global warming and climate change essay in malayalam

കാലാവസ്ഥാ വ്യതിയാനം ഒരു ആഗോള ആരോഗ്യ വെല്ലുവിളി

കാലാവസ്ഥാ വ്യതിയാനം മനുഷ്യജീവിതത്തിന്റെ എല്ലാതലങ്ങളെയും ഗൗരവതരമായി ബാധിച്ചു കൊണ്ടിരിക്കുകയാണ്. ലോകം ഇപ്പോൾ നേരിട്ടുകൊണ്ടിരിക്കുന്ന കാലാവസ്ഥാ വ്യതിയാനപരമായ വലിയൊരു വെല്ലുവിളി ആരോഗ്യപ്രശ്നങ്ങൾ വളർന്നു വരുന്നതാണ്. അന്തരീക്ഷ ഊഷ്മാവിന്റെ അളവ് ക്രമാതീതമായി വർധിക്കുന്നത് ജീവന്റെ നിലനിൽപ്പിനു തന്നെ ഭീഷണിയായി മാറിയിരിക്കുന്നു. താപ കാലാവസ്ഥാ താളക്രമത്തിൽ വന്ന മാറ്റം സാംക്രമിക രോഗങ്ങൾക്ക് ആക്കംകൂട്ടി. ഉഷ്ണമേഖലാ രോഗങ്ങളിൽ (Tropical Diseases) പ്രധാനികളായ ചിക്കുൻഗുനിയ, ഡെങ്കി തുടങ്ങിയ കൊതുകുജന്യ രോഗങ്ങളുടെ പ്രഹരശേഷിയും വ്യാപനതോതും അടുത്തിടെയായി വർധിച്ചു വരുന്നതായി കാണുന്നു.

ആര്‍ട്ടിക്കിള്‍ ഷോ

HindiVyakran

• नर्सरी निबंध
• सूक्तिपरक निबंध
• सामान्य निबंध
• दीर्घ निबंध
• संस्कृत निबंध
• संस्कृत पत्र
• संस्कृत व्याकरण
• संस्कृत कविता
• संस्कृत कहानियाँ
• संस्कृत शब्दावली
• Group Example 1
• Group Example 2
• Group Example 3
• Group Example 4
• संवाद लेखन
• जीवन परिचय
• Premium Content
• Message Box
• Horizontal Tabs
• Vertical Tab
• Accordion / Toggle
• Text Columns
• Contact Form
• विज्ञापन

Header$type=social_icons

• commentsSystem

Essay on Global Warming in Malayalam ആഗോളതാപനം ഉപന്യാസം

Essay on Global Warming in Malayalam Language: ആഗോളതാപനം ഉപന്യാസം / ആഗോള താപനവും കാലാവസ്ഥ വ്യതിയാനവും ഉപന്യാസം

Essay on Global Warming in Malayalam Language : ആഗോളതാപനം ഉപന്യാസം / ആഗോള താപനവും കാലാവസ്ഥ വ്യതിയാനവും ഉപന്യാസം

Essay on Global Warming inMalayalam

ഈ അടുത്തകാലത്തായി ലോകമെമ്പാടും ഏറെ ചർച്ചചെയ്യുന്ന ഒരു വാക്കാണ് ആഗോളതാപനം. ഒരു മഹാവിപത്തിന്റെ ഭീകരമായ ചിത്രം ഈ വാക്കിൽ നിറഞ്ഞുകിടക്കുന്നു. എന്താണ് ആഗോളതാപനം? ഭൗമോ പരിതലത്തിന് അടുത്തുള്ള വായുവിന്റെയും സമുദ്രത്തിന്റെയും താപനി ലയിൽ വന്നുകൊണ്ടിരിക്കുന്ന വർദ്ധനവിനെയാണ് ആഗോളതാപനം എന്നതുകൊണ്ട് ഉദ്ദേശിക്കുന്നത്. മനുഷ്യന്റെ പ്രകൃതിയുടെമേലുള്ള കടന്നുകയറ്റവും അനിയന്ത്രിതമായ പ്രവർത്തനങ്ങളും പ്രകൃതിയിൽ ഉണ്ടാക്കുന്ന ചില പ്രതിഭാസമാണ് ഭൗമാന്തരീക്ഷത്തിലെ ചൂട് കൂട്ടുന്നത്. ഭൗമോപരിതലത്തിൽ വന്നുപതിക്കുന്ന സൂര്യതാപത്തിൽ ഏറിയഭാഗവും പ്രതിഫലിച്ചു ചിതറിപ്പോകുന്നു. തന്മൂലം ചൂടിന്റെ കാഠിന്യം ഭൂമിയിൽ രൂക്ഷമാകുന്നില്ല. എന്നാൽ പ്രക്രിയയ്ക്ക് വിരുദ്ധമായി അവയിൽ കുറെഭാഗം അന്തരീക്ഷത്തിലുള്ള ചില വാതകങ്ങളും നീരാവിയും വലിച്ചെടുക്കുന്നു. കാർബൺ ഡൈ ഓക്സൈഡ്, മീഥേൻ, നൈട്രിക് ഓക്സൈഡ് തുടങ്ങിയവയാണ് ചൂട് വലിച്ചെടുക്കുന്ന വാതകങ്ങൾ. ഇവയെ "ഹരിതഗൃഹവാതകങ്ങൾ' എന്നു വിളിക്കുന്നു. സൂര്യനിൽ നിന്നും ഭൂമിയിലേക്കെത്തുന്ന ചൂടിന്റെ പ്രതിഫലനത്തെ ഈ വാതകങ്ങൾ തടയുന്നു. തൽഫലമായി ഭൂമിയിലെ താപനില വർദ്ധിക്കുന്നു. 1750 മുതലാണ് അന്തരീക്ഷത്തിലെ ഹരിതഗൃഹ വാതകങ്ങളുടെ അളവിലെ ഗണ്യമായ വർദ്ധനവ് ശ്രദ്ധിക്കപ്പെട്ടുതുടങ്ങിയത്. വ്യാവസായിക പുരോഗതിയാണ് ഇതിനു കാരണം. വ്യവസായസ്ഥാപനങ്ങൾ പുറ ത്തേക്കുവിടുന്ന പുകയും ഡീസൽ, കൽക്കരി തുടങ്ങിയ ഇന്ധനങ്ങൾ കത്തുന്നതുമൂലം ഉണ്ടാകുന്ന കരിയും പുകയും പ്രധാന കാരണമാണ്. വനനശീകരണം അന്തരീക്ഷത്തിലെ കാർബൺ ഡൈ ഓക്സൈഡിന്റെ അളവ് വളരെയേറെ വർദ്ധിക്കുവാൻ കാരണമായി. സസ്യങ്ങൾ പ്രകാ ശസംശ്ലേഷണം നടത്തുകവഴി കാർബൺ ഡൈ ഓക്സൈഡിന്റെ അളവ് നിയന്ത്രിതമായിരുന്നു. എന്നാൽ വിവേചനം കൂടാതെ സസ്യസ മ്പത്ത് നശിപ്പിക്കുന്നതു കാരണം ഈ സാധ്യത ഇല്ലാതാകുന്നു. ദിന ന്തോറും പെരുകുന്ന മോട്ടോർ വാഹനങ്ങൾ പുറത്തേക്കുവിടുന്ന പുക അന്തരീക്ഷത്തിലെ കാർബൺ ഡൈ ഓക്സൈഡിന്റെ തോത് ക്രമാതീതമായി വർദ്ധിപ്പിക്കുകയാണ്. ആഗോളതാപത്തിന് ഒരു പ്രധാന കാരണമാണ് ഇത്.

അന്തരീക്ഷത്തിൽ കാർബൺ ഡൈ ഓക്സൈഡിന്റെയും അപക ടകാരികളായ മറ്റു വാതകങ്ങളുടെയും അളവ് ക്രമാതീതമായി വർദ്ധി ച്ചപ്പോൾ സൂര്യനിൽനിന്നുള്ള താപം, ഈ വാതകങ്ങൾ കൂടുതലായി വലിച്ചെടുക്കുവാൻ തുടങ്ങി. തൽഫലമായി അന്തരീക്ഷത്തിലെ താപ നില ഉയരുവാൻ തുടങ്ങി. അന്തരീക്ഷത്തിലെ ചൂട് തീവ്രമാകാൻ തുടങ്ങി. ഈ സ്ഥിതി തുടരുകയാണെങ്കിൽ ഈ നൂറ്റാണ്ടിന്റെ അവ സാനമാകുമ്പോഴേക്കും അന്തരീക്ഷോഷ്മാവ് അഞ്ച് ഡിഗ്രി സെൽഷ്യ സിനു മീതെ വർദ്ധിക്കുമത്രേ. ഇത് പരിസ്ഥിതിക്ക് വലിയ ആഘാത മാണ് ഉണ്ടാക്കുവാൻ പോകുന്നത്. ഭൂമിയിൽ ജീവന്റെ നിലനില്പിനെ തന്നെ അപകടത്തിലാക്കുകയാണ്. 

അന്തരീക്ഷത്തിലെ ഊഷ്മാവ് വർദ്ധിപ്പിക്കുന്നതിൽ കോൺക്രീറ്റ് പരിസരങ്ങളും വലിയ പങ്ക് വഹിക്കുന്നുണ്ട്. ഫ്രിഡ്ജുകളും ഏസി കളും പ്രവർത്തിക്കുമ്പോൾ അന്തരീക്ഷത്തിലേക്കു വ്യാപിക്കുന്ന വാതകം ഓസോൺ പാളിയിൽ വിള്ളലുകൾ ഉണ്ടാക്കുന്നു. ഓസോൺപാളി യാണ് സൂര്യൻ നിൽനിന്നും വരുന്ന പല മാരകരശ്മികളെയും തടയു ന്നത്. ഇവ ഭൂമിയിൽ വന്നുപതിക്കുന്നത് ആപത്താണ്.

ആഗോളതാപനത്തിന്റെ ഫലമായി വർദ്ധിക്കുന്ന ചൂടിൽ ഏറിയ കൂറും ആഗിരണം ചെയ്യുന്നത് സമുദ്രങ്ങളാണ്. തൽഫലമായി സമുദ്രജലം ചൂടുപിടിക്കും. 3000 മീറ്റർ ആഴത്തിൽവരെ സമുദ്രജലം ചൂടുള്ളതായി ത്തീരും. ചൂടുപിടിച്ച് സമുദ്രജലത്തിന്റെ വ്യാപ്തം വർദ്ധിക്കും. ഇത് ജലനിരപ്പ് ഉയരുവാൻ കാരണമാകും. ധ്രുവപ്രദേശങ്ങളിൽ പത്ത് ഡിഗ്രി സെന്റിഗ്രേഡ് ചൂട് വർദ്ധിക്കും. ഇത് അവിടെയുള്ള മഞ്ഞുമലകൾ ഉരുകുവാൻ ഇടയാക്കും. അതോടെ സമുദ്രജലവിതാനം ഉയരും. ലോക ത്തിലെ പ്രധാന നഗരങ്ങൾ കടലിനടിയിലാവുകയും ചെയ്യും. 

ആഗോളതാപനം കാലാവസ്ഥയെ തകിടം മറിക്കും. സമുദ്രജലത്തിന്റെ ഘടനയിൽ സാരമായ മാറ്റങ്ങൾ ഉണ്ടാക്കും. വെള്ളപ്പൊക്കം, കനത്ത മഴ, വൻകൊടുങ്കാറ്റ് എന്നിവയ്ക്ക് വഴിവയ്ക്കും . ഈ വ്യതിയാനങ്ങൾ പ്രപഞ്ചജീവിതത്തിന്റെ താളം തെറ്റിക്കും. ജീവജാലങ്ങളുടെ നില നില്പിനെ ഇത് പ്രതികൂലമായി ബാധിക്കും. കാലാവസ്ഥയിലുള്ള വ്യതി യാനം കാർഷികമേഖലയ്ക്ക് വിനാശകരമായിത്തീരും. കൃഷിയിടങ്ങൾ മരുഭൂമികളായിമാറും. സൈബീരിയയായിരിക്കും ലോകത്തിന്റെ ഭക്ഷ്യ കലവറ. - കാലാവസ്ഥാവ്യതിയാനംമൂലം നദികളുടെ ഉത്ഭവസ്ഥാനത്ത മഞ്ഞ് ഉരുകുന്നതിനാൽ വൻനദികളുടെ നിലനില്പ്പോലും അവതാളത്തി ലാകും. ആഗോളതാപനം ഒരു നിയന്ത്രണവുമില്ലാതെ ഇങ്ങനെ തുടരു കയാണെങ്കിൽ ഭൂമിയുടെ സർവ്വനാശമായിരിക്കും ഫലം. ആഗോളതാപന ത്തെക്കുറിച്ചുള്ള പഠനങ്ങൾക്കും പ്രവർത്തനങ്ങൾക്കും ലോകരാഷ്ട്ര ങ്ങൾ ഇപ്പോൾ മുന്തിയ പരിഗണനയാണ് നൽകുന്നത്. 

അന്തരീക്ഷത്തിലെ താപവർദ്ധന കാലാവസ്ഥയിലുണ്ടാക്കുന്ന ദൂഷ്യ ഫലങ്ങൾ ഭീകരമായിരിക്കും. താപനില ഉയരുന്നത് മനുഷ്യന്റെ ആരോ ഗ്യസ്ഥിതി മോശമാകുവാൻ കാരണമാകും. പലതരത്തിലുള്ള രോഗ ങ്ങൾക്കും രോഗാണുക്കളുടെ വളർച്ചയ്ക്കും കാരണമാകും. മഞ്ഞ പ്പിത്തം, പലതരം കാൻസറുകൾ എന്നിവ വ്യാപകമാകും. പല ജീവജാ ലങ്ങളും ഭൂമിയിൽനിന്നും അപ്രത്യക്ഷമാകും. ആഗോളതാപനനിയന്ത്രണത്തിന് അന്തരീക്ഷത്തിലെ കാർബൺ ഡൈ ഓക്സൈഡിന്റെ അളവ് കുറയ്ക്കുക എന്നുള്ളതാണ് ഏകമാർഗ്ഗം. വനനശീകരണമാണ് അന്തരീക്ഷത്തിലെ കാർബൺ ഡൈ ഓക്സൈ ഡിന്റെ വർദ്ധനവിനു ഒരു പ്രധാന കാരണം. സസ്യങ്ങൾക്ക് അന്നജ നിർമ്മാണത്തിന് കാർബൺ ഡൈ ഓക്സൈഡ് ആവശ്യമാണ്. അതിന് ആവശ്യമായ വാതകം അന്തരീക്ഷത്തിൽനിന്നുമാണ് അവ സ്വീകരിക്കു ന്നത്. തന്മൂലം അന്തരീക്ഷത്തിലെ കാർബൺ ഡൈ ഓക്സൈഡിന്റെ അളവ് നിയന്ത്രിക്കപ്പെടും. എന്നാൽ വനനശീകരണവും ഹരിതസസ്യ ങ്ങളുടെ വിനാശംകൊണ്ടും മനുഷ്യൻ ഈ സാദ്ധ്യതകൾ ഇല്ലാതാക്കുക യാണ്. ഫലമോ അന്തരീക്ഷത്തിൽ കാർബൺ ഡൈ ഓക്സൈഡിന്റെ അളവ് വർദ്ധിക്കുന്നു. അതുകൊണ്ട് വൃക്ഷങ്ങൾ വച്ചുപിടിപ്പിച്ച് ഭൂമി യിലെ ഹരിതസസ്യങ്ങളെ സംരക്ഷിക്കണം. അനിയന്ത്രിതമായ വനന ശീകരണവും വൃക്ഷഹത്യകളും ഒഴിവാക്കിയേ മതിയാകൂ.

സമുദ്രജലത്തിലുള്ളതും ഓക്സിജൻ പുറത്തുവിടുന്നതുമായ സൂക്ഷ്മസസ്യങ്ങളും മലിനീകരണംമൂലം നശിച്ചുകൊണ്ടിരിക്കുക യാണ്. കടൽപ്പായലുകളും മറ്റും കരയിലെ സസ്യങ്ങളെയെന്നപോലെ അന്തരീക്ഷത്തിലെ കാർബൺഡൈഓക്സൈഡിന്റെ അളവ് നിയന്ത്രി ക്കുന്നതിലും ഓക്സിജൻ പ്രദാനം ചെയ്യുന്നതിലും നല്ല പങ്കു വഹി ക്കുന്നുണ്ട്. ആ സാദ്ധ്യതയെയാണ് സമുദ്രജഥമലിനീകരണം ഇല്ലാതാ ക്കുന്നത്. 

കാർബൺ ഡൈ ഓക്സൈഡിന്റെ അളവു വർദ്ധിക്കുവാനുള്ള മറ്റൊരു കാരണം വാഹനങ്ങളുടെ പുകയാണ്. ദിനംപ്രതി വാഹനങ്ങ ളുടെ എണ്ണം പെരുകുകയാണ്. സ്വകാര്യവാഹനങ്ങളുടെ ഉപയോഗം കുറച്ചുകൊണ്ടുവരുകയും പൊതുവാഹന സംവിധാനങ്ങൾ ഉപയോ ഗിക്കുവാൻ ജനങ്ങളെ നിർബ്ബന്ധിക്കുകയും ചെയ്താൽ വാഹനപ്പെരുപ്പം കുറയ്ക്കാനും അതുവഴി ലോകത്തെ നാശത്തിൽനിന്നും ഒരു പരിധി വരെ രക്ഷിക്കാനും സാധിക്കും. 

ആഗോള താപനത്തിന്റെ വിപത്തിനെപ്പറ്റി സാധാരണ ജനങ്ങൾ ബോധവാന്മാരല്ല. അതുകൊണ്ട് ഇക്കാര്യത്തിൽ പൊതുജനങ്ങളെ അറിവുള്ളവരാക്കേണ്ടിയിരിക്കുന്നു.

വ്യവസായവും സുഖസൗകര്യങ്ങളും മനുഷ്യരാശിക്കു ഒഴിച്ചു കൂടാനാവില്ല. വികസനവും പുരോഗതിയും വേണ്ടെന്നുവയ്ക്കാനും ആവില്ല. എന്നാൽ അതെല്ലാം വച്ചുപുലർത്തി അനുഭവിക്കാൻ നല്ല അന്തരീക്ഷവും പ്രപഞ്ചവും ജീവനും വേണം. ഭൂമിയിലെ ജീവരാശി യുടെ മേൽ വിനാശത്തിന്റെ തീമഴ ചൊരിയാൻ പാകത്തിൽ ആഗോള താപനം എന്ന പ്രതിഭാസം ഒരു ഭീഷണിയായി അനുദിനം പെരുകുക യാണ്. ഇതുണ്ടാക്കിവച്ച മനുഷ്യരാശിതന്നെ അതു പരിഹരിക്കാനും മുന്നോട്ടുവന്നില്ലെങ്കിൽ സർവ്വനാശമായിരിക്കും ഫലം.

Advertisement

Put your ad code here, 100+ social counters$type=social_counter.

• fixedSidebar
• showMoreText

/gi-clock-o/ WEEK TRENDING$type=list

• गम् धातु के रूप संस्कृत में – Gam Dhatu Roop In Sanskrit गम् धातु के रूप संस्कृत में – Gam Dhatu Roop In Sanskrit यहां पढ़ें गम् धातु रूप के पांचो लकार संस्कृत भाषा में। गम् धातु का अर्थ होता है जा...

• दो मित्रों के बीच परीक्षा को लेकर संवाद - Do Mitro ke Beech Pariksha Ko Lekar Samvad Lekhan दो मित्रों के बीच परीक्षा को लेकर संवाद लेखन : In This article, We are providing दो मित्रों के बीच परीक्षा को लेकर संवाद , परीक्षा की तैयार...

RECENT WITH THUMBS$type=blogging$m=0$cate=0$sn=0$rm=0$c=4$va=0

• 10 line essay
• 10 Lines in Gujarati
• Aapka Bunty
• Aarti Sangrah
• Akbar Birbal
• anuched lekhan
• asprishyata
• Bahu ki Vida
• Bengali Essays
• Bengali Letters
• bengali stories
• best hindi poem
• Bhagat ki Gat
• Bhagwati Charan Varma
• Bhishma Shahni
• Bhor ka Tara
• Boodhi Kaki
• Chandradhar Sharma Guleri
• charitra chitran
• Chief ki Daawat
• Chini Feriwala
• chitralekha
• Chota jadugar
• Claim Kahani
• Dairy Lekhan
• Daroga Amichand
• deshbhkati poem
• Dharmaveer Bharti
• Dharmveer Bharti
• Diary Lekhan
• Do Bailon ki Katha
• Dushyant Kumar
• Eidgah Kahani
• Essay on Animals
• festival poems
• French Essays
• funny hindi poem
• funny hindi story
• German essays
• Gujarati Nibandh
• gujarati patra
• Guliki Banno
• Gulli Danda Kahani
• Haar ki Jeet
• Harishankar Parsai
• hindi grammar
• hindi motivational story
• hindi poem for kids
• hindi poems
• hindi rhyms
• hindi short poems
• hindi stories with moral
• Information
• Jagdish Chandra Mathur
• Jahirat Lekhan
• jainendra Kumar
• jatak story
• Jayshankar Prasad
• Jeep par Sawar Illian
• jivan parichay
• Kashinath Singh
• kavita in hindi
• Kedarnath Agrawal
• Khoyi Hui Dishayen
• Kya Pooja Kya Archan Re Kavita
• Madhur madhur mere deepak jal
• Mahadevi Varma
• Mahanagar Ki Maithili
• Main Haar Gayi
• Maithilisharan Gupt
• Majboori Kahani
• malayalam essay
• malayalam letter
• malayalam speech
• malayalam words
• Mannu Bhandari
• Marathi Kathapurti Lekhan
• Marathi Nibandh
• Marathi Patra
• Marathi Samvad
• marathi vritant lekhan
• Mohan Rakesh
• Mohandas Naimishrai
• MOTHERS DAY POEM
• Narendra Sharma
• Nasha Kahani
• Neeli Jheel
• nursery rhymes
• odia letters
• Panch Parmeshwar
• panchtantra
• Parinde Kahani
• Paryayvachi Shabd
• Poos ki Raat
• Portuguese Essays
• Punjabi Essays
• Punjabi Letters
• Punjabi Poems
• Raja Nirbansiya
• Rajendra yadav
• Rakh Kahani
• Ramesh Bakshi
• Ramvriksh Benipuri
• Rani Ma ka Chabutra
• Russian Essays
• Sadgati Kahani
• samvad lekhan
• Samvad yojna
• Samvidhanvad
• Sandesh Lekhan
• sanskrit biography
• Sanskrit Dialogue Writing
• sanskrit essay
• sanskrit grammar
• sanskrit patra
• Sanskrit Poem
• sanskrit story
• Sanskrit words
• Sara Akash Upanyas
• Savitri Number 2
• Shankar Puntambekar
• Sharad Joshi
• Shatranj Ke Khiladi
• short essay
• spanish essays
• Striling-Pulling
• Subhadra Kumari Chauhan
• Subhan Khan
• Suchana Lekhan
• Sudha Arora
• Sukh Kahani
• suktiparak nibandh
• Suryakant Tripathi Nirala
• Swarg aur Prithvi
• Tasveer Kahani
• Telugu Stories
• UPSC Essays
• Usne Kaha Tha
• Vinod Rastogi
• Vrutant lekhan
• Wahi ki Wahi Baat
• Yahi Sach Hai kahani
• Yoddha Kahani
• Zaheer Qureshi
• कहानी लेखन
• कहानी सारांश
• तेनालीराम
• मेरी माँ
• लोककथा
• शिकायती पत्र
• सूचना लेखन
• हजारी प्रसाद द्विवेदी जी
• हिंदी कहानी

RECENT$type=list-tab$date=0$au=0$c=5

Replies$type=list-tab$com=0$c=4$src=recent-comments, random$type=list-tab$date=0$au=0$c=5$src=random-posts, /gi-fire/ year popular$type=one.

• अध्यापक और छात्र के बीच संवाद लेखन - Adhyapak aur Chatra ke Bich Samvad Lekhan अध्यापक और छात्र के बीच संवाद लेखन : In This article, We are providing अध्यापक और विद्यार्थी के बीच संवाद लेखन and Adhyapak aur Chatra ke ...

Join with us

Footer Social$type=social_icons

• loadMorePosts
• relatedPostsText
• relatedPostsNum

എന്താണ് ഹരിതഗൃഹപ്രഭാവം?

ഭൂ മിയുടെ അന്തരീക്ഷത്തിലെ ചില ഘടകങ്ങൾ (പ്രധാനമായും കാർബൺ ഡൈഓക്സൈഡ്) സൗരതാപത്തെ ആഗിരണം ചെയ്യുകയും ഭൂമിയുടെ താപനില ഉയർത്തുകയും ചെയ്യുന്ന പ്രതിഭാസമാണ് ഹരിതഗൃഹപ്രഭാവം. (Greenhouse effect). ഭൂമി തണുത്തുറഞ്ഞു പോകാതെ ജീവ​െൻറ നിലനിൽപിന് അനുയോജ്യമായ താപനില നിലനിർത്താൻ ഹരിതഗൃഹപ്രഭാവം സഹായിക്കുന്നു. എന്നാൽ, ഇപ്പോൾ മനുഷ്യ​െൻറ തെറ്റായ ചില പ്രവർത്തനങ്ങൾമൂലം അന്തരീക്ഷത്തിൽ കാർബൺ ഡൈഓക്സൈഡി​െൻറ അളവ് കൂടുകയും ഭൂമിയുടെ അന്തരീക്ഷ താപനില അനഭിലഷണീയമാം വിധം ഉയരുകയും ചെയ്യുന്നുണ്ട്. പരിസ്ഥിതി ശാസ്ത്രജ്ഞർക്കിടയിൽ ഹരിതഗൃഹപ്രഭാവം ഒരു സജീവ ചർച്ചാവിഷയമാകുന്നത് അതു കൊണ്ടാണ്.

പേരിനു പിന്നിൽ

ശൈത്യരാജ്യങ്ങളിൽ അതിശൈത്യം കാരണം ചിലയിനം ചെടികൾ നശിച്ചുപോവുക സാധാരണമാണ്. അതിനാൽ, കർഷകർ സംരക്ഷിക്കേണ്ട ചെടികളെ ഒരു ചില്ലു കൂടിനുള്ളിൽ വളർത്തുന്നു. ഈ ചില്ലുകൂടാണ് ഹരിതഗൃഹം (Green house). ചില്ല് സുതാര്യമായതിനാൽ പ്രകാശരശ്മികൾ ഉള്ളിൽ കയറും. എന്നാൽ, ചില്ലുകൂട് താപരശ്മികളെ പുറത്തുകടക്കാൻ അനുവദിക്കാതെ കെണിയിലാക്കുന്നു. അതിനാൽ, ചില്ലുകൂടിനുള്ളിലെ താപനില ഉയരുകയും ചെടികൾ അതിശൈത്യത്തിൽ നിന്നും രക്ഷപ്പെടുകയും ചെയ്യുന്നു. ഇത്തരം ചില്ലുകൂടുകൾ ചെയ്യുന്നതുപോലെ അന്തരീക്ഷത്തിലെ ചില വാതകങ്ങൾ ഭൂമി പ്രതിപതിപ്പിക്കുന്ന താപവികിരണങ്ങൾ ശൂന്യാകാശത്തിലേക്ക് നഷ്​ടപ്പെടാതെ തടയുന്നു. ഇതാണ് ഹരിതഗൃഹപ്രഭാവം.

അന്തരീക്ഷം ചൂടുപിടിക്കുന്നതെങ്ങനെ?

ഭൂമിയുടെ അന്തരീക്ഷതാപനില ഉയരുന്നത് ഭൂമിയിൽ നേരിട്ടുപതിക്കുന്ന സൂര്യരശ്​മികളാലല്ല. അവ ​ ഹ്രസ്വതരംഗങ്ങളായതിനാൽ അധികം വായുകണങ്ങളുമായി സമ്പർക്കത്തിൽ വരാത്തതാണ് കാരണം. എന്നാൽ, ഇവയേറ്റ് ചൂടുപിടിക്കുന്ന ഭൂമി, ഇൻഫ്രാറെഡ് വികിരണങ്ങൾ (ഉഷ്ണരശ്മികൾ) ഭൗമോപരിതലത്തിൽ നിന്നും പ്രതിപതിപ്പിക്കും. തരംഗദൈർഘ്യം കൂടുതലുള്ളതിനാൽ ഇവ വളഞ്ഞുപുളഞ്ഞ് സഞ്ചരിച്ച് അന്തരീക്ഷത്തിലെ കൂടുതൽ വായുകണങ്ങളുമായി സമ്പർക്കത്തിൽ വന്ന് അവയെ ചൂടുപിടിക്കും. ഇതാണ് അന്തരീക്ഷതാപനില ഉയർത്തുന്നത് (ഇൻറർലോക്കിട്ട മുറ്റമുള്ള വീടുകളിൽ നേരിട്ട് സൂര്യപ്രകാശമേൽക്കാത്ത സിറ്റൗട്ടിൽ ഇരിക്കുമ്പോൾ നമുക്ക് അത്യധികമായ ചൂട് അനുഭവപ്പെടാനുള്ള കാരണം ഈ ഭൗമവികിരണങ്ങളാണ്).

താപത്തെ കെണിയിലാക്കുന്നവർ

ഭൗമോപരിതലത്തിൽനിന്നും പ്രതിപതിക്കുന്ന താപവികിരണങ്ങളാണ് അന്തരീക്ഷത്തെ ചൂടുപിടിപ്പിക്കുന്നത് എന്നു നാം കണ്ടു. ഈ താപവികിരണങ്ങളെ ബഹിരാകാശത്തേക്ക് തിരിച്ചുപോകാൻ അനുവദിക്കാതെ അന്തരീക്ഷത്തിലെ ചില വാതകങ്ങൾ ആഗിരണം ചെയ്യുന്നു. ഇത്തരം വാതകങ്ങളാണ് ഹരിതഗൃഹവാതകങ്ങൾ (Green house gases). കാർബൺ ഡൈഓക്സൈഡ്, മീഥൈൻ, നൈട്രസ് ഓക്സൈഡ്, നീരാവി എന്നിവയാണ്പ്രധാന ഹരിതഗൃഹവാതകങ്ങൾ. ഈ വാതകങ്ങൾ അവ ആഗിരണം ചെയ്ത താപവികിരണങ്ങളെ വീണ്ടും താഴേക്കും മുകളിലേക്കും വശങ്ങളിലേക്കും പ്രതിപതിപ്പിക്കുന്നു. ഇവയെ ഭൗമോപരിതലവും മറ്റു ഹരിതഗൃഹ വാതകകണങ്ങളും വീണ്ടും ആഗിരണം ചെയ്യുകയും പുറത്തുവിടുകയും ചെയ്യുന്നു. ഈ ചാക്രിക പ്രക്രിയയാണ് അന്തരീക്ഷത്തെ ജീവ​ന്‍റെ നിലനിൽപിന് അനുയോജ്യമാം വിധം ചൂടുള്ളതാക്കുന്നത്.

അമിതമായാൽ അമൃതും വിഷം

സസ്യ-ജന്തുജാലങ്ങളുടെ നിലനിൽപിന് ആവശ്യമായ അളവിൽ അന്തരീക്ഷതാപം നിലനിർത്തുന്നത് ഹരിതഗൃഹവാതകങ്ങളിൽ പ്രധാനപ്പെട്ട കാർബൺ ഡൈഓക്സൈഡാണ്. എന്നാൽ, കഴിഞ്ഞ ഏതാനും പതിറ്റാണ്ടുകളായി കാർബൺ ഡൈഓക്സൈഡി​െൻറ അനുപാതം അന്തരീക്ഷത്തിൽ സാരമായി വർധിച്ചിരിക്കുകയാണ്. കൽക്കരി, പെട്രോളിയം, പ്രകൃതിവാതകം എന്നിവയുടെ അമിതമായ ഉപയോഗം, വനനശീകരണം എന്നിവയാണ് അന്തരീക്ഷത്തിൽ കാർബൺ ഡൈഓക്സൈഡി​െൻറ അളവു കൂടാൻ കാരണമായത്.പത്തൊമ്പതാം നൂറ്റാണ്ടിൽ അന്തരീക്ഷത്തിലെ കാർബൺ ഡൈഓക്സൈഡി​െൻറ അളവ് 280 പി.പി.എം ആയിരുന്നു (Parts per million അഥവാ പത്ത് ലക്ഷത്തിൽ ഒരംശം എന്നതാണ് പി.പി.എം കൊണ്ട് ഉദ്ദേശിക്കുന്നത്). ഇന്ന് അത് 350 പി.പി.എം ആണ്.

അന്തരീക്ഷത്തിൽ ഉണ്ടായിട്ടുള്ള കാർബൺ ഡൈഓക്സൈഡ് വർധനയുടെ 25 ശതമാനവും സംഭവിച്ചിട്ടുള്ളത് കഴിഞ്ഞ 40 വർഷങ്ങൾക്കുള്ളിലാണ്. ഈ നില തുടർന്നാൽ 2050ൽ കാർബൺ ഡൈഓക്സൈഡി​ന്‍റെ അളവ് 600 പി.പി.എം ആകും. ഇത് ആഗോളതാപനത്തിന് ഇടയാക്കും.

ഹരിതഗൃഹവാതകങ്ങളുടെ അളവു കൂടുന്നതുമൂലം അന്തരീക്ഷതാപനില ഉയരുന്നതാണ് ആഗോളതാപനം(Global warming). ഇരുപതാം നൂറ്റാണ്ടി​െൻറ രണ്ടാം പാതിയിൽ ഭൂമിയുടെ ശരാശരി താപ നില 0.8ഡിഗ്രി C മുതൽ 1.2ഡിഗ്രി Cവരെ ഉയർന്നു എന്നാണ് വിവിധ പഠനങ്ങൾ ചൂണ്ടിക്കാണിക്കുന്നത്. 1986 നും 2005നും ഇടയിൽ ഉണ്ടായ തോതിൽ അന്തരീക്ഷത്തിൽ കാർബൺ ഡൈഓക്സൈഡ് ഇനിയും എത്തിയാൽ 2100 ആകുമ്പോഴേക്കും ഭൂമിയുടെ ശരാശരി താപനില 5.8ഡിഗ്രി Cവരെ ഉയരുമെന്നാണ് കാലാവസ്ഥാ വ്യതിയാനത്തെക്കുറിച്ച് പഠനം നടത്തുന്ന ശാസ്ത്രജ്ഞരുടെ സംഘമായ IPCC (Intergovernmental Panel on Climate Change) മുന്നറിയിപ്പ് നൽകുന്നത്.

ഭൂമിയുടെ ഇപ്പോഴത്തെ ശരാശരി താപനില 15ഡിഗ്രി C മാത്രമാണെന്നിരിക്കെ ഈ വർധന എത്രമാത്രം ഭീതിദമാണ്! ഇതു പല പ്രശ്നങ്ങളും ഭൂമിയിൽ സൃഷ്​ടിക്കും. അൻറാർട്ടിക്കയിലെയും ഹിമാലയത്തിലെയും മഞ്ഞുരുകി ഇന്ത്യയിലെ മുംബൈ അടക്കം ലോകത്തെ പല വൻനഗരങ്ങളും ചില ദ്വീപരാജ്യങ്ങളും വെള്ളത്തിനടിയിലാകും. ഭൂമിയിലെ ഋതുഭേദങ്ങൾ മാറിമറിയും. ചിലയിടത്ത് പേമാരിയും ചിലയിടത്ത് വരൾച്ചയുമുണ്ടാകും. കൊടുങ്കാറ്റുകളും ചുഴലിക്കാറ്റുകളും സാർവത്രികമാകും. ഭൂമിയിലെ വിവിധ ആവാസ വ്യവസ്ഥകൾ നശിക്കും. കൃഷിനാശവും അതു വഴി ഭക്ഷ്യക്ഷാമവുമുണ്ടാകും. മനുഷ്യർക്ക് ത്വക് അർബുദം പോലുള്ള രോഗങ്ങളുണ്ടാകും.

ആഗോളതാപനത്തി​െൻറ ഗൗരവം ഉൾക്കൊണ്ടാണ് പല വർഷങ്ങളുടെയും ലോകപരിസ്ഥിതിദിനസന്ദേശങ്ങൾ പോലും രൂപപ്പെട്ടത്. മഞ്ഞുരുകൽ ഒരു ചൂടുള്ള വിഷയം, 'CO2- Kick the habit', 'Beat air pollution', 'ആഗോള താപനം - മരമാണ് മറുപടി' തുടങ്ങിയവ ഉദാഹരണങ്ങളാണ്.

Don't miss the exclusive news, Stay updated

Subscribe to our Newsletter

By subscribing you agree to our Terms & Conditions .

Your subscription means a lot to us

Still haven't registered? Click here to Register

• India Today
• Business Today
• Reader’s Digest
• Harper's Bazaar
• Brides Today
• Cosmopolitan
• Aaj Tak Campus

NOTIFICATIONS

• മലയാളം വാർത്ത

Global warming: ഡെങ്കിപ്പനി, മലേറിയ ഉൾപ്പെടെയുള്ള പകർച്ചവ്യാധികൾ വർധിക്കുന്നത് കാലാവസ്ഥാ വ്യതിയാനം മൂലമാണോ ?

Climate change: കാലാവസ്ഥാ വ്യതിയാനം മൂലം ഡെങ്കിപ്പനിയുടെയും ചിക്കുൻഗുനിയയുടെയും വ്യാപനം 1951-60 കാലത്തെ അപേക്ഷിച്ച് 2012-21 കാലയളവിൽ ശരാശരി 12 ശതമാനവും, സിക്ക 12.4 ശതമാനവും വർദ്ധിച്ചുവെന്നാണ് കണക്കുകൾ സൂചിപ്പിക്കുന്നത്..

Samrat Sharma

• 01 Nov 2022,
• (Updated 01 Nov 2022, 6:24 PM IST)

Climate change effects: ലോകത്തിലെ വിവിധ ഭാഗങ്ങളിൽ കാലാവസ്ഥാ വ്യതിയാനം (climate change) മനുഷ്യന്റെ ആരോഗ്യത്തെ നേരിട്ട് ബാധിക്കുന്നതിന്റെ ലക്ഷണങ്ങൾ പ്രകടമായി തുടങ്ങിയിട്ടുണ്ട്. ഭക്ഷ്യ-ജലജന്യ രോഗങ്ങൾ ഉൾപ്പെടെ നിരവധി പകർച്ചവ്യാധികളുടെ (pandemic) ഉയർച്ചയ്ക്ക് ഇത് കാരണമാകുന്നതായാണ് വിലയിരുത്തൽ. 

കാലാവസ്ഥാ വ്യതിയാനം മൂലം ഡെങ്കിപ്പനിയുടെയും ചിക്കുൻഗുനിയയുടെയും വ്യാപനം 1951-60 കാലത്തെ അപേക്ഷിച്ച് 2012-21 കാലയളവിൽ ശരാശരി 12 ശതമാനവും, സിക്ക 12.4 ശതമാനവും വർദ്ധിച്ചുവെന്നാണ് കണക്കുകൾ സൂചിപ്പിക്കുന്നത്. ഇതിനൊപ്പം രോഗത്തിന്റെ പകർച്ചാ കാലയളവ് ദൈർഘ്യത്തിൽ ഏകദേശം ആറ് ശതമാനത്തിന്റെ വർദ്ധനയും ഉണ്ടായിട്ടുണ്ട്.

"മാറിക്കൊണ്ടിരിക്കുന്ന കാലാവസ്ഥ സാഹചര്യങ്ങൾ ചൂടുമായി ബന്ധപ്പെട്ട രോഗങ്ങളുടെ അപകടസാധ്യത വർദ്ധിപ്പിക്കുന്നു. ഇത് പകർച്ചവ്യാധികൾ പകരുന്ന രീതി മാറ്റുന്നു. ഇത് ആരോഗ്യ അപകടങ്ങൾ വർദ്ധിപ്പിക്കുകയും, ശുചിത്വം ഇല്ലാതാക്കുകയും ചെയ്യുന്നുണ്ട്. ഭക്ഷ്യ-ജല സുരക്ഷയിൽ വലിയ  പ്രത്യാഘാതങ്ങളാണ് ഈ മാറ്റം സൃഷ്‌ടിക്കുന്നത്‌" ആരോഗ്യവും കാലാവസ്ഥാ വ്യതിയാനവും സംബന്ധിച്ച ലാൻസെറ്റ് കൗണ്ട്ഡൗണിന്റെ 2022ലെ റിപ്പോർട്ട് സൂചിപ്പിച്ചു.

"ഈ ആഘാതങ്ങൾ പലപ്പോഴും ഒരേസമയമാണ് സംഭവിക്കുന്നത്. ആരോഗ്യത്തിനും ആരോഗ്യ മേഖലയെ പിന്തുണയ്ക്കുന്ന സംവിധാനങ്ങൾക്കും ഇത് വലിയ സമ്മർദ്ദമാണ് ഉണ്ടാക്കുന്നത്. സാമൂഹികവും പ്രകൃതിദത്തവുമായ ആരോഗ്യ സംവിധാനങ്ങളിൽ കനത്ത ആഘാതമാണ് കാലാവസ്ഥാ വ്യതിയാനം മൂലമുണ്ടാവുന്നത്" റിപ്പോർട്ട് കൂട്ടിച്ചേർത്തു.

ഇതിന് പുറമെ കാലാവസ്ഥാ വ്യതിയാനം ഉഷ്‌ണ തരംഗ ദിനങ്ങളുടെ എണ്ണത്തിലും അനുബന്ധ മരണങ്ങളിലും ഗണ്യമായ വർദ്ധനവിന് കാരണമായിട്ടുണ്ട്. 65 വയസിന് മുകളിലുള്ളവരും, ഒരു വയസിന് താഴെയുള്ള കുട്ടികളും 1986-2005 കാലഘട്ടത്തിൽ ഉള്ളതിനേക്കാൾ 3.7 ബില്യൺ കൂടുതൽ ഉഷ്‌ണ തരംഗ ദിനങ്ങൾ അനുഭവിക്കേണ്ടി വന്നുവെന്നാണ് കണക്കുകൾ സൂചിപ്പിക്കുന്നത്. കൂടാതെ 2000-04നും 2017-21നും ഇടയിൽ ഇതുമായി  ബന്ധപ്പെട്ട മരണങ്ങൾ 68 ശതമാനം വർദ്ധിക്കുകയുമുണ്ടായി.

അതേസമയം, രാജ്യങ്ങൾ ഹരിതഗൃഹ വാതകങ്ങൾ പുറത്തുവിടുന്നത് കുറയ്ക്കുമ്പോഴും ഈ നൂറ്റാണ്ടിന്റെ അവസാനത്തോടെ ആഗോളതാപനം 1.5 ഡിഗ്രി സെൽഷ്യസായി പരിമിതപ്പെടുത്താനുള്ള ശ്രമങ്ങൾ അപര്യാപ്‌തമാണെന്നാണ് കാലാവസ്ഥാ വ്യതിയാനത്തെക്കുറിച്ചുള്ള ഐക്യരാഷ്ട്രസഭ കൺവെൻഷന്റെ ഏറ്റവും പുതിയ റിപ്പോർട്ട് ചൂണ്ടിക്കാണിക്കുന്നത്. 2010ലെ നിലവാരത്തെ അപേക്ഷിച്ച് 2030 ആകുമ്പോഴേക്കും ഹരിതഗൃഹ വാതകം പുറത്തിവിടുന്നത് 10.6 ശതമാനം വർദ്ധിക്കുമെന്നും റിപ്പോർട്ട് പറയുന്നു.

"ആഗോള താപനം 1.5 ഡിഗ്രി സെൽഷ്യസായി കുറയ്ക്കാനുള്ള ശ്രമങ്ങൾ ശരിയായ ദിശയിലല്ല. ഈ ലക്ഷ്യത്തിലേക്ക് അടുക്കണമെങ്കിൽ അതത് സർക്കാരുകൾ കൂടുതൽ മെച്ചപ്പെട്ട പദ്ധതികൾ തയ്യറാക്കി അടുത്ത എട്ട് വർഷത്തേക്ക് നടപ്പാക്കണം" UNFCC എക്‌സിക്യൂട്ടീവ് സെക്രട്ടറി സൈമൺ സ്‌റ്റിൽ വ്യക്തമാക്കി.

• climate change
• Global Warming

ഏറ്റവും പുതിയത്‌

Movies & Music

• kerala disaster

Ravages of climate change in Kerala

Mathrubhumi fact check desk, 03 november 2021, 09:31 am ist.

Despite being famed for its moderate tropical climate, the picturesque state of Kerala is now facing threats from extreme climate events. The intensity of climate change was realized by the common folks only when it knocked on their doorsteps in the form of disasters. Climate is not immune to changes. But the increase in the frequency and impact of climate events create panic. Kerala has been experiencing temperature rise, irregular monsoon and water scarcity for the past few years. But in recent times, these have become life-threatening in the form of extreme unforeseen disasters. Uninterrupted human activities have further enhanced the consequences of climate change.

With the onset of Cyclone Ockhi in 2017 unforeseen disasters had begun to haunt Kerala. Shortly afterwards, floods in 2018 and 19 devastated Kerala. Thousands of lives were lost. The time is not far off when natural disasters such as hurricanes, floods, landslides, floods, droughts and tsunamis will haunt us even more severely.

Were these tragedies unexpected?

Ockhi in 2017 was an unforeseen disaster which struck Kerala after the Tsunami. "Ockhi was an unprecedented cyclone and it quickly turned into a cyclone within 6 hours of low pressure. It was not possible to issue warnings according to the existing rules.” stated Amit Shah, Union Home Minister in Parliament. The catastrophic floods of 2018 and the subsequent floods and landslides from 2018 to 2021 gave Kerala unexpected misfortunes.

Each of these disasters due to climate change affects different regions each time. The disasters of 2019 did not occur where the landslides and floods of 2018 were terribly affected‌. There were landslides in Kerala in 2020 and 2021. They were also in different areas from previous years. There are probable chances that the next incident would happen somewhere else.

There was a special report by the IPCC in 2012 (Special Report on Extreme Events, IPCC 2012) that climate change would increase the number and magnitude of disasters and rainfall would be more intense. The changes we see in that sense are not unexpected, but the natural evolution of a changing climate.

According to the State Disaster Management Plan 2016, the presence of the Arabian Sea, the Western Ghats and the geographically slanting terrain makes Kerala a high risk area for climate change disasters. In connection with the disaster risks in Kerala, Dr. Murali Thummarukudy (Disaster Risk Reduction and Operations Manager, United Nations Environment Program (UNEP)) says: "Is the number of disasters increasing worldwide? Or is it because of the improvement in communication facilities that we are becoming more aware about the disasters? These are questions that baffle many. Disasters occur when forces that cause disasters (earthquakes, rain, wind, explosions in factories and roads) collide with objects that can cause damage (humans, animals, the environment, or immovables). All of them may not occur in the same way, for example, an earthquake is not caused by climate change. But others (the number of factories and road tankers) are increasing daily. The world's population is growing along with per capita wealth. Generally people have started to inhabit those places, where there were no settlements earlier. All this increases the risk of disaster. On top of all this, climate change is acting like a magnifying glass."

Kerala's high population density (860 people per sq km) increases the magnitude of natural disasters in the state. Rapid industrialization and accompanying urbanization are further expanding emission of greenhouse gases into the atmosphere. The illegal encroachments into environmentally sensitive areas, especially for industrial purposes, disrupt the ecological balances and escalate the impact of climate change in the State.

Studies and Observations

The Gadgil Report of 2011, which studied extensively about the environmental degradation happening in the Western Ghats, is one of the most important studies about the environment in Kerala. The report identifies certain areas in Western Ghats as Ecologically Sensitive Areas based on their biological characteristics, elevation, slope, climate, risk and historical significance. The report also pointed out that 64% of the area in Western Ghats constitutes an Ecologically Sensitive Area.

Gadgil had warned that many disasters would follow if the Western Ghats were not protected. Without acknowledging this, the Kasturirangan Committee was appointed to review the Gadgil report. According to the Kasturirangan report, only 37% of the Western Ghats is considered an Ecologically Sensitive Area.

Global warming and climate change affect each region in different ways. The Intergovernmental Panel on Climate Change (IPCC) was established internationally to provide scientific assessments on climate change, its implications and future risks, and to put forward mitigation measures. According to the report released by IPCC in 2021, the sea level will increase by 0.11m, and the sea will engulf shores. By 2130, many of the coastal places, including Kochi, will be submerged.

According to a study by the Indian Network of Climate Change, rainfall is expected to increase by about 6-8% in the Western Ghats and western coastal areas by 2030 when compared to the 1970s, and temperatures are expected to rise by 1-3 degrees Celsius. Ice melting and thermal expansion in the oceans (changes in shape, volume, and density caused by changes in the temperature of an object) will cause water levels to rise. In addition, global warming is causing atmospheric and sea temperatures to rise sharply. This causes more low pressure to form in the atmosphere. They are more likely to turn into hurricanes at any time in the future.

According to the Indian Meteorological Department (IMD), there was a 52% increase in development of cyclone movements in the Arabian Sea from 2001 to 2019 and an 8% increase in the Bay of Bengal. Four of the nine major depressions in 2020 were in the Arabian Sea. This is another central concern for Kerala.

Dangerous Coastline

The government studies indicate that 322 km of the 580 km long coastline of Kerala is prone to sea turbulence and coastal erosion. If the sea level rises by another one meter, 169 sq km of land off the coast of Kochi will be submerged. According to a report published by the National Centre for Coastal Research (NCCR), 41% of Kerala's coastal land has been degraded and 21% expanded so far.

In the future, the sea level will rise even higher. The existing shores will be washed away by the sea and sedimentation of sand will happen in some parts. Such changes and the resulting disasters will damage the habitat of humans and other organisms.

Irregular Monsoon and Landslides

Low pressure in the ocean causes heavy rainfall over land. In addition, irregular monsoon is a problem faced in Kerala. And 14.5% of the state is prone to floods. In addition to these causes, mining, illegal quarrying, deforestation, land encroachment and changes in farming practices increase the risk of landslides and debris flow in the hilly areas of Kerala. Due to this unpredictable and rapid occurrence of climate events, many lives were lost in landslides in Kerala.

Drought and Wildfire

Kerala is as prone to drought as it is to floods. Water scarcity is another issue. Kerala has experienced severe drought in previous years. If the drought conditions intensify, there is a high risk of wildfires in the future. There are 1,719 fire points in Kerala where there are chances of fires.

The Directorate of Environment & Climate Change works at the state level to coordinate activities against climate change. The department's main objective is to implement the Kerala State Environment Policy, State Action Plan for Climate Change, National Environmental Policy 2016 and Green Protocol. The State Disaster Management Authority and the District Disaster Management Authority are responsible for mitigating and preventing potential disasters in the State. For the necessary training and awareness programs to improve disaster mitigation plans, the state has The Institute for Land and Disaster Management. In addition, there are institutions like the Indian Meteorological Department and the National Center for Earth Science Studies for weather forecasting and monitoring. The Institute of Climate Change Studies has been established in Kottayam for research and study of climate change in Kerala.

Climate change is not something that can be prevented. The state must prepare itself to become more climate resilient. However, the only way to survive such climate events is to minimise the impact of this phenomenon. How is Kerala adapting to climate change and its resulting disasters? How ready is Kerala for this? How should Kerala society change to prevent disasters? The climate change series of Mathrubhumi fact check explores all these relevant issues.

Share this Article

Related topics, kerala disaster, get daily updates from mathrubhumi.com, more from this section.

Unidentified man attempts to kiss, gropes UK vloggers during Thrissur Pooram; ...

Four-member Malayali family killed in tragic road accident in America

Actress Anumol pens a heartfelt note about her 29 years of loss and longing

• Mathrubhumi News
• Media School

• Privacy Policy
• Terms of Use
• Subscription
• Classifieds

© Copyright Mathrubhumi 2024. All rights reserved.

• Kerala News
• Shashi Tharoor
• M G Radhakrishnan
• SR Suryanarayan
• Mini Krishnan
• Movie Review
• Sports News
• Scholarships
• Agriculture

Click on ‘Get News Alerts’ to get the latest news alerts from

Search the United Nations

• What Is Climate Change
• Myth Busters
• Renewable Energy
• Finance & Justice
• Initiatives
• Sustainable Development Goals
• Paris Agreement
• Climate Ambition Summit 2023
• Climate Conferences
• Press Material
• Communications Tips

Causes and Effects of Climate Change

Fossil fuels – coal, oil and gas – are by far the largest contributor to global climate change, accounting for over 75 per cent of global greenhouse gas emissions and nearly 90 per cent of all carbon dioxide emissions. As greenhouse gas emissions blanket the Earth, they trap the sun’s heat. This leads to global warming and climate change. The world is now warming faster than at any point in recorded history. Warmer temperatures over time are changing weather patterns and disrupting the usual balance of nature. This poses many risks to human beings and all other forms of life on Earth. 

Sacred plant helps forge a climate-friendly future in Paraguay

El Niño and climate crisis raise drought fears in Madagascar

The El Niño climate pattern, a naturally occurring phenomenon, can significantly disrupt global weather systems, but the human-made climate emergency is exacerbating the destructive effects.

“Verified for Climate” champions: Communicating science and solutions

Gustavo Figueirôa, biologist and communications director at SOS Pantanal, and Habiba Abdulrahman, eco-fashion educator, introduce themselves as champions for “Verified for Climate,” a joint initiative of the United Nations and Purpose to stand up to climate disinformation and put an end to the narratives of denialism, doomism, and delay.

Facts and figures

• What is climate change?
• Causes and effects
• Myth busters

Cutting emissions

• Explaining net zero
• High-level expert group on net zero
• Checklists for credibility of net-zero pledges
• Greenwashing
• What you can do

Clean energy

• Renewable energy – key to a safer future
• What is renewable energy
• Five ways to speed up the energy transition
• Why invest in renewable energy
• Clean energy stories
• A just transition

Adapting to climate change

• Climate adaptation
• Early warnings for all
• Youth voices

Financing climate action

• Finance and justice
• Loss and damage
• $100 billion commitment
• Why finance climate action
• Biodiversity
• Human Security

International cooperation

• What are Nationally Determined Contributions
• Acceleration Agenda
• Climate Ambition Summit
• Climate conferences (COPs)
• Youth Advisory Group
• Action initiatives
• Secretary-General’s speeches
• Press material
• Fact sheets
• Communications tips

Climate Change

Climate crisis in kerala: an integrated approach is needed to mitigate impact.

The state has been experiencing an onslaught of heavy rains, floods, landslides and droughts over the last few years

By Pradeep Balan, Jessy M.D

Published: tuesday 04 january 2022.

Kerala has been experiencing an onslaught of heavy rains, floods, landslides and droughts over the last few years. The state has received heavy rainfall in 1924, 1961, 2018 and 2021.

The carbon emitted by humans into the atmosphere since the Industrial Revolution is one of the major causes of the current climate crisis . But human interactions have accelerated the impacts of climate change.

In a densely populated (859 per square kilometres) and geographically small state like Kerala (38,863 sq km), it is very important to take appropriate measures to prevent the impact of natural disasters such as floods and landslides.

Climate change in Kerala is likely due to the combined effect of geography, land-use change, urbanisation, development activities and population density of the state.

The maximum distance between the eastern and western parts of Kerala is only 120 km (in some places it is only 35 km). Within this 120 km, there are places above 2,695 metres (Anamudi, Idukki district) and places up to 2 metres below sea level (Alappuzha and Kottayam districts).

One has to travel hardly 120 km to reach sea level, from a height of about 2,695 metres. Therefore, in case of heavy rainfall, water should flow smoothly from the eastern hills of Kerala to the west coast. When this is interrupted, the effects of impacts are likely to increase.

The water of 41 rivers flowing westwards in Kerala has to fall into the sea across 120 km. It is estimated that there are about 58 dams in Kerala. Although dams are a part of development, there are related factors that impede the natural flow of rivers.

Though dams can control flooding, the flow of water through rivers and their tributaries decreases only after the dams have been constructed. When the water recedes, people use the river banks for agricultural and household purposes.

Those living along the river banks are most affected when the dams are opened during the rainy season.

People have migrated to the foothills of the Western Ghats for agriculture and housing. The origin of many rivers in Kerala starts from these portions of the Western Ghats. Buildings, roads, agriculture and construction activities obstruct the natural flow of rainwater.

The total length of roads in Kerala is about 331,904 kilometres. Its total area is around 165,952 hectares if we arbitrarily assume the average width of a road is 5 metres.

Similarly, the total number of households in Kerala is 7.8 million. If we presume the average area of a house is around ​​5 cents, it covers an area of ​​about 157,827 ha of concrete buildings, all of which are permanent blockages. This prevents the infiltration rate of rainwater from reaching the ground.

The myth that plantation crops in Kerala’s Western Ghats are affected by landslides may be widespread, but extreme rainfall in an area can lead to landslides when the water saturation capacity of soils exceeds. It is highly likely to trigger landslides even in forested areas.

Landslides are triggered by the slope of an area, rainfall intensity, soil saturation capacity, soil depth and geological structure of a location. Plantation agriculture doesn’t disturb soils. 

This reduces the risk of a landslide. Science-based practices are crucial to minimise natural disasters. Plantation agriculture such as the rubber sector has issued advisories for rubber plantations grown in landslide-prone areas.

Quarrying, mining and large-scale construction activities, which affect the ecological stability of the landscape, could be the major factors causing these landslides. There are an estimated 5,924 quarries in Kerala.

The low-lying areas in the western part of Kerala are prone to flash floods. If the construction is done in areas with drainages, the natural flow of water can be obstructed. It is then highly likely that water will flow into areas where it can flow.

It can sometimes be through cities or even places where houses are located. Floods at Kochi International Airport in 2018 were an example of this. The airport is located in a low-lying area close to the watersheds / rivers, which is prone to flash floods. It is, therefore, vital to prepare flood risk zones at the micro level to identify, locate and manage the regions most vulnerable to floods.

While Kerala receives an annual average rainfall of 3,000 mm, the possibility of drought also looms large. The state, for example, experienced drought in 2017. The southern parts of the state (Kollam), central Kerala (Palakkad) and North Kerala (Kannur and Kasaragod districts) generally experience summer droughts (February to May).

Although geography and soil characteristics play an important role in drought , the major amount of rainfall received in Kerala falls into the sea in a short time because of the state's sloping terrain.

If more rainwater is infiltrated into the soil, it will enhance the amount of groundwater recharge. Rainwater harvesting and protection of watersheds can help alleviate drought to some extent.

It is essential to regulate climate disasters and create awareness in a densely populated state like Kerala. An integrated approach is needed to manage climate change impacts.

Views expressed are the authors’ own and don't necessarily reflect those of  Down To Earth

We are a voice to you; you have been a support to us. Together we build journalism that is independent, credible and fearless. You can further help us by making a donation. This will mean a lot for our ability to bring you news, perspectives and analysis from the ground so that we can make change together.

Comments are moderated and will be published only after the site moderator’s approval. Please use a genuine email ID and provide your name. Selected comments may also be used in the ‘Letters’ section of the Down To Earth print edition.

Climate Change: Evidence and Causes: Update 2020 (2020)

Chapter: conclusion, c onclusion.

This document explains that there are well-understood physical mechanisms by which changes in the amounts of greenhouse gases cause climate changes. It discusses the evidence that the concentrations of these gases in the atmosphere have increased and are still increasing rapidly, that climate change is occurring, and that most of the recent change is almost certainly due to emissions of greenhouse gases caused by human activities. Further climate change is inevitable; if emissions of greenhouse gases continue unabated, future changes will substantially exceed those that have occurred so far. There remains a range of estimates of the magnitude and regional expression of future change, but increases in the extremes of climate that can adversely affect natural ecosystems and human activities and infrastructure are expected.

Citizens and governments can choose among several options (or a mixture of those options) in response to this information: they can change their pattern of energy production and usage in order to limit emissions of greenhouse gases and hence the magnitude of climate changes; they can wait for changes to occur and accept the losses, damage, and suffering that arise; they can adapt to actual and expected changes as much as possible; or they can seek as yet unproven “geoengineering” solutions to counteract some of the climate changes that would otherwise occur. Each of these options has risks, attractions and costs, and what is actually done may be a mixture of these different options. Different nations and communities will vary in their vulnerability and their capacity to adapt. There is an important debate to be had about choices among these options, to decide what is best for each group or nation, and most importantly for the global population as a whole. The options have to be discussed at a global scale because in many cases those communities that are most vulnerable control few of the emissions, either past or future. Our description of the science of climate change, with both its facts and its uncertainties, is offered as a basis to inform that policy debate.

A CKNOWLEDGEMENTS

The following individuals served as the primary writing team for the 2014 and 2020 editions of this document:

• Eric Wolff FRS, (UK lead), University of Cambridge
• Inez Fung (NAS, US lead), University of California, Berkeley
• Brian Hoskins FRS, Grantham Institute for Climate Change
• John F.B. Mitchell FRS, UK Met Office
• Tim Palmer FRS, University of Oxford
• Benjamin Santer (NAS), Lawrence Livermore National Laboratory
• John Shepherd FRS, University of Southampton
• Keith Shine FRS, University of Reading.
• Susan Solomon (NAS), Massachusetts Institute of Technology
• Kevin Trenberth, National Center for Atmospheric Research
• John Walsh, University of Alaska, Fairbanks
• Don Wuebbles, University of Illinois

Staff support for the 2020 revision was provided by Richard Walker, Amanda Purcell, Nancy Huddleston, and Michael Hudson. We offer special thanks to Rebecca Lindsey and NOAA Climate.gov for providing data and figure updates.

The following individuals served as reviewers of the 2014 document in accordance with procedures approved by the Royal Society and the National Academy of Sciences:

• Richard Alley (NAS), Department of Geosciences, Pennsylvania State University
• Alec Broers FRS, Former President of the Royal Academy of Engineering
• Harry Elderfield FRS, Department of Earth Sciences, University of Cambridge
• Joanna Haigh FRS, Professor of Atmospheric Physics, Imperial College London
• Isaac Held (NAS), NOAA Geophysical Fluid Dynamics Laboratory
• John Kutzbach (NAS), Center for Climatic Research, University of Wisconsin
• Jerry Meehl, Senior Scientist, National Center for Atmospheric Research
• John Pendry FRS, Imperial College London
• John Pyle FRS, Department of Chemistry, University of Cambridge
• Gavin Schmidt, NASA Goddard Space Flight Center
• Emily Shuckburgh, British Antarctic Survey
• Gabrielle Walker, Journalist
• Andrew Watson FRS, University of East Anglia

The Support for the 2014 Edition was provided by NAS Endowment Funds. We offer sincere thanks to the Ralph J. and Carol M. Cicerone Endowment for NAS Missions for supporting the production of this 2020 Edition.

F OR FURTHER READING

For more detailed discussion of the topics addressed in this document (including references to the underlying original research), see:

• Intergovernmental Panel on Climate Change (IPCC), 2019: Special Report on the Ocean and Cryosphere in a Changing Climate [ https://www.ipcc.ch/srocc ]
• National Academies of Sciences, Engineering, and Medicine (NASEM), 2019: Negative Emissions Technologies and Reliable Sequestration: A Research Agenda [ https://www.nap.edu/catalog/25259 ]
• Royal Society, 2018: Greenhouse gas removal [ https://raeng.org.uk/greenhousegasremoval ]
• U.S. Global Change Research Program (USGCRP), 2018: Fourth National Climate Assessment Volume II: Impacts, Risks, and Adaptation in the United States [ https://nca2018.globalchange.gov ]
• IPCC, 2018: Global Warming of 1.5°C [ https://www.ipcc.ch/sr15 ]
• USGCRP, 2017: Fourth National Climate Assessment Volume I: Climate Science Special Reports [ https://science2017.globalchange.gov ]
• NASEM, 2016: Attribution of Extreme Weather Events in the Context of Climate Change [ https://www.nap.edu/catalog/21852 ]
• IPCC, 2013: Fifth Assessment Report (AR5) Working Group 1. Climate Change 2013: The Physical Science Basis [ https://www.ipcc.ch/report/ar5/wg1 ]
• NRC, 2013: Abrupt Impacts of Climate Change: Anticipating Surprises [ https://www.nap.edu/catalog/18373 ]
• NRC, 2011: Climate Stabilization Targets: Emissions, Concentrations, and Impacts Over Decades to Millennia [ https://www.nap.edu/catalog/12877 ]
• Royal Society 2010: Climate Change: A Summary of the Science [ https://royalsociety.org/topics-policy/publications/2010/climate-change-summary-science ]
• NRC, 2010: America’s Climate Choices: Advancing the Science of Climate Change [ https://www.nap.edu/catalog/12782 ]

Much of the original data underlying the scientific findings discussed here are available at:

• https://data.ucar.edu/
• https://climatedataguide.ucar.edu
• https://iridl.ldeo.columbia.edu
• https://ess-dive.lbl.gov/
• https://www.ncdc.noaa.gov/
• https://www.esrl.noaa.gov/gmd/ccgg/trends/
• http://scrippsco2.ucsd.edu
• http://hahana.soest.hawaii.edu/hot/

Climate change is one of the defining issues of our time. It is now more certain than ever, based on many lines of evidence, that humans are changing Earth's climate. The Royal Society and the US National Academy of Sciences, with their similar missions to promote the use of science to benefit society and to inform critical policy debates, produced the original Climate Change: Evidence and Causes in 2014. It was written and reviewed by a UK-US team of leading climate scientists. This new edition, prepared by the same author team, has been updated with the most recent climate data and scientific analyses, all of which reinforce our understanding of human-caused climate change.

Scientific information is a vital component for society to make informed decisions about how to reduce the magnitude of climate change and how to adapt to its impacts. This booklet serves as a key reference document for decision makers, policy makers, educators, and others seeking authoritative answers about the current state of climate-change science.

READ FREE ONLINE

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

A review of the global climate change impacts, adaptation, and sustainable mitigation measures

Kashif abbass.

1 School of Economics and Management, Nanjing University of Science and Technology, Nanjing, 210094 People’s Republic of China

Muhammad Zeeshan Qasim

2 Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Xiaolingwei 200, Nanjing, 210094 People’s Republic of China

Huaming Song

Muntasir murshed.

3 School of Business and Economics, North South University, Dhaka, 1229 Bangladesh

4 Department of Journalism, Media and Communications, Daffodil International University, Dhaka, Bangladesh

Haider Mahmood

5 Department of Finance, College of Business Administration, Prince Sattam Bin Abdulaziz University, 173, Alkharj, 11942 Saudi Arabia

Ijaz Younis

Associated data.

Data sources and relevant links are provided in the paper to access data.

Climate change is a long-lasting change in the weather arrays across tropics to polls. It is a global threat that has embarked on to put stress on various sectors. This study is aimed to conceptually engineer how climate variability is deteriorating the sustainability of diverse sectors worldwide. Specifically, the agricultural sector’s vulnerability is a globally concerning scenario, as sufficient production and food supplies are threatened due to irreversible weather fluctuations. In turn, it is challenging the global feeding patterns, particularly in countries with agriculture as an integral part of their economy and total productivity. Climate change has also put the integrity and survival of many species at stake due to shifts in optimum temperature ranges, thereby accelerating biodiversity loss by progressively changing the ecosystem structures. Climate variations increase the likelihood of particular food and waterborne and vector-borne diseases, and a recent example is a coronavirus pandemic. Climate change also accelerates the enigma of antimicrobial resistance, another threat to human health due to the increasing incidence of resistant pathogenic infections. Besides, the global tourism industry is devastated as climate change impacts unfavorable tourism spots. The methodology investigates hypothetical scenarios of climate variability and attempts to describe the quality of evidence to facilitate readers’ careful, critical engagement. Secondary data is used to identify sustainability issues such as environmental, social, and economic viability. To better understand the problem, gathered the information in this report from various media outlets, research agencies, policy papers, newspapers, and other sources. This review is a sectorial assessment of climate change mitigation and adaptation approaches worldwide in the aforementioned sectors and the associated economic costs. According to the findings, government involvement is necessary for the country’s long-term development through strict accountability of resources and regulations implemented in the past to generate cutting-edge climate policy. Therefore, mitigating the impacts of climate change must be of the utmost importance, and hence, this global threat requires global commitment to address its dreadful implications to ensure global sustenance.

Introduction

Worldwide observed and anticipated climatic changes for the twenty-first century and global warming are significant global changes that have been encountered during the past 65 years. Climate change (CC) is an inter-governmental complex challenge globally with its influence over various components of the ecological, environmental, socio-political, and socio-economic disciplines (Adger et al.  2005 ; Leal Filho et al.  2021 ; Feliciano et al.  2022 ). Climate change involves heightened temperatures across numerous worlds (Battisti and Naylor  2009 ; Schuurmans  2021 ; Weisheimer and Palmer  2005 ; Yadav et al.  2015 ). With the onset of the industrial revolution, the problem of earth climate was amplified manifold (Leppänen et al.  2014 ). It is reported that the immediate attention and due steps might increase the probability of overcoming its devastating impacts. It is not plausible to interpret the exact consequences of climate change (CC) on a sectoral basis (Izaguirre et al.  2021 ; Jurgilevich et al.  2017 ), which is evident by the emerging level of recognition plus the inclusion of climatic uncertainties at both local and national level of policymaking (Ayers et al.  2014 ).

Climate change is characterized based on the comprehensive long-haul temperature and precipitation trends and other components such as pressure and humidity level in the surrounding environment. Besides, the irregular weather patterns, retreating of global ice sheets, and the corresponding elevated sea level rise are among the most renowned international and domestic effects of climate change (Lipczynska-Kochany  2018 ; Michel et al.  2021 ; Murshed and Dao 2020 ). Before the industrial revolution, natural sources, including volcanoes, forest fires, and seismic activities, were regarded as the distinct sources of greenhouse gases (GHGs) such as CO 2 , CH 4 , N 2 O, and H 2 O into the atmosphere (Murshed et al. 2020 ; Hussain et al.  2020 ; Sovacool et al.  2021 ; Usman and Balsalobre-Lorente 2022 ; Murshed 2022 ). United Nations Framework Convention on Climate Change (UNFCCC) struck a major agreement to tackle climate change and accelerate and intensify the actions and investments required for a sustainable low-carbon future at Conference of the Parties (COP-21) in Paris on December 12, 2015. The Paris Agreement expands on the Convention by bringing all nations together for the first time in a single cause to undertake ambitious measures to prevent climate change and adapt to its impacts, with increased funding to assist developing countries in doing so. As so, it marks a turning point in the global climate fight. The core goal of the Paris Agreement is to improve the global response to the threat of climate change by keeping the global temperature rise this century well below 2 °C over pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5° C (Sharma et al. 2020 ; Sharif et al. 2020 ; Chien et al. 2021 .

Furthermore, the agreement aspires to strengthen nations’ ability to deal with the effects of climate change and align financing flows with low GHG emissions and climate-resilient paths (Shahbaz et al. 2019 ; Anwar et al. 2021 ; Usman et al. 2022a ). To achieve these lofty goals, adequate financial resources must be mobilized and provided, as well as a new technology framework and expanded capacity building, allowing developing countries and the most vulnerable countries to act under their respective national objectives. The agreement also establishes a more transparent action and support mechanism. All Parties are required by the Paris Agreement to do their best through “nationally determined contributions” (NDCs) and to strengthen these efforts in the coming years (Balsalobre-Lorente et al. 2020 ). It includes obligations that all Parties regularly report on their emissions and implementation activities. A global stock-take will be conducted every five years to review collective progress toward the agreement’s goal and inform the Parties’ future individual actions. The Paris Agreement became available for signature on April 22, 2016, Earth Day, at the United Nations Headquarters in New York. On November 4, 2016, it went into effect 30 days after the so-called double threshold was met (ratification by 55 nations accounting for at least 55% of world emissions). More countries have ratified and continue to ratify the agreement since then, bringing 125 Parties in early 2017. To fully operationalize the Paris Agreement, a work program was initiated in Paris to define mechanisms, processes, and recommendations on a wide range of concerns (Murshed et al. 2021 ). Since 2016, Parties have collaborated in subsidiary bodies (APA, SBSTA, and SBI) and numerous formed entities. The Conference of the Parties functioning as the meeting of the Parties to the Paris Agreement (CMA) convened for the first time in November 2016 in Marrakesh in conjunction with COP22 and made its first two resolutions. The work plan is scheduled to be finished by 2018. Some mitigation and adaptation strategies to reduce the emission in the prospective of Paris agreement are following firstly, a long-term goal of keeping the increase in global average temperature to well below 2 °C above pre-industrial levels, secondly, to aim to limit the rise to 1.5 °C, since this would significantly reduce risks and the impacts of climate change, thirdly, on the need for global emissions to peak as soon as possible, recognizing that this will take longer for developing countries, lastly, to undertake rapid reductions after that under the best available science, to achieve a balance between emissions and removals in the second half of the century. On the other side, some adaptation strategies are; strengthening societies’ ability to deal with the effects of climate change and to continue & expand international assistance for developing nations’ adaptation.

However, anthropogenic activities are currently regarded as most accountable for CC (Murshed et al. 2022 ). Apart from the industrial revolution, other anthropogenic activities include excessive agricultural operations, which further involve the high use of fuel-based mechanization, burning of agricultural residues, burning fossil fuels, deforestation, national and domestic transportation sectors, etc. (Huang et al.  2016 ). Consequently, these anthropogenic activities lead to climatic catastrophes, damaging local and global infrastructure, human health, and total productivity. Energy consumption has mounted GHGs levels concerning warming temperatures as most of the energy production in developing countries comes from fossil fuels (Balsalobre-Lorente et al. 2022 ; Usman et al. 2022b ; Abbass et al. 2021a ; Ishikawa-Ishiwata and Furuya  2022 ).

This review aims to highlight the effects of climate change in a socio-scientific aspect by analyzing the existing literature on various sectorial pieces of evidence globally that influence the environment. Although this review provides a thorough examination of climate change and its severe affected sectors that pose a grave danger for global agriculture, biodiversity, health, economy, forestry, and tourism, and to purpose some practical prophylactic measures and mitigation strategies to be adapted as sound substitutes to survive from climate change (CC) impacts. The societal implications of irregular weather patterns and other effects of climate changes are discussed in detail. Some numerous sustainable mitigation measures and adaptation practices and techniques at the global level are discussed in this review with an in-depth focus on its economic, social, and environmental aspects. Methods of data collection section are included in the supplementary information.

Review methodology

Related study and its objectives.

Today, we live an ordinary life in the beautiful digital, globalized world where climate change has a decisive role. What happens in one country has a massive influence on geographically far apart countries, which points to the current crisis known as COVID-19 (Sarkar et al.  2021 ). The most dangerous disease like COVID-19 has affected the world’s climate changes and economic conditions (Abbass et al. 2022 ; Pirasteh-Anosheh et al.  2021 ). The purpose of the present study is to review the status of research on the subject, which is based on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures” by systematically reviewing past published and unpublished research work. Furthermore, the current study seeks to comment on research on the same topic and suggest future research on the same topic. Specifically, the present study aims: The first one is, organize publications to make them easy and quick to find. Secondly, to explore issues in this area, propose an outline of research for future work. The third aim of the study is to synthesize the previous literature on climate change, various sectors, and their mitigation measurement. Lastly , classify the articles according to the different methods and procedures that have been adopted.

Review methodology for reviewers

This review-based article followed systematic literature review techniques that have proved the literature review as a rigorous framework (Benita  2021 ; Tranfield et al.  2003 ). Moreover, we illustrate in Fig.  1 the search method that we have started for this research. First, finalized the research theme to search literature (Cooper et al.  2018 ). Second, used numerous research databases to search related articles and download from the database (Web of Science, Google Scholar, Scopus Index Journals, Emerald, Elsevier Science Direct, Springer, and Sciverse). We focused on various articles, with research articles, feedback pieces, short notes, debates, and review articles published in scholarly journals. Reports used to search for multiple keywords such as “Climate Change,” “Mitigation and Adaptation,” “Department of Agriculture and Human Health,” “Department of Biodiversity and Forestry,” etc.; in summary, keyword list and full text have been made. Initially, the search for keywords yielded a large amount of literature.

Methodology search for finalized articles for investigations.

Source : constructed by authors

Since 2020, it has been impossible to review all the articles found; some restrictions have been set for the literature exhibition. The study searched 95 articles on a different database mentioned above based on the nature of the study. It excluded 40 irrelevant papers due to copied from a previous search after readings tiles, abstract and full pieces. The criteria for inclusion were: (i) articles focused on “Global Climate Change Impacts, adaptation, and sustainable mitigation measures,” and (ii) the search key terms related to study requirements. The complete procedure yielded 55 articles for our study. We repeat our search on the “Web of Science and Google Scholars” database to enhance the search results and check the referenced articles.

In this study, 55 articles are reviewed systematically and analyzed for research topics and other aspects, such as the methods, contexts, and theories used in these studies. Furthermore, this study analyzes closely related areas to provide unique research opportunities in the future. The study also discussed future direction opportunities and research questions by understanding the research findings climate changes and other affected sectors. The reviewed paper framework analysis process is outlined in Fig.  2 .

Framework of the analysis Process.

Natural disasters and climate change’s socio-economic consequences

Natural and environmental disasters can be highly variable from year to year; some years pass with very few deaths before a significant disaster event claims many lives (Symanski et al.  2021 ). Approximately 60,000 people globally died from natural disasters each year on average over the past decade (Ritchie and Roser  2014 ; Wiranata and Simbolon  2021 ). So, according to the report, around 0.1% of global deaths. Annual variability in the number and share of deaths from natural disasters in recent decades are shown in Fig.  3 . The number of fatalities can be meager—sometimes less than 10,000, and as few as 0.01% of all deaths. But shock events have a devastating impact: the 1983–1985 famine and drought in Ethiopia; the 2004 Indian Ocean earthquake and tsunami; Cyclone Nargis, which struck Myanmar in 2008; and the 2010 Port-au-Prince earthquake in Haiti and now recent example is COVID-19 pandemic (Erman et al.  2021 ). These events pushed global disaster deaths to over 200,000—more than 0.4% of deaths in these years. Low-frequency, high-impact events such as earthquakes and tsunamis are not preventable, but such high losses of human life are. Historical evidence shows that earlier disaster detection, more robust infrastructure, emergency preparedness, and response programmers have substantially reduced disaster deaths worldwide. Low-income is also the most vulnerable to disasters; improving living conditions, facilities, and response services in these areas would be critical in reducing natural disaster deaths in the coming decades.

Global deaths from natural disasters, 1978 to 2020.

Source EMDAT ( 2020 )

The interior regions of the continent are likely to be impacted by rising temperatures (Dimri et al.  2018 ; Goes et al.  2020 ; Mannig et al.  2018 ; Schuurmans  2021 ). Weather patterns change due to the shortage of natural resources (water), increase in glacier melting, and rising mercury are likely to cause extinction to many planted species (Gampe et al.  2016 ; Mihiretu et al.  2021 ; Shaffril et al.  2018 ).On the other hand, the coastal ecosystem is on the verge of devastation (Perera et al.  2018 ; Phillips  2018 ). The temperature rises, insect disease outbreaks, health-related problems, and seasonal and lifestyle changes are persistent, with a strong probability of these patterns continuing in the future (Abbass et al. 2021c ; Hussain et al.  2018 ). At the global level, a shortage of good infrastructure and insufficient adaptive capacity are hammering the most (IPCC  2013 ). In addition to the above concerns, a lack of environmental education and knowledge, outdated consumer behavior, a scarcity of incentives, a lack of legislation, and the government’s lack of commitment to climate change contribute to the general public’s concerns. By 2050, a 2 to 3% rise in mercury and a drastic shift in rainfall patterns may have serious consequences (Huang et al. 2022 ; Gorst et al.  2018 ). Natural and environmental calamities caused huge losses globally, such as decreased agriculture outputs, rehabilitation of the system, and rebuilding necessary technologies (Ali and Erenstein  2017 ; Ramankutty et al.  2018 ; Yu et al.  2021 ) (Table ​ (Table1). 1 ). Furthermore, in the last 3 or 4 years, the world has been plagued by smog-related eye and skin diseases, as well as a rise in road accidents due to poor visibility.

Main natural danger statistics for 1985–2020 at the global level

Source: EM-DAT ( 2020 )

Climate change and agriculture

Global agriculture is the ultimate sector responsible for 30–40% of all greenhouse emissions, which makes it a leading industry predominantly contributing to climate warming and significantly impacted by it (Grieg; Mishra et al.  2021 ; Ortiz et al.  2021 ; Thornton and Lipper  2014 ). Numerous agro-environmental and climatic factors that have a dominant influence on agriculture productivity (Pautasso et al.  2012 ) are significantly impacted in response to precipitation extremes including floods, forest fires, and droughts (Huang  2004 ). Besides, the immense dependency on exhaustible resources also fuels the fire and leads global agriculture to become prone to devastation. Godfray et al. ( 2010 ) mentioned that decline in agriculture challenges the farmer’s quality of life and thus a significant factor to poverty as the food and water supplies are critically impacted by CC (Ortiz et al.  2021 ; Rosenzweig et al.  2014 ). As an essential part of the economic systems, especially in developing countries, agricultural systems affect the overall economy and potentially the well-being of households (Schlenker and Roberts  2009 ). According to the report published by the Intergovernmental Panel on Climate Change (IPCC), atmospheric concentrations of greenhouse gases, i.e., CH 4, CO 2 , and N 2 O, are increased in the air to extraordinary levels over the last few centuries (Usman and Makhdum 2021 ; Stocker et al.  2013 ). Climate change is the composite outcome of two different factors. The first is the natural causes, and the second is the anthropogenic actions (Karami 2012 ). It is also forecasted that the world may experience a typical rise in temperature stretching from 1 to 3.7 °C at the end of this century (Pachauri et al. 2014 ). The world’s crop production is also highly vulnerable to these global temperature-changing trends as raised temperatures will pose severe negative impacts on crop growth (Reidsma et al. 2009 ). Some of the recent modeling about the fate of global agriculture is briefly described below.

Decline in cereal productivity

Crop productivity will also be affected dramatically in the next few decades due to variations in integral abiotic factors such as temperature, solar radiation, precipitation, and CO 2 . These all factors are included in various regulatory instruments like progress and growth, weather-tempted changes, pest invasions (Cammell and Knight 1992 ), accompanying disease snags (Fand et al. 2012 ), water supplies (Panda et al. 2003 ), high prices of agro-products in world’s agriculture industry, and preeminent quantity of fertilizer consumption. Lobell and field ( 2007 ) claimed that from 1962 to 2002, wheat crop output had condensed significantly due to rising temperatures. Therefore, during 1980–2011, the common wheat productivity trends endorsed extreme temperature events confirmed by Gourdji et al. ( 2013 ) around South Asia, South America, and Central Asia. Various other studies (Asseng, Cao, Zhang, and Ludwig 2009 ; Asseng et al. 2013 ; García et al. 2015 ; Ortiz et al. 2021 ) also proved that wheat output is negatively affected by the rising temperatures and also caused adverse effects on biomass productivity (Calderini et al. 1999 ; Sadras and Slafer 2012 ). Hereafter, the rice crop is also influenced by the high temperatures at night. These difficulties will worsen because the temperature will be rising further in the future owing to CC (Tebaldi et al. 2006 ). Another research conducted in China revealed that a 4.6% of rice production per 1 °C has happened connected with the advancement in night temperatures (Tao et al. 2006 ). Moreover, the average night temperature growth also affected rice indicia cultivar’s output pragmatically during 25 years in the Philippines (Peng et al. 2004 ). It is anticipated that the increase in world average temperature will also cause a substantial reduction in yield (Hatfield et al. 2011 ; Lobell and Gourdji 2012 ). In the southern hemisphere, Parry et al. ( 2007 ) noted a rise of 1–4 °C in average daily temperatures at the end of spring season unti the middle of summers, and this raised temperature reduced crop output by cutting down the time length for phenophases eventually reduce the yield (Hatfield and Prueger 2015 ; R. Ortiz 2008 ). Also, world climate models have recommended that humid and subtropical regions expect to be plentiful prey to the upcoming heat strokes (Battisti and Naylor 2009 ). Grain production is the amalgamation of two constituents: the average weight and the grain output/m 2 , however, in crop production. Crop output is mainly accredited to the grain quantity (Araus et al. 2008 ; Gambín and Borrás 2010 ). In the times of grain set, yield resources are mainly strewn between hitherto defined components, i.e., grain usual weight and grain output, which presents a trade-off between them (Gambín and Borrás 2010 ) beside disparities in per grain integration (B. L. Gambín et al. 2006 ). In addition to this, the maize crop is also susceptible to raised temperatures, principally in the flowering stage (Edreira and Otegui 2013 ). In reality, the lower grain number is associated with insufficient acclimatization due to intense photosynthesis and higher respiration and the high-temperature effect on the reproduction phenomena (Edreira and Otegui 2013 ). During the flowering phase, maize visible to heat (30–36 °C) seemed less anthesis-silking intermissions (Edreira et al. 2011 ). Another research by Dupuis and Dumas ( 1990 ) proved that a drop in spikelet when directly visible to high temperatures above 35 °C in vitro pollination. Abnormalities in kernel number claimed by Vega et al. ( 2001 ) is related to conceded plant development during a flowering phase that is linked with the active ear growth phase and categorized as a critical phase for approximation of kernel number during silking (Otegui and Bonhomme 1998 ).

The retort of rice output to high temperature presents disparities in flowering patterns, and seed set lessens and lessens grain weight (Qasim et al. 2020 ; Qasim, Hammad, Maqsood, Tariq, & Chawla). During the daytime, heat directly impacts flowers which lessens the thesis period and quickens the earlier peak flowering (Tao et al. 2006 ). Antagonistic effect of higher daytime temperature d on pollen sprouting proposed seed set decay, whereas, seed set was lengthily reduced than could be explicated by pollen growing at high temperatures 40◦C (Matsui et al. 2001 ).

The decline in wheat output is linked with higher temperatures, confirmed in numerous studies (Semenov 2009 ; Stone and Nicolas 1994 ). High temperatures fast-track the arrangements of plant expansion (Blum et al. 2001 ), diminution photosynthetic process (Salvucci and Crafts‐Brandner 2004 ), and also considerably affect the reproductive operations (Farooq et al. 2011 ).

The destructive impacts of CC induced weather extremes to deteriorate the integrity of crops (Chaudhary et al. 2011 ), e.g., Spartan cold and extreme fog cause falling and discoloration of betel leaves (Rosenzweig et al. 2001 ), giving them a somehow reddish appearance, squeezing of lemon leaves (Pautasso et al. 2012 ), as well as root rot of pineapple, have reported (Vedwan and Rhoades 2001 ). Henceforth, in tackling the disruptive effects of CC, several short-term and long-term management approaches are the crucial need of time (Fig.  4 ). Moreover, various studies (Chaudhary et al. 2011 ; Patz et al. 2005 ; Pautasso et al. 2012 ) have demonstrated adapting trends such as ameliorating crop diversity can yield better adaptability towards CC.

Schematic description of potential impacts of climate change on the agriculture sector and the appropriate mitigation and adaptation measures to overcome its impact.

Climate change impacts on biodiversity

Global biodiversity is among the severe victims of CC because it is the fastest emerging cause of species loss. Studies demonstrated that the massive scale species dynamics are considerably associated with diverse climatic events (Abraham and Chain 1988 ; Manes et al. 2021 ; A. M. D. Ortiz et al. 2021 ). Both the pace and magnitude of CC are altering the compatible habitat ranges for living entities of marine, freshwater, and terrestrial regions. Alterations in general climate regimes influence the integrity of ecosystems in numerous ways, such as variation in the relative abundance of species, range shifts, changes in activity timing, and microhabitat use (Bates et al. 2014 ). The geographic distribution of any species often depends upon its ability to tolerate environmental stresses, biological interactions, and dispersal constraints. Hence, instead of the CC, the local species must only accept, adapt, move, or face extinction (Berg et al. 2010 ). So, the best performer species have a better survival capacity for adjusting to new ecosystems or a decreased perseverance to survive where they are already situated (Bates et al. 2014 ). An important aspect here is the inadequate habitat connectivity and access to microclimates, also crucial in raising the exposure to climate warming and extreme heatwave episodes. For example, the carbon sequestration rates are undergoing fluctuations due to climate-driven expansion in the range of global mangroves (Cavanaugh et al. 2014 ).

Similarly, the loss of kelp-forest ecosystems in various regions and its occupancy by the seaweed turfs has set the track for elevated herbivory by the high influx of tropical fish populations. Not only this, the increased water temperatures have exacerbated the conditions far away from the physiological tolerance level of the kelp communities (Vergés et al. 2016 ; Wernberg et al. 2016 ). Another pertinent danger is the devastation of keystone species, which even has more pervasive effects on the entire communities in that habitat (Zarnetske et al. 2012 ). It is particularly important as CC does not specify specific populations or communities. Eventually, this CC-induced redistribution of species may deteriorate carbon storage and the net ecosystem productivity (Weed et al. 2013 ). Among the typical disruptions, the prominent ones include impacts on marine and terrestrial productivity, marine community assembly, and the extended invasion of toxic cyanobacteria bloom (Fossheim et al. 2015 ).

The CC-impacted species extinction is widely reported in the literature (Beesley et al. 2019 ; Urban 2015 ), and the predictions of demise until the twenty-first century are dreadful (Abbass et al. 2019 ; Pereira et al. 2013 ). In a few cases, northward shifting of species may not be formidable as it allows mountain-dwelling species to find optimum climates. However, the migrant species may be trapped in isolated and incompatible habitats due to losing topography and range (Dullinger et al. 2012 ). For example, a study indicated that the American pika has been extirpated or intensely diminished in some regions, primarily attributed to the CC-impacted extinction or at least local extirpation (Stewart et al. 2015 ). Besides, the anticipation of persistent responses to the impacts of CC often requires data records of several decades to rigorously analyze the critical pre and post CC patterns at species and ecosystem levels (Manes et al. 2021 ; Testa et al. 2018 ).

Nonetheless, the availability of such long-term data records is rare; hence, attempts are needed to focus on these profound aspects. Biodiversity is also vulnerable to the other associated impacts of CC, such as rising temperatures, droughts, and certain invasive pest species. For instance, a study revealed the changes in the composition of plankton communities attributed to rising temperatures. Henceforth, alterations in such aquatic producer communities, i.e., diatoms and calcareous plants, can ultimately lead to variation in the recycling of biological carbon. Moreover, such changes are characterized as a potential contributor to CO 2 differences between the Pleistocene glacial and interglacial periods (Kohfeld et al. 2005 ).

Climate change implications on human health

It is an understood corporality that human health is a significant victim of CC (Costello et al. 2009 ). According to the WHO, CC might be responsible for 250,000 additional deaths per year during 2030–2050 (Watts et al. 2015 ). These deaths are attributed to extreme weather-induced mortality and morbidity and the global expansion of vector-borne diseases (Lemery et al. 2021; Yang and Usman 2021 ; Meierrieks 2021 ; UNEP 2017 ). Here, some of the emerging health issues pertinent to this global problem are briefly described.

Climate change and antimicrobial resistance with corresponding economic costs

Antimicrobial resistance (AMR) is an up-surging complex global health challenge (Garner et al. 2019 ; Lemery et al. 2021 ). Health professionals across the globe are extremely worried due to this phenomenon that has critical potential to reverse almost all the progress that has been achieved so far in the health discipline (Gosling and Arnell 2016 ). A massive amount of antibiotics is produced by many pharmaceutical industries worldwide, and the pathogenic microorganisms are gradually developing resistance to them, which can be comprehended how strongly this aspect can shake the foundations of national and global economies (UNEP 2017 ). This statement is supported by the fact that AMR is not developing in a particular region or country. Instead, it is flourishing in every continent of the world (WHO 2018 ). This plague is heavily pushing humanity to the post-antibiotic era, in which currently antibiotic-susceptible pathogens will once again lead to certain endemics and pandemics after being resistant(WHO 2018 ). Undesirably, if this statement would become a factuality, there might emerge certain risks in undertaking sophisticated interventions such as chemotherapy, joint replacement cases, and organ transplantation (Su et al. 2018 ). Presently, the amplification of drug resistance cases has made common illnesses like pneumonia, post-surgical infections, HIV/AIDS, tuberculosis, malaria, etc., too difficult and costly to be treated or cure well (WHO 2018 ). From a simple example, it can be assumed how easily antibiotic-resistant strains can be transmitted from one person to another and ultimately travel across the boundaries (Berendonk et al. 2015 ). Talking about the second- and third-generation classes of antibiotics, e.g., most renowned generations of cephalosporin antibiotics that are more expensive, broad-spectrum, more toxic, and usually require more extended periods whenever prescribed to patients (Lemery et al. 2021 ; Pärnänen et al. 2019 ). This scenario has also revealed that the abundance of resistant strains of pathogens was also higher in the Southern part (WHO 2018 ). As southern parts are generally warmer than their counterparts, it is evident from this example how CC-induced global warming can augment the spread of antibiotic-resistant strains within the biosphere, eventually putting additional economic burden in the face of developing new and costlier antibiotics. The ARG exchange to susceptible bacteria through one of the potential mechanisms, transformation, transduction, and conjugation; Selection pressure can be caused by certain antibiotics, metals or pesticides, etc., as shown in Fig.  5 .

A typical interaction between the susceptible and resistant strains.

Source: Elsayed et al. ( 2021 ); Karkman et al. ( 2018 )

Certain studies highlighted that conventional urban wastewater treatment plants are typical hotspots where most bacterial strains exchange genetic material through horizontal gene transfer (Fig.  5 ). Although at present, the extent of risks associated with the antibiotic resistance found in wastewater is complicated; environmental scientists and engineers have particular concerns about the potential impacts of these antibiotic resistance genes on human health (Ashbolt 2015 ). At most undesirable and worst case, these antibiotic-resistant genes containing bacteria can make their way to enter into the environment (Pruden et al. 2013 ), irrigation water used for crops and public water supplies and ultimately become a part of food chains and food webs (Ma et al. 2019 ; D. Wu et al. 2019 ). This problem has been reported manifold in several countries (Hendriksen et al. 2019 ), where wastewater as a means of irrigated water is quite common.

Climate change and vector borne-diseases

Temperature is a fundamental factor for the sustenance of living entities regardless of an ecosystem. So, a specific living being, especially a pathogen, requires a sophisticated temperature range to exist on earth. The second essential component of CC is precipitation, which also impacts numerous infectious agents’ transport and dissemination patterns. Global rising temperature is a significant cause of many species extinction. On the one hand, this changing environmental temperature may be causing species extinction, and on the other, this warming temperature might favor the thriving of some new organisms. Here, it was evident that some pathogens may also upraise once non-evident or reported (Patz et al. 2000 ). This concept can be exemplified through certain pathogenic strains of microorganisms that how the likelihood of various diseases increases in response to climate warming-induced environmental changes (Table ​ (Table2 2 ).

Examples of how various environmental changes affect various infectious diseases in humans

Source: Aron and Patz ( 2001 )

A recent example is an outburst of coronavirus (COVID-19) in the Republic of China, causing pneumonia and severe acute respiratory complications (Cui et al. 2021 ; Song et al. 2021 ). The large family of viruses is harbored in numerous animals, bats, and snakes in particular (livescience.com) with the subsequent transfer into human beings. Hence, it is worth noting that the thriving of numerous vectors involved in spreading various diseases is influenced by Climate change (Ogden 2018 ; Santos et al. 2021 ).

Psychological impacts of climate change

Climate change (CC) is responsible for the rapid dissemination and exaggeration of certain epidemics and pandemics. In addition to the vast apparent impacts of climate change on health, forestry, agriculture, etc., it may also have psychological implications on vulnerable societies. It can be exemplified through the recent outburst of (COVID-19) in various countries around the world (Pal 2021 ). Besides, the victims of this viral infection have made healthy beings scarier and terrified. In the wake of such epidemics, people with common colds or fever are also frightened and must pass specific regulatory protocols. Living in such situations continuously terrifies the public and makes the stress familiar, which eventually makes them psychologically weak (npr.org).

CC boosts the extent of anxiety, distress, and other issues in public, pushing them to develop various mental-related problems. Besides, frequent exposure to extreme climatic catastrophes such as geological disasters also imprints post-traumatic disorder, and their ubiquitous occurrence paves the way to developing chronic psychological dysfunction. Moreover, repetitive listening from media also causes an increase in the person’s stress level (Association 2020 ). Similarly, communities living in flood-prone areas constantly live in extreme fear of drowning and die by floods. In addition to human lives, the flood-induced destruction of physical infrastructure is a specific reason for putting pressure on these communities (Ogden 2018 ). For instance, Ogden ( 2018 ) comprehensively denoted that Katrina’s Hurricane augmented the mental health issues in the victim communities.

Climate change impacts on the forestry sector

Forests are the global regulators of the world’s climate (FAO 2018 ) and have an indispensable role in regulating global carbon and nitrogen cycles (Rehman et al. 2021 ; Reichstein and Carvalhais 2019 ). Hence, disturbances in forest ecology affect the micro and macro-climates (Ellison et al. 2017 ). Climate warming, in return, has profound impacts on the growth and productivity of transboundary forests by influencing the temperature and precipitation patterns, etc. As CC induces specific changes in the typical structure and functions of ecosystems (Zhang et al. 2017 ) as well impacts forest health, climate change also has several devastating consequences such as forest fires, droughts, pest outbreaks (EPA 2018 ), and last but not the least is the livelihoods of forest-dependent communities. The rising frequency and intensity of another CC product, i.e., droughts, pose plenty of challenges to the well-being of global forests (Diffenbaugh et al. 2017 ), which is further projected to increase soon (Hartmann et al. 2018 ; Lehner et al. 2017 ; Rehman et al. 2021 ). Hence, CC induces storms, with more significant impacts also put extra pressure on the survival of the global forests (Martínez-Alvarado et al. 2018 ), significantly since their influences are augmented during higher winter precipitations with corresponding wetter soils causing weak root anchorage of trees (Brázdil et al. 2018 ). Surging temperature regimes causes alterations in usual precipitation patterns, which is a significant hurdle for the survival of temperate forests (Allen et al. 2010 ; Flannigan et al. 2013 ), letting them encounter severe stress and disturbances which adversely affects the local tree species (Hubbart et al. 2016 ; Millar and Stephenson 2015 ; Rehman et al. 2021 ).

Climate change impacts on forest-dependent communities

Forests are the fundamental livelihood resource for about 1.6 billion people worldwide; out of them, 350 million are distinguished with relatively higher reliance (Bank 2008 ). Agro-forestry-dependent communities comprise 1.2 billion, and 60 million indigenous people solely rely on forests and their products to sustain their lives (Sunderlin et al. 2005 ). For example, in the entire African continent, more than 2/3rd of inhabitants depend on forest resources and woodlands for their alimonies, e.g., food, fuelwood and grazing (Wasiq and Ahmad 2004 ). The livings of these people are more intensely affected by the climatic disruptions making their lives harder (Brown et al. 2014 ). On the one hand, forest communities are incredibly vulnerable to CC due to their livelihoods, cultural and spiritual ties as well as socio-ecological connections, and on the other, they are not familiar with the term “climate change.” (Rahman and Alam 2016 ). Among the destructive impacts of temperature and rainfall, disruption of the agroforestry crops with resultant downscale growth and yield (Macchi et al. 2008 ). Cruz ( 2015 ) ascribed that forest-dependent smallholder farmers in the Philippines face the enigma of delayed fruiting, more severe damages by insect and pest incidences due to unfavorable temperature regimes, and changed rainfall patterns.

Among these series of challenges to forest communities, their well-being is also distinctly vulnerable to CC. Though the detailed climate change impacts on human health have been comprehensively mentioned in the previous section, some studies have listed a few more devastating effects on the prosperity of forest-dependent communities. For instance, the Himalayan people have been experiencing frequent skin-borne diseases such as malaria and other skin diseases due to increasing mosquitoes, wild boar as well, and new wasps species, particularly in higher altitudes that were almost non-existent before last 5–10 years (Xu et al. 2008 ). Similarly, people living at high altitudes in Bangladesh have experienced frequent mosquito-borne calamities (Fardous; Sharma 2012 ). In addition, the pace of other waterborne diseases such as infectious diarrhea, cholera, pathogenic induced abdominal complications and dengue has also been boosted in other distinguished regions of Bangladesh (Cell 2009 ; Gunter et al. 2008 ).

Pest outbreak

Upscaling hotter climate may positively affect the mobile organisms with shorter generation times because they can scurry from harsh conditions than the immobile species (Fettig et al. 2013 ; Schoene and Bernier 2012 ) and are also relatively more capable of adapting to new environments (Jactel et al. 2019 ). It reveals that insects adapt quickly to global warming due to their mobility advantages. Due to past outbreaks, the trees (forests) are relatively more susceptible victims (Kurz et al. 2008 ). Before CC, the influence of factors mentioned earlier, i.e., droughts and storms, was existent and made the forests susceptible to insect pest interventions; however, the global forests remain steadfast, assiduous, and green (Jactel et al. 2019 ). The typical reasons could be the insect herbivores were regulated by several tree defenses and pressures of predation (Wilkinson and Sherratt 2016 ). As climate greatly influences these phenomena, the global forests cannot be so sedulous against such challenges (Jactel et al. 2019 ). Table ​ Table3 3 demonstrates some of the particular considerations with practical examples that are essential while mitigating the impacts of CC in the forestry sector.

Essential considerations while mitigating the climate change impacts on the forestry sector

Source : Fischer ( 2019 )

Climate change impacts on tourism

Tourism is a commercial activity that has roots in multi-dimensions and an efficient tool with adequate job generation potential, revenue creation, earning of spectacular foreign exchange, enhancement in cross-cultural promulgation and cooperation, a business tool for entrepreneurs and eventually for the country’s national development (Arshad et al. 2018 ; Scott 2021 ). Among a plethora of other disciplines, the tourism industry is also a distinct victim of climate warming (Gössling et al. 2012 ; Hall et al. 2015 ) as the climate is among the essential resources that enable tourism in particular regions as most preferred locations. Different places at different times of the year attract tourists both within and across the countries depending upon the feasibility and compatibility of particular weather patterns. Hence, the massive variations in these weather patterns resulting from CC will eventually lead to monumental challenges to the local economy in that specific area’s particular and national economy (Bujosa et al. 2015 ). For instance, the Intergovernmental Panel on Climate Change (IPCC) report demonstrated that the global tourism industry had faced a considerable decline in the duration of ski season, including the loss of some ski areas and the dramatic shifts in tourist destinations’ climate warming.

Furthermore, different studies (Neuvonen et al. 2015 ; Scott et al. 2004 ) indicated that various currently perfect tourist spots, e.g., coastal areas, splendid islands, and ski resorts, will suffer consequences of CC. It is also worth noting that the quality and potential of administrative management potential to cope with the influence of CC on the tourism industry is of crucial significance, which renders specific strengths of resiliency to numerous destinations to withstand against it (Füssel and Hildén 2014 ). Similarly, in the partial or complete absence of adequate socio-economic and socio-political capital, the high-demanding tourist sites scurry towards the verge of vulnerability. The susceptibility of tourism is based on different components such as the extent of exposure, sensitivity, life-supporting sectors, and capacity assessment factors (Füssel and Hildén 2014 ). It is obvious corporality that sectors such as health, food, ecosystems, human habitat, infrastructure, water availability, and the accessibility of a particular region are prone to CC. Henceforth, the sensitivity of these critical sectors to CC and, in return, the adaptive measures are a hallmark in determining the composite vulnerability of climate warming (Ionescu et al. 2009 ).

Moreover, the dependence on imported food items, poor hygienic conditions, and inadequate health professionals are dominant aspects affecting the local terrestrial and aquatic biodiversity. Meanwhile, the greater dependency on ecosystem services and its products also makes a destination more fragile to become a prey of CC (Rizvi et al. 2015 ). Some significant non-climatic factors are important indicators of a particular ecosystem’s typical health and functioning, e.g., resource richness and abundance portray the picture of ecosystem stability. Similarly, the species abundance is also a productive tool that ensures that the ecosystem has a higher buffering capacity, which is terrific in terms of resiliency (Roscher et al. 2013 ).

Climate change impacts on the economic sector

Climate plays a significant role in overall productivity and economic growth. Due to its increasingly global existence and its effect on economic growth, CC has become one of the major concerns of both local and international environmental policymakers (Ferreira et al. 2020 ; Gleditsch 2021 ; Abbass et al. 2021b ; Lamperti et al. 2021 ). The adverse effects of CC on the overall productivity factor of the agricultural sector are therefore significant for understanding the creation of local adaptation policies and the composition of productive climate policy contracts. Previous studies on CC in the world have already forecasted its effects on the agricultural sector. Researchers have found that global CC will impact the agricultural sector in different world regions. The study of the impacts of CC on various agrarian activities in other demographic areas and the development of relative strategies to respond to effects has become a focal point for researchers (Chandioet al. 2020 ; Gleditsch 2021 ; Mosavi et al. 2020 ).

With the rapid growth of global warming since the 1980s, the temperature has started increasing globally, which resulted in the incredible transformation of rain and evaporation in the countries. The agricultural development of many countries has been reliant, delicate, and susceptible to CC for a long time, and it is on the development of agriculture total factor productivity (ATFP) influence different crops and yields of farmers (Alhassan 2021 ; Wu  2020 ).

Food security and natural disasters are increasing rapidly in the world. Several major climatic/natural disasters have impacted local crop production in the countries concerned. The effects of these natural disasters have been poorly controlled by the development of the economies and populations and may affect human life as well. One example is China, which is among the world’s most affected countries, vulnerable to natural disasters due to its large population, harsh environmental conditions, rapid CC, low environmental stability, and disaster power. According to the January 2016 statistical survey, China experienced an economic loss of 298.3 billion Yuan, and about 137 million Chinese people were severely affected by various natural disasters (Xie et al. 2018 ).

Mitigation and adaptation strategies of climate changes

Adaptation and mitigation are the crucial factors to address the response to CC (Jahanzad et al. 2020 ). Researchers define mitigation on climate changes, and on the other hand, adaptation directly impacts climate changes like floods. To some extent, mitigation reduces or moderates greenhouse gas emission, and it becomes a critical issue both economically and environmentally (Botzen et al. 2021 ; Jahanzad et al. 2020 ; Kongsager 2018 ; Smit et al. 2000 ; Vale et al. 2021 ; Usman et al. 2021 ; Verheyen 2005 ).

Researchers have deep concern about the adaptation and mitigation methodologies in sectoral and geographical contexts. Agriculture, industry, forestry, transport, and land use are the main sectors to adapt and mitigate policies(Kärkkäinen et al. 2020 ; Waheed et al. 2021 ). Adaptation and mitigation require particular concern both at the national and international levels. The world has faced a significant problem of climate change in the last decades, and adaptation to these effects is compulsory for economic and social development. To adapt and mitigate against CC, one should develop policies and strategies at the international level (Hussain et al. 2020 ). Figure  6 depicts the list of current studies on sectoral impacts of CC with adaptation and mitigation measures globally.

Sectoral impacts of climate change with adaptation and mitigation measures.

Conclusion and future perspectives

Specific socio-agricultural, socio-economic, and physical systems are the cornerstone of psychological well-being, and the alteration in these systems by CC will have disastrous impacts. Climate variability, alongside other anthropogenic and natural stressors, influences human and environmental health sustainability. Food security is another concerning scenario that may lead to compromised food quality, higher food prices, and inadequate food distribution systems. Global forests are challenged by different climatic factors such as storms, droughts, flash floods, and intense precipitation. On the other hand, their anthropogenic wiping is aggrandizing their existence. Undoubtedly, the vulnerability scale of the world’s regions differs; however, appropriate mitigation and adaptation measures can aid the decision-making bodies in developing effective policies to tackle its impacts. Presently, modern life on earth has tailored to consistent climatic patterns, and accordingly, adapting to such considerable variations is of paramount importance. Because the faster changes in climate will make it harder to survive and adjust, this globally-raising enigma calls for immediate attention at every scale ranging from elementary community level to international level. Still, much effort, research, and dedication are required, which is the most critical time. Some policy implications can help us to mitigate the consequences of climate change, especially the most affected sectors like the agriculture sector;

Warming might lengthen the season in frost-prone growing regions (temperate and arctic zones), allowing for longer-maturing seasonal cultivars with better yields (Pfadenhauer 2020 ; Bonacci 2019 ). Extending the planting season may allow additional crops each year; when warming leads to frequent warmer months highs over critical thresholds, a split season with a brief summer fallow may be conceivable for short-period crops such as wheat barley, cereals, and many other vegetable crops. The capacity to prolong the planting season in tropical and subtropical places where the harvest season is constrained by precipitation or agriculture farming occurs after the year may be more limited and dependent on how precipitation patterns vary (Wu et al. 2017 ).

The genetic component is comprehensive for many yields, but it is restricted like kiwi fruit for a few. Ali et al. ( 2017 ) investigated how new crops will react to climatic changes (also stated in Mall et al. 2017 ). Hot temperature, drought, insect resistance; salt tolerance; and overall crop production and product quality increases would all be advantageous (Akkari 2016 ). Genetic mapping and engineering can introduce a greater spectrum of features. The adoption of genetically altered cultivars has been slowed, particularly in the early forecasts owing to the complexity in ensuring features are expediently expressed throughout the entire plant, customer concerns, economic profitability, and regulatory impediments (Wirehn 2018 ; Davidson et al. 2016 ).

To get the full benefit of the CO 2 would certainly require additional nitrogen and other fertilizers. Nitrogen not consumed by the plants may be excreted into groundwater, discharged into water surface, or emitted from the land, soil nitrous oxide when large doses of fertilizer are sprayed. Increased nitrogen levels in groundwater sources have been related to human chronic illnesses and impact marine ecosystems. Cultivation, grain drying, and other field activities have all been examined in depth in the studies (Barua et al. 2018 ).

• The technological and socio-economic adaptation

The policy consequence of the causative conclusion is that as a source of alternative energy, biofuel production is one of the routes that explain oil price volatility separate from international macroeconomic factors. Even though biofuel production has just begun in a few sample nations, there is still a tremendous worldwide need for feedstock to satisfy industrial expansion in China and the USA, which explains the food price relationship to the global oil price. Essentially, oil-exporting countries may create incentives in their economies to increase food production. It may accomplish by giving farmers financing, seedlings, fertilizers, and farming equipment. Because of the declining global oil price and, as a result, their earnings from oil export, oil-producing nations may be unable to subsidize food imports even in the near term. As a result, these countries can boost the agricultural value chain for export. It may be accomplished through R&D and adding value to their food products to increase income by correcting exchange rate misalignment and adverse trade terms. These nations may also diversify their economies away from oil, as dependence on oil exports alone is no longer economically viable given the extreme volatility of global oil prices. Finally, resource-rich and oil-exporting countries can convert to non-food renewable energy sources such as solar, hydro, coal, wind, wave, and tidal energy. By doing so, both world food and oil supplies would be maintained rather than harmed.

IRENA’s modeling work shows that, if a comprehensive policy framework is in place, efforts toward decarbonizing the energy future will benefit economic activity, jobs (outweighing losses in the fossil fuel industry), and welfare. Countries with weak domestic supply chains and a large reliance on fossil fuel income, in particular, must undertake structural reforms to capitalize on the opportunities inherent in the energy transition. Governments continue to give major policy assistance to extract fossil fuels, including tax incentives, financing, direct infrastructure expenditures, exemptions from environmental regulations, and other measures. The majority of major oil and gas producing countries intend to increase output. Some countries intend to cut coal output, while others plan to maintain or expand it. While some nations are beginning to explore and execute policies aimed at a just and equitable transition away from fossil fuel production, these efforts have yet to impact major producing countries’ plans and goals. Verifiable and comparable data on fossil fuel output and assistance from governments and industries are critical to closing the production gap. Governments could increase openness by declaring their production intentions in their climate obligations under the Paris Agreement.

It is firmly believed that achieving the Paris Agreement commitments is doubtlful without undergoing renewable energy transition across the globe (Murshed 2020 ; Zhao et al. 2022 ). Policy instruments play the most important role in determining the degree of investment in renewable energy technology. This study examines the efficacy of various policy strategies in the renewable energy industry of multiple nations. Although its impact is more visible in established renewable energy markets, a renewable portfolio standard is also a useful policy instrument. The cost of producing renewable energy is still greater than other traditional energy sources. Furthermore, government incentives in the R&D sector can foster innovation in this field, resulting in cost reductions in the renewable energy industry. These nations may export their technologies and share their policy experiences by forming networks among their renewable energy-focused organizations. All policy measures aim to reduce production costs while increasing the proportion of renewables to a country’s energy system. Meanwhile, long-term contracts with renewable energy providers, government commitment and control, and the establishment of long-term goals can assist developing nations in deploying renewable energy technology in their energy sector.

Author contribution

KA: Writing the original manuscript, data collection, data analysis, Study design, Formal analysis, Visualization, Revised draft, Writing-review, and editing. MZQ: Writing the original manuscript, data collection, data analysis, Writing-review, and editing. HS: Contribution to the contextualization of the theme, Conceptualization, Validation, Supervision, literature review, Revised drapt, and writing review and editing. MM: Writing review and editing, compiling the literature review, language editing. HM: Writing review and editing, compiling the literature review, language editing. IY: Contribution to the contextualization of the theme, literature review, and writing review and editing.

Availability of data and material

Declarations.

Not applicable.

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Kashif Abbass, Email: nc.ude.tsujn@ssabbafihsak .

Muhammad Zeeshan Qasim, Email: moc.kooltuo@888misaqnahseez .

Huaming Song, Email: nc.ude.tsujn@gnimauh .

Muntasir Murshed, Email: [email protected] .

Haider Mahmood, Email: moc.liamtoh@doomhamrediah .

Ijaz Younis, Email: nc.ude.tsujn@sinuoyzaji .

• Abbass K, Begum H, Alam ASA, Awang AH, Abdelsalam MK, Egdair IMM, Wahid R (2022) Fresh Insight through a Keynesian Theory Approach to Investigate the Economic Impact of the COVID-19 Pandemic in Pakistan. Sustain 14(3):1054
• Abbass K, Niazi AAK, Qazi TF, Basit A, Song H (2021a) The aftermath of COVID-19 pandemic period: barriers in implementation of social distancing at workplace. Library Hi Tech
• Abbass K, Song H, Khan F, Begum H, Asif M (2021b) Fresh insight through the VAR approach to investigate the effects of fiscal policy on environmental pollution in Pakistan. Environ Scie Poll Res 1–14 [ PubMed ]
• Abbass K, Song H, Shah SM, Aziz B. Determinants of Stock Return for Non-Financial Sector: Evidence from Energy Sector of Pakistan. J Bus Fin Aff. 2019; 8 (370):2167–0234. [ Google Scholar ]
• Abbass K, Tanveer A, Huaming S, Khatiya AA (2021c) Impact of financial resources utilization on firm performance: a case of SMEs working in Pakistan
• Abraham E, Chain E. An enzyme from bacteria able to destroy penicillin. 1940. Rev Infect Dis. 1988; 10 (4):677. [ PubMed ] [ Google Scholar ]
• Adger WN, Arnell NW, Tompkins EL. Successful adaptation to climate change across scales. Glob Environ Chang. 2005; 15 (2):77–86. doi: 10.1016/j.gloenvcha.2004.12.005. [ CrossRef ] [ Google Scholar ]
• Akkari C, Bryant CR. The co-construction approach as approach to developing adaptation strategies in the face of climate change and variability: A conceptual framework. Agricultural Research. 2016; 5 (2):162–173. doi: 10.1007/s40003-016-0208-8. [ CrossRef ] [ Google Scholar ]
• Alhassan H (2021) The effect of agricultural total factor productivity on environmental degradation in sub-Saharan Africa. Sci Afr 12:e00740
• Ali A, Erenstein O. Assessing farmer use of climate change adaptation practices and impacts on food security and poverty in Pakistan. Clim Risk Manag. 2017; 16 :183–194. doi: 10.1016/j.crm.2016.12.001. [ CrossRef ] [ Google Scholar ]
• Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Hogg ET. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag. 2010; 259 (4):660–684. doi: 10.1016/j.foreco.2009.09.001. [ CrossRef ] [ Google Scholar ]
• Anwar A, Sinha A, Sharif A, Siddique M, Irshad S, Anwar W, Malik S (2021) The nexus between urbanization, renewable energy consumption, financial development, and CO2 emissions: evidence from selected Asian countries. Environ Dev Sust. 10.1007/s10668-021-01716-2
• Araus JL, Slafer GA, Royo C, Serret MD. Breeding for yield potential and stress adaptation in cereals. Crit Rev Plant Sci. 2008; 27 (6):377–412. doi: 10.1080/07352680802467736. [ CrossRef ] [ Google Scholar ]
• Aron JL, Patz J (2001) Ecosystem change and public health: a global perspective: JHU Press
• Arshad MI, Iqbal MA, Shahbaz M. Pakistan tourism industry and challenges: a review. Asia Pacific Journal of Tourism Research. 2018; 23 (2):121–132. doi: 10.1080/10941665.2017.1410192. [ CrossRef ] [ Google Scholar ]
• Ashbolt NJ. Microbial contamination of drinking water and human health from community water systems. Current Environmental Health Reports. 2015; 2 (1):95–106. doi: 10.1007/s40572-014-0037-5. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Asseng S, Cao W, Zhang W, Ludwig F (2009) Crop physiology, modelling and climate change: impact and adaptation strategies. Crop Physiol 511–543
• Asseng S, Ewert F, Rosenzweig C, Jones JW, Hatfield JL, Ruane AC, Cammarano D. Uncertainty in simulating wheat yields under climate change. Nat Clim Chang. 2013; 3 (9):827–832. doi: 10.1038/nclimate1916. [ CrossRef ] [ Google Scholar ]
• Association A (2020) Climate change is threatening mental health, American Psychological Association, “Kirsten Weir, . from < https://www.apa.org/monitor/2016/07-08/climate-change >, Accessed on 26 Jan 2020.
• Ayers J, Huq S, Wright H, Faisal A, Hussain S. Mainstreaming climate change adaptation into development in Bangladesh. Clim Dev. 2014; 6 :293–305. doi: 10.1080/17565529.2014.977761. [ CrossRef ] [ Google Scholar ]
• Balsalobre-Lorente D, Driha OM, Bekun FV, Sinha A, Adedoyin FF (2020) Consequences of COVID-19 on the social isolation of the Chinese economy: accounting for the role of reduction in carbon emissions. Air Qual Atmos Health 13(12):1439–1451
• Balsalobre-Lorente D, Ibáñez-Luzón L, Usman M, Shahbaz M. The environmental Kuznets curve, based on the economic complexity, and the pollution haven hypothesis in PIIGS countries. Renew Energy. 2022; 185 :1441–1455. doi: 10.1016/j.renene.2021.10.059. [ CrossRef ] [ Google Scholar ]
• Bank W (2008) Forests sourcebook: practical guidance for sustaining forests in development cooperation: World Bank
• Barua S, Valenzuela E (2018) Climate change impacts on global agricultural trade patterns: evidence from the past 50 years. In Proceedings of the Sixth International Conference on Sustainable Development (pp. 26–28)
• Bates AE, Pecl GT, Frusher S, Hobday AJ, Wernberg T, Smale DA, Colwell RK. Defining and observing stages of climate-mediated range shifts in marine systems. Glob Environ Chang. 2014; 26 :27–38. doi: 10.1016/j.gloenvcha.2014.03.009. [ CrossRef ] [ Google Scholar ]
• Battisti DS, Naylor RL. Historical warnings of future food insecurity with unprecedented seasonal heat. Science. 2009; 323 (5911):240–244. doi: 10.1126/science.1164363. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Beesley L, Close PG, Gwinn DC, Long M, Moroz M, Koster WM, Storer T. Flow-mediated movement of freshwater catfish, Tandanus bostocki, in a regulated semi-urban river, to inform environmental water releases. Ecol Freshw Fish. 2019; 28 (3):434–445. doi: 10.1111/eff.12466. [ CrossRef ] [ Google Scholar ]
• Benita F (2021) Human mobility behavior in COVID-19: A systematic literature review and bibliometric analysis. Sustain Cities Soc 70:102916 [ PMC free article ] [ PubMed ]
• Berendonk TU, Manaia CM, Merlin C, Fatta-Kassinos D, Cytryn E, Walsh F, Pons M-N. Tackling antibiotic resistance: the environmental framework. Nat Rev Microbiol. 2015; 13 (5):310–317. doi: 10.1038/nrmicro3439. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Berg MP, Kiers ET, Driessen G, Van DerHEIJDEN M, Kooi BW, Kuenen F, Ellers J. Adapt or disperse: understanding species persistence in a changing world. Glob Change Biol. 2010; 16 (2):587–598. doi: 10.1111/j.1365-2486.2009.02014.x. [ CrossRef ] [ Google Scholar ]
• Blum A, Klueva N, Nguyen H. Wheat cellular thermotolerance is related to yield under heat stress. Euphytica. 2001; 117 (2):117–123. doi: 10.1023/A:1004083305905. [ CrossRef ] [ Google Scholar ]
• Bonacci O. Air temperature and precipitation analyses on a small Mediterranean island: the case of the remote island of Lastovo (Adriatic Sea, Croatia) Acta Hydrotechnica. 2019; 32 (57):135–150. doi: 10.15292/acta.hydro.2019.10. [ CrossRef ] [ Google Scholar ]
• Botzen W, Duijndam S, van Beukering P (2021) Lessons for climate policy from behavioral biases towards COVID-19 and climate change risks. World Dev 137:105214 [ PMC free article ] [ PubMed ]
• Brázdil R, Stucki P, Szabó P, Řezníčková L, Dolák L, Dobrovolný P, Suchánková S. Windstorms and forest disturbances in the Czech Lands: 1801–2015. Agric for Meteorol. 2018; 250 :47–63. doi: 10.1016/j.agrformet.2017.11.036. [ CrossRef ] [ Google Scholar ]
• Brown HCP, Smit B, Somorin OA, Sonwa DJ, Nkem JN. Climate change and forest communities: prospects for building institutional adaptive capacity in the Congo Basin forests. Ambio. 2014; 43 (6):759–769. doi: 10.1007/s13280-014-0493-z. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bujosa A, Riera A, Torres CM. Valuing tourism demand attributes to guide climate change adaptation measures efficiently: the case of the Spanish domestic travel market. Tour Manage. 2015; 47 :233–239. doi: 10.1016/j.tourman.2014.09.023. [ CrossRef ] [ Google Scholar ]
• Calderini D, Abeledo L, Savin R, Slafer GA. Effect of temperature and carpel size during pre-anthesis on potential grain weight in wheat. J Agric Sci. 1999; 132 (4):453–459. doi: 10.1017/S0021859699006504. [ CrossRef ] [ Google Scholar ]
• Cammell M, Knight J. Effects of climatic change on the population dynamics of crop pests. Adv Ecol Res. 1992; 22 :117–162. doi: 10.1016/S0065-2504(08)60135-X. [ CrossRef ] [ Google Scholar ]
• Cavanaugh KC, Kellner JR, Forde AJ, Gruner DS, Parker JD, Rodriguez W, Feller IC. Poleward expansion of mangroves is a threshold response to decreased frequency of extreme cold events. Proc Natl Acad Sci. 2014; 111 (2):723–727. doi: 10.1073/pnas.1315800111. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cell CC (2009) Climate change and health impacts in Bangladesh. Clima Chang Cell DoE MoEF
• Chandio AA, Jiang Y, Rehman A, Rauf A (2020) Short and long-run impacts of climate change on agriculture: an empirical evidence from China. Int J Clim Chang Strat Manag
• Chaudhary P, Rai S, Wangdi S, Mao A, Rehman N, Chettri S, Bawa KS (2011) Consistency of local perceptions of climate change in the Kangchenjunga Himalaya landscape. Curr Sci 504–513
• Chien F, Anwar A, Hsu CC, Sharif A, Razzaq A, Sinha A (2021) The role of information and communication technology in encountering environmental degradation: proposing an SDG framework for the BRICS countries. Technol Soc 65:101587
• Cooper C, Booth A, Varley-Campbell J, Britten N, Garside R. Defining the process to literature searching in systematic reviews: a literature review of guidance and supporting studies. BMC Med Res Methodol. 2018; 18 (1):1–14. doi: 10.1186/s12874-018-0545-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Costello A, Abbas M, Allen A, Ball S, Bell S, Bellamy R, Kett M. Managing the health effects of climate change: lancet and University College London Institute for Global Health Commission. The Lancet. 2009; 373 (9676):1693–1733. doi: 10.1016/S0140-6736(09)60935-1. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cruz DLA (2015) Mother Figured. University of Chicago Press. Retrieved from, 10.7208/9780226315072
• Cui W, Ouyang T, Qiu Y, Cui D (2021) Literature Review of the Implications of Exercise Rehabilitation Strategies for SARS Patients on the Recovery of COVID-19 Patients. Paper presented at the Healthcare [ PMC free article ] [ PubMed ]
• Davidson D. Gaps in agricultural climate adaptation research. Nat Clim Chang. 2016; 6 (5):433–435. doi: 10.1038/nclimate3007. [ CrossRef ] [ Google Scholar ]
• Diffenbaugh NS, Singh D, Mankin JS, Horton DE, Swain DL, Touma D, Tsiang M. Quantifying the influence of global warming on unprecedented extreme climate events. Proc Natl Acad Sci. 2017; 114 (19):4881–4886. doi: 10.1073/pnas.1618082114. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dimri A, Kumar D, Choudhary A, Maharana P. Future changes over the Himalayas: mean temperature. Global Planet Change. 2018; 162 :235–251. doi: 10.1016/j.gloplacha.2018.01.014. [ CrossRef ] [ Google Scholar ]
• Dullinger S, Gattringer A, Thuiller W, Moser D, Zimmermann N, Guisan A. Extinction debt of high-mountain plants under twenty-first-century climate change. Nat Clim Chang: Nature Publishing Group; 2012. [ Google Scholar ]
• Dupuis I, Dumas C. Influence of temperature stress on in vitro fertilization and heat shock protein synthesis in maize (Zea mays L.) reproductive tissues. Plant Physiol. 1990; 94 (2):665–670. doi: 10.1104/pp.94.2.665. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Edreira JR, Otegui ME. Heat stress in temperate and tropical maize hybrids: a novel approach for assessing sources of kernel loss in field conditions. Field Crop Res. 2013; 142 :58–67. doi: 10.1016/j.fcr.2012.11.009. [ CrossRef ] [ Google Scholar ]
• Edreira JR, Carpici EB, Sammarro D, Otegui M. Heat stress effects around flowering on kernel set of temperate and tropical maize hybrids. Field Crop Res. 2011; 123 (2):62–73. doi: 10.1016/j.fcr.2011.04.015. [ CrossRef ] [ Google Scholar ]
• Ellison D, Morris CE, Locatelli B, Sheil D, Cohen J, Murdiyarso D, Pokorny J. Trees, forests and water: Cool insights for a hot world. Glob Environ Chang. 2017; 43 :51–61. doi: 10.1016/j.gloenvcha.2017.01.002. [ CrossRef ] [ Google Scholar ]
• Elsayed ZM, Eldehna WM, Abdel-Aziz MM, El Hassab MA, Elkaeed EB, Al-Warhi T, Mohammed ER. Development of novel isatin–nicotinohydrazide hybrids with potent activity against susceptible/resistant Mycobacterium tuberculosis and bronchitis causing–bacteria. J Enzyme Inhib Med Chem. 2021; 36 (1):384–393. doi: 10.1080/14756366.2020.1868450. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• EM-DAT (2020) EMDAT: OFDA/CRED International Disaster Database, Université catholique de Louvain – Brussels – Belgium. from http://www.emdat.be
• EPA U (2018) United States Environmental Protection Agency, EPA Year in Review
• Erman A, De Vries Robbe SA, Thies SF, Kabir K, Maruo M (2021) Gender Dimensions of Disaster Risk and Resilience
• Fand BB, Kamble AL, Kumar M. Will climate change pose serious threat to crop pest management: a critical review. Int J Sci Res Publ. 2012; 2 (11):1–14. [ Google Scholar ]
• FAO (2018).The State of the World’s Forests 2018 - Forest Pathways to Sustainable Development.
• Fardous S Perception of climate change in Kaptai National Park. Rural Livelihoods and Protected Landscape: Co-Management in the Wetlands and Forests of Bangladesh, 186–204
• Farooq M, Bramley H, Palta JA, Siddique KH. Heat stress in wheat during reproductive and grain-filling phases. Crit Rev Plant Sci. 2011; 30 (6):491–507. doi: 10.1080/07352689.2011.615687. [ CrossRef ] [ Google Scholar ]
• Feliciano D, Recha J, Ambaw G, MacSween K, Solomon D, Wollenberg E (2022) Assessment of agricultural emissions, climate change mitigation and adaptation practices in Ethiopia. Clim Policy 1–18
• Ferreira JJ, Fernandes CI, Ferreira FA (2020) Technology transfer, climate change mitigation, and environmental patent impact on sustainability and economic growth: a comparison of European countries. Technol Forecast Soc Change 150:119770
• Fettig CJ, Reid ML, Bentz BJ, Sevanto S, Spittlehouse DL, Wang T. Changing climates, changing forests: a western North American perspective. J Forest. 2013; 111 (3):214–228. doi: 10.5849/jof.12-085. [ CrossRef ] [ Google Scholar ]
• Fischer AP. Characterizing behavioral adaptation to climate change in temperate forests. Landsc Urban Plan. 2019; 188 :72–79. doi: 10.1016/j.landurbplan.2018.09.024. [ CrossRef ] [ Google Scholar ]
• Flannigan M, Cantin AS, De Groot WJ, Wotton M, Newbery A, Gowman LM. Global wildland fire season severity in the 21st century. For Ecol Manage. 2013; 294 :54–61. doi: 10.1016/j.foreco.2012.10.022. [ CrossRef ] [ Google Scholar ]
• Fossheim M, Primicerio R, Johannesen E, Ingvaldsen RB, Aschan MM, Dolgov AV. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat Clim Chang. 2015; 5 (7):673–677. doi: 10.1038/nclimate2647. [ CrossRef ] [ Google Scholar ]
• Füssel HM, Hildén M (2014) How is uncertainty addressed in the knowledge base for national adaptation planning? Adapting to an Uncertain Climate (pp. 41–66): Springer
• Gambín BL, Borrás L, Otegui ME. Source–sink relations and kernel weight differences in maize temperate hybrids. Field Crop Res. 2006; 95 (2–3):316–326. doi: 10.1016/j.fcr.2005.04.002. [ CrossRef ] [ Google Scholar ]
• Gambín B, Borrás L. Resource distribution and the trade-off between seed number and seed weight: a comparison across crop species. Annals of Applied Biology. 2010; 156 (1):91–102. doi: 10.1111/j.1744-7348.2009.00367.x. [ CrossRef ] [ Google Scholar ]
• Gampe D, Nikulin G, Ludwig R. Using an ensemble of regional climate models to assess climate change impacts on water scarcity in European river basins. Sci Total Environ. 2016; 573 :1503–1518. doi: 10.1016/j.scitotenv.2016.08.053. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• García GA, Dreccer MF, Miralles DJ, Serrago RA. High night temperatures during grain number determination reduce wheat and barley grain yield: a field study. Glob Change Biol. 2015; 21 (11):4153–4164. doi: 10.1111/gcb.13009. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Garner E, Inyang M, Garvey E, Parks J, Glover C, Grimaldi A, Edwards MA. Impact of blending for direct potable reuse on premise plumbing microbial ecology and regrowth of opportunistic pathogens and antibiotic resistant bacteria. Water Res. 2019; 151 :75–86. doi: 10.1016/j.watres.2018.12.003. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gleditsch NP (2021) This time is different! Or is it? NeoMalthusians and environmental optimists in the age of climate change. J Peace Res 0022343320969785
• Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Toulmin C. Food security: the challenge of feeding 9 billion people. Science. 2010; 327 (5967):812–818. doi: 10.1126/science.1185383. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Goes S, Hasterok D, Schutt DL, Klöcking M (2020) Continental lithospheric temperatures: A review. Phys Earth Planet Inter 106509
• Gorst A, Dehlavi A, Groom B. Crop productivity and adaptation to climate change in Pakistan. Environ Dev Econ. 2018; 23 (6):679–701. doi: 10.1017/S1355770X18000232. [ CrossRef ] [ Google Scholar ]
• Gosling SN, Arnell NW. A global assessment of the impact of climate change on water scarcity. Clim Change. 2016; 134 (3):371–385. doi: 10.1007/s10584-013-0853-x. [ CrossRef ] [ Google Scholar ]
• Gössling S, Scott D, Hall CM, Ceron J-P, Dubois G. Consumer behaviour and demand response of tourists to climate change. Ann Tour Res. 2012; 39 (1):36–58. doi: 10.1016/j.annals.2011.11.002. [ CrossRef ] [ Google Scholar ]
• Gourdji SM, Sibley AM, Lobell DB. Global crop exposure to critical high temperatures in the reproductive period: historical trends and future projections. Environ Res Lett. 2013; 8 (2):024041. doi: 10.1088/1748-9326/8/2/024041. [ CrossRef ] [ Google Scholar ]
• Grieg E Responsible Consumption and Production
• Gunter BG, Rahman A, Rahman A (2008) How Vulnerable are Bangladesh’s Indigenous People to Climate Change? Bangladesh Development Research Center (BDRC)
• Hall CM, Amelung B, Cohen S, Eijgelaar E, Gössling S, Higham J, Scott D. On climate change skepticism and denial in tourism. J Sustain Tour. 2015; 23 (1):4–25. doi: 10.1080/09669582.2014.953544. [ CrossRef ] [ Google Scholar ]
• Hartmann H, Moura CF, Anderegg WR, Ruehr NK, Salmon Y, Allen CD, Galbraith D. Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol. 2018; 218 (1):15–28. doi: 10.1111/nph.15048. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hatfield JL, Prueger JH. Temperature extremes: Effect on plant growth and development. Weather and Climate Extremes. 2015; 10 :4–10. doi: 10.1016/j.wace.2015.08.001. [ CrossRef ] [ Google Scholar ]
• Hatfield JL, Boote KJ, Kimball B, Ziska L, Izaurralde RC, Ort D, Wolfe D. Climate impacts on agriculture: implications for crop production. Agron J. 2011; 103 (2):351–370. doi: 10.2134/agronj2010.0303. [ CrossRef ] [ Google Scholar ]
• Hendriksen RS, Munk P, Njage P, Van Bunnik B, McNally L, Lukjancenko O, Kjeldgaard J. Global monitoring of antimicrobial resistance based on metagenomics analyses of urban sewage. Nat Commun. 2019; 10 (1):1124. doi: 10.1038/s41467-019-08853-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Huang S (2004) Global trade patterns in fruits and vegetables. USDA-ERS Agriculture and Trade Report No. WRS-04–06
• Huang W, Gao Q-X, Cao G-L, Ma Z-Y, Zhang W-D, Chao Q-C. Effect of urban symbiosis development in China on GHG emissions reduction. Adv Clim Chang Res. 2016; 7 (4):247–252. doi: 10.1016/j.accre.2016.12.003. [ CrossRef ] [ Google Scholar ]
• Huang Y, Haseeb M, Usman M, Ozturk I (2022) Dynamic association between ICT, renewable energy, economic complexity and ecological footprint: Is there any difference between E-7 (developing) and G-7 (developed) countries? Tech Soc 68:101853
• Hubbart JA, Guyette R, Muzika R-M. More than drought: precipitation variance, excessive wetness, pathogens and the future of the western edge of the eastern deciduous forest. Sci Total Environ. 2016; 566 :463–467. doi: 10.1016/j.scitotenv.2016.05.108. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hussain M, Butt AR, Uzma F, Ahmed R, Irshad S, Rehman A, Yousaf B. A comprehensive review of climate change impacts, adaptation, and mitigation on environmental and natural calamities in Pakistan. Environ Monit Assess. 2020; 192 (1):48. doi: 10.1007/s10661-019-7956-4. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hussain M, Liu G, Yousaf B, Ahmed R, Uzma F, Ali MU, Butt AR. Regional and sectoral assessment on climate-change in Pakistan: social norms and indigenous perceptions on climate-change adaptation and mitigation in relation to global context. J Clean Prod. 2018; 200 :791–808. doi: 10.1016/j.jclepro.2018.07.272. [ CrossRef ] [ Google Scholar ]
• Intergov. Panel Clim Chang 33 from 10.1017/CBO9781107415324
• Ionescu C, Klein RJ, Hinkel J, Kumar KK, Klein R. Towards a formal framework of vulnerability to climate change. Environ Model Assess. 2009; 14 (1):1–16. doi: 10.1007/s10666-008-9179-x. [ CrossRef ] [ Google Scholar ]
• IPCC (2013) Summary for policymakers. Clim Chang Phys Sci Basis Contrib Work Gr I Fifth Assess Rep
• Ishikawa-Ishiwata Y, Furuya J (2022) Economic evaluation and climate change adaptation measures for rice production in vietnam using a supply and demand model: special emphasis on the Mekong River Delta region in Vietnam. In Interlocal Adaptations to Climate Change in East and Southeast Asia (pp. 45–53). Springer, Cham
• Izaguirre C, Losada I, Camus P, Vigh J, Stenek V. Climate change risk to global port operations. Nat Clim Chang. 2021; 11 (1):14–20. doi: 10.1038/s41558-020-00937-z. [ CrossRef ] [ Google Scholar ]
• Jactel H, Koricheva J, Castagneyrol B (2019) Responses of forest insect pests to climate change: not so simple. Current opinion in insect science [ PubMed ]
• Jahanzad E, Holtz BA, Zuber CA, Doll D, Brewer KM, Hogan S, Gaudin AC. Orchard recycling improves climate change adaptation and mitigation potential of almond production systems. PLoS ONE. 2020; 15 (3):e0229588. doi: 10.1371/journal.pone.0229588. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Jurgilevich A, Räsänen A, Groundstroem F, Juhola S. A systematic review of dynamics in climate risk and vulnerability assessments. Environ Res Lett. 2017; 12 (1):013002. doi: 10.1088/1748-9326/aa5508. [ CrossRef ] [ Google Scholar ]
• Karami E (2012) Climate change, resilience and poverty in the developing world. Paper presented at the Culture, Politics and Climate change conference
• Kärkkäinen L, Lehtonen H, Helin J, Lintunen J, Peltonen-Sainio P, Regina K, . . . Packalen T (2020) Evaluation of policy instruments for supporting greenhouse gas mitigation efforts in agricultural and urban land use. Land Use Policy 99:104991
• Karkman A, Do TT, Walsh F, Virta MP. Antibiotic-resistance genes in waste water. Trends Microbiol. 2018; 26 (3):220–228. doi: 10.1016/j.tim.2017.09.005. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kohfeld KE, Le Quéré C, Harrison SP, Anderson RF. Role of marine biology in glacial-interglacial CO2 cycles. Science. 2005; 308 (5718):74–78. doi: 10.1126/science.1105375. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kongsager R. Linking climate change adaptation and mitigation: a review with evidence from the land-use sectors. Land. 2018; 7 (4):158. doi: 10.3390/land7040158. [ CrossRef ] [ Google Scholar ]
• Kurz WA, Dymond C, Stinson G, Rampley G, Neilson E, Carroll A, Safranyik L. Mountain pine beetle and forest carbon feedback to climate change. Nature. 2008; 452 (7190):987. doi: 10.1038/nature06777. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lamperti F, Bosetti V, Roventini A, Tavoni M, Treibich T (2021) Three green financial policies to address climate risks. J Financial Stab 54:100875
• Leal Filho W, Azeiteiro UM, Balogun AL, Setti AFF, Mucova SA, Ayal D, . . . Oguge NO (2021) The influence of ecosystems services depletion to climate change adaptation efforts in Africa. Sci Total Environ 146414 [ PubMed ]
• Lehner F, Coats S, Stocker TF, Pendergrass AG, Sanderson BM, Raible CC, Smerdon JE. Projected drought risk in 1.5 C and 2 C warmer climates. Geophys Res Lett. 2017; 44 (14):7419–7428. doi: 10.1002/2017GL074117. [ CrossRef ] [ Google Scholar ]
• Lemery J, Knowlton K, Sorensen C (2021) Global climate change and human health: from science to practice: John Wiley & Sons
• Leppänen S, Saikkonen L, Ollikainen M (2014) Impact of Climate Change on cereal grain production in Russia: Mimeo
• Lipczynska-Kochany E. Effect of climate change on humic substances and associated impacts on the quality of surface water and groundwater: a review. Sci Total Environ. 2018; 640 :1548–1565. doi: 10.1016/j.scitotenv.2018.05.376. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• livescience.com. New coronavirus may have ‘jumped’ to humans from snakes, study finds, live science,. from < https://www.livescience.com/new-coronavirus-origin-snakes.html > accessed on Jan 2020
• Lobell DB, Field CB. Global scale climate–crop yield relationships and the impacts of recent warming. Environ Res Lett. 2007; 2 (1):014002. doi: 10.1088/1748-9326/2/1/014002. [ CrossRef ] [ Google Scholar ]
• Lobell DB, Gourdji SM. The influence of climate change on global crop productivity. Plant Physiol. 2012; 160 (4):1686–1697. doi: 10.1104/pp.112.208298. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ma L, Li B, Zhang T. New insights into antibiotic resistome in drinking water and management perspectives: a metagenomic based study of small-sized microbes. Water Res. 2019; 152 :191–201. doi: 10.1016/j.watres.2018.12.069. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Macchi M, Oviedo G, Gotheil S, Cross K, Boedhihartono A, Wolfangel C, Howell M (2008) Indigenous and traditional peoples and climate change. International Union for the Conservation of Nature, Gland, Suiza
• Mall RK, Gupta A, Sonkar G (2017) Effect of climate change on agricultural crops. In Current developments in biotechnology and bioengineering (pp. 23–46). Elsevier
• Manes S, Costello MJ, Beckett H, Debnath A, Devenish-Nelson E, Grey KA, . . . Krause C (2021) Endemism increases species’ climate change risk in areas of global biodiversity importance. Biol Conserv 257:109070
• Mannig B, Pollinger F, Gafurov A, Vorogushyn S, Unger-Shayesteh K (2018) Impacts of climate change in Central Asia Encyclopedia of the Anthropocene (pp. 195–203): Elsevier
• Martínez-Alvarado O, Gray SL, Hart NC, Clark PA, Hodges K, Roberts MJ. Increased wind risk from sting-jet windstorms with climate change. Environ Res Lett. 2018; 13 (4):044002. doi: 10.1088/1748-9326/aaae3a. [ CrossRef ] [ Google Scholar ]
• Matsui T, Omasa K, Horie T. The difference in sterility due to high temperatures during the flowering period among japonica-rice varieties. Plant Production Science. 2001; 4 (2):90–93. doi: 10.1626/pps.4.90. [ CrossRef ] [ Google Scholar ]
• Meierrieks D (2021) Weather shocks, climate change and human health. World Dev 138:105228
• Michel D, Eriksson M, Klimes M (2021) Climate change and (in) security in transboundary river basins Handbook of Security and the Environment: Edward Elgar Publishing
• Mihiretu A, Okoyo EN, Lemma T. Awareness of climate change and its associated risks jointly explain context-specific adaptation in the Arid-tropics. Northeast Ethiopia SN Social Sciences. 2021; 1 (2):1–18. [ Google Scholar ]
• Millar CI, Stephenson NL. Temperate forest health in an era of emerging megadisturbance. Science. 2015; 349 (6250):823–826. doi: 10.1126/science.aaa9933. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mishra A, Bruno E, Zilberman D (2021) Compound natural and human disasters: Managing drought and COVID-19 to sustain global agriculture and food sectors. Sci Total Environ 754:142210 [ PMC free article ] [ PubMed ]
• Mosavi SH, Soltani S, Khalilian S (2020) Coping with climate change in agriculture: Evidence from Hamadan-Bahar plain in Iran. Agric Water Manag 241:106332
• Murshed M (2020) An empirical analysis of the non-linear impacts of ICT-trade openness on renewable energy transition, energy efficiency, clean cooking fuel access and environmental sustainability in South Asia. Environ Sci Pollut Res 27(29):36254–36281. 10.1007/s11356-020-09497-3 [ PMC free article ] [ PubMed ]
• Murshed M. Pathways to clean cooking fuel transition in low and middle income Sub-Saharan African countries: the relevance of improving energy use efficiency. Sustainable Production and Consumption. 2022; 30 :396–412. doi: 10.1016/j.spc.2021.12.016. [ CrossRef ] [ Google Scholar ]
• Murshed M, Dao NTT. Revisiting the CO2 emission-induced EKC hypothesis in South Asia: the role of Export Quality Improvement. GeoJournal. 2020 doi: 10.1007/s10708-020-10270-9. [ CrossRef ] [ Google Scholar ]
• Murshed M, Abbass K, Rashid S. Modelling renewable energy adoption across south Asian economies: Empirical evidence from Bangladesh, India, Pakistan and Sri Lanka. Int J Finan Eco. 2021; 26 (4):5425–5450. doi: 10.1002/ijfe.2073. [ CrossRef ] [ Google Scholar ]
• Murshed M, Nurmakhanova M, Elheddad M, Ahmed R. Value addition in the services sector and its heterogeneous impacts on CO2 emissions: revisiting the EKC hypothesis for the OPEC using panel spatial estimation techniques. Environ Sci Pollut Res. 2020; 27 (31):38951–38973. doi: 10.1007/s11356-020-09593-4. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Murshed M, Nurmakhanova M, Al-Tal R, Mahmood H, Elheddad M, Ahmed R (2022) Can intra-regional trade, renewable energy use, foreign direct investments, and economic growth reduce ecological footprints in South Asia? Energy Sources, Part B: Economics, Planning, and Policy. 10.1080/15567249.2022.2038730
• Neuvonen M, Sievänen T, Fronzek S, Lahtinen I, Veijalainen N, Carter TR. Vulnerability of cross-country skiing to climate change in Finland–an interactive mapping tool. J Outdoor Recreat Tour. 2015; 11 :64–79. doi: 10.1016/j.jort.2015.06.010. [ CrossRef ] [ Google Scholar ]
• npr.org. Please Help Me.’ What people in China are saying about the outbreak on social media, npr.org, . from < https://www.npr.org/sections/goatsandsoda/2020/01/24/799000379/please-help-me-what-people-in-china-are-saying-about-the-outbreak-on-social-medi >, Accessed on 26 Jan 2020.
• Ogden LE. Climate change, pathogens, and people: the challenges of monitoring a moving target. Bioscience. 2018; 68 (10):733–739. doi: 10.1093/biosci/biy101. [ CrossRef ] [ Google Scholar ]
• Ortiz AMD, Outhwaite CL, Dalin C, Newbold T. A review of the interactions between biodiversity, agriculture, climate change, and international trade: research and policy priorities. One Earth. 2021; 4 (1):88–101. doi: 10.1016/j.oneear.2020.12.008. [ CrossRef ] [ Google Scholar ]
• Ortiz R. Crop genetic engineering under global climate change. Ann Arid Zone. 2008; 47 (3):343. [ Google Scholar ]
• Otegui MAE, Bonhomme R. Grain yield components in maize: I. Ear growth and kernel set. Field Crop Res. 1998; 56 (3):247–256. doi: 10.1016/S0378-4290(97)00093-2. [ CrossRef ] [ Google Scholar ]
• Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, . . . Dasgupta P (2014) Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change: Ipcc
• Pal JK. Visualizing the knowledge outburst in global research on COVID-19. Scientometrics. 2021; 126 (5):4173–4193. doi: 10.1007/s11192-021-03912-3. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Panda R, Behera S, Kashyap P. Effective management of irrigation water for wheat under stressed conditions. Agric Water Manag. 2003; 63 (1):37–56. doi: 10.1016/S0378-3774(03)00099-4. [ CrossRef ] [ Google Scholar ]
• Pärnänen KM, Narciso-da-Rocha C, Kneis D, Berendonk TU, Cacace D, Do TT, Jaeger T. Antibiotic resistance in European wastewater treatment plants mirrors the pattern of clinical antibiotic resistance prevalence. Sci Adv. 2019; 5 (3):eaau9124. doi: 10.1126/sciadv.aau9124. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Parry M, Parry ML, Canziani O, Palutikof J, Van der Linden P, Hanson C (2007) Climate change 2007-impacts, adaptation and vulnerability: Working group II contribution to the fourth assessment report of the IPCC (Vol. 4): Cambridge University Press
• Patz JA, Campbell-Lendrum D, Holloway T, Foley JA. Impact of regional climate change on human health. Nature. 2005; 438 (7066):310–317. doi: 10.1038/nature04188. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Patz JA, Graczyk TK, Geller N, Vittor AY. Effects of environmental change on emerging parasitic diseases. Int J Parasitol. 2000; 30 (12–13):1395–1405. doi: 10.1016/S0020-7519(00)00141-7. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pautasso M, Döring TF, Garbelotto M, Pellis L, Jeger MJ. Impacts of climate change on plant diseases—opinions and trends. Eur J Plant Pathol. 2012; 133 (1):295–313. doi: 10.1007/s10658-012-9936-1. [ CrossRef ] [ Google Scholar ]
• Peng S, Huang J, Sheehy JE, Laza RC, Visperas RM, Zhong X, Cassman KG. Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci. 2004; 101 (27):9971–9975. doi: 10.1073/pnas.0403720101. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pereira HM, Ferrier S, Walters M, Geller GN, Jongman R, Scholes RJ, Cardoso A. Essential biodiversity variables. Science. 2013; 339 (6117):277–278. doi: 10.1126/science.1229931. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Perera K, De Silva K, Amarasinghe M. Potential impact of predicted sea level rise on carbon sink function of mangrove ecosystems with special reference to Negombo estuary, Sri Lanka. Global Planet Change. 2018; 161 :162–171. doi: 10.1016/j.gloplacha.2017.12.016. [ CrossRef ] [ Google Scholar ]
• Pfadenhauer JS, Klötzli FA (2020) Zonal Vegetation of the Subtropical (Warm–Temperate) Zone with Winter Rain. In Global Vegetation (pp. 455–514). Springer, Cham
• Phillips JD. Environmental gradients and complexity in coastal landscape response to sea level rise. CATENA. 2018; 169 :107–118. doi: 10.1016/j.catena.2018.05.036. [ CrossRef ] [ Google Scholar ]
• Pirasteh-Anosheh H, Parnian A, Spasiano D, Race M, Ashraf M (2021) Haloculture: A system to mitigate the negative impacts of pandemics on the environment, society and economy, emphasizing COVID-19. Environ Res 111228 [ PMC free article ] [ PubMed ]
• Pruden A, Larsson DJ, Amézquita A, Collignon P, Brandt KK, Graham DW, Snape JR. Management options for reducing the release of antibiotics and antibiotic resistance genes to the environment. Environ Health Perspect. 2013; 121 (8):878–885. doi: 10.1289/ehp.1206446. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Qasim MZ, Hammad HM, Abbas F, Saeed S, Bakhat HF, Nasim W, Fahad S. The potential applications of picotechnology in biomedical and environmental sciences. Environ Sci Pollut Res. 2020; 27 (1):133–142. doi: 10.1007/s11356-019-06554-4. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Qasim MZ, Hammad HM, Maqsood F, Tariq T, Chawla MS Climate Change Implication on Cereal Crop Productivity
• Rahman M, Alam K. Forest dependent indigenous communities’ perception and adaptation to climate change through local knowledge in the protected area—a Bangladesh case study. Climate. 2016; 4 (1):12. doi: 10.3390/cli4010012. [ CrossRef ] [ Google Scholar ]
• Ramankutty N, Mehrabi Z, Waha K, Jarvis L, Kremen C, Herrero M, Rieseberg LH. Trends in global agricultural land use: implications for environmental health and food security. Annu Rev Plant Biol. 2018; 69 :789–815. doi: 10.1146/annurev-arplant-042817-040256. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rehman A, Ma H, Ahmad M, Irfan M, Traore O, Chandio AA (2021) Towards environmental Sustainability: devolving the influence of carbon dioxide emission to population growth, climate change, Forestry, livestock and crops production in Pakistan. Ecol Indic 125:107460
• Reichstein M, Carvalhais N. Aspects of forest biomass in the Earth system: its role and major unknowns. Surv Geophys. 2019; 40 (4):693–707. doi: 10.1007/s10712-019-09551-x. [ CrossRef ] [ Google Scholar ]
• Reidsma P, Ewert F, Boogaard H, van Diepen K. Regional crop modelling in Europe: the impact of climatic conditions and farm characteristics on maize yields. Agric Syst. 2009; 100 (1–3):51–60. doi: 10.1016/j.agsy.2008.12.009. [ CrossRef ] [ Google Scholar ]
• Ritchie H, Roser M (2014) Natural disasters. Our World in Data
• Rizvi AR, Baig S, Verdone M. Ecosystems based adaptation: knowledge gaps in making an economic case for investing in nature based solutions for climate change. Gland, Switzerland: IUCN; 2015. p. 48. [ Google Scholar ]
• Roscher C, Fergus AJ, Petermann JS, Buchmann N, Schmid B, Schulze E-D. What happens to the sown species if a biodiversity experiment is not weeded? Basic Appl Ecol. 2013; 14 (3):187–198. doi: 10.1016/j.baae.2013.01.003. [ CrossRef ] [ Google Scholar ]
• Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Khabarov N. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci. 2014; 111 (9):3268–3273. doi: 10.1073/pnas.1222463110. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rosenzweig C, Iglesius A, Yang XB, Epstein PR, Chivian E (2001) Climate change and extreme weather events-implications for food production, plant diseases, and pests
• Sadras VO, Slafer GA. Environmental modulation of yield components in cereals: heritabilities reveal a hierarchy of phenotypic plasticities. Field Crop Res. 2012; 127 :215–224. doi: 10.1016/j.fcr.2011.11.014. [ CrossRef ] [ Google Scholar ]
• Salvucci ME, Crafts-Brandner SJ. Inhibition of photosynthesis by heat stress: the activation state of Rubisco as a limiting factor in photosynthesis. Physiol Plant. 2004; 120 (2):179–186. doi: 10.1111/j.0031-9317.2004.0173.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Santos WS, Gurgel-Gonçalves R, Garcez LM, Abad-Franch F. Deforestation effects on Attalea palms and their resident Rhodnius, vectors of Chagas disease, in eastern Amazonia. PLoS ONE. 2021; 16 (5):e0252071. doi: 10.1371/journal.pone.0252071. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sarkar P, Debnath N, Reang D (2021) Coupled human-environment system amid COVID-19 crisis: a conceptual model to understand the nexus. Sci Total Environ 753:141757 [ PMC free article ] [ PubMed ]
• Schlenker W, Roberts MJ. Nonlinear temperature effects indicate severe damages to US crop yields under climate change. Proc Natl Acad Sci. 2009; 106 (37):15594–15598. doi: 10.1073/pnas.0906865106. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schoene DH, Bernier PY. Adapting forestry and forests to climate change: a challenge to change the paradigm. Forest Policy Econ. 2012; 24 :12–19. doi: 10.1016/j.forpol.2011.04.007. [ CrossRef ] [ Google Scholar ]
• Schuurmans C (2021) The world heat budget: expected changes Climate Change (pp. 1–15): CRC Press
• Scott D. Sustainable Tourism and the Grand Challenge of Climate Change. Sustainability. 2021; 13 (4):1966. doi: 10.3390/su13041966. [ CrossRef ] [ Google Scholar ]
• Scott D, McBoyle G, Schwartzentruber M. Climate change and the distribution of climatic resources for tourism in North America. Climate Res. 2004; 27 (2):105–117. doi: 10.3354/cr027105. [ CrossRef ] [ Google Scholar ]
• Semenov MA. Impacts of climate change on wheat in England and Wales. J R Soc Interface. 2009; 6 (33):343–350. doi: 10.1098/rsif.2008.0285. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shaffril HAM, Krauss SE, Samsuddin SF. A systematic review on Asian’s farmers’ adaptation practices towards climate change. Sci Total Environ. 2018; 644 :683–695. doi: 10.1016/j.scitotenv.2018.06.349. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shahbaz M, Balsalobre-Lorente D, Sinha A (2019) Foreign direct Investment–CO2 emissions nexus in Middle East and North African countries: Importance of biomass energy consumption. J Clean Product 217:603–614
• Sharif A, Mishra S, Sinha A, Jiao Z, Shahbaz M, Afshan S (2020) The renewable energy consumption-environmental degradation nexus in Top-10 polluted countries: Fresh insights from quantile-on-quantile regression approach. Renew Energy 150:670–690
• Sharma R. Impacts on human health of climate and land use change in the Hindu Kush-Himalayan region. Mt Res Dev. 2012; 32 (4):480–486. doi: 10.1659/MRD-JOURNAL-D-12-00068.1. [ CrossRef ] [ Google Scholar ]
• Sharma R, Sinha A, Kautish P. Examining the impacts of economic and demographic aspects on the ecological footprint in South and Southeast Asian countries. Environ Sci Pollut Res. 2020; 27 (29):36970–36982. doi: 10.1007/s11356-020-09659-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Smit B, Burton I, Klein RJ, Wandel J (2000) An anatomy of adaptation to climate change and variability Societal adaptation to climate variability and change (pp. 223–251): Springer
• Song Y, Fan H, Tang X, Luo Y, Liu P, Chen Y (2021) The effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on ischemic stroke and the possible underlying mechanisms. Int J Neurosci 1–20 [ PMC free article ] [ PubMed ]
• Sovacool BK, Griffiths S, Kim J, Bazilian M (2021) Climate change and industrial F-gases: a critical and systematic review of developments, sociotechnical systems and policy options for reducing synthetic greenhouse gas emissions. Renew Sustain Energy Rev 141:110759
• Stewart JA, Perrine JD, Nichols LB, Thorne JH, Millar CI, Goehring KE, Wright DH. Revisiting the past to foretell the future: summer temperature and habitat area predict pika extirpations in California. J Biogeogr. 2015; 42 (5):880–890. doi: 10.1111/jbi.12466. [ CrossRef ] [ Google Scholar ]
• Stocker T, Qin D, Plattner G, Tignor M, Allen S, Boschung J, . . . Midgley P (2013) Climate change 2013: The physical science basis. Working group I contribution to the IPCC Fifth assessment report: Cambridge: Cambridge University Press. 1535p
• Stone P, Nicolas M. Wheat cultivars vary widely in their responses of grain yield and quality to short periods of post-anthesis heat stress. Funct Plant Biol. 1994; 21 (6):887–900. doi: 10.1071/PP9940887. [ CrossRef ] [ Google Scholar ]
• Su H-C, Liu Y-S, Pan C-G, Chen J, He L-Y, Ying G-G. Persistence of antibiotic resistance genes and bacterial community changes in drinking water treatment system: from drinking water source to tap water. Sci Total Environ. 2018; 616 :453–461. doi: 10.1016/j.scitotenv.2017.10.318. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sunderlin WD, Angelsen A, Belcher B, Burgers P, Nasi R, Santoso L, Wunder S. Livelihoods, forests, and conservation in developing countries: an overview. World Dev. 2005; 33 (9):1383–1402. doi: 10.1016/j.worlddev.2004.10.004. [ CrossRef ] [ Google Scholar ]
• Symanski E, Han HA, Han I, McDaniel M, Whitworth KW, McCurdy S, . . . Delclos GL (2021) Responding to natural and industrial disasters: partnerships and lessons learned. Disaster medicine and public health preparedness 1–4 [ PMC free article ] [ PubMed ]
• Tao F, Yokozawa M, Xu Y, Hayashi Y, Zhang Z. Climate changes and trends in phenology and yields of field crops in China, 1981–2000. Agric for Meteorol. 2006; 138 (1–4):82–92. doi: 10.1016/j.agrformet.2006.03.014. [ CrossRef ] [ Google Scholar ]
• Tebaldi C, Hayhoe K, Arblaster JM, Meehl GA. Going to the extremes. Clim Change. 2006; 79 (3–4):185–211. doi: 10.1007/s10584-006-9051-4. [ CrossRef ] [ Google Scholar ]
• Testa G, Koon E, Johannesson L, McKenna G, Anthony T, Klintmalm G, Gunby R (2018) This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as
• Thornton PK, Lipper L (2014) How does climate change alter agricultural strategies to support food security? (Vol. 1340): Intl Food Policy Res Inst
• Tranfield D, Denyer D, Smart P. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br J Manag. 2003; 14 (3):207–222. doi: 10.1111/1467-8551.00375. [ CrossRef ] [ Google Scholar ]
• UNEP (2017) United nations environment programme: frontiers 2017. from https://www.unenvironment.org/news-and-stories/press-release/antimicrobial-resistance - environmental-pollution-among-biggest
• Usman M, Balsalobre-Lorente D (2022) Environmental concern in the era of industrialization: Can financial development, renewable energy and natural resources alleviate some load? Ene Policy 162:112780
• Usman M, Makhdum MSA (2021) What abates ecological footprint in BRICS-T region? Exploring the influence of renewable energy, non-renewable energy, agriculture, forest area and financial development. Renew Energy 179:12–28
• Usman M, Balsalobre-Lorente D, Jahanger A, Ahmad P. Pollution concern during globalization mode in financially resource-rich countries: Do financial development, natural resources, and renewable energy consumption matter? Rene. Energy. 2022; 183 :90–102. doi: 10.1016/j.renene.2021.10.067. [ CrossRef ] [ Google Scholar ]
• Usman M, Jahanger A, Makhdum MSA, Balsalobre-Lorente D, Bashir A (2022a) How do financial development, energy consumption, natural resources, and globalization affect Arctic countries’ economic growth and environmental quality? An advanced panel data simulation. Energy 241:122515
• Usman M, Khalid K, Mehdi MA. What determines environmental deficit in Asia? Embossing the role of renewable and non-renewable energy utilization. Renew Energy. 2021; 168 :1165–1176. doi: 10.1016/j.renene.2021.01.012. [ CrossRef ] [ Google Scholar ]
• Urban MC. Accelerating extinction risk from climate change. Science. 2015; 348 (6234):571–573. doi: 10.1126/science.aaa4984. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Vale MM, Arias PA, Ortega G, Cardoso M, Oliveira BF, Loyola R, Scarano FR (2021) Climate change and biodiversity in the Atlantic Forest: best climatic models, predicted changes and impacts, and adaptation options The Atlantic Forest (pp. 253–267): Springer
• Vedwan N, Rhoades RE. Climate change in the Western Himalayas of India: a study of local perception and response. Climate Res. 2001; 19 (2):109–117. doi: 10.3354/cr019109. [ CrossRef ] [ Google Scholar ]
• Vega CR, Andrade FH, Sadras VO, Uhart SA, Valentinuz OR. Seed number as a function of growth. A comparative study in soybean, sunflower, and maize. Crop Sci. 2001; 41 (3):748–754. doi: 10.2135/cropsci2001.413748x. [ CrossRef ] [ Google Scholar ]
• Vergés A, Doropoulos C, Malcolm HA, Skye M, Garcia-Pizá M, Marzinelli EM, Vila-Concejo A. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proc Natl Acad Sci. 2016; 113 (48):13791–13796. doi: 10.1073/pnas.1610725113. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Verheyen R (2005) Climate change damage and international law: prevention duties and state responsibility (Vol. 54): Martinus Nijhoff Publishers
• Waheed A, Fischer TB, Khan MI. Climate Change Policy Coherence across Policies, Plans, and Strategies in Pakistan—implications for the China-Pakistan Economic Corridor Plan. Environ Manage. 2021; 67 (5):793–810. doi: 10.1007/s00267-021-01449-y. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wasiq M, Ahmad M (2004) Sustaining forests: a development strategy: The World Bank
• Watts N, Adger WN, Agnolucci P, Blackstock J, Byass P, Cai W, Cooper A. Health and climate change: policy responses to protect public health. The Lancet. 2015; 386 (10006):1861–1914. doi: 10.1016/S0140-6736(15)60854-6. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Weed AS, Ayres MP, Hicke JA. Consequences of climate change for biotic disturbances in North American forests. Ecol Monogr. 2013; 83 (4):441–470. doi: 10.1890/13-0160.1. [ CrossRef ] [ Google Scholar ]
• Weisheimer A, Palmer T (2005) Changing frequency of occurrence of extreme seasonal temperatures under global warming. Geophys Res Lett 32(20)
• Wernberg T, Bennett S, Babcock RC, De Bettignies T, Cure K, Depczynski M, Hovey RK. Climate-driven regime shift of a temperate marine ecosystem. Science. 2016; 353 (6295):169–172. doi: 10.1126/science.aad8745. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• WHO (2018) WHO, 2018. Antimicrobial resistance
• Wilkinson DM, Sherratt TN. Why is the world green? The interactions of top–down and bottom–up processes in terrestrial vegetation ecology. Plant Ecolog Divers. 2016; 9 (2):127–140. doi: 10.1080/17550874.2016.1178353. [ CrossRef ] [ Google Scholar ]
• Wiranata IJ, Simbolon K. Increasing awareness capacity of disaster potential as a support to achieve sustainable development goal (sdg) 13 in lampung province. Jurnal Pir: Power in International Relations. 2021; 5 (2):129–146. doi: 10.22303/pir.5.2.2021.129-146. [ CrossRef ] [ Google Scholar ]
• Wiréhn L. Nordic agriculture under climate change: a systematic review of challenges, opportunities and adaptation strategies for crop production. Land Use Policy. 2018; 77 :63–74. doi: 10.1016/j.landusepol.2018.04.059. [ CrossRef ] [ Google Scholar ]
• Wu D, Su Y, Xi H, Chen X, Xie B. Urban and agriculturally influenced water contribute differently to the spread of antibiotic resistance genes in a mega-city river network. Water Res. 2019; 158 :11–21. doi: 10.1016/j.watres.2019.03.010. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wu HX (2020) Losing Steam?—An industry origin analysis of China’s productivity slowdown Measuring Economic Growth and Productivity (pp. 137–167): Elsevier
• Wu H, Qian H, Chen J, Huo C. Assessment of agricultural drought vulnerability in the Guanzhong Plain. China Water Resources Management. 2017; 31 (5):1557–1574. doi: 10.1007/s11269-017-1594-9. [ CrossRef ] [ Google Scholar ]
• Xie W, Huang J, Wang J, Cui Q, Robertson R, Chen K (2018) Climate change impacts on China’s agriculture: the responses from market and trade. China Econ Rev
• Xu J, Sharma R, Fang J, Xu Y. Critical linkages between land-use transition and human health in the Himalayan region. Environ Int. 2008; 34 (2):239–247. doi: 10.1016/j.envint.2007.08.004. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yadav MK, Singh R, Singh K, Mall R, Patel C, Yadav S, Singh M. Assessment of climate change impact on productivity of different cereal crops in Varanasi. India J Agrometeorol. 2015; 17 (2):179–184. doi: 10.54386/jam.v17i2.1000. [ CrossRef ] [ Google Scholar ]
• Yang B, Usman M. Do industrialization, economic growth and globalization processes influence the ecological footprint and healthcare expenditures? Fresh insights based on the STIRPAT model for countries with the highest healthcare expenditures. Sust Prod Cons. 2021; 28 :893–910. [ Google Scholar ]
• Yu Z, Razzaq A, Rehman A, Shah A, Jameel K, Mor RS (2021) Disruption in global supply chain and socio-economic shocks: a lesson from COVID-19 for sustainable production and consumption. Oper Manag Res 1–16
• Zarnetske PL, Skelly DK, Urban MC. Biotic multipliers of climate change. Science. 2012; 336 (6088):1516–1518. doi: 10.1126/science.1222732. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Zhang M, Liu N, Harper R, Li Q, Liu K, Wei X, Liu S. A global review on hydrological responses to forest change across multiple spatial scales: importance of scale, climate, forest type and hydrological regime. J Hydrol. 2017; 546 :44–59. doi: 10.1016/j.jhydrol.2016.12.040. [ CrossRef ] [ Google Scholar ]
• Zhao J, Sinha A, Inuwa N, Wang Y, Murshed M, Abbasi KR (2022) Does Structural Transformation in Economy Impact Inequality in Renewable Energy Productivity? Implications for Sustainable Development. Renew Energy 189:853–864. 10.1016/j.renene.2022.03.050

Knowledge is power

Stay in the know about climate impacts and solutions. Subscribe to our weekly newsletter.

By clicking submit, you agree to share your email address with the site owner and Mailchimp to receive emails from the site owner. Use the unsubscribe link in those emails to opt out at any time.

Yale Climate Connections

Scientists agree: Climate change is real and caused by people

Share this:

• Click to share on Facebook (Opens in new window)
• Click to share on X (Opens in new window)

[Leer en español aquí]

The scientific consensus that climate change is happening and that it is human-caused is strong. Scientific investigation of global warming began in the 19th century , and by the early 2000s, this research began to coalesce into confidence about the reality, causes, and general range of adverse effects of global warming. This conclusion was drawn from studying air and ocean temperatures, the atmosphere’s composition, satellite records, ice cores, modeling, and more.

In 1988 the United Nations and World Meteorological Organization founded the Intergovernmental Panel on Climate Change, IPCC, to provide regular updates on the scientific evidence on global warming. In a 2013 report , the IPCC concluded that scientific evidence of warming is “unequivocal” and that the largest cause is an increase of carbon dioxide in the atmosphere as a result of humans burning fossil fuels. The IPCC continues to assess this science, periodically issuing new reports.

Climate change is real and caused by humans

The IPCC is not the only scientific group that has reached a clear consensus on the scientific evidence of human-caused global warming. As this NASA page points out, 200 global scientific organizations, 11 international science academies, and 18 American science associations have released statements in alignment with this consensus.

Amanda Staudt is the senior director for climate, atmospheric and polar sciences at the National Academies of Science, Engineering and Medicine, where she has worked since 2001. The Academies, she said, first began studying climate change in 1979, researching how much warming would likely happen if the amount of carbon dioxide concentrations in the atmosphere were doubled.

Four decades later, those findings have held up and have been strengthened based on scores of continued studies and analysis. “The remarkable thing about that study,” she said, “is that they basically got the right answer” from the start. This 1979 study by the National Research Council, Staudt said, led to investment in climate science in the U.S. 

Though this consensus has been thoroughly established, scientific research and new findings continue. Staudt said countless attempted rebuttals of climate science findings have been researched and disproved.

“We did a lot of studies in that time period, looking at those questions,” she said, ”and one by one, putting them to bed and convincing ourselves over and over again, that humans were affecting climate, and that we could document that effect.”

At the National Academies, reaching consensus requires open sessions and dialogue with scientists and agreement from committees, which typically consist of 12-15 experts. Their draft reports go through peer review, and reviewers’ concerns are resolved before publication is approved. The goal is for the complex science of climate change to become as thoroughly researched and substantiated as possible.

“One of the things I think about scientists is that we’re all inherently skeptics at some level,” Staudt said. “That’s what drives us to science, that we have questions about the world around us. And we want to prove that for ourselves.”

Scientists consistently reaffirm evidence that climate change is happening

Climate scientists worldwide go through similar processes before their findings are published. And their research papers, too, show a strong consensus about global warming. As NASA states on its website , “Multiple studies published in peer-reviewed scientific journals show that 97 percent or more of actively publishing climate scientists agree: Climate-warming trends over the past century are extremely likely due to human activities.” (By sound practice, scientists resist saying science is for all times “certain” or that its findings are “final,” and the “extremely likely” language respects that practice.)

One of the studies about the consensus was led by John Cook, a fellow at the Climate Change Communication Research Hub at Monash University in Melbourne, Australia. Cook and colleagues reviewed nearly 12,000 scientific papers to examine how aligned published research is on major findings on climate change. That study found that 97 percent of scholarly papers that take a position on climate change do endorse the consensus. The papers that rejected the consensus position contained errors, according to subsequent research .

In reviewing the papers, Cook has said he and his colleagues found the consensus to have been so widely accepted by 2013 that many researchers by then no longer felt a need to mention or reaffirm it in their research papers.

Also see: Causes of global warming: How scientists know that humans are responsible

Samantha Harrington

Samantha Harrington, director of audience experience for Yale Climate Connections, is a journalist and graphic designer with a background in digital media and entrepreneurship. Sam is especially interested... More by Samantha Harrington

What’s the difference between climate change and global warming?

The terms “global warming” and “climate change” are sometimes used interchangeably, but "global warming" is only one aspect of climate change.

“Global warming” refers to the long-term warming of the planet. Global temperature shows a well-documented rise since the early 20th century and most notably since the late 1970s. Worldwide since 1880, the average surface temperature has risen about 1 ° C (about 2 ° F), relative to the mid-20th century baseline (of 1951-1980). This is on top of about an additional 0.15 ° C of warming from between 1750 and 1880.

“Climate change” encompasses global warming, but refers to the broader range of changes that are happening to our planet. These include rising sea levels; shrinking mountain glaciers; accelerating ice melt in Greenland, Antarctica and the Arctic; and shifts in flower/plant blooming times. These are all consequences of warming, which is caused mainly by people burning fossil fuels and putting out heat-trapping gases into the air.

• Overview: Weather, Global Warming and Climate Change

Discover More Topics From NASA

Explore Earth Science

Earth Science in Action

Earth Science Data

Facts About Earth

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

82k Accesses

3427 Altmetric

Metrics details

• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

Similar content being viewed by others

Climate damage projections beyond annual temperature

Investment incentive reduced by climate damages can be restored by optimal policy

Climate economics support for the UN climate targets

Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Glanemann, N., Willner, S. N. & Levermann, A. Paris Climate Agreement passes the cost-benefit test. Nat. Commun. 11 , 110 (2020).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Burke, M., Hsiang, S. M. & Miguel, E. Global non-linear effect of temperature on economic production. Nature 527 , 235–239 (2015).

Article   ADS   CAS   PubMed   Google Scholar  

Kalkuhl, M. & Wenz, L. The impact of climate conditions on economic production. Evidence from a global panel of regions. J. Environ. Econ. Manag. 103 , 102360 (2020).

Article   Google Scholar  

Moore, F. C. & Diaz, D. B. Temperature impacts on economic growth warrant stringent mitigation policy. Nat. Clim. Change 5 , 127–131 (2015).

Article   ADS   Google Scholar  

Drouet, L., Bosetti, V. & Tavoni, M. Net economic benefits of well-below 2°C scenarios and associated uncertainties. Oxf. Open Clim. Change 2 , kgac003 (2022).

Ueckerdt, F. et al. The economically optimal warming limit of the planet. Earth Syst. Dyn. 10 , 741–763 (2019).

Kotz, M., Wenz, L., Stechemesser, A., Kalkuhl, M. & Levermann, A. Day-to-day temperature variability reduces economic growth. Nat. Clim. Change 11 , 319–325 (2021).

Kotz, M., Levermann, A. & Wenz, L. The effect of rainfall changes on economic production. Nature 601 , 223–227 (2022).

Kousky, C. Informing climate adaptation: a review of the economic costs of natural disasters. Energy Econ. 46 , 576–592 (2014).

Harlan, S. L. et al. in Climate Change and Society: Sociological Perspectives (eds Dunlap, R. E. & Brulle, R. J.) 127–163 (Oxford Univ. Press, 2015).

Bolton, P. et al. The Green Swan (BIS Books, 2020).

Alogoskoufis, S. et al. ECB Economy-wide Climate Stress Test: Methodology and Results European Central Bank, 2021).

Weber, E. U. What shapes perceptions of climate change? Wiley Interdiscip. Rev. Clim. Change 1 , 332–342 (2010).

Markowitz, E. M. & Shariff, A. F. Climate change and moral judgement. Nat. Clim. Change 2 , 243–247 (2012).

Riahi, K. et al. The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Change 42 , 153–168 (2017).

Auffhammer, M., Hsiang, S. M., Schlenker, W. & Sobel, A. Using weather data and climate model output in economic analyses of climate change. Rev. Environ. Econ. Policy 7 , 181–198 (2013).

Kolstad, C. D. & Moore, F. C. Estimating the economic impacts of climate change using weather observations. Rev. Environ. Econ. Policy 14 , 1–24 (2020).

Dell, M., Jones, B. F. & Olken, B. A. Temperature shocks and economic growth: evidence from the last half century. Am. Econ. J. Macroecon. 4 , 66–95 (2012).

Newell, R. G., Prest, B. C. & Sexton, S. E. The GDP-temperature relationship: implications for climate change damages. J. Environ. Econ. Manag. 108 , 102445 (2021).

Kikstra, J. S. et al. The social cost of carbon dioxide under climate-economy feedbacks and temperature variability. Environ. Res. Lett. 16 , 094037 (2021).

Article   ADS   CAS   Google Scholar  

Bastien-Olvera, B. & Moore, F. Persistent effect of temperature on GDP identified from lower frequency temperature variability. Environ. Res. Lett. 17 , 084038 (2022).

Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9 , 1937–1958 (2016).

Byers, E. et al. AR6 scenarios database. Zenodo https://zenodo.org/records/7197970 (2022).

Burke, M., Davis, W. M. & Diffenbaugh, N. S. Large potential reduction in economic damages under UN mitigation targets. Nature 557 , 549–553 (2018).

Kotz, M., Wenz, L. & Levermann, A. Footprint of greenhouse forcing in daily temperature variability. Proc. Natl Acad. Sci. 118 , e2103294118 (2021).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Myhre, G. et al. Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci. Rep. 9 , 16063 (2019).

Min, S.-K., Zhang, X., Zwiers, F. W. & Hegerl, G. C. Human contribution to more-intense precipitation extremes. Nature 470 , 378–381 (2011).

England, M. R., Eisenman, I., Lutsko, N. J. & Wagner, T. J. The recent emergence of Arctic Amplification. Geophys. Res. Lett. 48 , e2021GL094086 (2021).

Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5 , 560–564 (2015).

Pfahl, S., O’Gorman, P. A. & Fischer, E. M. Understanding the regional pattern of projected future changes in extreme precipitation. Nat. Clim. Change 7 , 423–427 (2017).

Callahan, C. W. & Mankin, J. S. Globally unequal effect of extreme heat on economic growth. Sci. Adv. 8 , eadd3726 (2022).

Diffenbaugh, N. S. & Burke, M. Global warming has increased global economic inequality. Proc. Natl Acad. Sci. 116 , 9808–9813 (2019).

Callahan, C. W. & Mankin, J. S. National attribution of historical climate damages. Clim. Change 172 , 40 (2022).

Burke, M. & Tanutama, V. Climatic constraints on aggregate economic output. National Bureau of Economic Research, Working Paper 25779. https://doi.org/10.3386/w25779 (2019).

Kahn, M. E. et al. Long-term macroeconomic effects of climate change: a cross-country analysis. Energy Econ. 104 , 105624 (2021).

Desmet, K. et al. Evaluating the economic cost of coastal flooding. National Bureau of Economic Research, Working Paper 24918. https://doi.org/10.3386/w24918 (2018).

Hsiang, S. M. & Jina, A. S. The causal effect of environmental catastrophe on long-run economic growth: evidence from 6,700 cyclones. National Bureau of Economic Research, Working Paper 20352. https://doi.org/10.3386/w2035 (2014).

Ritchie, P. D. et al. Shifts in national land use and food production in Great Britain after a climate tipping point. Nat. Food 1 , 76–83 (2020).

Dietz, S., Rising, J., Stoerk, T. & Wagner, G. Economic impacts of tipping points in the climate system. Proc. Natl Acad. Sci. 118 , e2103081118 (2021).

Bastien-Olvera, B. A. & Moore, F. C. Use and non-use value of nature and the social cost of carbon. Nat. Sustain. 4 , 101–108 (2021).

Carleton, T. et al. Valuing the global mortality consequences of climate change accounting for adaptation costs and benefits. Q. J. Econ. 137 , 2037–2105 (2022).

Bastien-Olvera, B. A. et al. Unequal climate impacts on global values of natural capital. Nature 625 , 722–727 (2024).

Malik, A. et al. Impacts of climate change and extreme weather on food supply chains cascade across sectors and regions in Australia. Nat. Food 3 , 631–643 (2022).

Article   ADS   PubMed   Google Scholar  

Kuhla, K., Willner, S. N., Otto, C., Geiger, T. & Levermann, A. Ripple resonance amplifies economic welfare loss from weather extremes. Environ. Res. Lett. 16 , 114010 (2021).

Schleypen, J. R., Mistry, M. N., Saeed, F. & Dasgupta, S. Sharing the burden: quantifying climate change spillovers in the European Union under the Paris Agreement. Spat. Econ. Anal. 17 , 67–82 (2022).

Dasgupta, S., Bosello, F., De Cian, E. & Mistry, M. Global temperature effects on economic activity and equity: a spatial analysis. European Institute on Economics and the Environment, Working Paper 22-1 (2022).

Neal, T. The importance of external weather effects in projecting the macroeconomic impacts of climate change. UNSW Economics Working Paper 2023-09 (2023).

Deryugina, T. & Hsiang, S. M. Does the environment still matter? Daily temperature and income in the United States. National Bureau of Economic Research, Working Paper 20750. https://doi.org/10.3386/w20750 (2014).

Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146 , 1999–2049 (2020).

Cucchi, M. et al. WFDE5: bias-adjusted ERA5 reanalysis data for impact studies. Earth Syst. Sci. Data 12 , 2097–2120 (2020).

Adler, R. et al. The New Version 2.3 of the Global Precipitation Climatology Project (GPCP) Monthly Analysis Product 1072–1084 (University of Maryland, 2016).

Lange, S. Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0). Geosci. Model Dev. 12 , 3055–3070 (2019).

Wenz, L., Carr, R. D., Kögel, N., Kotz, M. & Kalkuhl, M. DOSE – global data set of reported sub-national economic output. Sci. Data 10 , 425 (2023).

Article   PubMed   PubMed Central   Google Scholar  

Gennaioli, N., La Porta, R., Lopez De Silanes, F. & Shleifer, A. Growth in regions. J. Econ. Growth 19 , 259–309 (2014).

Board of Governors of the Federal Reserve System (US). U.S. dollars to euro spot exchange rate. https://fred.stlouisfed.org/series/AEXUSEU (2022).

World Bank. GDP deflator. https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS (2022).

Jones, B. & O’Neill, B. C. Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett. 11 , 084003 (2016).

Murakami, D. & Yamagata, Y. Estimation of gridded population and GDP scenarios with spatially explicit statistical downscaling. Sustainability 11 , 2106 (2019).

Koch, J. & Leimbach, M. Update of SSP GDP projections: capturing recent changes in national accounting, PPP conversion and Covid 19 impacts. Ecol. Econ. 206 (2023).

Carleton, T. A. & Hsiang, S. M. Social and economic impacts of climate. Science 353 , aad9837 (2016).

Article   PubMed   Google Scholar  

Bergé, L. Efficient estimation of maximum likelihood models with multiple fixed-effects: the R package FENmlm. DEM Discussion Paper Series 18-13 (2018).

Kalkuhl, M., Kotz, M. & Wenz, L. DOSE - The MCC-PIK Database Of Subnational Economic output. Zenodo https://zenodo.org/doi/10.5281/zenodo.4681305 (2021).

Kotz, M., Wenz, L. & Levermann, A. Data and code for “The economic commitment of climate change”. Zenodo https://zenodo.org/doi/10.5281/zenodo.10562951 (2024).

Dasgupta, S. et al. Effects of climate change on combined labour productivity and supply: an empirical, multi-model study. Lancet Planet. Health 5 , e455–e465 (2021).

Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3 , 497–501 (2013).

Zhao, C. et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. 114 , 9326–9331 (2017).

Wheeler, T. R., Craufurd, P. Q., Ellis, R. H., Porter, J. R. & Prasad, P. V. Temperature variability and the yield of annual crops. Agric. Ecosyst. Environ. 82 , 159–167 (2000).

Rowhani, P., Lobell, D. B., Linderman, M. & Ramankutty, N. Climate variability and crop production in Tanzania. Agric. For. Meteorol. 151 , 449–460 (2011).

Ceglar, A., Toreti, A., Lecerf, R., Van der Velde, M. & Dentener, F. Impact of meteorological drivers on regional inter-annual crop yield variability in France. Agric. For. Meteorol. 216 , 58–67 (2016).

Shi, L., Kloog, I., Zanobetti, A., Liu, P. & Schwartz, J. D. Impacts of temperature and its variability on mortality in New England. Nat. Clim. Change 5 , 988–991 (2015).

Xue, T., Zhu, T., Zheng, Y. & Zhang, Q. Declines in mental health associated with air pollution and temperature variability in China. Nat. Commun. 10 , 2165 (2019).

Article   ADS   PubMed   PubMed Central   Google Scholar  

Liang, X.-Z. et al. Determining climate effects on US total agricultural productivity. Proc. Natl Acad. Sci. 114 , E2285–E2292 (2017).

Desbureaux, S. & Rodella, A.-S. Drought in the city: the economic impact of water scarcity in Latin American metropolitan areas. World Dev. 114 , 13–27 (2019).

Damania, R. The economics of water scarcity and variability. Oxf. Rev. Econ. Policy 36 , 24–44 (2020).

Davenport, F. V., Burke, M. & Diffenbaugh, N. S. Contribution of historical precipitation change to US flood damages. Proc. Natl Acad. Sci. 118 , e2017524118 (2021).

Dave, R., Subramanian, S. S. & Bhatia, U. Extreme precipitation induced concurrent events trigger prolonged disruptions in regional road networks. Environ. Res. Lett. 16 , 104050 (2021).

Download references

Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

Author information

Authors and affiliations.

Research Domain IV, Research Domain IV, Potsdam Institute for Climate Impact Research, Potsdam, Germany

Maximilian Kotz, Anders Levermann & Leonie Wenz

Institute of Physics, Potsdam University, Potsdam, Germany

Maximilian Kotz & Anders Levermann

Mercator Research Institute on Global Commons and Climate Change, Berlin, Germany

Leonie Wenz

You can also search for this author in PubMed   Google Scholar

Contributions

All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

Corresponding author

Correspondence to Leonie Wenz .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Xin-Zhong Liang, Chad Thackeray and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

Supplementary information

Supplementary information, peer review file, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

Download citation

Received : 25 January 2023

Accepted : 21 February 2024

Published : 17 April 2024

Issue Date : 18 April 2024

DOI : https://doi.org/10.1038/s41586-024-07219-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Opinion And Analysis
• Entertainment +
• Off The Beat
• Europe News
• Other Countries
• Readers Corner
• Thiruvananthapuram
• Pathanamthitta
• Kerala Lottery Results
• Other Sports
• Local Sports
• Love & Life
• Art & Culture
• Spirituality
• Agriculture
• Environment
• Methodology
• Correction Policy
• Financial Planning
• Smart Spending
• Business News
• News Updates
• Latest Info
• GLOBAL MANORAMA
• ENTERTAINMENT
• TECH & GADGETS
• GREEN WORLD
• YOUTH & KIDS
• WEB STORIES
• THOZHILVEEDHI
• GULF MANORAMA
• US MANORAMA
• MANORAMA NEWS TV
• MANORAMAMAX
• RADIO MANGO
• HELLO ADDRESS
• MANORAMA HORIZON
• MANORAMA YEARBOOK
• MALAYALA MANORAMA
• MANORAMA NEWS
• CALENDAR 2023 PRO
• CALENDAR 2023 FREE
• MANORAMA PRODUCTS
• TERMS OF USE
• CAREERS @ MANORAMA
• Site follows the DNPA’s code of conduct

Signed in as

• Change Password

• Look up a Term Close Panel
• CLAM Bake Close Clams

Climate Science Glossary

Term lookup.

Enter a term in the search box to find its definition.

Use the controls in the far right panel to increase or decrease the number of terms automatically displayed (or to completely turn that feature off).

Latest Posts

2024 sks weekly climate change & global warming news roundup #17.

• Fact Brief - Is Antarctica gaining land ice?
• Simon Clark: The climate lies you'll hear this year
• Skeptical Science New Research for Week #17 2024
• Water is at the heart of farmers’ struggle to survive in Benin
• At a glance - The difference between weather and climate
• India makes a big bet on electric buses
• 2024 SkS Weekly Climate Change & Global Warming News Roundup #16
• EGU2024 - An intense week of joining sessions virtually
• Skeptical Science New Research for Week #16 2024
• How extreme was the Earth's temperature in 2023
• At a glance - Is the science settled?
• What is Mexico doing about climate change?
• 2024 SkS Weekly Climate Change & Global Warming News Roundup #15
• Fact Brief - Did global warming stop in 1998?
• Skeptical Science New Research for Week #15 2024
• EGU2024 - Picking and chosing sessions to attend virtually
• At a glance - The Pacific Decadal Oscillation (PDO) is not causing global warming
• Climate Adam: Is Global Warming Speeding Up?
• 2024 SkS Weekly Climate Change & Global Warming News Roundup #14
• Gigafact and Skeptical Science collaborate to create fact briefs
• Skeptical Science New Research for Week #14 2024
• How can I make my retirement plan climate-friendly?
• At a glance - Global warming and the El Niño Southern Oscillation
• A data scientist’s case for ‘cautious optimism’ about climate change
• 2024 SkS Weekly Climate Change & Global Warming News Roundup #13
• Skeptical Science New Research for Week #13 2024
• You can start applying for the American Climate Corps next month
• At a glance - Human fingerprints on climate change rule out natural cycles
• Want clean electricity? These are the overlooked elected officials who get to decide.

Posted on 28 April 2024 by BaerbelW, Doug Bostrom, John Hartz

Story of the week.

Anthropogenic climate change may be the ultimate shaggy dog story — but with a twist, because here endless subplots definitely depend upon one central element in the unfolding drama of our grand physics accident: the dominant story mechanic is that we're changing Earth's climate. This leads to outcomes. One way of seeing this is via the abstraction of statistics, while another perspective is that of individual experiences each of which is only an anecdote but together lead us back to statistics. Our story of the week is  Carbon Brief's annual summary  State of the  climate : 2024 off to a record-warm start :

This year is shaping up to either match or surpass 2023 as the hottest year on record. Global temperatures have been exceptionally high over the past three months – at around 1.6C above  pre-industrial levels  – following the peak of current  El Niño  event at the start of 2024. The past 10 months have all set new all-time monthly temperature records, though the margin by which new records have been set has fallen from around 0.3C last year to 0.1C over the first three months of 2024.  April 2024 is on track to extend this streak to 11 record months in a row.

Author Zeke Hausfather continues this informative summary by delivering a complete numerical rundown of where we stand with regard to global surface temperature. In sum we're we're living a spike. Our shock is belated.  Expert opinion suggests we're  experiencing another wiggle in a upward-trending graph. We've seen this before in historical records, only less remarked given we're only now having our first brush with 1.5  ° C of overall warming.  In any case, the directed herky jerky plot of global warming inevitably unfolds a bevy of subplots exemplified by other stories from this week's roundup, "anecdotes" in the grand scheme of global temperature records:

• The big dry: forests and shrublands are dying in parched Western Australia
• Europe is the fastest-warming continent, at nearly twice the average global rate, report says
• 'We were in disbelief': Antarctica is behaving in a way we've never seen before. Can it recover?
• Warming climate is putting more metals into Colorado's mountain streams

This shaggy dog story will continue to proliferate and evolve while we wait to reach our next record year. 

Stories we promoted this week, by publication date:

Before April 21

• Climate change is also a chance to transform education in America - again , TheHill, Andrés Henríquez.
• The big dry: forests and shrublands are dying in parched Western Australia , Environment & Energy, The Conversation AU, by Joe Fontaine, George Matusick, Jatin Kala, Kerryn Hawke & Nate Anderson.
• A Planetary Crisis Awaits the Next President , New York Times, Guest Essay by Stephen Markley.
• 2024 SkS Weekly Climate Change & Global Warming News Roundup #16 , Skeptical Science, Bärbel Winkler, Doug Bostrom & John Hartz. A listing of 29 news and opinion articles we found interesting and shared on social media during the past week: Sun, April 14, 2024 thru Sat, April 20, 2024.
• 100% wind and solar is coming! , "Just have a think" on Youtube, Dave Borlace.
• Climate Doom Is Out. `Apocalyptic Optimism` Is In. , NYT, Alexis Soloski. Focusing on disaster hasn’t changed the planet’s trajectory. Will a more upbeat approach show a way forward?
• Europe is the fastest-warming continent, at nearly twice the average global rate, report says , The Independent News, Jamey Keaten. Two top climate monitoring organizations are reporting that Europe is the fastest-warming continent and its temperatures are rising at roughly twice the global average
• We were in disbelief': Antarctica is behaving in a way we've never seen before. Can it recover? , Livescience, Ben Turner. Antarctic sea ice has been disappearing over the last several summers. Now, climate scientists are wondering whether it will ever come back.
• Europe`s warming up at nearly twice the global average, says new report , The Verge - Science Posts, Amrita Khalid. In 2023, the continent experienced extreme heatwaves, severe flooding, and its largest ever recorded wildfire.
• Good News and Nothing But , The Crucial Years, Bill McKibben. One Day Only--Happy Earth Day
• Can individuals make an impact on climate change? Here`s where local experts say to start , Yahoo News - Latest News , Michelle Alfini.
• Carbon Dioxide Levels Have Passed a New Milestone , The Upshot, New York Times, Aatish Bhatia.
• How to talk to a climate doomer (even if that doomer is you) , Yale Climate Connections, Daisy Simmons. It’s not too late to tackle climate change, but sometimes it sure feels that way.
• State of the climate: 2024 off to a record-warm start , Carbon Brief, Zeke Hausfather. This year is shaping up to either match or surpass 2023 as the hottest year on record.
• A powerful volcano is erupting. Here’s what that could mean for weather and climate , Climate, CNN, Mary Gilbert.
• Warming climate is putting more metals into Colorado's mountain streams , Phys.org, Liza Lester.
• Your most pressing climate questions , NYT, Ryan McCarthy. Introducing Ask NYT Climate, where we’ll explore how climate intersects with your everyday life
• Africa’s megacities threatened by heat, floods and disease – urgent action is needed to start greening and adapt to climate change , Climate, The Conversation AF, April 23, 2024, Meelan Thondoo.
• Earth Day reflections from the next generation , Environmental Health News (EHN, Editors. "This week we're featuring essays from Houston-area eighth graders to hear what the youth think about the state of our planet."
• Yellowstone Lake's weird resistance to climate change could be about to crack , Livescience, Ben Turner. Yellowstone's lake's ice cover has remained unaffected by increasing temperatures due to increased snowfall. But this could make it vulnerable to a sudden shift.
• `Outrageous` climate activists get in the faces of politicians and oil bosses - will it work? , The Guardian, Oliver Milman. As the climate crisis has deepened, protesters have become more confrontational – and their ambitions have grown
• Pinning down climate change's role in extreme weather , The Climate Brink, Andrew Dessler. and did climate change contribute to the flooding in Dubai?
• We might be closer to changing course on climate change than we realized , Climate, Vox, Umair Irfan. "Greenhouse gas emissions might have already peaked. Now they need to fall — fast."
• Skeptical Science New Research for Week #17 2024 , Skeptical Science, Doug Bostrom & Marc Kodack. A weekly overview of research on matters of human-caused climate change.
• Climate change could become the main driver of biodiversity decline by mid-century , iDiv, iDiv. Press release from the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig about the largest modelling study of its kind, published in Science
• Scientists and comedians join forces to get climate crisis message across , The Guardian, Jeremy Plester. Video series launched in which comics translate climate science into down-to-earth language
• The Paris Effect: Human Rights in Light of International Climate Goals and Commitments , Climate Law Blog, Jannika Jahn.
• Nixon Advisers` Climate Research Plan: Another Lost Chance on the Road to Crisis , Inside Climate News, Marianne Lavelle. A 1971 plan for a global carbon dioxide monitoring network never came to fruition. The proposal is detailed in a document newly unearthed by the National Security Archive.
• Climate skeptic dismisses severity of Great Barrier Reef bleaching , AFP Fact Check, Manon Jacob. Scientists predict recent bleaching at Australia's Great Barrier Reef will be the worst on record, but skeptics online dismiss the damage by claiming coral populations are at a record high.
• Fact Brief - Is Antarctica gaining land ice? , Skeptical Science, SkS Team.
• They turned cattle ranches into tropical forest - then climate change hit , The Verge, Justine Calma. They brought forests back to life in Costa Rica. Their next challenge? Restoring ecosystems in a warming world.

If you happen upon high quality climate-science and/or climate-myth busting articles from reliable sources while surfing the web, please feel free to submit them via  this Google form so that we may share them widely. Thanks!

There have been no comments posted yet.

You need to be logged in to post a comment. Login via the left margin or if you're new, register here .

What caused Dubai floods? Experts cite climate change, not cloud seeding

• Medium Text

DID CLOUD SEEDING CAUSE THE STORM?

CAN'T CREATE CLOUDS FROM NOTHING

Sign up here.

Reporting by Alexander Cornwell; editing by Maha El Dahan and Alexandra Hudson

Our Standards: The Thomson Reuters Trust Principles. New Tab , opens new tab

World Chevron

Thai foreign minister quits after cabinet reshuffle

Thai Foreign Minister Parnpree Bahiddha-Nukara has resigned, the government said on Sunday, after being removed from the post of deputy prime minister in a cabinet reshuffle, at a time when the Southeast Asian country seeks to help resolve a conflict in Myanmar.

• Share full article

Advertisement

Supported by

Drought Pushes Millions Into ‘Acute Hunger’ in Southern Africa

The disaster, intensified by El Niño, is devastating communities across several countries, killing crops and livestock and sending food prices soaring.

By Somini Sengupta and Manuela Andreoni

An estimated 20 million people in southern Africa are facing what the United Nations calls “acute hunger” as one of the worst droughts in more than four decades shrivels crops, decimates livestock and, after years of rising food prices brought on by pandemic and war, spikes the price of corn, the region’s staple crop.

Malawi, Zambia and Zimbabwe have all declared national emergencies.

It is a bitter foretaste of what a warming climate is projected to bring to a region that’s likely to be acutely affected by climate change, though scientists said on Thursday that the current drought is more driven by the natural weather cycle known as El Niño than by global warming.

Its effects are all the more punishing because in the past few years the region had been hit by cyclones, unusually heavy rains and a widening outbreak of cholera.

‘Urgent help’ is needed

The rains this year began late and were lower than average. In February, when crops need it most, parts of Zimbabwe, Zambia, Malawi, Angola, Mozambique and Botswana received a fifth of the typical rainfall.

That’s devastating for these largely agrarian countries, where farmers rely entirely on the rains.

In southern Malawi, in a district called Chikwawa, some residents were wading into a river rife with crocodiles to collect a wild tuber known as nyika to curb their hunger. “My area needs urgent help,” the local leader, who identified himself as Chief Chimombo, said.

Elsewhere, cattle in search of water walked into fields still muddy from last year’s heavy rains, only to get stuck, said Chikondi Chabvuta, a Malawi-based aid worker with CARE, the international relief organization. Thousands of cattle deaths have been reported in the region, according to the group.

The first few months of every year, just before the harvest begins in late April and May, are usually a lean season. This year, because harvests are projected to be significantly lower , the lean season is likely to last longer. “The food security situation is very bad and is expected to get worse,” Ms. Chabvuta said.

Local corn prices have risen sharply. In Zambia, the price more than doubled between January 2022 and January of this year, according to the United Nations Food and Agriculture Organization . In Malawi, it rose fourfold.

The F.A.O. pointed out that, in addition to low yields, grain prices have been abnormally high because of the war in Ukraine, one of the world’s biggest grain exporters, as well as weak currencies in several southern African countries, making it expensive to buy imported food, fuel and fertilizers.

Why it’s happening

According to an analysis published Thursday by World Weather Attribution, an international coalition of scientists that focuses on rapid assessment of extreme weather events, the driving force behind the current drought is El Niño, a natural weather phenomenon that heats parts of the Pacific Ocean every few years and tweaks the weather in different ways in different parts of the world. In Southern Africa, El Niños tend to bring below-average rainfall.

El Niño made this drought twice as likely, the study concluded. That weather pattern is now weakening, but a repeat is expected soon.

The drought may also have been worsened by deforestation, which throws off local rainfall patterns and degrades soils, the study concluded.

Droughts are notoriously hard to attribute to global warming. That is particularly true in regions like Southern Africa, in part because it doesn’t have a dense network of weather stations offering detailed historical data.

Scientists are uncertain as to whether climate change played a role in this particular drought. However, there is little uncertainty about the long-term effects of climate change in this part of the world.

The average temperature in Southern Africa has risen by 1.04 to 1.8 degrees Celsius in the past 50 years , according to the Intergovernmental Panel on Climate Change, and the number of hot days has increased. That makes a dry year worse. Plants and animals are thirstier. Moisture evaporates. Soils dry out. Scientific models indicate that Southern Africa is becoming drier overall .

The Intergovernmental Panel on Climate Change calls Southern Africa a climate change “hot spot in terms of both hot extremes and drying.”

The costs of adaptation

To the millions of people trying to cope with this drought, it hardly matters whether climate change or something else is responsible for why the skies have gone dry.

What matters is whether these communities can adapt fast enough to weather shocks.

“It’s really important that resilience to droughts, especially in these parts of the continent, should really be improved,” said Joyce Kimutai, one of the authors of the study and a researcher at the Grantham Institute, a climate and environment center at Imperial College London.

There are existing solutions that need money to put into effect: early warning systems that inform people about what to expect, insurance and other social safety programs to help them prepare, as well as diversifying what farmers plant. Corn is extremely vulnerable to heat and erratic rains.

Golden Matonga contributed reporting.

Somini Sengupta is the international climate reporter on the Times climate team. More about Somini Sengupta

Manuela Andreoni is a Times climate and environmental reporter and a writer for the Climate Forward newsletter. More about Manuela Andreoni

Learn More About Climate Change

Have questions about climate change? Our F.A.Q. will tackle your climate questions, big and small .

Paris is becoming a city of bikes. Across China, people are snapping up $5,000 electric cars. Here’s a look at a few bright spots  for emission reductions.

In theory, online shopping can be more efficient  than driving to the store. But you may still want to think before you add to cart.

“Buying Time,” a new series from The New York Times, looks at the risky ways  humans are starting to manipulate nature  to fight climate change.

Big brands like Procter & Gamble and Nestlé say a new generation of recycling plants will help them meet environmental goals, but the technology is struggling to deliver .

Did you know the ♻ symbol doesn’t mean something is actually recyclable ? Read on about how we got here, and what can be done.