impact of lockdown on global warming essay

How did COVID-19 lockdowns affect the climate?

May 2021 - A new study shows how COVID-19 lockdowns have temporarily reduced global emissions of CO2 and other pollutants.

In this article, Met Office Research Fellow Chris Jones discusses the study and what it tells us about limiting global temperature rise.

The COVID-19 pandemic has had a massive impact on our lives, and how we go about day-to-day business. It has directly affected millions of people and put our infrastructure under huge pressure. In this new study we look to see if the changes to our daily lives over the last 12 months have had an effect on the climate. On a global scale, the new study by Jones et al. (2021) (which is also the subject of a Research Spotlight in EOS)  finds very little changes are detectable, but the way that we rebuild our economies does offer an unprecedented opportunity to “build back better” and shows potential pathways to meet long-term climate goals.

Many nations, including the UK, responded to the COVID-19 pandemic by restricting travel and other activities during 2020, and into 2021. This caused a temporary reduction in global greenhouse gas emissions and local air pollution.

Empty roads in the UK during the COVID-19 pandemic

Atmospheric concentrations of greenhouse gases have been increasing since the mid-1800s. Once in the atmosphere, these gases form a blanket around the planet trapping heat from the sun and increasing global temperatures. Aerosols , which are tiny particles suspended in the atmosphere, can also affect the Earth’s climate. Pollution from cars and factories is a major source of man-made aerosols but they can also be produced naturally. Aerosols play a key role in Earth’s energy balance through influencing the amount of energy from the sun that is either absorbed in the atmosphere or reflected back into space.  

Figure 1.  The map shows the average change in aerosols across the Earth system models. AOD refers to aerosol optical depth. Green shading shows regions with reduced aerosol amounts in the atmosphere, and purple shows regions with increased aerosols.

As seen in Figure 1, the results show a consensus that aerosol amounts were reduced, especially over southern and eastern Asia, for 2020. This led to increases in solar radiation reaching the surface in that region. However, we could not detect any associated impact on temperature or rainfall in that region or globally.

The results are publicly available through a climate model archive known as the Earth System Grid . The climate research community will benefit from these experiments for many years. We recommend that more analyses on regional scales and analysis of extreme weather and air quality would be useful to further understand the impact of emission reductions due to COVID-19 on the climate.

How have emissions changed due to the pandemic?

As the pandemic rapidly spread and affected more and more countries it became clear that it had the potential to disrupt daily life on a global scale and change the way we use energy, burn fuel and emit pollutants into the atmosphere. Several studies analysed activity data – such as that available from Apple or Google – to estimate how this might change greenhouse gas and aerosol emissions. Le Quéré et al. (2020) estimated a drop in carbon dioxide emissions of about 17% during April 2020, and projected this would lead to a decrease of about 7% for the year as a whole. Forster et al. (2020) applied similar analysis to emissions of other greenhouse gases – such as methane, and precursors of ozone and aerosols such as sulphates and nitrogen oxides. All of these saw a peak reduction during April with expected sustained reductions during the rest of 2020. Carbon Brief reported that in the UK, these reductions saw greenhouse gas emissions drop to 51% below 1990 levels – equivalent to temporarily being halfway to meeting the UK’s net zero by 2050 target.

What was the effect of the change in emissions?

How these emissions affect the content of the atmosphere varies for each different gas or aerosol. We know that aerosols only stay in the air for a few days and so the amounts of them can change very quickly. Many places in the world saw big improvements in air quality and visibility due to the reduction in aerosols.

Gases like carbon dioxide have a very long lifetime in the atmosphere and so changes to emissions only affect them very slowly. While a decrease in emissions of 7% is unprecedented, it still means that 93% of our normal emissions went into the atmosphere and carbon dioxide levels continued to build up. A bit like filling a bathtub – we slowed the flow from the taps very slightly, but the water level continues to rise .

How did the climate respond?

Some experiments with the Canadian climate model, CanESM5, showed that the global climate response to emissions reductions like those observed in 2020 is likely to be small (Fyfe et al., 2021). But we know that different models can give different answers, so in our new study we wanted to use as many models as we could to check the robustness of the conclusions. Assuming that the emissions reductions would last for two years before beginning to recover back to previous levels, the models were used to simulate the climate of the five year period from 2020-2024.

Figure 1 shows how the reduced emissions in 2020 led to reduced aerosol amounts, especially over southern and eastern Asia. This also led to small increases in the amount of sunlight reaching the Earth’s surface in that region, as seen in Figure 2, but this was not enough to change the climate. Both for that region, and globally, the annual temperature and rainfall did not change significantly.

Can we build-back “greener”?

To try to understand the effect all of these changes would have on our climate, Forster et al. (2020) used a simplified climate model to look at the conflicting effects. Despite initially very little effect on climate, on longer timescales over many years decreased carbon dioxide emissions will cause a cooling effect. The message being that if we can continue to reduce our emissions then we still have a chance of limiting the level of future warming and the severity of future climate change impacts.

With vaccination programmes bringing the prospect of brighter horizons, now is a good time to consider how we can plan our economic recovery and development in a way which ensures a sustainable future and transition to low-carbon. The goals of the Paris Agreement commit countries to try to limit warming to well below 2°C above pre-industrial levels, while pursuing efforts to limit it to 1.5°C. Reducing emissions to net zero from about the middle of the century is required to limit global warming; this means no longer adding any greenhouse gases to the atmosphere from that date. Many countries have pledged to achieve these net zero goals, but doing so requires far reaching transformation across all parts of society. This includes long-term planning for infrastructure from power generation, to domestic heating, to electric vehicle infrastructure. How we achieve this is up to society, but scientific understanding of the climate system helps with planning possible solutions. The impact of the Covid restrictions has provided a unique opportunity for society to pursue ways forward which could bring permanent changes towards a climate-resilient future.

Forster, P. M., Forster, H. I., Evans, M. J., Gidden, M. J., Jones, C. D., Keller, C. A., et al. (2020). Current and future global climate impacts resulting from COVID-19. Nat. Clim. Chang. doi: 10.1038/s41558-020-0883-0 .

Fyfe, J.C., Kharin, V.V., Swart, N., Flato, G.M., Sigmond, M., Gillett, N. P. (2021). Quantifying the Influence of Short-term Emission Reductions on Climate. Sci. Adv. in press.

Jones, C. D., Hickman, J. E., Rumbold, S. T., Walton, J., Lamboll, R. D., Skeie, R. B., et al. (2021). The Climate Response to Emissions Reductions due to COVID‐19: Initial Results from CovidMIP. Geophys. Res. Lett. doi: 10.1029/2020GL091883 .

Lamboll, R. D., Jones, C. D., Skeie, R. B., Fiedler, S., Samset, B. H., Gillett, N. P., Rogelj, J., and Forster, P. M. (2020). Modifying emission scenario projections to account for the effects of COVID-19: protocol for Covid-MIP. Geosci. Model Dev. Discuss. doi: 10.5194/gmd-2020-373 .

Le Quéré, C., Jackson, R. B., Jones, M. W., Smith, A. J. P., Abernethy, S., Andrew, R. M., et al. (2020). Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement. Nat. Clim. Chang. 10, 647–653. doi: 10.1038/s41558-020-0797-x .

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COVID-19’s Long-Term Effects on Climate Change—For Better or Worse

As a result of the lockdowns around the world to control COVID-19, huge decreases in transportation and industrial activity resulted in a drop in daily global carbon emissions of 17 percent in April. Nonetheless, CO2 levels in the atmosphere reached their highest monthly average ever recorded in May — 417.1 parts per million. This is because the carbon dioxide humans have already emitted can remain in the atmosphere for a hundred years; some of it could last tens of thousands of years.

Beyond carbon emissions, however, COVID-19 is resulting in changes in individual behavior and social attitudes, and in responses by governments that will have impacts on the environment and on our ability to combat climate change. Many of these will make matters worse, while others could make them better. While it’s unclear how these factors will balance out in the end, one thing is certain: more large-scale actions will be essential to avoid the worst impacts of climate change.

Delay of COP26

The Paris climate accord of 2015, adopted by every country, all of which pledged to take action to keep global average temperatures from rising more than 2° C beyond preindustrial levels, was set to reconvene in November this year at COP26. The countries were to announce plans to ratchet up climate actions, since the plans they submitted in 2015 could still allow global temperatures to rise by a potentially catastrophic 3°C. Now COP26 has been delayed a year. If the conference occurred this fall, countries would likely be more compelled to introduce economic recovery plans for COVID-19 that also further their climate change goals. The delay, however, could enable countries to enact stimulus plans that do not incorporate climate change strategies.

International negotiations delayed

A variety of international negotiations to protect the environment have also been delayed. The World Conservation Congress to evaluate global conservation measures has been postponed to January 2021. The Convention on Biological Diversity, which would have established new global rules to protect wildlife and plants from climate change and other threats, has been postponed until next year. The 2020 U.N. Ocean Conference scheduled for June to plan for sustainable solutions to manage the oceans has been delayed but no new date has been set. And a meeting to finalize the High Seas Treaty to establish agreements for conservation and sustainable development for ocean biodiversity in international waters — a meeting that took years of negotiations to arrange— has been pushed to 2021. These delays could allow some countries to shift their priorities away from the environment.

Deforestation in the Amazon

Brazilian president Jair Bolsonaro has been calling for more commercial development in the Amazon rainforest, which absorbs two billion tons of CO2 from the atmosphere a year.

Now as Brazil, hard hit by COVID-19, is focused on controlling the virus, illegal loggers and miners are taking advantage of the situation to cut down large swaths of the Amazon. Between January and April, 464 square miles of the rainforest were razed , 55 percent more area than was destroyed in the same period in 2019. The cleared area will be burned to make it suitable for cattle grazing, which could increase the chance of wildfires; wildfires burning out of control in 2019 destroyed an estimated 3,500 square miles of rainforest.

Weakening of climate policies

Some countries and private companies may delay or cancel investments in renewable energy or climate action policies if their finances have been impacted by the pandemic. For example, airlines, responsible for two to three percent of global carbon emissions, have been hard hit financially by the cessation of travel. They are clamoring to defer impending carbon taxes for flights within Europe. And after years of negotiation, a global plan to reduce aviation emissions, set to go into effect in 2021, would compel airlines to improve their international flights’ fuel economy by capping emissions at a 2020 baseline; any increase in future emissions would need to be offset by carbon reduction projects. But because a 2020 baseline would be relatively low, if air travel returns to its “normal” levels, they would be counted as growth and increase the burden on airlines; the United Nations’ International Civil Aviation Organization is considering making 2019 the baseline.

Rollback of U.S. environmental measures

President Trump signed an executive order that enables federal agencies to waive environmental review for infrastructure projects such as highways and pipelines to speed the economic recovery. It weakens the National Environmental Policy Act (NEPA) that requires government agencies to conduct a review of potential environmental and public health impacts before a project is approved and enables local communities to weigh in. The executive order gives NEPA “flexibility” in emergency situations, and allows agencies to put aside normal environmental reviews and make alternative plans.

The EPA has announced that it will temporarily “exercise enforcement discretion” with regard to violations of environmental laws as a result of COVID-19. New guidelines enable companies to monitor themselves to determine if they are violating air and water quality regulations. In other words, entities unable to comply with regulations due to social distancing or shortage of workers will not be penalized. States and environmental groups are suing the EPA for abdicating its duty. Gina McCarthy, head of the EPA under the Obama administration, now president of the Natural Resources Defense Council, called it “an open license to pollute.”

One result of the EPA’s action is that manufacturing or energy production facilities, coal mines, industrial waste landfills and others can delay reporting of their greenhouse gas emissions. This emissions data is necessary to help the EPA assess its existing greenhouse gas regulations and determine if additional ones are necessary.

Using the pandemic as cover, President Trump is continuing his efforts to weaken environmental regulations. The EPA has proposed a new rule that would alter the cost-benefit formulas used in Clean Air Act regulations. “Co-benefits” such as improvements in public health from reducing pollution, will no longer be given as much weight in justifying regulations.

In addition, Trump signed another executive order opening up a marine conservation area off New England to commercial fishing. The Northeast Canyons and Seamounts Marine National Monument established by President Obama is a haven for endangered right whales and other vulnerable marine creatures.

The Pipeline and Hazardous Materials Safety Administration declared that it would exercise discretion in enforcing natural gas pipeline safety regulations during the pandemic. This could result in more methane (a greenhouse gas with 80 times more global warming potential than CO2 over a 20-year span) being emitted from leaking pipelines. The EPA estimates  that the natural gas pipeline system was responsible for almost 13 percent of national methane emissions in 2018.

Less money for climate resilience and renewable energy

The need for more emergency services coupled with a reduction in tax revenue has taken an economic toll on cities and states. As a result, some have had to delay and divert funding away from climate resilience projects and renewable energy. Miami, which began elevating its flood-prone roads in 2015, had only completed about 20 percent of the work when COVID-19 struck and cut tourism income. The city has lost about one quarter of its total revenue, which will make finishing the job more challenging.

The Obama administration’s $1 billion National Disaster Resilience Competition set aside $1 billion in funding for innovative projects that make cities and states more resilient to climate change, but the funds must be spent by fall 2022. Many projects will need an extension.

For instance, Virginia, which won $121 million to build a flood wall, raise roads and incorporate green infrastructure and pumps to curb flooding in Norfolk, has broken ground on the project, but needs more time to spend all the funds. If Congress does not extend the deadline, most of the 13 projects will not be completed.

While U.S. renewable energy generation doubled over the past 10 years, COVID-19 may undo much of this progress—600,000 jobs in renewable energy, energy efficiency, green vehicles and energy storage have been lost since March. The wind industry estimates it could lose 35,000 jobs, and the Solar Energy Industries Association predicts half its workforce will be out of work by the end of 2020. For example, sales and installations in Illinois, a once booming solar market due to its Future Energy Jobs Act enacted in 2016 to move the state to a clean energy future, have slowed due to COVID-19. Many workers have already been laid off or furloughed with more job losses expected; smaller companies may not survive.

Scientific research disrupted

Due to lockdowns and travel bans, scientists have been unable to travel to do their fieldwork , and there’s a limit to how much some can accomplish with data and computers alone. Columbia University’s Lamont-Doherty Earth Observatory (LDEO) closed its labs in March, affecting its researchers. Jacqueline Austermann, an LDEO earth scientist, had a National Science Foundation grant to collect coral fossil samples in the Bahamas this spring; the samples would have helped researchers better understand historical sea levels and how climate change might affect future sea level. The project was put on hold.

Galen McKinley, a professor of Earth and Environmental Sciences at Columbia University and LDEO, studies the ocean and the carbon cycle, working mostly on the computer, running models and simulations. She depends on data collected by investigators who collect surface ocean carbon data, but many research cruises have been cancelled due to COVID-19.

McKinley explained that in some parts of the ocean, carbon uptake is only measured once every decade or so. “These sections [of ocean research] are very expensive to do. You have to have a ship out there for a couple months to accomplish it with people and equipment. If these sections get cancelled midstream, as one was in the Pacific, those data won’t be taken. So we’ll have a hole in our ability to observe the change in the total uptake of carbon and heat by the ocean. There will be a 10-year gap in our ability to monitor that and understand how the ocean is responding to climate change.”

The cancellation of research cruises not only means a gap in the data, it also means the loss of an unprecedented opportunity. COVID-19 may result in an approximately five to eight percent reduction in average global emissions for the year, and while this is a small amount in the context of the whole system, it offers a rare opportunity to see how Earth responds to cuts on carbon emissions. “All of our observations of the Earth system have been made under a situation where atmospheric CO2 is going up exponentially every year,” said McKinley. “We don’t really know what the Earth will do when we start cutting our emissions, but this is what we want and need to do under the Paris accord. That is one reason why this is a valuable opportunity to tease out any signals of what we can expect the Earth system to do in response to cutting emissions.”

McKinley and colleagues recently found that the ocean’s capacity to absorb carbon dioxide from the atmosphere depends on the amount of CO2 in the atmosphere; in other words, as CO2 emissions decrease, the ocean’s absorption of CO2 will slow . As we cut our emissions, the ocean will eventually begin to release carbon back into the atmosphere. But we don’t know whether this will happen in a few years or a few decades, and the current dip in emissions could provide some clues if researchers could go out in the field to take measurements. Understanding how the ocean circulation and carbon cycle work is key to making more accurate predictions about future conditions.

More plastic

COVID-19 has vastly increased our use of plastic: gloves and masks, plexiglass dividers in stores and offices, and disposable shopping bags.

Discarded gloves and masks are littering streets and parks, and personal protective gear is already washing up on beaches around the world. The use of plastic packaging and bags has soared because restaurants rely on take-out and delivery food. Ordering all sorts of other items online has also resulted in more packaging materials, increasing the carbon footprint of e-commerce. Some cities and states have temporarily banned reusable shopping bags, and delayed or rolled back plastic-bag bans. Most large cities are continuing with recycling, but some smaller communities such as Fayetteville, AK and Dalton, GA, have curtailed it altogether.

The CDC has recommended that people returning to work minimize contact with others, and urged companies to offer incentives to encourage people to ride or drive alone. These guidelines are prompting more individual car use, which will cause traffic congestion and air pollution, and increase greenhouse gas emissions. Apple Maps data have detected many more requests for directions from people driving cars. The CDC advice will also increase the fear many have of taking public transportation.

According to a recent poll, about one third of Americans are considering moving out of cities to less dense areas in the wake of COVID-19. Real estate agents have reported a boom in demand from New York City residents for suburban homes in New Jersey and Connecticut. But suburban living means more driving. A 2014 report found that half the household carbon footprint of the U.S. comes from suburban living, as a result of transportation, household energy use and consumption of food and services.

Green recovery in other countries

The European Commission, the executive branch of the European Union, has put forth the world’s greenest stimulus plan — a 750 billion euro ($825 billion) economic recovery plan with the goal for the EU to be carbon neutral by 2050. It includes financing for renewable energy, electric vehicle charging and other emissions-friendly projects, including retrofitting old buildings and developing no-carbon fuels like hydrogen. The stimulus plan still needs to be approved by the EU’s 27 member states.

“To the extent that Europe takes moves, that will make it more attractive for other countries to act,” said Scott Barrett, vice dean at Columbia University’s School of International and Public Affairs. “But I don’t think example is enough. I think what’s more powerful would be not only their demonstration that it can be done, but a change in the economic calculus—because technology’s changed, because systems are interconnected, and because when Europe did it, it actually became more economic and easier, and possibly necessary for others to do it. If they [EU] are able to lower the cost of alternative energy sources, then those actions would actually make other countries be more inclined to use those alternatives. That leverage creates a positive feedback so that when more countries do more, others want to do more.”

Some countries are also using the pandemic as an opportunity to make their societies more resilient to the looming climate crisis. Germany’s $145 billion stimulus plan devotes about one third of its funds to public transportation, electric vehicles and renewable energy, with no money provided for combustion engine vehicles. The government is also driving down the cost of clean energy, increasing research and development of green hydrogen, and investing in more sustainable agriculture and forest management as well as initiatives to decrease shipping and airlines emissions.

France is investing $8.8 billion to help its car industry, with the aim of becoming the main producer of electric vehicles in Europe. Its plan includes financial incentives to encourage people to exchange their old cars for lower-emissions vehicles and to buy electric cars.

South Korea has introduced a Green New Deal that would make it the first East Asian country to commit to a goal of net-zero emissions by 2050. The plan, which still needs to be signed into law, would include a carbon tax, more investment in renewable energy, training for workers displaced by the transition to clean energy, and an end to public financing of fossil fuel projects.

While the U.S.’s relief plans have so far lacked policies that help combat climate change, House Democrats have proposed a $1.5 green infrastructure plan with much of it focused on green initiatives, resiliency, and reducing the emissions of the transportation sector. It allots $300 billion to fixing and building bridges and roads. The plan also includes funding for education, broadband, clean water and housing. The Republican-led Senate, however, is likely to oppose the plan.

A renewable energy extension

The U.S. Treasury Department has given renewable energy projects more time to take advantage of the production tax credit and the investment tax credit. Renewable energy facilities will now have five years (instead of four) to complete projects that commenced in 2016 and 2017 and still be eligible for the tax credits.

More biking and walking

To help residents trying to avoid public transportation, many cities have closed off streets for pedestrians and increased bike lanes.

Oakland, CA introduced Slow Streets, which banned cars on 74 miles of streets, encouraged slower driving, and promoted biking and walking. New York, San Francisco, Minneapolis and Seattle have followed suit. Brookline, MA, a Boston suburb, used temporary structures to widen sidewalks and increase bike lanes.

European cities have also expanded biking. Barcelona added 13 miles of city streets for biking; Berlin has 14 new miles of bike lanes and Rome is building 93 miles for biking. Paris opened almost 400 miles of bikeways as of May.

Less international travel

Transportation is responsible for 23 percent of global carbon emissions, with 11 percent of the sector’s greenhouse gas emissions attributable to aviation. The enormous decrease in international air travel due to COVID-19 has reduced CO2 and nitrogen oxide emissions as well as ozone creation and particulate matter.

As people realize they can be equally or more productive at home, remote working will likely become much more common in the future. This may mean more teleconferencing and less international business travel. International trade may also decrease as countries recognize the need to produce more goods domestically.

McKinley said that oceanography research has a particularly large carbon footprint; because collaborators are all over the world, the work entails a lot of long trips. She has been heartened by the success of COVID-19-induced virtual meetings because they actually enable more international colleagues to attend and participate.

She cited the example of a virtual meeting in May at Lamont studying the ocean carbon cycle. The working group was only 15 people, but because the meeting was virtual, they ended up with 150 people around the world listening in. Not only did the virtual meeting make for a smaller carbon footprint than an in-person meeting, “I think it really opened up the ideas to a much broader community,” said McKinley. She would still want some scientific meetings be in person, however, because she feels it’s important for young scientists to get to know others face-to-face. “So much of the educational experience of becoming a scientist, particularly for graduate students, is the experience of being part of a scientific community,” she said.

Living more simply

Lockdowns and quarantines have compelled people to stay at home and cook, which benefits the environment because it requires fewer resources than ordering in or eating out—processing, packaging and transporting food add to its carbon footprint. And because COVID-19 has hit people with preexisting conditions harder and meat prices rose, more people may be trying to eat less meat and instead opt for more organic, vegetarian or vegan foods. Having experienced the sight of empty shelves in grocery stores during the pandemic, they may also be inclined to waste less food. People who want to know where their food comes from may move away from processed foods, and eat more locally or grow a garden.

Living simply within our homes has encouraged many people to reexamine their pre-pandemic more materialistic and consumerist lives. Do we really need the latest fashion or the newest gadget? Consumer goods contribute to climate change throughout their life cycles: raw materials extraction, processing, logistics, retail and storage, consumer use and disposal all result in carbon emissions. Perhaps we will no longer be as susceptible to the planned obsolescence inherent in fashion and many other consumer products.

With stores, restaurants and movie theaters shuttered, people have sought relief by walking outside in parks and in nature. This experience could foster a new appreciation for nature, and more understanding about the impacts humans have on the environment. Hopefully it will translate into an impetus to protect and care for the environment.

Renewed faith in science and expertise

Our experience with COVID-19 should help people realize the importance of science and of preparing for what is to come, whether that’s a pandemic or climate change, as both are phenomena that scientists have foreseen.

“Scientists have been waiting for a pandemic like this for a very long time, so for the infectious disease experts and historians who understand pathogens and interactions between humans and their environment, this is not an unusual thing,” said Barrett. “I think what’s been interesting has been how the public and some policy makers have been paying attention to what the infectious disease community, especially the modelers, are telling them. Also, we’re now very aware of the delay between the time you act and the time you start to see results. It’s pretty clear that if we had acted when we should have acted in the U.S., we would have saved a lot of people. This is a reminder that expertise matters. Nature is real. Scientists do understand how it works. We need to heed what they tell us and the warnings that they’ve given us.”

Barrett believes that problems like COVID-19 and climate are collective problems that have to be addressed collectively. “Ultimately, we’re only going to address these problems if countries work together,” he said. He feels this is a real opportunity. If countries can work collaboratively to develop a vaccine and ultimately eliminate COVID-19, “I think people would say, ‘Wow,’ we can really do something together. Let’s go back to this climate problem.”

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You’ve got to be kidding… This is what you concluded from the global economic shut down of the past 8 months? What ever happened to Human caused global warming? The theory of AGW is under the microscope. If we humans are the cause of global warming, this past 8 months should have created a mini-ice age down the road. On the other hand, if very little impact was made, then it proves to me that the theory of global warming AGW is flawed.

Lol that’s not how climate works. The level of carbon dioxide in the atmosphere is estimated to be around 412 parts per million — higher than it’s ever been for at least the past 800,000 years. Just because we made less emissions this year, doesn’t mean the rest of it goes away.

I read another article, written in Dec 2020 that said the shift in emmisions was miniscule. What is needed is more technological change as co2 enmisions will take years to be reduced from the atmosphere. I do agree that there is stuff that humans can do like recycling however focus should now be on countries producing the most emissions, that’s where we will see the most dramatic changes. The west alone cannot save the world.

I would also like to add that the authors biases against trump is very apparent. I found this article via my own searches for a year end review on climate and although it has some good points I am Inclined to ignore most opinions because I can see it may be tainted. 2020 has taught this European well.

It’s not the writer’s fault that Trump has a terrible track record on climate change.

i think its both cause it has harmed us as humans cause millions have died and have been very sick it has increased the rate of depression and anxiety but also good cause while we are on lock down animals have been out roaming around and happy

I have to say that I am surprised that there is no appreciable change in the atmospheric CO2 trajectory despite the very significant decrease in human generated greenhouse gases during COVID. I realize that humans only account for about 3.5% of CO2 entering the atmosphere, but still…. Forests, and even more so, wetlands are the biggest natural carbon sinks so I cannot help wondering if draining wetlands and cutting down forests has had a bigger destabilizing effect on CO2 balance in the atmosphere than scientists, politicians, and the media acknowledge. Sea level change for example has been very stable for the last 200 years which implies to me that human land use patterns rather than fossil fuels may be a driving factor in climate change. In my books the biggest environmental issue facing us and nature as a whole is human population increase, rather than the more narrow, human production of greenhouse gasses.

I really liked this article. It is rich and informative and supplies me with just the information I need for my project.

The “Hockey Stick” started in the 1970s, but no investigation of this unique pattern or explanation. EPA started controlling the number of emissions and fining abusers. Boeing introduced the 747 and the beginning of high-altitude flying, which became the norm. No investigation thus far of the real damage caused by it. A cause for reflection, no added cars by the millions, no World War III, and nuclear energy displacing coal for energy. The question is, should somebody investigate the Why?

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How will Covid-19 ultimately impact climate change?

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Business closures. Travel restrictions. Working and learning from home. These and other dramatic responses to Covid-19 have caused sharp reductions in economic activity — and associated fossil fuel consumption — around the world. As a result, many nations are reporting significant reductions in greenhouse gas emissions for the year 2020, edging them a bit closer to meeting the initial emissions targets to which they committed under the Paris Agreement on climate change. While the pandemic may have accelerated progress toward these targets over the past year, will that trend continue through this decade and beyond? 

According to a new study in the journal Humanities and Social Sciences Communications, the answer to that question will depend, in part, on the pandemic’s long-term effect on economic activity and energy use around the world. To assess that impact, the study’s co-authors, all researchers at the MIT Joint Program on the Science and Policy of Global Change , compared two estimates of global economic activity through 2035: one projecting economic recession and recovery from Covid-19, the other forecasting economic growth had Covid-19 not occurred.

Assuming a return to pre-pandemic levels of employment by 2035, the study finds that Covid-19 produces a steep, 8.2 percent reduction in global gross domestic product (GDP) in 2020, but only a 2 percent reduction in 2035. Assuming that Paris Agreement national climate targets through 2030 are fulfilled despite economic disruption, the lower GDP numbers result in a 3.4 percent reduction in annual greenhouse gas emissions in 2020, but only a 1 percent reduction in 2030.

The researchers also note that while various structural changes in the economy that may result from the pandemic (e.g., less air travel, commuting, and commercial activity at brick-and-mortar shops and restaurants, as well as lingering effects of larger government deficits) could reduce emissions further, these post-pandemic reductions would pale in comparison to those observed in 2020. In any case, they are unlikely to contribute substantially to global efforts to meet the long-term climate goals of the Paris Agreement.

“Our projections of global economic activity with and without the pandemic show only a small impact of Covid-19 on emissions in 2030 and beyond,” says MIT Joint Program Co-Director Emeritus John Reilly , the study’s lead author. “While pandemic-induced economic shocks will likely have little direct effect on long-term emissions, they may well have a significant indirect effect on the level of investment that nations are willing to commit to meet or beat their Paris emissions targets.” 

The study shows that reduced economic activity resulting from Covid-19 lowers the cost of meeting these targets, making such commitments more politically palatable. Moreover, fiscal stimulus measures to accelerate economic recovery present an opportunity for major investments in emissions reduction efforts. Keeping global warming well below 2 degrees Celsius — the central goal of the Paris Agreement — will require further commitment and action by countries worldwide to reduce emissions. 

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Covid-19, climate change, and the environment: a sustainable, inclusive, and resilient global recovery

Read our latest coverage of the climate emergency.

• Related content
• Peer review
• Nicholas Stern ,
• IG Patel , professor of economics and government and chair ,
• Bob Ward , policy and communications director
• Grantham Research Institute on Climate Change and the Environment, London School of Economics and Political Science, UK
• r.e.ward{at}lse.ac.uk

We are at a critical moment in history, facing growing crises in climate change, biodiversity, and environmental degradation—as well as covid-19. But we also have an enormous opportunity to transform the global economy and usher in an era of greater wellbeing and prosperity, write Nick Stern and Bob Ward

The covid-19 pandemic has shown how vulnerable and exposed the world is to global threats. The effects of the disease and the measures that have been taken to control it have had serious consequences for lives and livelihoods. In addition to the tragic toll of illness and death, economies have been hit hard, particularly in developing countries.

Continuing to tackle the disease must be the priority, particularly by ensuring access to vaccines and treatments in all countries. Rich countries have a critical responsibility not just to safeguard their own populations but to support the distribution of vaccines to developing countries.

Every country will remain potentially exposed and vulnerable to the SARS-CoV-2 virus as long as it is able to spread rapidly through unvaccinated populations in any part of the world. Common humanity and self-interest point in the same direction.

Governments have tried to limit and reverse the economic damage through rescue and recovery packages. The rescue efforts have understandably focused on protecting existing jobs and companies, but recovery offers the chance to accelerate the transition towards a more inclusive, sustainable, and resilient form of economic development and growth.

A report prepared at the request of the British prime minister, Boris Johnson, for the G7 Leaders’ Summit in Carbis Bay, Cornwall, in June 2021 laid out the case for an investment led recovery from the pandemic. 1 It pointed out that an increase in annual investment of $1tn (£0.7tn; €0.9tn), equivalent to 2% of the collective national output, across the G7 countries over the coming decade and beyond would drive strong growth out of the economic difficulties arising from the pandemic and from the relatively low levels of investment, particularly since the financial crisis in 2008-9, which have been a major cause of sluggish growth in many rich countries over the past decade.

Most of this increase in investment will be made by the private sector, but governments also need to lead by example through their spending programmes both to kickstart growth and play their parts in crucial infrastructure investment, particularly in zero carbon and climate resilient energy, transport, and buildings.

The rich countries should also work to support investment in developing countries to foster sustainable, resilient, and inclusive development and growth. Most global investment in the next two decades will be in emerging markets and developing countries, and the nature of that investment will shape the future for us all in terms of wellbeing and its sustainability.

These investments in both developed and developing countries should aim both to reduce greenhouse gas emissions and to improve resilience against the effects of climate change that cannot now be avoided. Many relevant investments spur development, reduce emissions, and strengthen resilience. There are examples across all sectors: protecting and restoring mangroves; restoring degraded land; expanding and protecting forests; improving public transport; installing decentralised solar energy systems; and constructing and retrofitting buildings to make them more efficient and resilient. All of these can boost economic development, climate change mitigation, and adaptation.

Central to these changes will be extra finance, much of it concessional, from the national and multilateral development banks. This will be crucial to reducing and managing risk for both private and public investment. The scale of the challenge implies that its scale must be expanded.

Growing effects of climate change

The growing consequences of climate change have been all too visible across the world this year with severe heatwaves, floods, wildfires, and tropical cyclones. A new assessment of the science by the Intergovernmental Panel on Climate Change (IPCC), published in August 2021, concluded that there is now a clear link between rising greenhouse gas concentrations in the atmosphere and increases in the frequency and intensity of extreme weather events. 2 It states: “Climate change is already affecting every inhabited region across the globe, with human influence contributing to many observed changes in weather and climate extremes.”

Although the IPCC’s review of the effects of climate change on people and wildlife is not due to be published until next year, losses are clearly mounting around the world. One of the great injustices of climate change is that the poorest people around the world are often most exposed and vulnerable to the effects, even though they are least responsible for the driving cause: the rise in concentrations of carbon dioxide and other greenhouse gases in the atmosphere.

The most recent Human Development Report, 3 published by the United Nations Development Programme in December 2020, pointed out that climate change has played a large role in reducing average incomes, particularly in low income countries, increasing the number of people experiencing hunger and expanding the number of people affected by climate and weather disasters.

Climate change has been making it more difficult to achieve many of the United Nations Sustainable Development Goals (SDGs), even before the pandemic. In his 2021 annual progress report on the SDGs, 4 the United Nations secretary general, António Guterres, said: “The pandemic related economic downturn has pushed between 119 and 124 million more people into extreme poverty in 2020, further compounding challenges to poverty eradication such as conflict, climate change, and natural disasters.”

The mounting damage from climate change is clearly harming efforts to overcome poverty and raise living standards, particularly in developing countries. Global mean surface temperature is already more than 1°C above its pre-industrial level. A special report by the IPCC in October 2018 provided a detailed review of the evidence about the risks of warming exceeding 1.5°C. 5 There is a growing consensus that those risks pose an unacceptable threat.

The IPCC report concluded that, to prevent warming exceeding 1.5°C by the end of the century, greenhouse gas emissions would need to be cut sharply over the coming decades, with net carbon dioxide emissions reduced to zero by 2050—this means that any residual emissions from human activities would need to be compensated by equivalent removals from the atmosphere by planting more vegetation or through other artificial methods involving carbon capture, use, and storage. Many countries have now pledged to reach net zero annual emissions of greenhouse gases by 2050.

New form of economic development and growth

Greater understanding of the urgency required to cut emissions has been accompanied by mounting evidence that it does not mean sacrificing economic development and growth. Annual emissions by the United Kingdom, for example, fell by 43.8% between 1990 and 2019, 6 whereas its gross domestic product rose by 78% over the same period. 7 This is a critically important insight, particularly for developing countries that understandably view economic growth as essential to improving the lives of their citizens. The increase in economic activity is usually accompanied by more jobs, higher incomes, and less hunger, as well as potentially higher tax revenues for governments to invest in public services, including health and education.

Some people argue that greenhouse gas emissions can only be eliminated by killing economic growth. But this is analytically incorrect. There is nothing inherent about economic growth that requires emissions. Energy can be generated from sources other than fossil fuels, which are the main driver of emissions. Furthermore, commitment to the new path for economic development and growth is already generating rapid innovation and cost reduction for most countries. Round-the-clock renewable electricity is now cheaper than fossil fuel electricity in many places, for example. Electric vehicles are more efficient than those driven by internal combustion engines. Resource efficiency (including the circular economy) improves productivity. And progress is rapid.

As countries emerge from the pandemic, investments in the rapid transition away from fossil fuels towards cleaner sources of energy will have multiple economic benefits. It will, for example, drastically reduce the number of deaths from air pollution, which kills more than seven million people worldwide every year, according to the World Health Organization, 8 and knocks several percentage points off economic output, 9 particularly in countries like China and India.

Investments in sustainable infrastructure, such as renewable energy and electric trains, can improve the economic competitiveness of countries and transform cities into more attractive places where people can live, move, and breathe more easily. Infrastructure that is not sustainable has the opposite effect—creating more pollution, waste, and congestion.

An investment led recovery that accelerates the transformation to sustainable, inclusive, and resilient economic development and growth will not only avoid the worst potential consequences of climate change, biodiversity loss, and environmental degradation, but will also create meaningful job opportunities and improve the lives of people around the world. A new form of clean, sustainable, efficient and inclusive development and growth is now in our hands. It will involve strong investment and some dislocation. It is important that the transition is, and is seen to be, just. All this will require strong commitment and leadership. But if offers us a much better future.

Biographies

Nick Stern is a cross bench member of the UK House of Lords. He has been president of the British Academy, the Royal Economic Society, and the European Economic Association. He was head of the UK Government Economic Service from 2003 to 2007 and head of the Stern Review on the Economics of Climate Change , published in 2006. He was chief economist of the European Bank for Reconstruction and Development between 1994 and 1999, and chief economist and senior vice president at the World Bank between 2000 and 2003.

Robert Ward is deputy chair of the London Climate Change Partnership and a fellow of the Geological Society, the Royal Geographical Society, and the Energy Institute. He was previously director of public policy at Risk Management Solutions between 2006 and 2008, and senior manager for policy communication at the Royal Society between 1999 and 2006. He has also worked as a freelance science journalist

Commissioned, not externally peer reviewed.

Competing interests: We have read and understood BMJ policy on declaration of interests and declare the following: NS oversaw the preparation of the G7 report by the Grantham Research Institute on Climate Change and the Environment, which he has chaired since its foundation in 2008, and RW, who has been policy and communications director at the institute since its foundation, was one of the writing team.

This article is made freely available for use in accordance with BMJ's website terms and conditions for the duration of the covid-19 pandemic or until otherwise determined by BMJ. You may use, download and print the article for any lawful, non-commercial purpose (including text and data mining) provided that all copyright notices and trade marks are retained.

• ↵ Stern N. G7 leadership for sustainable, resilient, and inclusive economic recovery and growth: An independent report requested by the UK Prime Minister for the G7. London: Grantham Research Institute on Climate Change and the Environment. June 2021. https://www.lse.ac.uk/granthaminstitute/publication/g7-leadership-for-sustainable-resilient-and-inclusive-economic-recovery-and-growth/ .
• ↵ Intergovernmental Panel on Climate Change. Climate change 2021: the physical science basis. 2021. https://www.ipcc.ch/report/ar6/wg1/#FullReport
• ↵ United Nations Development Programme. Human development report 2020. 2020. http://hdr.undp.org/en/2020-report
• ↵ United Nations Secretary-General. Progress towards the Sustainable Development Goals: report of the secretary-general. 30 April 2021. https://unstats.un.org/sdgs/files/report/2021/secretary-general-sdg-report-2021--EN.pdf
• ↵ Intergovernmental Panel on Climate Change. Global warming of 1.5°C: 2018. https://www.ipcc.ch/sr15/
• ↵ Department for Business, Energy, and Industrial Strategy. 2019 UK greenhouse gas emissions, final figures. 2021. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/957887/2019_Final_greenhouse_gas_emissions_statistical_release.pdf
• ↵ Office for National Statistics. Gross domestic product: chained volume measures: seasonally adjusted £m. 2021. https://www.ons.gov.uk/economy/grossdomesticproductgdp/timeseries/abmi/pn2
• ↵ World Health Organization. Air pollution. 2021. https://www.who.int/health-topics/air-pollution#tab=tab_1
• ↵ World Bank, Institute for Health Metrics and Evaluation. The cost of air pollution: strengthening the economic case for action. 2016. https://documents1.worldbank.org/curated/en/781521473177013155/pdf/108141-REVISED-Cost-of-PollutionWebCORRECTEDfile.pdf

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• Published: 07 August 2020

Current and future global climate impacts resulting from COVID-19

• Piers M. Forster   ORCID: orcid.org/0000-0002-6078-0171 1 ,
• Harriet I. Forster 2 ,
• Mat J. Evans   ORCID: orcid.org/0000-0003-4775-032X 3 , 4 ,
• Matthew J. Gidden 5 , 6 ,
• Chris D. Jones   ORCID: orcid.org/0000-0002-7141-9285 7 ,
• Christoph A. Keller 8 , 9 ,
• Robin D. Lamboll   ORCID: orcid.org/0000-0002-8410-037X 10 ,
• Corinne Le Quéré   ORCID: orcid.org/0000-0003-2319-0452 11 , 12 ,
• Joeri Rogelj   ORCID: orcid.org/0000-0003-2056-9061 6 , 10 ,
• Deborah Rosen 1 ,
• Carl-Friedrich Schleussner   ORCID: orcid.org/0000-0001-8471-848X 5 , 13 ,
• Thomas B. Richardson 1 ,
• Christopher J. Smith   ORCID: orcid.org/0000-0003-0599-4633 1 , 6 &
• Steven T. Turnock   ORCID: orcid.org/0000-0002-0036-4627 1 , 7  

Nature Climate Change volume  10 ,  pages 913–919 ( 2020 ) Cite this article

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• Atmospheric chemistry
• Climate and Earth system modelling
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A Publisher Correction to this article was published on 14 August 2020

This article has been updated

The global response to the COVID-19 pandemic has led to a sudden reduction of both GHG emissions and air pollutants. Here, using national mobility data, we estimate global emission reductions for ten species during the period February to June 2020. We estimate that global NO x emissions declined by as much as 30% in April, contributing a short-term cooling since the start of the year. This cooling trend is offset by ~20% reduction in global SO 2 emissions that weakens the aerosol cooling effect, causing short-term warming. As a result, we estimate that the direct effect of the pandemic-driven response will be negligible, with a cooling of around 0.01 ± 0.005 °C by 2030 compared to a baseline scenario that follows current national policies. In contrast, with an economic recovery tilted towards green stimulus and reductions in fossil fuel investments, it is possible to avoid future warming of 0.3 °C by 2050.

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By the time the World Health Organization declared COVID-19 (scientifically referred to as the severe acute respiratory syndrome–coronavirus 2 or SARS-CoV-2) a pandemic on 11 March 2020, the virus had already spread from China to other Asian countries, Europe and the United States. As of 5 July 2020, cases have been identified in 188 countries or regions 1 . This has led to unprecedented enforced and voluntary restrictions on travel and work. This in turn has led to reductions of both GHG emissions and air pollutants 2 , 3 , 4 . Analysis of mobility data from Google 5 and Apple 6 shows that mobility declined by 10% or more during April 2020 in all but one of the 125 nations tracked. Mobility declined by 80% in five or more nations (Supplementary Fig. 1 ). Associated declines in air pollution have been observed from satellite data and from local ground-based observations 7 , 8 . The large pollution declines are expected to be temporary as pollution levels are already returning to near-normal in parts of Asia 9 , 10 .

Here we build an estimate of emission changes in GHGs and air pollution due to the COVID-19 global restrictions during the period February–June 2020 and project these into the future. These emission changes are then used to make a prediction of the resultant global temperature response. We examine the temperature response of a direct recovery to pre-COVID-19 national policies and emission levels, and also explore responses where the economic recovery to COVID-19 is driven by either a green stimulus package or an increase in fossil fuel use.

Emission trends

Bottom-up emission-trend analyses have traditionally relied on laborious collection of various energy-industry-related indicators and statistics from multiple sources 11 . The unprecedented recent access to global mobility data from Google and Apple gives a unique opportunity to compare trends across many countries with a consistent approach. We use these data to develop a new method of emission-trend analysis. The advantage over previous approaches is the possibility of near-real-time analysis, national granularity and a systematic consistent approach across nations and over time. The disadvantages are the loss of a direct connection between energy and emissions and the need to make assumptions about these relationships. There are also disadvantages over the short time history of the mobility data and opacity from the data providers around their detailed methodologies and uncertainties. Here we make a simple set of assumptions to deduce estimates of emissions change from the mobility data and test the estimates extensively against the approach of Le Quéré et al. 3 .

Google and Apple mobility changes and the Le Quéré et al. 3 data all indicate that >50% of the world’s population reduced travel by >50% during April 2020 (Fig. 1a ). Google mobility trends indicate that >80% of the population in the 114 countries in the dataset (4 billion people) reduced their travel by >50%. Google mobility data and emission reduction estimates based on confinement level analysis in Le Quéré et al. 3 agree on country-level surface-transport trends to within ~20% (Fig. 1b and Supplementary Fig. 1 ). When we examine the trends for the countries that we expect have contributed most to the overall surface-transport emission change (for example, the United States, European nations and India), good agreement between the datasets is observed and their trends are well-correlated in time (see Fig. 1b and Extended Data Fig. 1 ). Workplace, retail and residential movement data from Google also compare relatively well with corresponding industry, public and residential sector emission changes but only if the high estimate of the emission change in the Le Quéré et al. 3 dataset (Fig. 1b,c and Supplementary Figs. 3 and 4 ) is used.

a , Population-weighted histogram of surface-transport trends from Apple driving data, Google transit mobility data and the high estimate from Le Quéré et al. 3 for available countries in the different datasets averaged over April 2020. b , Violin plots showing the distribution, minimum, maximum and median levels of national trends weighted by CO 2 emissions for the Google and Le Quéré et al. 3 datasets and the differences between the datasets evaluated over April 2020. c , Estimates of emission changes for the datasets across four sectors for April 2020 and the sum of the four sectors. The CO 2 emission estimates from Liu et al. 2 are also shown on this panel. In b and c , data are shown for 60 countries with overlapping data in the Google and Le Quéré et al. 3 datasets (representing 60% of global CO 2 emissions). In c , Apple data are shown for 57 countries, covering 58% of the global emissions. The Liu et al. 2 estimate is for a global emission change. The high estimate from Le Quéré et al. 3 data is used in a and b . Panel c shows the Le Quéré et al. 3 low and high estimates as the range of the error bar on the mid-level estimate. For baselines, see Methods .

Using mobility data outside of the surface-transport sector is likely to overestimate the emission change and this appears to be the case for CO 2 emissions when compared to two previous estimates 1 , 2 . Nevertheless, our national and US state-level mobility-derived emission estimates are well-correlated in time with emission changes from the Le Quéré et al. 3 study (see examples in Supplementary Figs. 3 and 4 ). For the industry sector, differences may be due to the fact that the emissions from industrial activity are less correlated with mobility trends, due to automated machinery, inertia in closing operations or alternative modes of work, or a baseline level of industrial emission from heavy industry in the absence of production, neither which would be captured by the Google mobility data which only report changes in phone locations. For the residential sector, the 20% median increase matches the UK smart meter analysis by Octopus Energy for the situation when previously empty houses were occupied during the day after lockdown restrictions began 12 . However, many households were already occupied during the day and in these situations, when an additional occupant was added, energy use only increased by 4%. These factors probably mean that our Google-based trends overestimate the emission change from these sectors, leading to our Google-based total emission-trend estimate agreeing better with the high emission estimate from the Le Quéré et al. 3 dataset. Our analysis also suggests considerably larger trends than those found in Liu et al. 2 (compare datasets in Fig. 1c ). There is also a question about how representative the Apple and Google datasets are of wider national behaviour and how the use and penetration of these phone operating systems varies across regions 13 . For example, the >80% drop in Apple driving mobility in India (Fig. 1a and Supplementary Fig. 1 ), may only represent the part of the population that are able to work from home. Therefore, the emissions trends in our work which are largely derived from Google mobility data should be taken as a high estimate of the COVID-19 emission-driven change (see Methods ).

In the following, we construct 2020 emission changes largely from Google mobility data to estimate emissions changes from the restriction measures in response to the COVID-19 virus, as illustrated in Fig. 1c . As Google data are not available everywhere, we use the Le Quéré et al. 3 analysis to cover important missing countries, in particular, China and Iran, which are large emitters whose citizens have been under considerable restrictions related to COVID-19. We also use Le Quéré et al. 3 data to provide additional trend estimates from aviation and shipping sectors (see Methods ).

Our bottom-up analysis uses 123 countries covering >99% of global fossil fuel CO 2 emissions, extending the 69 countries analysed in Le Quéré et al. 3 . Daily national emission trends in six sectors are analysed for January–June 2020 (surface transport, residential, power, industry, public and aviation). These are then weighted by the national and sector split of seven emitted species covering the major GHGs and short-lived pollutants. The estimated changes in these non-CO 2 species covers their total anthropogenic emissions, although agricultural and waste emissions are assumed not to change ( Methods ). National and sector data are taken from the Emissions Database for Global Atmospheric Research (EDGAR) v.5.0 database for 2015 14 . These data are combined to generate national and globally averaged daily emission changes in 2020 by species and sector.

To assess changes due to the COVID-19 pandemic, we establish a baseline scenario. We take a central estimate of emissions pathways 15 , in which countries are assumed to meet their stated nationally determined contributions (NDCs) by 2030. In this baseline, no further strengthening of climate action after 2030 is assumed to take place. These pathways account for both GHG and air pollutant emission changes (see Methods ). To derive changes from this scenario, a three-stage process is followed (see Methods ). First, fractional Google mobility data use the five-week period (3 January to 6 February 2020) as reference. Absolute emission trends are then computed by multiplying these fractional changes by either the 2019 CO 2 emissions from Le Quéré et al. 3 or, for other species, the 2015 emissions in the EDGAR database 14 . Finally, these absolute changes are then applied to a steadily rising emission pathway based on pre-COVID-19 national pledges (see Table 1 ). Only the globally average emission changes are used in this paper (see Fig. 2a ) but national and spatially gridded data are made available for other interested researchers ( https://doi.org/10.5281/zenodo.3957826 ).

a , Percentage globally averaged emission changes for the considered species as a function of day in the year of 2020. The changes are for fossil fuel CO 2 emissions and total anthropogenic emissions from the other sectors. The vertical grey dashed lines mark the first day of the months Febuary to June to aid orientation. b , A breakdown of the April 2020 average global emission reductions compared to a recent year for the different species. The breakdown is for major emission-producing nations, including international aviation. Global percentage emission changes from the baseline are shown on the x axis (see details in Supplementary Figs. 5 and 6 ). Trends are relative to 2019 for CO 2 ; for the other species they are relative to 2015. The low, middle and high estimates of the total changes based on Le Quéré et al. 3 and Liu et al. 2 trends are shown for comparison as the black circles, error bars and red triangle. EU27+UK, the 27 countries in the European Union plus the United Kingdom.

Our analysis shows that emission reductions probably peaked in mid-April 2020 and that these reductions are species dependent. The data suggest that global fossil fuel CO 2 emissions and total NO x emissions could have decreased by as much as 30% in April 2020 driven by a decline in surface-transport emissions (Fig. 2a,b and Supplementary Fig. 5 ). Conversely, organic carbon (OC) has increased by <1% as it is primarily affected by rising residential emissions (Fig. 2b and Supplementary Fig. 5 ). Methane changes are driven by power sector declines and SO 2 is most strongly affected by declining industrial emissions. Generally, changes in surface transport are the biggest driver of change for most species analysed (Supplementary Fig. 5 ). The analysis in Fig. 2b also applies our methods to the Le Quéré et al. 3 data for non-CO 2 species and reports both previous estimates of CO 2 trend. Our estimated trends are close to the high Le Quéré et al. estimate and almost twice as large as the CO 2 trend estimate of Liu et al. 2 .

Our data suggest that changes in emissions are not confined to the major emitting countries and mobility restrictions have been of worldwide proportions (despite the extent of measures—and therefore relative emissions changes—varying globally) during April 2020 (Fig. 1 and Supplementary Fig. 1 ). This manifests itself in many countries contributing to the emission decline. For the short-lived species, Europe and the United States, in spite of their large fractional national emission change, make up a small percentage of the global response due their relatively low levels of emissions from pollution (Fig. 2b and Supplementary Fig. 6 ).

Observational evidence

Detecting a COVID-19-related signal in CO 2 concentrations is challenging due to the long atmospheric lifetime of CO 2 which makes any perturbation small. While the airborne fraction of CO 2 emissions is ~50% on multi-annual timescales 11 , the airborne fraction of emissions changes is probably above 90% on subannual timescales 16 . Because CO 2 is not well mixed on the timescale of weeks to months, individual observing stations will not reflect the global CO 2 burden—for example, Mauna Loa in the Northern hemisphere Pacific Ocean may see a larger signal than at the South Pole from the emissions reductions due to COVID-19 restrictions. The magnitude of natural (terrestrial and marine) fluxes of CO 2 compared with anthropogenic emissions make it extremely difficult to detect changes in emissions at national level from CO 2 concentrations themselves. We estimate these CO 2 concentration changes in the temperature response to restrictions section (see Fig. 5b ) and find maximum reductions compared to our baseline scenario of around 2 ppm in two years’ time (Extended Data Fig. 2 ).

Even though the CO 2 change cannot readily be observed, changes in the concentrations of air pollutants can be used to test the veracity of the bottom-up emission reduction estimates. A decline in NO 2 has been observed globally and in several countries and cities 7 , 8 . NO 2 is short-lived (~5 h), provides a relatively linear response to emission changes (unlike other pollutants, such as O 3 and PM2.5) and reductions in its emissions are expected to be well-correlated to CO 2 emission reductions (see Fig. 2a and Le Quéré et al. 3 ). Changes in its concentration thus act as a useful bellwether for changes in CO 2 emissions. A number of studies report COVID-19-induced changes in NO 2 concentration from both surface- and satellite-platforms over China 17 , 18 . However, it remains challenging to get a quantifiable estimate of the emission-driven NO 2 change as it is hard to separate that signal from meteorological variability. To address this we follow previous work 19 and develop a machine-learning method to derive meteorology and chemistry-normalized changes in NO 2 surface concentrations at air quality monitoring stations around the globe (see Methods ). We aggregate these changes for 32 nations and show how these observationally based national time series of NO 2 concentration changes compare to our mobility-based estimate of NO x emissions change in Supplementary Fig. 7 . Figure 3 shows the predicted mobility-based NO x emissions change versus the average observationally derived NO 2 change for each country in 2020. Some differences between the emission estimates and observed changes would be expected: monitoring stations tend to focus on sites with high surface-transport emission and so may be less sensitive to changes in industrial or residential activity; much of the surface-transport emissions of NO x arises from commercial vehicles (64% of surface-transport emission in the United Kingdom 20 ) which may show different responses to the population aggregated mobility data used here (see Methods and Supplementary Fig. 2 ). However, the comparisons for the individual countries (Supplementary Fig. 7 ) are generally good and there is a quantitative relationship between the average predicted change in the emissions and observed reduction in concentrations (Fig. 3 ). Most countries show a smaller (20% or roughly two percentage points) decrease in observed NO 2 than the predicted reduction in NO x emissions, whereas China and India show larger observed reductions than predicted (28% and 48%, respectively). This could be due to the Le Quéré et al. 3 analysis being used to estimate trends in China as Google data were not available and also due a possible lack of representativeness in the phone mobility data for India (see Emission trends ). As China is the largest emitter, our analyses might be affected by a possible significant underestimate of Chinese NO x trends and hence global trend in the early part of the record, although any global underestimate is unlikely to have persisted into April, where the contribution of China to the global trend is relatively modest (Fig. 2b ).

Country-level comparison of the mean predicted NO x emissions change against the meteorologically normalized observed mean fractional reduction in NO 2 concentration for the period 1 January to 11 May 2020. Circle size indicates the mass of NO x emitted each day for that country from EDGAR emissions. The blue line shows the line of best fit (orthogonal regression) excluding China and India shown in red, weighted by the number of observations in those countries, with the shaded area showing the 95% confidence interval. Not all countries are labelled. Brazil shows an increase in NO 2 concentrations and is not shown but is included in the statistical fit (see also Supplementary Fig. 7 ).

The temperature response to restrictions

The immediate response of the warming comes from a combination of an aerosol-induced warming trend and a cooling trend both from CO 2 reductions and the NO x -driven tropospheric ozone cooling loss (Fig. 4 ). To estimate the surface temperature response beyond April 2020, the emission trends are projected forward in time under four simple ‘what-if’ assumptions. The temperature changes from these pathways were simulated by the FaIR v1.5 climate emulator 21 which was set up to represent the response expected from the latest generation of climate models (see Methods ). As significant social distancing conditions may be necessary for two years 22 , we begin by assuming in all pathways that the emissions decrease will remain at 66% of their June 2020 values until the end of 2021. In the simplest ‘two-year blip’ pathway, emissions return linearly to the baseline pathway by the end of 2022 (Table 1 and Fig. 4a ). Under such a pathway, we project a longer term cooling from reductions in CO 2 of around 0.01 ± 0.005  o C compared to baseline, with a cancellation of the influence of the removal of short-term pollutants (Fig. 4b and Extended Data Fig. 2 ).

a , b , Component effective radiative forcing ( a ) and component temperature response ( b ). Results are for the two-year blip pathway compared to the baseline pathway. The response is broken down by the major forcing contributors, as emulated by the FaIR v.1.5 model. The 5–95% Monte Carlo sampled uncertainties are shown and weighted according to their historical fit to the surface temperature record (see Methods ).

As the global temperature response due to COVID-19 restrictions will probably be small, climate scientists are encouraged to look for regional climate signatures. In particular, changes in aerosol loadings may contribute to increasing regional risks posed by extreme weather, such as heat waves or heavy precipitation 23 , 24 . Such near-term changes require particular attention as hazards posed by extreme weather will compound with the ongoing pandemic situation, as exemplified tragically by tropical cyclone Amphan hitting Kolkata on 21 May 2020. With considerable overlaps of vulnerable groups (for example, heat waves and the elderly) or challenges related to the implementation of effective responses (evacuation in case of flooding), as well as potential impacts on crop yields 25 and initial studies suggesting that the spread of COVID-19 may itself be influenced by climatic factors 22 , this will put the ability of society and governments to manage compound risks to the test 26 .

In our estimates, declines in NO x of as much as 30% will contribute a short-term cooling of up to 0.01  o C over the period 2020–2025 almost exclusively from reductions in tropospheric ozone. NO x trends also contribute an insignificant warming effect from the decrease in nitrate aerosol. As the ozone response is expected to have strong regional variation, we test the ozone response in a more sophisticated emulator 27 , 28 that takes these variations into account (see Methods ). This estimates an annual mean radiative forcing of −0.029 W m −2 for 2020, in very close agreement with the forcing seen in Fig. 4a (−0.030 W m −2 ). The emulator also provides an estimate of the regional mean surface ozone changes (Supplementary Table 4 ). In contrast to NO x , reductions in emissions of other short-lived pollutants, especially SO 2 , cause warming from weakening negative aerosol forcing. These two effects more-or-less cancel in our simulations, although on balance we expect a small warming effect over the next two years (Fig. 4b ).

In spite of the uncertainty, our results indicate that reductions of NO x have a cooling effect which will probably offset a considerable fraction of the warming that comes from reductions in emissions of other short-lived pollutants. This suggests that policies directed at limiting pollution from road transport could offset the short-term warming that might come from policies that reduce pollution from the power and industry sector. Therefore, we recommend that policies are enacted to cut pollution from all three sectors at the same time. This is a useful way forward for net-zero transition pathways so we can avoid any short-term warming effects that might come from reductions in aerosol pollution 29 .

The need for a green recovery

As we have shown, the climate effect of the immediate COVID-19-related restrictions is close to negligible and lasting effects, if any, will only arise from the recovery strategy adopted in the medium term. To that end, we assess the effect of different scenarios including a fossil-fuelled recovery and two different scenarios of green stimulus (all pathway assumptions are summarized in Table 1 ).

Due to the different warming and cooling trends from short-lived pollutants, the 2020–2030 climate response to the different pathways remains uncertain but is probably negligible whatever path the recovery takes (Figs. 4 and 5 and Extended Data Figs. 3 and 4 ). However, differences manifest themselves after 2030. Figure 5 shows estimated changes in CO 2 emissions and the climatic responses for the four assessed pathways. We find that both the two-year blip pathway, where the economic recovery maintains current investment levels, and the fossil-fuelled recovery pathway are likely to exceed 1.5 °C above pre-industrial limit by 2050 (>80%; Extended Data Fig. 5 ). Conversely, choosing a pathway with strong green stimulus assumptions (~1.2% of global gross domestic product), including climate policy measures, has a good chance (~55%; Extended Data Fig. 5 ) of keeping global temperature change above pre-industrial within the 1.5  o C limit, saving around 0.3  o C of future warming by 2050 (0.2 °C for the moderate green stimulus pathway).

a – c , Emissions of CO 2 ( a ), CO 2 concentrations ( b ) and the global surface air temperature response ( c ) for the what-if pathways from Table 1 , emulated by the FaIR v.1.5 model. The baseline pathway is also plotted but largely obscured by the two-year-blip pathway. The 5–95% Monte Carlo sampled uncertainties are shown and weighted according to their historical fit to observations (red dotted line) 32 shown in c (see Methods ).

Our work shows that the global temperature signal due to the short-term dynamics of the pandemic is likely to be small. These results highlight that without underlying long-term system-wide decarbonization of economies, even massive shifts in behaviour, only lead to modest reductions in the rate of warming. However, economic investment choices for the recovery will strongly affect the warming trajectory by mid-century. Pursuing a green stimulus recovery out of the post-COVID-19 economic crisis can set the world on track for keeping the long-term temperature goal of the Paris Agreement within sight.

Lastly, by combining large datasets from surface air quality networks with mobility data, we have illustrated the science benefits from timely and easy access to big data. Such data syntheses can help epidemiology and environmental sciences to provide the evidence base for the solutions that are urgently needed to build a resilient recovery to the devastating pandemic. Google, Apple and other big data providers are encouraged to continue to provide and expand their data offerings.

CO 2 emission estimates

The google mobility analysis.

Google 5 and Apple 6 mobility data were accessed on 5 July 2020. National average Google data were used for 114 countries and the US states. Mobility was provided in six categories of which we used four in our analyses (transit stations, residential, workplaces and retail and recreation). These data represent the number of Android phones at assigned locations, representing transit stations, homes, workplaces, retail outlets and parks. Apple mobility data were from phone movement changes available for 57 countries providing data on changes in transit use, walking and driving, depending on country. Google data were referenced to the day-of-week average in the five-week period from 3 January to 6 February 2020. Apple used a baseline of 13 February and did not account for day-of-week effects. The Apple data were considerably more variable and were only used as a check on the other datasets. Our tests found that the Google transit mobility trends agreed well with Apple driving trends in the 56 nations with overlapping data (Fig. 1a , Supplementary Fig. 1 and Extended Data Fig. 1 ) and this gave us confidence to use the Google mobility data as an estimate of general trends in emissions from surface transport. Correlations of the Apple driving data with Google transit data were stronger than 0.8 (during February–June 2020) for 37 countries and their trends typically agreed to within 20% for April 2020 (Extended Data Fig. 1 ). For the United Kingdom, Apple driving data agree well with government analysis of car journeys (Supplementary Fig. 2 ), whereas Google transit data appear to be more of a hybrid measure. Note, as discussed in the observational evidence section, NO x emissions might be expected to be more closely aligned to commercial vehicles. Changes for these vehicles in the United Kingdom over the period of COVID-19 restrictions were less than indicated by either Apple or Google data (compare light van and heavy goods vehicle use to Google and Apple data in Supplementary Fig. 2 ). Therefore, we expect the Google mobility data to overestimate emission trends in the other sectors and we compare our approach for estimating granular near-real-time emission changes with the previous approaches of Liu et al. 2 and Le Quéré et al. 3 and with observations of NO 2 to test the assumptions.

The Le Quéré et al. sector analysis

Le Quéré et al. 3 analysed fossil fuel CO 2 emission changes in eight sectors (power, surface transport, residential, public and commercial, industry, national shipping, international shipping, national aviation and international aviation) and 69 countries representing 97% of global emissions. The Le Quéré et al. estimates are based on a global estimate of sector emission reductions according to a 1–3 level of confinement. The confinement level estimates were obtained from government (where accessible) and cross-media reports, while the sectoral activity data were from multiple streams of data for each sector including industry reports and were available daily or weekly. Changes in emissions as a function of the confinement level, for each sector, were estimated by quantifying changes in individual and industrial activity in each sector as a function of the observed level of confinement for all countries together. The data are then extrapolated for each country and each day, depending on their level of confinement and their mean emission levels in each sector. The United States and China were treated at state level and provincial level, respectively. Low, medium and high estimates of the emission changes resulting from uncertainty in the activity data among countries for different confinement levels were tested against our data. It was found that the high estimates agreed best with the Google transit trends during January to June 2020 (see Fig. 1 and Supplementary Figs. 1 and 2b). Projections for 2020 were also provided.

Mobility-based emission estimates

As mobility analysis does not cover all sectors or countries to make a global emission estimate we combine the mobility analysis with components of the analysis in Le Quéré et al. 3 to estimate global emission changes for CO 2 and other pollutants that were due to the COVID-19 restrictions.

We adopt the sector approach of Le Quéré et al. 3 but substitute their percentage changes in the emissions from surface transport, residential, public and commercial and industry sectors, with Google mobility changes in transit, residential, retail and recreation, and workplaces respectively. For the power sector, we used a hybrid approach, using a combined weighting of workplace, residential and retail mobility weighted by the 2019 national split of industrial, residential and commercial emissions. Then we used this weighted mobility measure to scale the power sector emissions. Finally, applying a scaling to match the global emission change in the power sector of the Le Quéré et al. high estimate. We also directly used the Le Quéré et al. emission trends for international and national aviation and shipping. In the 45 countries with only Google data available, the average emission changes from the 69 nations of Le Quéré et al. were used in the sectors not covered by the Google mobility data. Note that for simplicity and following Le Quéré et al., shipping changes are added to the surface-transport trends in the analyses presented in Fig. 2 and Supplementary Figs. 3 and 4 . All emission changes are compared to a daily emission rate which is the annual averaged 2019 emission estimated for that country divided by 365 (using the data and approach from Le Quéré et al.). This assumption was tested by analysing the Liu et al. 2 data which included daily seasonal variation from 2019 and repeating our analysis on Climate Model Intercomparison Project Phase 6 (CMIP6) emission data 33 for NO x as a test. We found that adding a seasonal cycle would decrease the January to May 2020 emission change estimate by 3%. However, as the Google analysis also does not account for a seasonal cycle, it is difficult to gauge the overall error in our estimates. To aid comparison with Le Quéré et al. and for consistency with the simple climate modelling approach discussed in Surface temperature change estimates , we choose not to introduce a seasonal cycle in our analyses. The combined dataset gives daily CO 2 emission changes for 2020, across eight sectors and 123 countries, covering 99% of global emissions. The high estimate of Le Quéré et al. and new mobility-based emission estimates were found to agree well with each other, both at the individual US state level and at the country level for the 56 countries with overlapping data (Supplementary Figs. 1 , 3 and 4 and Fig. 1b ).

Supplementary Table 1 compares the global average trends and that from some major nations to the CO 2 estimates in Le Quéré et al. 3 and that of Liu et al. 2 . Our trends are expected to be higher than the other datasets but this does not manifest itself for first-quarter trends in all countries. As the Google trends only start on 15 February, our analysis will underestimate first-quarter trend estimates where changes occurred before this date. More interesting are the differences with the Liu et al. 2 dataset for India and Russia, where their trends are considerably smaller. This could be caused by the differences with the reference assumptions. The approach of Liu et al. 2 makes a daily reference comparison with 2019 emissions and both nations show declining emissions in the first quarter of 2019, whereas our reference is taken as the Google mobility base-period of 3 January to 6 February (see The Google mobility analysis ). As the emission data of Le Quéré et al. 3 are well-correlated in time with the Google mobility estimates and also quantitatively agree (see Supplementary Figs. 3 and 4 ), we assume that the mobility trends we see are largely a response to COVID-19. However, more work will be needed to fully understand and resolve these differences.

Non-CO 2 emission estimates

The Emission Database for Global Atmospheric Research (EDGAR) v.5.0 (ref. 14 ) provides gridded and national-level sectoral emissions on methane, nitrous oxide and several short-lived species. The last year available is 2015. The sectors used in the EDGAR analyses are mapped onto the sectors from Le Quéré et al. 3 used here, according to the breakdown in Supplementary Table 2 . The national- and sector-level emission changes for 2020 are then estimated by equation ( 1 ).

where Δ E in,is ( t ) is the emission change (in kt day −1 ) of the species as a function of nation (in) and sector (is). E base in,is is the annual emission divided by 365 of the species from the sector and nation for 2015. Δ C in,is ( t ) and C base in,is are the CO 2 emission change over 2020 and the average daily baseline emission, respectively, in the sector and nation being considered (CO 2 is in units of MtCO 2  day −1 ). Similar equations are used for international aviation and shipping, where the global emission from aviation or shipping is ratioed by the globally averaged CO 2 emission change in the corresponding sum over the national change in sectors from the data of Le Quéré et al. 3 . The resulting changes are shown in Figs. 2 and 3 and Supplementary Figs. 5 and 6 . Note that only fossil fuel CO 2 emissions were accounted for in Le Quéré et al., so the fractional changes refer to fossil fuel only. Agricultural and waste emissions are included in non-CO 2 analyses but assumed not to change. This leads to a reduced fraction of global emissions for non-CO 2 gases being covered and smaller emission changes for many species (Fig. 2 ). The assumption that a national sector’s emission change will respond uniformly is obviously an important one. There are limited data to explore this assumption, although Liu et al. 2 and Le Quéré et al. 3 discuss how well it applies for CO 2 in specific sectors in specific countries. Extended Data Fig. 1 and Supplementary Fig. 2 and the discussion in CO 2 emissions estimates show that Google mobility data are unlikely to be a perfect proxy for NO x trends in the United Kingdom but at least would be expected to be strongly correlated and close to the right magnitude. This is also supported by the NO 2 analysis in Fig. 3 and Supplementary Fig. 7 . Our approach of assuming that national sectors change in the same way may partly explain why time series for CO 2 and non-CO 2 species evolve in a similar fashion in Fig. 2a . However, Supplementary Fig. 5 shows that sectors do evolve differently for different species. To examine this, we performed substitution tests where we crudely made large changes to specific national sector emissions time series or set them to zero. These tests suggested that the similar patterns seen across species in Fig. 2a are more a product of national restrictions evolving more-or-less together than of non-varying abatement choices within a national sector.

Emission scenarios

The generated datasets firstly combine sector-specific mobility changes referenced to the 3 January to 6 February 2020 period, with national lockdown measures. The method then uses published national emission inventories for either 2019 (for CO 2 ) or 2015 (for non-CO 2 ) to derive absolute emission changes which would also be relative to the early 2020 period. This reference is then projected out to 2030 to form an emission baseline representing current NDCs 15 . To explore the temperature response to emission changes relative to this baseline, the bottom-up emission change estimates from the first four months of 2020 have been extended according to the scenarios illustrated in Table 1 . Four scenarios are explored: two-year blip, fossil-fuelled recovery, moderate green stimulus and strong green stimulus. The two-year blip scenario assumes climate action to continue at the same level of ambition as implied by the current NDCs 15 until 2030—approximated by the implied global carbon price consistent with the emission reduction resulting from the NDCs. The fossil-fuelled recovery follows a path that lies 10% higher than the NDC path. The moderate green stimulus assumes about a 35% reduction in total global GHG emissions relative to the baseline NDC path and a further decline of global CO 2 emissions towards zero emissions in 2060. The Kyoto emissions totals of these NDC baskets are broken into components using the Silicone package 34 by interpolating between the MESSAGE-GLOBIOM implementations of the middle-of-the-road shared socioeconomic pathway (SSP2) scenarios 35 , 36 . Where CO 2 is defined directly, we interpolate from that instead. The strong green stimulus assumes about a 52% reduction in total global GHG emissions relative to the baseline NDC path and a further decline of global CO 2 emissions towards zero emissions in 2050. Non-CO 2 emissions are estimated by interpolating between the sustainability SSP1 scenarios implemented by the IMAGE model 37 . Scenarios are given as emissions of 39 species from anthropogenic and natural sources and volcanic and solar radiative forcing (see Smith et al. 21 for details). Only the ten species evaluated in this paper are changed. The original dataset gives annual emissions from 1750 to 2100 and these are linearly interpolated to monthly values, to provide higher time resolution for the subsequent calculations of effective radiative forcing and temperature.

Comparison to NO 2 observations

Hourly observations of NO 2 are taken from the OpenAQ database ( https://openaq.org/ ) between 1 January 2018 and 3 May 2020, giving 1,747,189 hourly observations from 2,873 sites around the world. For each observation, a spatially and temporally co-located model value for the meteorological, chemical and emissions state is acquired from the NASA GEOS Composition Forecast (GEOS-CF) system. GEOS-CF integrates the GEOS-Chem chemistry model into the GEOS Earth System Model 38 providing global hourly analyses of atmospheric composition at 25 × 25 km 2 spatial resolution in near-real-time. Anthropogenic NO x emissions are prescribed using monthly HTAP bottom-up emissions 39 , with annual scale factors based on OMI satellite data applied to it to account for year-over-year changes. GEOS-CF does not account for emission reductions related to COVID-19, providing a business-as-usual estimate of NO 2 that serves as a reference baseline for surface observations. For each site, a function describing the time-dependent model bias (observed value – modelled value) is developed using the 2018 and 2019 observations on the basis of the XGBoost algorithm 40 , with the model meteorological, chemical and emissions states as the dependent variables. Of these data, 50% are used for training and 50% for testing. For 2020, we predict the concentration of NO 2 , by taking the model output time series of NO 2 at each station and add the bias predicted by our trained algorithm. This then provides a counterfactual for the NO 2 concentration had COVID-19 restrictions not been put into place. We calculate the ratio between the actual concentration and that predicted for each site and then take the mean across all sites within a country. These data are compared to 26 country-level emission estimates in Supplementary Fig. 7 and the country-mean reductions compared to that predicted from the mobility data are shown in Fig. 2b .

Surface temperature change estimates

From the emission scenarios in Table 1 , global averaged effective radiative forcing (ERF) and near-surface air temperature are computed. First, ERFs are calculated using the FaIR v.1.5 model and the methodology outlined in ref. 21 for 13 different forcing components. Uncertainties are estimated by 10,000 Monte Carlo samples of relative ERF uncertainties, using ranges based on IPCC Assessment Report 5 (ref. 41 ), see ref. 21 for details. NO x emissions affect direct forcing from nitrate aerosol and tropospheric ozone radiative forcing. Additionally, the ERF from aviation contrails and contrail-induced cirrus are assumed to scale with NO x emissions from the aviation sector.

The two-layer energy balance model of Geoffroy et al. 42 , 43 including efficacy of deep ocean heat uptake is used to translate these ERF time series into surface temperature estimates. The five free parameters in this model are chosen to match individual CMIP6 model behaviour by fitting the parameters to 4 × CO 2 abrupt simulations in 35 models; these parameter fits are shown in Supplementary Table 3 . To estimate uncertainties, parameters corresponding to an individual model are picked randomly 10,000 times and paired to a sampled ERF parameter range for each of the 13 ERF time series. The two-layer model is then run with each of these parameter sets to make a surface temperature projection. The resulting plume of possible projections is then compared to Cowtan and Way 32 observed surface temperature record. The Cowtan and Way data have been adjusted to allow for the fact that the near-surface air temperature has warmed more than the sea surface temperature. To make this adjustment, the CMIP6 ratio of near-surface air temperature to blended near-surface air temperature and surface ocean temperatures is made over the historical period and found to converge towards 8% in recent years 44 . This is then used to scale the observations upwards. The root mean square errors of the simple model projections are then compared to these scaled observations over the period 1850–2019 inclusive. The goodness of fit is then used to provide projected probability distribution based on a weighted average of the goodness of fit. This follows the method outlined in Knutti et al. 45 , with the exception that we do not downweight ensemble members on the basis of independence.

Testing the ozone forcing parameterization

The FaIR v.1.5 model used adopts a simple global annual mean emission-forcing relationship for tropospheric ozone which may not capture the seasonal and regional nuances of the atmospheric chemical response to the changes in NO x and other emissions. To test this, a second ozone parameterization was used based upon source–receptor relationships from models that participated in the Task Force on Hemispheric Transport of Air Pollutants (TF-HTAP) project 46 . The parameterization 27 , 28 emulates the ozone response in models to applied perturbations in ozone precursor emissions (NO x , CO and NMVOCs) and global CH 4 abundance. For emission perturbations in CO and NMVOCs a linear scaling factor is used whereas a nonlinear factor is used for changes in NO x and CH 4 . The 2020 annual mean tropospheric ozone radiative forcing and annual mean tropospheric ozone burden change deduced from this parameterization were −0.029 W m −2 and 7.5 Tg for the high emission scenario used here.

Data availability

A GitHub repository of the generated datasets is available from https://github.com/Priestley-Centre/COVID19_emissions and on Zenodo https://doi.org/10.5281/zenodo.3957826 . Google LLC mobility data are available from https://www.google.com/covid19/mobility/ Apple LLC mobility data are available from https://www.apple.com/covid19/mobility EDGAR gridded emissions data are available from https://data.europa.eu/doi/10.2904/JRC_DATASET_EDGAR Cowtan and Way temperature observations are available from https://www-users.york.ac.uk/~kdc3/papers/coverage2013/had4_krig_annual_v2_0_0.txt Le Quéré et al. 3 emissions data are available from https://www.icos-cp.eu/gcp-covid19 The air quality data are available from https://openaq.org/ . The GEOS modelled air pollution data used in this study/project have been provided by the Global Modelling and Assimilation Office at NASA Goddard Space Flight Center and are available from https://opendap.nccs.nasa.gov/dods/gmao/geos-cf/assim .

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A Correction to this paper has been published: https://doi.org/10.1038/s41558-020-0904-z

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Acknowledgements

Funding was provided by the European Union’s Horizon 2020 Research and Innovation Programme under grant nos. 820829 (CONSTRAIN) and UKRI NERC grant no. NE/N006038/1 (SMURPHS). C.D.J. was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101) and CRESCENDO (EU Project 641816). C.L.Q. was supported by the Royal Society grant no. RP\R1\191063 and the European Commission H2020 4C grant no. 821003. M.J.E. is grateful for computational support from the University of York’s HPC service (Viking). We thank S. Forster for proofreading the paper and for useful ideas.

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P.M.F. and H.I.F. designed the study. P.M.F. performed the main analyses with contributions from H.I.F. C.L.Q. provided the original data and contributed design ideas. C.J.S. provided the CMIP6 tuning of the two-layer model. C.K. and M.E. provided the surface NO 2 analyses. S.T. provided the ozone emulator analyses. M.G. and C.-F.S. contributed future scenario ideas. J.R. provided the scenario emission data with contributions from R.L. C.D.J. contributed the CO 2 concentration change discussion. D.R. contributed the wider air quality and societal context discussion. R.L. with initial work by T.R. provided the gridded online materials. All authors contributed to the writing.

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Correspondence to Piers M. Forster .

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Extended data

Extended data fig. 1 comparison of google and apple data..

Comparison of Google and Apple data. The Apple driving change in April plotted against the Google transit change for available nations. Example countries are highlighted. The size of the symbol gives a measure of the correlation over Feb-June 2020, ranging from 0.39 for Sweden to over 0.96 (India). The dashed line indicates equality.

Extended Data Fig. 2 Two-year blip scenario.

Two-year blip scenario. Emissions, and best estimates of CO 2 concentration and effective radiative forcing (ERFs) components from the two-year blip scenario. Component ERFs are shown with minor ERFs in panel b and the three largest ERF changes in c .

Extended Data Fig. 3 Longer term climate projections to 2030.

Longer term climate projections to 2030. Emissions, ERF and temperature response from the three scenarios over 2019-2030 (top). The probabilities are generated by varying the emulated CMIP6 model (one of 35) and ERF ranges with a 10,000 Monte Carlo sample. Distributions are weighted according to their goodness of fit over the historical period (see Methods surface temperature change estimates section).

Extended Data Fig. 4 Longer term climate projections to 2050.

Longer term climate projections to 2050. As Extended Data Fig. 3 except for the period extended to 2019-2050.

Extended Data Fig. 5 Probability distributions of passing 2050 global warming levels.

Probability distributions of passing 2050 global warming levels. Levels are relative to 1850-1900 for the scenarios in Table 1 , generated by varying the emulated CMIP6 model (choosing one of 35 model formulations) and ERF ranges. Distributions are weighted according to their goodness of fit over the historical period (see Methods surface temperature change estimates section).

Supplementary information

Supplementary information.

Supplementary Figs. 1–7 and Tables 1–4.

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Forster, P.M., Forster, H.I., Evans, M.J. et al. Current and future global climate impacts resulting from COVID-19. Nat. Clim. Chang. 10 , 913–919 (2020). https://doi.org/10.1038/s41558-020-0883-0

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Issue Date : October 2020

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Could Covid lockdown have helped save the planet?

Slowdown of human activity was too short to reverse years of destruction, but we saw a glimpse of post-fossil fuel world

W hen lockdown began, climate scientists were horrified at the unfolding tragedy, but also intrigued to observe what they called an “inadvertent experiment” on a global scale. To what extent, they asked, would the Earth system respond to the steepest slowdown in human activity since the second world war?

Environmental activists put the question more succinctly: how much would it help to save the planet?

Almost one year on from the first reported Covid case, the short answer is: not enough. In fact, experts say the pandemic may have made some environmental problems worse, though there is still a narrow window of opportunity for something good to come from something bad if governments use their economic stimulus packages to promote a green recovery.

During the northern hemisphere spring, when restrictions were at their strictest, the human footprint softened to a level not seen in decades. Flights halved , road traffic in the UK fell by more than 70%. Industrial emissions in China, the world’s biggest source of carbon, were down about 18% between early February and mid-March – a cut of 250m tonnes. Car use in the United States declined by 40%. So light was humankind’s touch on the Earth that seismologists were able to detect lower vibrations from “cultural noise” than before the pandemic.

The respite was too short to reverse decades of destruction, but it did provide a glimpse of what the world might feel like without fossil fuels and with more space for nature.

Wildlife did not have time to reclaim lost territory but it had scope for exploration. Alongside apocalyptic images of deserted roads, the internet briefly buzzed with heartwarming clips of sheep in a deserted playground in Monmouthshire, Wales, coyotes on the Golden Gate Bridge in San Francisco, wild boar snuffling through the streets of Barcelona, and deer grazing not far from the White House in Washington DC. Wildflowers flourished on roadsides because verges were cut less frequently.

In the global south, the picture was more mixed. Rhino poaching declined in Tanzania due to disruption of supply chains and restrictions on cross-border movements, but bushmeat hunting, illegal firewood collection and incursions into protected areas increased in India , Nepal and Kenya because local communities lost tourist income and sought other ways to care for their families.

In Brazil, traditional guardians of the Amazon have been weakened. The Xavante and Yanomami indigenous groups have been strongly impacted by the disease, and the lockdown has kept forest rangers at home. Meanwhile, land grabbers, fire-starters and illegal miners were busier than ever. Deforestation in Brazil hit a 12-year high .

Elsewhere, there were health gains, though probably not enough to offset the losses. Providing a little relief from rising Covid death tolls were projections in Europe of at least 11,000 fewer fatalities from air pollution. Breathing cleaner air also meant 6,000 fewer children developing asthma, 1,900 avoiding A&E visits and 600 fewer being born preterm.

In the UK, 2 million people with respiratory conditions experienced reduced symptoms . The change was visible from space , where satellite picked up clear reductions of smog belts over Wuhan in China and Turin in Italy. Residents in many cities could also see the difference. In Kathmandu, Nepal, residents were astonished to make out Mount Everest for the first time in decades. In Manila, the Sierra Madre became visible again.

But the gains were short-lived. Once lockdown eased, traffic surged back and so did air pollution . In a survey of 49 British towns and cities, 80% had contamination levels that were now the same or worse than before the pandemic. Elsewhere, sightings of distant mountain peaks and wild animals are fading in the memory.

The story is equally disheartening when it comes to global carbon emissions, which fell steeply but not for long enough to dent climate fears. Months of empty roads and skies and sluggish economic activity reduced global greenhouse gas discharges by an estimated 7%, the sharpest annual fall ever recorded.

8 ways to offset your carbon emissions

These eight companies

These eight companies are among those who will help you offset your carbon emissions. Here is an indication of the cost, and where the money will be spent:

Climate Care Cost : £17.61 for 2.35 tonnes Projects : Safe drinking water in Malawi, fuel efficiency in Ghana, landfill energy in Thailand, rainforests in Brazil

Atmosfair Cost : £106 for 5 tonnes Projects : Energy efficiency in South Africa, biogas in Nepal, wind and hydro power

Carbonfund.org Cost : $47 for 2.24 tonnes Projects : Water treatment in Kenya, hydroelectric in India

Clevel Cost : £38.74 for 2.6 tonnes Projects : Restoring grasslands in Mongolia, reducing deforestation in Tanzania

Flygreen Cost : £20.79 for 2.4 tonnes Projects : Solar panels in India

Myclimate Cost : £72 for 3.1 tonnes Projects : Energy efficiency in Africa, reforestation in Nicaragua

Carbonfootprint.com Cost : Between £7 and £30 a tonne Projects : Borehole rehabilitation in Uganda, hydroelectric in Chile

Gold Standard Cost : Asks you to calculate your own emissions and choose a project Projects : Water purifiers in Cambodia, fuel-efficient stoves in Sudan

That is a saving of 1.5 to 2.5bn metric tons of CO2 pollution, but it merely slowed the accumulation of carbon in the atmosphere, leaving the world on course for more than 3.2C of warming by the end of this century. In its annual emissions gap report, the United Nations environment programme said the impact of the lockdown was “negligible”, equivalent to just 0.01Cdifference by 2030 .

On a more optimistic note, it said ambitious green recovery spending could put the world back on track for the Paris agreement target of less than 2C of warming.

There is scant sign of that so far. Although China, the EU, the UK, Japan and South Korea have all recently announced carbon neutral targets by the mid-century, no nation is doing enough to achieve such a goal. Most stimulus spending is going to fossil fuel industries that are making the climate worse rather than to renewables that could make it better. These twisted priorities have raised concerns that the Covid lockdown may end up like the 2008-09 financial crisis, which led to a brief fall in emissions followed by a surge back to record highs.

“Based on how little of the roughly $15tn in stimulus spending has gone to green energy and clean tech, I think Covid will delay the transition to a carbon-free future,” said Rob Jackson, the chair of Global Carbon Project . In China, he said, emissions were already back to 2019 levels, while other governments were using the pandemic as an excuse to delay climate action in the aviation sector.

In the US, Donald Trump has gone further in his demonstration of crisis capitalism by rolling back a raft of environmental protections and ramping up support for fossil fuels.

The situation is not entirely bleak. This exceptional year has strengthened the economic argument for renewable energy, which has proved a robust, cheap alternative during the lockdown. Analysts predict 2020 will confirm the terminal decline of coal, the dirtiest of fuels, and also heighten doubts about investments in oil. Crude prices at one point fell to minus dollars a barrel.

By comparison, wind and solar power is stable and clean. “The virus has highlighted the health damage of oil-based transportation through air pollution. We caught a glimpse of a future with cleaner air in our cities without fossil fuel pollution from vehicles,” Jackson said.

Whether this is a blip or a turning point depends on action at the national and international level. As the climate-limp stimulus packages have shown, national governments are reluctant to change direction alone. Global cooperation is therefore essential.

But here too, coronavirus has proved an impediment. World leaders were supposed to meet in Glasgow this month for a UN climate summit that was designed to ramp up ambition, but that physical meeting had to be postponed until 2021. The virtual gathering that the UK hosts organised instead barely maintained the momentum. Very few of the participating nations came forward with concrete steps.

It was a similar story with international biodiversity talks that were supposed to have taken place in Kunming. They have been pushed back until next May at the earliest and recalcitrant nations such as Brazil have been accused of impeding progress by throwing up questions about online processes. As with the climate, it would not be accurate to say this was a lost year in international decision-making, but schedules have definitely been set back even as world leaders warn that time is running out.

The necessity for action was driven home by another year of horrifying climate news: 2020 saw record smoke plumes from bushfires in Australia, a freakishly protracted heatwave in Siberia, the most tropical storms ever registered in the Atlantic, devastating blazes in Brazil’s Pantanal wetlands, the highest flood levels recorded in east Africa, unusually devastating cyclones and typhoons in India, Indonesia and the Philippines, the hottest northern hemisphere summer in history, and temperature records in the Antarctic and the Arctic, where winter ice formation was delayed for longer than in any season in the satellite era.

Extreme heat has become more common in recent years

• Temperatures stayed over 34C for six consecutive days last week in the UK, the longest such run since comparable records began in the 1960s

• Spring was the sunniest on record in the UK, even as millions of people were stuck indoors by lockdown. There were more hours of sunshine than in any year since the series began in 1929, and May was the driest in more than a century

• February was the UK’s wettest ever, with 202.1mm of rainfall as storms battered the country

• July was unusually wet and cool

• In April, meteorologists forecast that 2020 would be the  world’s hottest year since records began

• Last year was  Europe’s hottest on record , with 11 of the 12 hottest years on record having occurred in the past two decades

• Siberia has experienced temperatures more than 10C above average this summer, in an Arctic heatwave that has alarmed scientists

• Last summer, Arctic sea ice was at its  second lowest extent on record . This year may surpass records, and recent research suggests Arctic sea ice is on track to disappear in summer by 2035

• Antarctica hit a record high of 20.75C in February, recorded on Seymour Island by Brazilian scientists, at the close of its summer

• The last decade was the earth’s hottest on record

January and November registered all-time heat records, while 2020 as a whole is certain to ensure the last seven years are the hottest since measurements began.

The interconnectedness of the world’s multiple crises is also increasingly apparent. Epidemiologists and conservationists have warned that outbreaks of coronavirus-like diseases are more likely in the future as a result of deforestation , global heating and humankind’s treatment of nature.

“The emergence of the pandemic is not an accident, as there have been repeated warnings for years that we were exerting too much pressure on the natural world by our destructive practices. Habitat loss, intensive agriculture and the over-exploitation of wildlife are key drivers of the emergence of novel infectious diseases like Covid,” said Paul De Ornellas, chief wildlife adviser at WWF-UK.

The secretary general of the UN, António Guterres, went further. In an impassioned state of the planet address this month, he declared making peace with nature the defining task of the 21st century. “Humanity is waging war on nature. This is suicidal,” he said. “Nature always strikes back – and it is already doing so with growing force and fury”

Work on a truce will have a better chance of getting under way next year with a new vaccine, a new president in the White House, a newfound respect for science and a new awareness of how rapidly change can come. It remains to be seen whether that leads to transformative improvement of the Earth system or a resumption of tinkering around the edges.

• Climate crisis
• The lost year: 12 months of coronavirus
• Coronavirus
• Greenhouse gas emissions
• Air pollution

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Climate Matters • April 28, 2021

Covid-19 and Climate Change

KEY CONCEPTS

Global carbon dioxide emissions dropped about 7% in 2020, according to the Global Carbon Project—the biggest annual decrease since the end of World War II. In the U.S., annual CO2 emissions dropped by nearly 13%.

But as Covid-19 restrictions and lockdowns ended, emissions returned to their normal climb  and the brief drop in CO2 emissions had a negligible impact on rising global temperatures. 

The results for other air pollutants— nitrogen dioxide (NO2) and particulate matter (PM2.5) —were mixed. When everyone’s mobility was severely restricted, NO2 concentrations dropped in cities around the world. But pandemic lockdowns did not lower PM2.5 levels beyond their normal range and 2020’s record-breaking wildfire season wiped out air quality gains made during that time.

Last year, when the Covid-19 pandemic put the brakes on global economic activity, greenhouse gas emissions and some air pollutants saw a sharp but temporary reduction. Global carbon dioxide emissions dropped by about 7% in 2020, according to the Global Carbon Project—the biggest annual decrease since the end of World War II. In the U.S., annual CO2 emissions dropped by nearly 13%.  But researchers found most of the decreases occurred early in the year, with the biggest drop in April. As restrictions and lockdowns ended, emissions returned to their normal climb. 

Even with the declines in emissions , humans still added a huge amount of new CO2 to the atmosphere , and concentrations of this heat-trapping gas continued to rise. ( Emissions are the amount of pollutant matter released from a specific source and in a specific time interval; concentrations are the amount of pollutant matter in the atmosphere per volume unit.) Earlier this month, the Mauna Loa Observatory measured the concentration of atmospheric CO2 at more than 420 parts per million —setting a new record.

That brief drop in CO2 emissions had a negligible impact on rising global temperatures, as CO2 remains in the atmosphere long after it is emitted. To keep the planet from warming more than 1.5°C above pre-industrial levels, a goal of the Paris Agreement , CO2 emissions would need to decrease roughly the same amount every year (7.6%) for the next decade.

Results were mixed for decreases of the air pollutants nitrogen dioxide (NO2) and particulate matter (PM2.5) .

NO2 is primarily emitted by vehicles and airplanes during fuel combustion and is one of the chemicals that contributes to the creation of unhealthy surface ozone . Unlike CO2, it has a relatively short lifetime in the atmosphere, lasting only a few hours before it disappears. When everyone’s mobility was severely restricted, NO2 concentrations dropped in cities around the world. This was partially due to shutdowns; weather and long-term improvements in air quality were also at play. 

PM2.5 comes from a number of sources, including transportation (especially diesel vehicles), industry, wood-burning stoves, and wildfires. In the U.S., research found mixed results for PM2.5 concentrations, but essentially the pandemic lockdowns did not lower PM2.5 levels beyond their normal range. Another report found that pollutants from 2020’s record-breaking wildfire season wiped out any air quality improvements made during Covid. 

So what did we learn from the Covid-19 experience that we can apply to solving climate change? 

We need to transform our energy systems.

When individuals cut back on flying and driving due to the pandemic, the impact was really small compared to the baseline carbon emissions required to power homes, run factories, and move goods across the planet. In order to get to net-zero emissions , we need to switch to renewable energy and electrify our transportation systems. The shift to renewables can be a huge economic driver, unlike Covid-19. A Princeton University study found taking actions to achieve net-zero emissions could create 500,000 to 1 million new energy jobs in the U.S. during this decade alone.

Covid and climate change affect populations disproportionately. 

In the U.S., Covid infections and death rates are highest among Black, Latino and indigenous communities. These same populations are also bearing the brunt of climate impacts , after years of exposure to environmental stressors through the redlining of neighborhoods and systemic racism. Going forward, Covid recovery policies and climate mitigation and adaptation approaches should be designed to be inclusive, equitable and accessible.

Science matters.

The pandemic put scientists and health experts front and center. Broad investment in basic science led to the technology that produced novel vaccines and diagnostic tests. Similarly, investment in research decades ago helped to create improved batteries that power electric vehicles and to advance climate projections. Governments, scientists, and the business community worked together to fast track the development of vaccines and deliver them to the public safely. If any progress is going to be made on climate change, it must lean heavily on science and scientists in policy and decision making.

RESOURCES FOR LOCAL REPORTING

Looking to report on local carbon emissions and air quality? Check your state’s Carbon Dioxide Emissions Data from the U.S. Energy Information Administration.  You can find local air quality data at AirNow.gov and NASA’s Global Nitrogen Dioxide Monitoring Page . Currently, 24 states and the District of Columbia have established greenhouse gas emissions targets—tracked here by C2ES.

More resources on Covid-19 and emissions:

NASA’s COVID-19 Dashboard : Air Quality and COVID-19

Global Monitoring Laboratory: Can We See a Change in the CO2 Record because of COVID-19?

NASA powerpoint -- “NASA Satellite Data Enable Research on the Impact of COVID-19 on World Air Quality”

NASA & ESA Earth Observing Dashboard : The Impact of COVID-19 Pandemic on Atmospheric Greenhouse Gases

Articles on Covid-19 and climate change

Five Lessons from COVID-19 for Advancing Climate Change Mitigation

COVID-19: Lessons for the climate change emergency

Lessons from COVID-19 for the climate crisis (American Psychological Association)

4 lessons from COVID-19 to help fight climate change (MIT) 

5 things COVID-19 can teach us about fighting climate change (World Economic Forum)

COVID-19, Black Women and Why Equitable Recovery Matters

4 Priorities for Climate Action and Social Equity in the COVID-19 Recovery

LOCAL EXPERTS

The SciLine service , 500 Women Scientists or the press offices of local universities may be able to connect you with local scientists who have expertise on climate change in your area. The American Association of State Climatologists is a professional scientific organization composed of all 50 state climatologists . Find and contact your state climatologist. 

NATIONAL EXPERTS

Pieter Tans , Ph.D. Senior Scientist, Global Monitoring Laboratory, NOAA [email protected]

Mona Sarfaty , MD MPH FAAFP Executive Director, Medical Society Consortium on Climate and Health, Center for Climate Change Communication George Mason University [email protected]

Emission Reductions From Pandemic Had Unexpected Effects on Atmosphere

Worldwide restrictions during the COVID-19 pandemic caused huge reductions in travel and other economic activities, resulting in lower emissions. Seen here, almost-empty highways in Colombia during the pandemic. Credit: International Monetary Fund

By Carol Rasmussen, NASA's Jet Propulsion Laboratory

Earth’s atmosphere reacted in surprising ways to the lowering of emissions during the pandemic, showing how closely climate warming and air pollution are linked.

The COVID-19 pandemic and resulting limitations on travel and other economic sectors by countries around the globe drastically decreased air pollution and greenhouse gas emissions within just a few weeks. That sudden change gave scientists an unprecedented view of results that would take regulations years to achieve.

A comprehensive new survey of the effects of the pandemic on the atmosphere, using satellite data from NASA and other international space agencies, reveals some unexpected findings. The study also offers insights into addressing the dual threats of climate warming and air pollution. “We’re past the point where we can think of these as two separate problems,” said Joshua Laughner, lead author of the new study and a postdoctoral fellow at Caltech in Pasadena, California. “To understand what is driving changes to the atmosphere, we must consider how air quality and climate influence each other.”

Published Nov. 9 in the Proceedings of the National Academy of Sciences, the paper grew from a workshop sponsored by Caltech’s W.M. Keck Institute for Space Studies, led by scientists at that institution and at the Jet Propulsion Laboratory in Southern California, which is managed by Caltech. Participants from about 20 U.S. and international universities, federal and state agencies, and laboratories pinpointed four atmospheric components for in-depth study: the two most important greenhouse gases, carbon dioxide and methane; and two air pollutants, nitrogen oxides and microscopic nitrate particles.

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Carbon Dioxide

The most surprising result, the authors noted, is that while carbon dioxide (CO 2 ) emissions fell by 5.4% in 2020, the amount of CO 2 in the atmosphere continued to grow at about the same rate as in preceding years. “During previous socioeconomic disruptions, like the 1973 oil shortage, you could immediately see a change in the growth rate of CO 2 ,” said David Schimel, head of JPL’s carbon group and a co-author of the study. “We all expected to see it this time, too.”

Using data from NASA’s Orbiting Carbon Observatory-2 satellite launched in 2014 and the NASA Goddard Earth Observing System atmospheric model, the researchers identified several reasons for this result. First, while the 5.4% drop in emissions was significant, the growth in atmospheric concentrations was within the normal range of year-to-year variation caused by natural processes. Also, the ocean didn’t absorb as much CO 2 from the atmosphere as it has in recent years – probably in an unexpectedly rapid response to the reduced pressure of CO 2 in the air at the ocean’s surface.

Air Pollutants and Methane

Nitrogen oxides (NOx) in the presence of sunlight can react with other atmospheric compounds to create ozone, a danger to human, animal, and plant health. That’s by no means their only reaction, however. “NOx chemistry is this incredibly complicated ball of yarn, where you tug on one part and five other parts change,” said Laughner.

As reported earlier , COVID-related drops in NOx quickly led to a global reduction in ozone. The new study used satellite measurements of a variety of pollutants to uncover a less-positive effect of limiting NOx. That pollutant reacts to form a short-lived molecule called the hydroxyl radical, which plays an important role in breaking down long-lived gases in the atmosphere. By reducing NOx emissions – as beneficial as that was in cleaning up air pollution – the pandemic also limited the atmosphere's ability to cleanse itself of another important greenhouse gas: methane.

Molecule for molecule, methane is far more effective than CO 2 at trapping heat in the atmosphere. Estimates of how much methane emissions dropped during the pandemic are uncertain because some human causes, such as poor maintenance of oilfield infrastructure, are not well documented, but one study calculated that the reduction was 10%.

However, as with CO 2 , the drop in emissions didn’t decrease the concentration of methane in the atmosphere. Instead, methane grew by 0.3% in the past year – a faster rate than at any other time in the last decade. With less NOx, there was less hydroxyl radical to scrub methane away, so it stayed in the atmosphere longer.

Lessons From the Pandemic

The study took a step back to ask what the pandemic could teach about how a lower-emissions future might look and how the world might get there.

Notably, emissions returned to near-pre-pandemic levels by the latter part of 2020, despite reduced activity in many sectors of the economy. The authors reason that this rebound in emissions was probably necessary for businesses and individuals to maintain even limited economic productivity, using the worldwide energy infrastructure that exists today. “This suggests that reducing activity in these industrial and residential sectors is not practical in the short term” as a means of cutting emissions, the study noted. “Reducing these sectors’ emissions permanently will require their transition to low-carbon-emitting technology.”

News Media Contacts

Jane J. Lee / Ian J. O’Neill Jet Propulsion Laboratory, Pasadena, Calif. 818-354-0307 / 818-354-2649 [email protected] / [email protected]

Whitney Clavin Caltech 626-395-1944 [email protected]

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What is climate change mitigation and why is it urgent?

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• Climate change mitigation involves actions to reduce or prevent greenhouse gas emissions from human activities.
• Mitigation efforts include transitioning to renewable energy sources, enhancing energy efficiency, adopting regenerative agricultural practices and protecting and restoring forests and critical ecosystems.
• Effective mitigation requires a whole-of-society approach and structural transformations to reduce emissions and limit global warming to 1.5°C above pre-industrial levels.
• International cooperation, for example through the Paris Agreement, is crucial in guiding and achieving global and national mitigation goals.
• Mitigation efforts face challenges such as the world's deep-rooted dependency on fossil fuels, the increased demand for new mineral resources and the difficulties in revamping our food systems.
• These challenges also offer opportunities to improve resilience and contribute to sustainable development.

What is climate change mitigation?

Climate change mitigation refers to any action taken by governments, businesses or people to reduce or prevent greenhouse gases, or to enhance carbon sinks that remove them from the atmosphere. These gases trap heat from the sun in our planet’s atmosphere, keeping it warm. 

Since the industrial era began, human activities have led to the release of dangerous levels of greenhouse gases, causing global warming and climate change. However, despite unequivocal research about the impact of our activities on the planet’s climate and growing awareness of the severe danger climate change poses to our societies, greenhouse gas emissions keep rising. If we can slow down the rise in greenhouse gases, we can slow down the pace of climate change and avoid its worst consequences.

Reducing greenhouse gases can be achieved by:

• Shifting away from fossil fuels : Fossil fuels are the biggest source of greenhouse gases, so transitioning to modern renewable energy sources like solar, wind and geothermal power, and advancing sustainable modes of transportation, is crucial.
• Improving energy efficiency : Using less energy overall – in buildings, industries, public and private spaces, energy generation and transmission, and transportation – helps reduce emissions. This can be achieved by using thermal comfort standards, better insulation and energy efficient appliances, and by improving building design, energy transmission systems and vehicles.
• Changing agricultural practices : Certain farming methods release high amounts of methane and nitrous oxide, which are potent greenhouse gases. Regenerative agricultural practices – including enhancing soil health, reducing livestock-related emissions, direct seeding techniques and using cover crops – support mitigation, improve resilience and decrease the cost burden on farmers.
• The sustainable management and conservation of forests : Forests act as carbon sinks , absorbing carbon dioxide and reducing the overall concentration of greenhouse gases in the atmosphere. Measures to reduce deforestation and forest degradation are key for climate mitigation and generate multiple additional benefits such as biodiversity conservation and improved water cycles.
• Restoring and conserving critical ecosystems : In addition to forests, ecosystems such as wetlands, peatlands, and grasslands, as well as coastal biomes such as mangrove forests, also contribute significantly to carbon sequestration, while supporting biodiversity and enhancing climate resilience.
• Creating a supportive environment : Investments, policies and regulations that encourage emission reductions, such as incentives, carbon pricing and limits on emissions from key sectors are crucial to driving climate change mitigation.

Photo: Stephane Bellerose/UNDP Mauritius

Photo: La Incre and Lizeth Jurado/PROAmazonia

What is the 1.5°C goal and why do we need to stick to it?

In 2015, 196 Parties to the UN Climate Convention in Paris adopted the Paris Agreement , a landmark international treaty, aimed at curbing global warming and addressing the effects of climate change. Its core ambition is to cap the rise in global average temperatures to well below 2°C above levels observed prior to the industrial era, while pursuing efforts to limit the increase to 1.5°C.

The 1.5°C goal is extremely important, especially for vulnerable communities already experiencing severe climate change impacts. Limiting warming below 1.5°C will translate into less extreme weather events and sea level rise, less stress on food production and water access, less biodiversity and ecosystem loss, and a lower chance of irreversible climate consequences.

To limit global warming to the critical threshold of 1.5°C, it is imperative for the world to undertake significant mitigation action. This requires a reduction in greenhouse gas emissions by 45 percent before 2030 and achieving net-zero emissions by mid-century.

What are the policy instruments that countries can use to drive mitigation?

Everyone has a role to play in climate change mitigation, from individuals adopting sustainable habits and advocating for change to governments implementing regulations, providing incentives and facilitating investments. The private sector, particularly those businesses and companies responsible for causing high emissions, should take a leading role in innovating, funding and driving climate change mitigation solutions. 

International collaboration and technology transfer is also crucial given the global nature and size of the challenge. As the main platform for international cooperation on climate action, the Paris Agreement has set forth a series of responsibilities and policy tools for its signatories. One of the primary instruments for achieving the goals of the treaty is Nationally Determined Contributions (NDCs) . These are the national climate pledges that each Party is required to develop and update every five years. NDCs articulate how each country will contribute to reducing greenhouse gas emissions and enhance climate resilience.   While NDCs include short- to medium-term targets, long-term low emission development strategies (LT-LEDS) are policy tools under the Paris Agreement through which countries must show how they plan to achieve carbon neutrality by mid-century. These strategies define a long-term vision that gives coherence and direction to shorter-term national climate targets.

Photo: Mucyo Serge/UNDP Rwanda

Photo: William Seal/UNDP Sudan

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Another critical aspect is ensuring a just transition for workers and communities that depend on the fossil fuel industry and its many connected industries. This process must prioritize social equity and create alternative employment opportunities as part of the shift towards renewable energy and more sustainable practices.

For emerging economies, innovation and advancements in technology have now demonstrated that robust economic growth can be achieved with clean, sustainable energy sources. By integrating renewable energy technologies such as solar, wind and geothermal power into their growth strategies, these economies can reduce their emissions, enhance energy security and create new economic opportunities and jobs. This shift not only contributes to global mitigation efforts but also sets a precedent for sustainable development.

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Furthermore, due to the significant demand for these minerals and the urgency of the energy transition, the scaled-up investment in the sector has the potential to exacerbate environmental degradation, economic and governance risks, and social inequalities, affecting the rights of Indigenous Peoples, local communities, and workers. Addressing these concerns necessitates implementing social and environmental safeguards, embracing circular economy principles, and establishing and enforcing responsible policies and regulations .

Agriculture is currently the largest driver of deforestation worldwide. A transformation in our food systems to reverse the impact that agriculture has on forests and biodiversity is undoubtedly a complex challenge. But it is also an important opportunity. The latest IPCC report highlights that adaptation and mitigation options related to land, water and food offer the greatest potential in responding to the climate crisis. Shifting to regenerative agricultural practices will not only ensure a healthy, fair and stable food supply for the world’s population, but also help to significantly reduce greenhouse gas emissions.  

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What are some examples of climate change mitigation?

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In Indonesia , UNDP has been working with the government for over a decade to support sustainable palm oil production. In 2019, the country adopted a National Action Plan on Sustainable Palm Oil, which was collaboratively developed by government, industry and civil society representatives. The plan increased the adoption of practices to minimize the adverse social and environmental effects of palm oil production and to protect forests. Since 2015, 37 million tonnes of direct greenhouse gas emissions have been avoided and 824,000 hectares of land with high conservation value have been protected.

In Moldova and Paraguay , UNDP has helped set up Green City Labs that are helping build more sustainable cities. This is achieved by implementing urban land use and mobility planning, prioritizing energy efficiency in residential buildings, introducing low-carbon public transport, implementing resource-efficient waste management, and switching to renewable energy sources. 

UNDP has supported the governments of Brazil, Costa Rica, Ecuador and Indonesia to implement results-based payments through the REDD+ (Reducing emissions from deforestation and forest degradation in developing countries) framework. These include payments for environmental services and community forest management programmes that channel international climate finance resources to local actors on the ground, specifically forest communities and Indigenous Peoples. 

UNDP is also supporting small island developing states like the Comoros to invest in renewable energy and sustainable infrastructure. Through the Africa Minigrids Program , solar minigrids will be installed in two priority communities, Grand Comore and Moheli, providing energy access through distributed renewable energy solutions to those hardest to reach.

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UNDP aims to assist countries with their climate change mitigation efforts, guiding them towards sustainable, low-carbon and climate-resilient development. This support is in line with achieving the Sustainable Development Goals (SDGs), particularly those related to affordable and clean energy (SDG7), sustainable cities and communities (SDG11), and climate action (SDG13). Specifically, UNDP’s offer of support includes developing and improving legislation and policy, standards and regulations, capacity building, knowledge dissemination, and financial mobilization for countries to pilot and scale-up mitigation solutions such as renewable energy projects, energy efficiency initiatives and sustainable land-use practices. 

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Open Access

Peer-reviewed

Research Article

Do economic effects of the anti-COVID-19 lockdowns in different regions interact through supply chains?

Roles Conceptualization, Methodology, Project administration, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Graduate School of Information Science, University of Hyogo, Kobe, Hyogo, Japan

Roles Methodology, Validation, Writing – original draft

Affiliation RIKEN Center for Computational Science, Kobe, Hyogo, Japan

Roles Conceptualization, Methodology, Writing – original draft, Writing – review & editing

Affiliation Graduate School of Economics, Waseda University, Tokyo, Japan

• Hiroyasu Inoue, 
• Yohsuke Murase, 
• Yasuyuki Todo

• Published: July 30, 2021
• https://doi.org/10.1371/journal.pone.0255031
• Reader Comments

To prevent the spread of COVID-19, many cities, states, and countries have ‘locked down’, restricting economic activities in non-essential sectors. Such lockdowns have substantially shrunk production in most countries. This study examines how the economic effects of lockdowns in different regions interact through supply chains, which are a network of firms for production, by simulating an agent-based model of production using supply-chain data for 1.6 million firms in Japan. We further investigate how the complex network structure affects the interactions between lockdown regions, emphasising the role of upstreamness and loops by decomposing supply-chain flows into potential and circular flow components. We find that a region’s upstreamness, intensity of loops, and supplier substitutability in supply chains with other regions largely determine the economic effect of the lockdown in the region. In particular, when a region lifts its lockdown, its economic recovery substantially varies depending on whether it lifts the lockdown alone or together with another region closely linked through supply chains. These results indicate that the economic effect produced by exogenous shocks in a region can affect other regions and therefore this study proposes the need for inter-region policy coordination to reduce economic loss due to lockdowns.

Citation: Inoue H, Murase Y, Todo Y (2021) Do economic effects of the anti-COVID-19 lockdowns in different regions interact through supply chains? PLoS ONE 16(7): e0255031. https://doi.org/10.1371/journal.pone.0255031

Editor: Ashkan Memari, Sunway University, MALAYSIA

Received: November 1, 2020; Accepted: July 8, 2021; Published: July 30, 2021

Copyright: © 2021 Inoue et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data for supply chain network are based on a survey done by Tokyo Shoko Research (TSR), one of the leading credit research agencies in Tokyo, supplied to us through the Research Institute of Economy, Trade and Industry (RIETI). The data are not in the public domain but are commercially available from Tokyo Shoko Research, Ltd., http://www.tsr-net.co.jp/ , [email protected] . The authors had no special access privileges to the data.

Funding: H.I., 18K04615, Japan Society for the Promotion of Science, https://www.jsps.go.jp/english/index.html , The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Y.T, 18H03642, Japan Society for the Promotion of Science, https://www.jsps.go.jp/english/index.html , The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

COVID-19, a novel coronavirus (SARS-CoV-2) disease, has been spreading worldwide. To prevent its spread, many cities, regions, and countries were or have been under lockdown, suppressing economic activities. On 18 April 2020, 158 countries out of 181 implemented measures that required temporary closure or work-from-home for some sectors in some or all cities. Although some countries later lifted their lockdowns, 95 countries remained under lockdown on 30 July 2020 [ 1 ].

Closing workplaces shrinks the economic output of regions under lockdown. The negative economic effect of a lockdown in one region may diffuse through supply chains, i.e., supplier-client relationships of firms, and to other regions that are not necessarily in a lockdown. When a firm is closed due to a lockdown strategy, its client firms located elsewhere would suffer decreased production due to the lack of supply of intermediate goods and services. Suppliers of the closed firms would also see reduced production because of a shortage of demand.

Many studies have empirically confirmed the propagation of economic shocks through supply chains, particularly shocks originating from natural disasters [ 2 – 7 ]. Some examine the diffusion of the effect of lockdowns because of COVID-19 on production across regions and countries and estimate the total effect using input–output (IO) linkages at the country-sector level [ 8 – 11 ] and supply chains at the firm level [ 12 ].

Several studies focusing on natural disasters [ 5 , 6 ] examine how the network structure of supply chains affects the propagation of shocks. They find that scale-free property, non-substitutability of suppliers, and loops are major drivers of such propagation. However, the role of the network structure has not been fully examined in the context of the propagation of the lockdown effect. This issue should be of great interest from the perspective of network science for the following two reasons.

First, the literature on network interventions has investigated the types of individuals or groups in a network, such as those with high centrality, who should be targeted to promote (prevent) the diffusion of positive (negative) behaviours and outcomes [ 13 , 14 ]. Similarly, we are interested in how the economic effect of imposing and lifting a lockdown in one region, an example of a network intervention, diffuses to other regions. Compared to existing research, this study is novel in many respects. For example, we consider interventions in a network of firms and their economic outcomes, while previous studies focus on the health behaviours and outcomes in human networks [ 15 ], with a few exceptions that examine economic outcomes in human networks [ 16 ]. In addition, because a lockdown is usually imposed in a city, state, or country, the scale of interventions is large. Firms targeted by such interventions are exogenously determined by geography, and thus we should assess the network characteristics of exogenously grouped nodes, rather than the endogenously connected ones identified by network centrality [ 13 , 17 ] or community detection algorithms [ 18 ].

Second, at any point during the spread of COVID-19, some regions imposed a lockdown, while others remained open. Therefore, when we evaluate the lockdown strategy of a region, the interactions between the strategies of different regions need to be considered. In other words, the economic effect of a lockdown in a region depends on whether other regions connected to it through supply chains are similarly locked down. For example, Sweden did not impose a strict lockdown, unlike other European countries. However, it still expects a 4.5% reduction in gross domestic product (GDP) in 2020, a decline comparable to that in neighbouring countries that did impose a lockdown, possibly because of its close economic ties with its neighbours [ 19 ]. Motivated by the Swedish experience, this study examines the network structure between regions—an aspect that is usually ignored in the literature on network interventions—and discusses the need for policy coordination among regions depending on their network characteristics. Some studies call for inter-regional and international policy coordination in the presence of spillover effects in the context of health, environment, and macroeconomics [ 20 , 21 ], but they do not explicitly incorporate the network structure.

The present study fills the above gaps in research on network interventions and regional interactions. We conduct a simulation analysis by applying actual supply-chain data of 1.6 million firms and their experiences of the lockdowns in Japan to an agent-based model of production. Specifically, we analyse the network characteristics of a prefecture in Japan that led to greater economic recovery by lifting its lockdown when all other prefectures remained locked down. In addition, to further highlight the interactions between regions, our simulation investigates how the characteristics of the supply-chain links between two prefectures affect their economic recovery when they simultaneously lift their lockdowns. One novelty of our study is to decompose supply-chain flows into potential and loop flow components and test the role of upstreamness (potential) in supply chains and intra- and inter-prefectural loops in diffusion.

The data used in this study are taken from the Company Information Database and Company Linkage Database compiled by Tokyo Shoko Research (TSR), one of the largest credit research companies in Japan. The former database includes information about the attributes of each firm, including the location, industry, sales, and number of employees, and the latter includes the major customers and suppliers of each firm. Due to availability, we use the data on firm attributes and supply chains from 2016. The number of firms in the data is 1,668,567 and the number of supply-chain links is 5,943,073. Hence, our data identify the major supply chains of most firms in Japan, although they lack information about supply-chain links with foreign entities. Because the transaction value of each supply-chain tie is not available in the data, we estimate sales from a supplier to each of its customers and consumers using the total sales of the supplier and the 2015 Input-Output (IO) Tables for Japan [ 22 ]. In this estimation process, we drop firms without any sales information. Accordingly, the number of firms in our final analysis is 966,627 and the number of links is 3,544,343. Although the firms in the TSR data are classified into 1,460 industries according to the Japan Standard Industrial Classification [ 23 ], we simplify this into the 187 industries classified in the IO tables. S1 Appendix provides details on the data construction process.

In the supply-chain data described above, the degree, or the number of links, of firms follows a power-law distribution [ 5 ], as often found in the literature [ 24 ]. The average path length between firms, or the number of steps between them through supply chains, is 4.8, indicating a small-world network. Using the same dataset, previous studies [ 5 , 25 ] find that 46–48% of firms are included in the giant strongly connected component (GSCC), in which all firms are indirectly connected to each other through supply chains. The large size of the GSCC clearly shows that the network has a significant number of cycles unlike the common image of a layered or tree-like supply-chain structure.

Agent-based models that incorporate the interactions of agents through networks have been widely used in the social sciences [ 26 – 28 ]. Following the literature, we employ the dynamic agent-based model of Inoue and Todo [ 5 , 6 ], an extension of Hallegatte’s [ 29 ] model, which assumes that supply chains are at the firm level. In the model, each firm utilises the inputs purchased from other firms to produce an output and sells it to other firms and consumers. Firms in the same industry are assumed to produce the same output. Supply chains are predetermined, and do not change over time in the following two respects. First, each firm utilises a firm-specific set of input varieties and does not change the input set over time. Second, each firm is linked with fixed suppliers and customers and cannot be linked with any new firm over time, even after a supply-chain disruption. Accordingly, our analysis focuses on short-term changes in production. Furthermore, we assume that each firm keeps inventories of each input at a level randomly determined from the Poisson distribution. Following Inoue and Todo [ 5 ], in which parameter values are calibrated from the case of the Great East Japan earthquake, we assume that firms aim to keep inventories for 10 days of production on average (see S2 Appendix for the details).

When a restriction is imposed on a firm’s production, both its upstream and downstream of the firm are affected. On the one hand, the firm’s demand for parts and components from its suppliers immediately declines, and thus suppliers have to shrink their production. Because demand for the products of suppliers’ suppliers also declines, the negative effect of the restriction propagates upstream. On the other hand, the supply of products from the directly restricted firm to its customer firms declines. Therefore, one way for customer firms to maintain the current level of production is to use their inventories of inputs. Alternatively, customers can procure inputs from other suppliers in the same industry that were already connected before the restriction, provided these suppliers have additional production capacity. If the inventories and inputs from substitute suppliers are insufficient, customers have to shrink their production because of a shortage of inputs. Accordingly, the effect of the restriction propagates downstream through supply chains. Such downstream propagation is likely to be slower than upstream propagation because of the inventory buffer and input substitution.

3.2 Lockdowns in Japan

In Japan, lockdown strategies were implemented at the prefecture level under the state of emergency [ 30 ] first declared on 7 April, 2020 in seven prefectures with a large number of confirmed COVID-19 cases. Because populated regions tended to be affected more and earlier, these seven prefectures are industrial clusters in Japan, including Tokyo, Osaka, Fukuoka, and their neighbouring prefectures. The state of emergency was expanded to all 47 prefectures on 16 April. The state of emergency was lifted for 39 prefectures on 14 May and for an additional three on 21 May; it was lifted for the remaining five prefectures on 25 May. (The summary of the timeline of the lockdowns in different prefectures can be found in Fig A.3 of [ 31 ]).

Although the national government declared a state of emergency, the extent to which the restrictions were imposed was determined by each prefecture’s government. Therefore, the level of lockdown in each prefecture may have varied. Although all prefectures were in the state of emergency from 16 April to 14 May, prefectures with larger numbers of confirmed COVID-19 cases, such as the seven prefectures in which a state of emergency was first declared, requested more stringent restrictions than others. The national or prefectural government can only request closing workplaces, staying at home, and social distancing rather than enforcing these actions through legal enforcement or punishment. However, strong social pressure in Japan led people and businesses to voluntarily restrict their activities to a large extent. As a result, production activities including those in sectors not officially restricted shrunk substantially (Mainichi Newspaper, 27 May 2020).

3.3 Simulation procedure

3.3.1 replication of the actual effect..

In our simulation analysis, we first confirm whether our model and data can replicate the actual reduction in production caused by the lockdown in Japan during this state of emergency. Because we cannot observe the extent to which each firm reduces its production capacity by obeying government requests, the rate of reduction in production capacity for each sector assumed in our simulation analysis depends on its characteristics. As the reduction rate, particularly during the lockdowns in Japan is not available, we follow the literature that defines the reduction rate in general settings. Specifically, the rate of reduction in a sector is the product of the level of reduction determined by the degree of exposure to the virus given by [ 9 ] and the share of workers who cannot work from home given by [ 8 ]. For example, in lifeline/essential sectors such as utilities, health, and transport, the rate of reduction is assumed to be zero; in other words, the production capacity in these sectors does not change during a lockdown. In sectors in which it is assumed that exposure to the virus is low (50%) and 13.4% of workers can work from home, such as the agriculture and fishery sectors, the rate of reduction is 43.3% (= 0.5 × (1 − 0.134)). Sectors with ordinary exposure (100%) and 47.5% of workers were working from home, such as the retail and wholesale sectors, show a reduction in production capacity by 52.5% (= 1 × (1 − 0.475)). See S1 Table for the rate of reduction of each sector.

After the lockdown in a prefecture is lifted, all the firms in that prefecture immediately return to their pre-lockdown production capacity. Moreover, we assume that inventories do not decay over time: inventories stocked before the lockdown can be fully utilised after the lockdown is lifted. The results given below are an averaged of over 30 Monte Carlo runs.

3.3.2 Interactions among regions.

After checking the accuracy of our simulation model, we examine how changing the restriction level of the lockdown in a region affects production in another region. For this purpose, we experiment with different sets of sector-specific rates of reduction in production capacity by multiplying the benchmark rates of reduction defined above by a multiplier such as 0.4 or 0.8. For example, when the benchmark rate of reduction in a sector is 52.5%, as in the case of the iron and other metal product sectors, and the multiplier is 0.4, we alternatively assume a rate of reduction of 21.0%.

Moreover, we assume that the rates of reduction can vary among prefectures, because each prefecture can determine its own level of restrictions under the state of emergency (Section 3.2). In practice, the restrictions requested by the prefectural governments were tougher and people were more obedient to the requests in the seven prefectures in which the state of emergency was first declared because of the larger COVID-19 caseloads than in other prefectures. Accordingly, we run the same simulation assuming different rates of reduction for the two types of prefectures, defined as more and less restricted groups, to investigate how different rates of reduction in one group affect production in the other.

3.3.3 Lifting lockdown in only one region.

In practice, some prefectures lifted their lockdowns earlier than others (Section 3.2). Although this may have led to the recovery of value added production, or gross regional product (GRP), the extent of such a recovery should have been affected by the links between firms in the prefecture and others still under lockdown. To highlight this network effect, we simulate what would happen to the GRP of a prefecture if it lifted its lockdown while all others were still imposing lockdowns. Next, we investigate what network characteristics of each prefecture determine the recovery from lockdown, measured by the ratio of the increase in the GRP of the prefecture by lifting its lockdown to the reduction in its GRP because of the lockdown of all prefectures.

In particular, we focus on four types of network characteristics. First, when a prefecture is more isolated from others in the supply-chain network, the effect of others’ lockdowns should be smaller. We measure the level of isolation using the number of links within the prefecture relative to the total degree of firms (total number of links from and to firms) in the prefecture.

Second, an alternative and more interesting measure of isolation is the intensity of loops in supply chains. Although supply chains usually flow from suppliers of materials to those of parts and components and then to assemblers, some suppliers use final products such as machinery and computers as inputs. This results in many complex loops in supply chains [ 32 ], in which negative shocks circulate and can become aggravated [ 5 ]. Such loops in a network are found to generate instability in the system dynamics literature [ 33 ] and more recently in the context of supply chains [ 34 ]. In the case of lifting the lockdown in only one prefecture, the loops within that prefecture may magnify its recovery because of the circulation of positive effects in the loops.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Each dot indicates a firm. Firms with a higher Helmholtz–Hodge (HH) potential are located more upward in both panels. In the left panel, the grey lines illustrate the potential flows computed from the HHD. The red and blue node colours represent higher and lower HH potentials, respectively. The right panel shows loop flows computed from HHD, while the different colours represent different cycles.

https://doi.org/10.1371/journal.pone.0255031.g001

Third, we pay attention to the upstreamness of firms in supply chains. Theoretically, upstream firms are affected by supply-chain disruptions through a lack of demand, whereas downstream firms are affected through a lack of supply. However, the effect of upstream and downstream links can differ in size. A recent sectoral analysis [ 36 ] finds that the profits of more upstream sectors in global value chains are substantially lower than those of more downstream sectors, implying that negative economic shocks propagate upstream more than downstream. To clarify the possible effect of upstreamness, we define the upstream position of each firm i in supply chains by its Helmholtz–Hodge (HH) potential, φ i computed from the HHD. In other words, the hierarchical position of a firm can be consistently defined by focusing on gradient flows, in other words, all flows less loop flows. The HH potential is higher when the firm is located in a more upstream position. In practice, it is generally higher for firms in the mining, manufacturing, and information and communication sectors, while lower for those in the wholesale, retail, finance, healthcare, and accommodation and food service sectors [ 32 ]. We average the HH potential over the firms in each prefecture to measure the upstreamness of the prefecture in supply chains. The visualization on the map can be found in Fig B.2 of [ 31 ].

Our measure of upstreamness based on the HH potential, is conceptually similar to the upstreamness measures developed and widely used in the literature on international trade [ 37 – 41 ] in that both measure the hierarchical position in supply chains. However, a clear difference between the two types of measures is that ours is based on firm-level data while others are based on sector-level IO tables. Therefore, our measure can incorporate firm-level heterogeneity within the same sector that is ignored in others. In addition, our measure is defined by gradient flows in supply chains that are constructed by eliminating loop flows from all flows. Although many loops at the firm level are found in supply chains, even within the industry [ 32 ], upstream measures based on IO tables do not incorporate such loops. For these reasons, we will rely on our upstreamness measures at the firm level, and not on existing measures at the sector level.

Finally, even when the supply of parts and components from other prefectures is shut down because of their lockdowns, the negative effect can be mitigated if suppliers can be replaced by those in the prefecture lifting its lockdown. Existing studies [ 2 , 5 ] have found that input substitutability can largely mitigate the propagation of negative economic shocks through supply chains. By assumption, suppliers of firms in prefecture a that are in other prefectures currently under lockdown can be replaced by suppliers in prefecture a that are in the same industry and already connected. To measure the degree of supplier substitutability for prefecture a , we divide the number of the latter suppliers by the number of the former.

3.3.4 Lifting lockdowns in two regions simultaneously.

S2 Appendix describes how to calculate Pot ab , Pot ba , and Loop ab using a simple example.

Finally, when suppliers of firms in prefecture a are located outside prefectures a and b and thus are locked down, they can be replaced by suppliers in the same industry in prefecture b that are already connected with firms in prefecture a . To measure the degree of this supplier substitutability, we divide the total number of the latter suppliers by the total number of the former. See S2 Appendix for the details.

4.1 Simulation of the effect of the actual lockdown

In Fig 2 , the blue lines indicate the results of the 30 Monte Carlo runs conducted to estimate the effect of the actual lockdown in Japan given the sector-specific rates of reduction in production capacity assumed in the literature [ 9 , 36 ] and shown in S1 Table . The horizontal axis indicates the number of days since the declaration of the state of emergency (7 April) and the vertical axis represents the total value added production, or GDP, of Japan on each day. See Section 3.2 for the sequence of the state of emergency across the country. Averaged over the 30 runs, the estimated loss in GDP is 35.0 trillion yen (3,280 billion U.S. dollars), or 6.60% of yearly GDP.

The blue and green lines indicate the simulation results given the sector-specific rates of reduction in production capacity assumed in the literature [ 9 , 36 ] and shown in S1 Table and the 26.7% of those rates to calibrate the actual production dynamics, respectively. Each line represents the daily GDP from one Monte Carlo run. The red segments indicate the daily GDP estimated from pre-lockdown GDP and the post-lockdown monthly Indices of All Industry Activity (IAIA) for April and May.

https://doi.org/10.1371/journal.pone.0255031.g002

Without relying on our model and simulation, we also estimate the changes in daily GDP from pre-lockdown GDP and the post-lockdown monthly Indices of All Industry Activity (IAIA) [ 42 ]. The average daily GDP in April and May estimated from the IAIA is indicated by the red lines in Fig 2 (see S3 Appendix for the detailed procedures). The total loss of GDP estimated by the IAIA, or the pink area in Fig 2 , is 7.52 trillion yen (1.44% of yearly GDP), 21.5% of the estimate from our simulations. Our simulation thus overestimates the loss of GDP from the lockdown, possibly because the assumed rates of reduction in production capacity due to the lockdown taken from the literature [ 8 , 9 ] are larger than the actual rates in Japan. Therefore, we experiment with different rates of reduction in production capacity by multiplying the benchmark rates by a weight to calibrate changes in production. We find that a weight of 26.7% results in a close fit between our estimates and those from the IAIA, and indicate the results using green lines in Fig 2 .

In either case (blue or green lines), the production loss rises during the lockdown. For example, the value added declined monotonically from days 9 to 37, when all prefectures were in a state of emergency, assuming a fixed rate of reduction in production capacity throughout the period. This is because the economic contraction in different regions interacted with each other through supply chains, and thus worsened over time. This worsening trend in GDP is consistent with GDP estimated using the IAIA.

Another notable finding from the simulation is that prefectures that were not locked down were heavily affected by those under lockdowns. The visualization on the map can be found in Fig 3 of [ 31 ]. In addition, a video presents a temporal and geographical visualisation of this. See S3 Appendix .

Moreover, because of the network effect, the earlier lifting of the lockdown in some prefectures does not result in a full recovery of production in these prefectures. Notably, when the lockdown was lifted in 39 prefectures on day 37 (14 May), the simulated GDP show a sharp recovery but drops again substantially a few days after the recovery. This drop occurred because the lockdown remained active in eight prefectures including the top two industrial clusters in Japan, greater Tokyo and greater Osaka. Although economic activities returned to normal in these 39 prefectures, their production did not recover monotonically but rather declined again because the major industrial clusters linked with them were still locked down. This finding points to the interactions of the economic effect of lockdown between regions through firm-level supply chains.

4.2 Interactions between lockdowns in different regions

Next, we experiment with simulations assuming different levels of restrictions, or different sets of multipliers for the sector-specific benchmark rates of reduction in production capacity, between the more and less restricted groups (Section 3.3). The more restricted group comprises the seven prefectures with a large number of COVID-19 cases, whereas the less restricted group includes the other 40 prefectures. The left, middle, and right panels of Fig 3 indicate the loss in GDP for different multipliers for the more restricted group when fixing the multiplier for the less restricted group at 0%, 50%, and 100%, respectively. Here, 100% corresponds to the rates of reduction shown in S1 Table and used in the previous subsection and 0% implies no restriction. In each bar, the blue and red portions indicate the loss of value added in the more and less restricted groups, respectively.

A restriction level is defined by a multiplier for the sector-specific benchmark rates of reduction in production capacity. For example, the left bar presents the result assuming a multiplier of 0% (i.e., no restriction) for the less restricted group and 20% for the more restricted group. The red and blue portions of each bar show the loss of value added in the less and more restricted groups, respectively, as a percentage of GDP.

https://doi.org/10.1371/journal.pone.0255031.g003

As shown, the total loss of GDP increases in the levels of restrictions in both groups. For example, the total production loss is 4.18% of GDP when the multiplier is 50% for both groups (the left bar in the middle panel), while it is larger, or 9.39%, when the multiplier is 100% for both (the right panel). More interestingly, the left panel shows that while the group with fewer restrictions imposes no restrictions, its value added decreases more (i.e., the red portion in Fig 3 increases) as the group with more restrictions imposes more restrictions. When the level of restrictions in the group with more restrictions is the highest (i.e., the multiplier is 100%), the loss in value added in the group with fewer restrictions without any lockdown is large: 18.6 trillion yen, or 3.51% of its pre-lockdown value added. These results clearly indicate that even when prefectures are not locked down, their economies can be damaged because of the propagation of the effect of the lockdowns in other prefectures through supply chains.

4.3 Effect of lifting the lockdown in one region

We further examine, how the recovery of a prefecture where lockdown is lifted is determined by its network characteristics, when only one prefecture lifts its lockdown and others remain locked down. We define the recovery rate of each prefecture as the ratio of the total gain of its value added or gross regional production (GRP) from lifting the lockdown to its total loss from the lockdown of all the prefectures for two weeks. The visualization of the recovery rate can be found in Fig 5 of [ 31 ]. See S6 Fig for the bar plot of the recovery rate of each prefecture.

One notable finding is that the prefectures that recover the most, including Hokkaido, Shimane, and Okinawa, which are remote from industrial hubs in terms of both geography and supply chains, suggesting the effect of network characteristics on economic recovery by lifting a lockdown. The name and location of each prefecture can be found in Fig A.2 of [ 31 ].

We further examine the correlation between the recovery rate and network measures explained in Section 3.3 (i.e. those for isolation, loops, upstreamness, and supplier substitution) and test the significance of the correlation using ordinary least squares (OLS) estimations. Fig 4 illustrates the correlation between the recovery rate and network measures. To control for the effect of the prefecture’s economic size on its recovery ( Fig 4(f) ), we include GRP in logs in all the OLS estimations and exclude the effect of GRP from the recovery rate in Fig 4 . The number of links of each prefecture could also be controlled for; however, because its correlation coefficient with GRP is 0.965 ( S3 Table ), we do not use the total links in our regressions to avoid multicollinearity. S4 Table presents the OLS results.

The vertical axis indicates the recovery rate, defined as the ratio of the increase in the GRP of a prefecture by lifting its own lockdown to its decrease because of the lockdown of all prefectures. Except for panel (f), the effect of GRP is excluded from the recovery rate. The horizontal axis indicates the share of the links within the prefecture to its all links in (a), the share of the loop flows within the prefecture to its total flows in (b), the share of the links to other prefectures to all links in (c), the standardised potential flows in (d), the share of substitutable suppliers to all suppliers outside the prefecture in (e), and GRP in logs in panel (f). The orange line in each panel specifies the fitted value from a linear regression that controls for the effect of GRP. The blue, black, and red dots show prefectures whose GRP is among the top 10, bottom 10, and others, respectively.

https://doi.org/10.1371/journal.pone.0255031.g004

In panels (a) and (b) of Fig 4 , the supply-chain links and loops within the prefecture are found to be positively correlated with the recovery rate. These results suggest that when a prefecture is more isolated in the network and has more loops within it, the positive effect of lifting a lockdown circulates in the loops, which can mitigate the propagation of the negative effects of other prefectures’ lockdowns. By contrast, the outward links to other prefectures and the HH potential of the prefecture are negatively and significantly correlated with the recovery rate (panels (c) and (d)). These findings imply that prefectures with more upstream firms in supply chains tend to recover less from lifting their own lockdowns. Panel (e) indicates that the recovery rate is higher when more suppliers in other prefectures under lockdown can be replaced by those in the prefecture lifting its lockdown.

4.4 Effect of lifting the lockdowns in two regions simultaneously

Finally, we simulate the effect on the production of prefecture a if it lifted its lockdown together with prefecture b . We compare the recovery in prefecture a ’s GRP by lifting its lockdown together with prefecture b and that by lifting its lockdown alone, and compute the relative recovery measure, as shown in S7 Fig . Using a regression framework as above, we investigate how the relative recovery measure of prefecture a is affected by the network relationships between prefectures a and b . Fig 5 illustrates the correlation between selected key variables and the relative recovery. In the regression analysis, we always control for the GRP of prefecture b , its squares, and the number of links between prefectures a and b that may affect the relative recovery ( Fig 5(e) and 5(f) ). Following this, we exclude these effects from the relative recovery in panels (a)–(d) in the figure. S6 Table presents the results of the OLS estimations.

The vertical axis indicates the relative recovery of prefecture a , defined as the ratio of the increase in the GRP of prefecture a by lifting its lockdown together with prefecture b to its increase by lifting its lockdown alone. The effect of the GRP of b and total links between the two are excluded from the relative recovery measure. The variable in the horizontal axis is given by Eqs 3 and 4 in panels (a) and (b), respectively, Eq 5 in (c), the share of substitutable suppliers in b for those in a among a ’s locked-down suppliers in (d), the number of links between prefectures a and b in (e) and the GRP of b in logs in (f). The orange line in each panel signifies the fitted value from a linear regression that controls for the effect of the GRP of b and total number of links between a and b in (a)–(d). The blue, black, and red dots show the pairs of prefectures a and b for which the GRP of b is among the top 10, bottom 10, and others, respectively.

https://doi.org/10.1371/journal.pone.0255031.g005

Panels (a) and (b) of Fig 5 show that even after controlling for the effect of economic size and number of links between the two prefectures, the ratio of potential flows from prefecture a to b and from b to a to the total flows of a is positively correlated with the relative recovery. S8 Fig shows a similarly positive correlation for the number of links between the two, rather than potential flows, and the relative recovery. These results suggest that the recovery from lifting a lockdown is greater when two prefectures closely linked through their supply chains, regardless of the direction, lift their lockdowns together. Further, we find that prefecture a recovers more when prefectures a and b are linked through more circular flows (panel (c)), confirming that the positive impacts of lifting a lockdown can circulate and be strengthened in inter-regional supply-chain loops. Panel (d) indicates that if prefecture a ’s suppliers in other prefectures are in lockdown but can be replaced by suppliers in prefecture b easily, prefecture a ’s recovery is higher when the two prefectures lift their lockdowns together. Although the correlation between the relative recovery measure and network variables seems to be largely driven by the observations for which the GRP of prefecture b is large (depicted by the blue dots in Fig 5 ), we find that the positive correlation still exists without these observations ( S9 Fig ).

5 Discussion and conclusion

Our simulation analysis reveals that the economic effects of lockdowns in different regions interact with each other through supply chains. Our results and their implications can be summarised as follows.

First, when a firm is locked down, its suppliers and customer firms are affected because of a lack of demand and supply, respectively. Therefore, a region’s production can improve more if prefectures lift their lockdowns together when they are closely linked through supply chains in either direction ( Fig 5(a) and 5(b) ). In addition to the total number of links between the two regions, the intensity of such links compared with those with others is also important.

Second, when the firms in a region are in more upstream positions in the whole network or are predominantly suppliers of simple parts, the production of the region does not recover substantially by lifting its lockdown alone ( Fig 4(d) ). Although the negative economic effect of a lockdown can propagate downstream and upstream, firms can mitigate downstream propagation easily by using inventory or by replacing suppliers who are under lockdown. The difference between the downstream and upstream effects of lockdown is aggravated as the effect propagates further through supply chains. This finding is in line with the literature [ 36 , 43 ] that also finds the upstream accumulation of negative effects on profits and assets. In practice, our result implies that a region with many small- and medium-sized suppliers of simple materials and parts should be cautious about whether it lifts its lockdown, which may not result in a large economic benefit but could still promote the spread of COVID-19.

Third, the production of a region can recover more by lifting its lockdown when it is more isolated in the network or embodies more supply-chain loops within the region ( Fig 4(a) and 4(b) ). Similarly, the production of the two regions can recover more by lifting their lockdowns together when their inter-regional links have more loops ( Fig 5(c) ). These results imply that the positive economic effect of lifting a lockdown circulates and is intensified in loops, consistent with those in [ 5 ]. Supply-chain loops exist between two regions when the final goods produced are used as inputs by suppliers, while suppliers provide parts and components to final-good producers and the loop stretches across two regions. The importance of loops in the diffusion of the economic effects in networks is not fully recognised, either in academic literature or in policymaking.

Finally, the recovery of a region from its lockdown is greater when suppliers who are still under lockdown can be replaced by those within the region or in other regions without a lockdown in place (Figs 4(e) and 5(f) ). The role of the substitutability of suppliers in mitigating the propagation effect through supply chains has been empirically found in the literature [ 2 , 5 – 7 ]. In practice, this finding suggests two management strategies for regional governments and firms. To minimise the economic loss from lockdown, a region should develop a full set of industries to allow for the possibility of the substitution of any industry. Alternatively, the firms in a region should be linked with geographically diverse suppliers so that suppliers in a region under lockdown can be replaced by those in other regions without a lockdown.

All these results point to the need for policy coordination among regions when regional governments impose or lift a lockdown. Although this study uses the inter-firm supply chains within a country and considers the economic effect of prefecture-level lockdowns, our results can be applied to examine the effect of country-level lockdowns propagating through international supply chains. For example, many suppliers of German firms are located in Eastern Europe and many suppliers of US firms are in Mexico. Our results thus suggest that the economic gains of Eastern Europe and Mexico from lifting their lockdowns are minimal if Germany and the United States, respectively, remain under lockdown. In addition, our framework can be applied to the case of other infectious diseases, and it is likely to suggest a need for the inter-regional and international coordination of lockdown strategies to prevent the spread of infection.

Since our model does not incorporate how lockdown strategies affect the spread of COVID-19, and because it is unclear how human and economic loss should be balanced to maximise social welfare, we cannot explicitly conclude in which cases a lockdown should be imposed or lifted. However, our analysis points to the importance of coordination between lockdown strategies among regions and countries that consider their economic effect in addition to their health effect.

Supporting information

S1 appendix. data..

https://doi.org/10.1371/journal.pone.0255031.s001

S2 Appendix. Methods.

https://doi.org/10.1371/journal.pone.0255031.s002

S3 Appendix. Results.

https://doi.org/10.1371/journal.pone.0255031.s003

S1 Fig. An example of the HHD and loop and potential flow measures of prefectures.

The left panel shows the supply chains of the six firms in the two prefectures. The right top and bottom panels present the potential flows and loop flows, respectively obtained from the HHD.

https://doi.org/10.1371/journal.pone.0255031.s004

S2 Fig. An example of the substitutability measure for two regions.

The bottom shows the equation. A i is the total number of suppliers outside prefectures a and b . The lowest two suppliers are applicable. A supplier in prefecture b belongs to the same industry as the upper firm of the outside suppliers, whereas the lower firm of the outside suppliers is not substitutable. Hence, A i = 2 and B i = 1.

https://doi.org/10.1371/journal.pone.0255031.s005

S3 Fig. Loss in value added as a percentage of total GDP, assuming different restriction levels for a lockdown of 14 days, between the groups with fewer and greater restrictions.

A restriction level is defined by a multiplier for the sector-specific benchmark rates of reduction in production capacity. The red and blue parts of each bar show the loss of value added in the less and more restricted groups, respectively, as a percentage of GDP.

https://doi.org/10.1371/journal.pone.0255031.s006

S4 Fig. Loss in value added as a percentage of total GDP, assuming different restriction levels for a lockdown of 30 days, between the groups with fewer and greater restrictions.

A restriction level is defined by a multiplier for the sector-specific benchmark rates of reduction in production capacity. The red and blue parts of each bar show the loss of value added in the less and more restricted groups, respectively as a percentage of GDP.

https://doi.org/10.1371/journal.pone.0255031.s007

S5 Fig. The ratio of the improvement in GDP by lifting the lockdown in each prefecture.

The improvement is defined as the ratio of the increase in the national GDP by each prefecture lifting its lockdown to the decrease in GDP by all prefectures’ lockdowns. The horizontal axis indicates the JIS codes of the prefectures. The yellow, dark green, and light green bars show the ratio of the improvement when lockdowns persist for 14, 30, and 60 days, respectively.

https://doi.org/10.1371/journal.pone.0255031.s008

S6 Fig. Recovery rate in GRP by lifting the lockdown in each prefecture.

The recovery rate is defined as the ratio of the increase in the GRP of each prefecture by lifting its lockdown to the decrease in its GRP by all prefectures’ lockdowns. The horizontal axis indicates the JIS codes of the prefectures. The yellow, dark green, and light green bars show the recovery rate when lockdowns persist for 14, 30, and 60 days, respectively.

https://doi.org/10.1371/journal.pone.0255031.s009

S7 Fig. Relative recovery from lifting the lockdown together to the recovery from lifting the lockdown alone.

The relative recovery measure is defined as the ratio of the increase in the GRP of prefecture a when it lifts its lockdown together with prefecture b to its increase when prefecture a lifts its lockdown alone. The horizontal axis shows the JIS code of prefecture a . The colour of each dot indicates whether the GRP of prefecture b is among the top 10 (blue), the bottom 10 (black), or others (red).

https://doi.org/10.1371/journal.pone.0255031.s010

S8 Fig. Correlation between the relative recovery and selected network measures.

The vertical axis indicates the relative recovery of prefecture a , defined as the ratio of the increase in the GRP of prefecture a by lifting its lockdown together with prefecture b to its increase by lifting its lockdown alone. The effect of the GRP of b and total links between the two are excluded from the relative recovery measure. The variable in the horizontal axis is given by Eqs 1 and 2 in panels (a) and (b), respectively. The orange line in each panel signifies the fitted value from a linear regression that controls for the effect of the GRP of b and total number of links between a and b . The blue, black, and red dots indicate the pairs of prefectures a and b for which the GRP of b is among the top 10, bottom 10, and others, respectively.

https://doi.org/10.1371/journal.pone.0255031.s011

S9 Fig. Correlation between the relative recovery and selected network measures.

See the caption of Fig 5 and S8 Fig . for the definitions of the variables used here. The green line in each panel signifies the fitted value from a linear regression that controls for the effect of the GRP of b and total number of links between a and b in (a)–(g). The black and red dots indicate the pairs of prefectures a and b for which the GRP of b is among the bottom 10 and between 11 and 37, respectively.

https://doi.org/10.1371/journal.pone.0255031.s012

S10 Fig. Correlation between the recovery rate and selected network measures.

See the caption of Fig 4 for the definitions of the variables used here. The orange line in each panel specifies the fitted value from a linear regression that controls for the effect of GRP in (b)–(f). The blue, black, and red dots indicate the prefectures whose GRP is among the top 10, the bottom 10, or others, respectively.

https://doi.org/10.1371/journal.pone.0255031.s013

S11 Fig. Correlation between the relative recovery and selected network measures.

See the caption of Fig 5 for the definitions of the variables used here. The red line in each panel signifies the fitted value from a linear regression that controls for the effect of the GRP of b and total number of links between a and b in (a)–(g). The blue, black, and red dots indicate the pairs of prefectures a and b for which the GRP of b is among the top 10, the bottom 10, or others, respectively.

https://doi.org/10.1371/journal.pone.0255031.s014

S1 Table. Sector-specific rates of reduction in production capacity.

Sectors are classified by the JSIC [ 23 ] at the two-digit level, except for industries 560, 561, and 569 for which we use three-digit codes to reflect the actual circumstances. The sector names are abbreviated. S1 Table lists the sector descriptions and abbreviations.

https://doi.org/10.1371/journal.pone.0255031.s015

S2 Table. Sector classifications and abbreviations.

https://doi.org/10.1371/journal.pone.0255031.s016

S3 Table. Correlation matrix of the variables used in Section 4.3.

The definitions of the variables are as follows. RecRatio: the recovery rate defined as the ratio of the increase in the GRP of each prefecture by lifting its lockdown to the decrease in its GRP by all prefectures’ lockdowns. GRP: gross regional product (log). Links: the degree (log). InLink: the share of links within the prefecture to all its links. InLoop: the share of loop flows within the prefecture to all its flows. OutLink: the share of outward inter-prefectural links to all the links of the prefecture. Potential: the average HH potential of the firms in the prefecture. Sub: the share of substitutable suppliers to all suppliers of the prefecture located outside the prefecture.

https://doi.org/10.1371/journal.pone.0255031.s017

S4 Table. Regression results for Section 4.3.

The dependent variable is the recovery rate. See the caption of Table S3 Table for the definitions of the independent variables. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

https://doi.org/10.1371/journal.pone.0255031.s018

S5 Table. Correlation matrix of the variables used in Section 4.4.

The definitions of the variables are as follows. Recov a : the relative recovery of prefecture a defined as the ratio of the increase in the GRP of prefecture a by lifting its lockdown together with prefecture b to its increase by lifting its lockdown alone. Link ab : the share of links from a to b to all links from a . Link ba : the share of links from b to a to all links from a . Pot ab : the share of potential flows from b to a to the total links of a . Pot ba : the share of potential flows from a to b to the total links of a . Sub ab : the share of suppliers substitutable by those in b to a ’s suppliers outside a and b . Sub ba : the share of suppliers substitutable by those in a to b ’s suppliers outside a and b . Loop ab : the share of loop flows between a and b to the total flows between the two. Bi ab : the number of inter-prefecture links between a and b in logs. GRP j : GRP of b in logs.

https://doi.org/10.1371/journal.pone.0255031.s019

S6 Table. Regression results for Section 4.4.

The dependent variable is the relative recovery measure. See the caption of Table S5 Table for the definitions of the independent variables. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

https://doi.org/10.1371/journal.pone.0255031.s020

Acknowledgments

This study used the computational resources of the supercomputer Fugaku (the evaluation environment in the trial phase) provided by the RIKEN Center for Computational Science. OACIS [ 44 ] and CARAVAN [ 45 ] were used for the simulations in this study. This study was conducted as part of a project entitled ‘Research on relationships between economic and social networks and globalization’ undertaken at the Research Institute of Economy, Trade, and Industry (RIETI). We thank Yoshi Fujiwara for advise on the Helmholtz–Hodge decomposition (HHD) computation.

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Positive global environmental impacts of the COVID-19 pandemic lockdown: a review

Hong chuan loh.

1 Clinical Research Centre, Hospital Seberang Jaya, Ministry of Health Malaysia, 13700 Seberang Jaya, Penang Malaysia

2 Medical Department, Hospital Seberang Jaya, Ministry of Health Malaysia, 13700 Seberang Jaya, Penang Malaysia

Alan Swee Hock Ch’ng

Khang wen goh.

3 School of Computing, Faculty of Computing and Engineering, Quest International University, 30250 Ipoh, Perak Malaysia

Long Chiau Ming

4 PAP Rashidah Sa’adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam

Kean Hua Ang

5 School of Biological Sciences, Faculty of Integrated Life Sciences, Quest International University, 30250 Ipoh, Perak Malaysia

Global environmental change is mainly due to human behaviours and is a major threat to sustainability. Despite all the health and economic consequences, the impact of the COVID-19 pandemic lockdown on environmental health warrants the scientific community’s attention. Thus, this article examined and narratively reviewed the impact of several drastic measures taken on the macro environment and holistic planetary health. We note that the amount of pollution in the air, water, soil, and noise showed a significant decline during the pandemic. Global air quality improved due to lower anthropogenic emissions of air pollutants and atmospheric particles. Water ecosystems also demonstrated signs of recuperation in many countries. Less commercial fishing internationally resulted in the restoration of some aquatic life. Additionally, significant reduction of solid and water waste led to less soil pollution. Some places experienced cleaner beaches and ocean water while wildlife sightings in urban areas across the world occurred more often. Lastly, the COVID-19 pandemic lockdown also led to a worldwide decline in noise pollution. However, the beneficial environmental effects will not be permanent as the world gradually returns to its pre-pandemic status quo. Therefore, behavioural changes such as adopting a lifestyle that reduces carbon footprint are needed to make a positive impact on the environment. In addition, world leaders should consider the national policy changes necessary to ensure continuity of as many of the positive environmental impacts from the COVID-19 pandemic lockdown as possible. Those changes would also serve to lessen the likelihood of another zoonotic calamity.

Introduction

Coronavirus disease-2019 (COVID-19) is now considered one of the world’s greatest challenges and biggest catastrophes since World War II (Gautam, 2020b ). As of today, 12 July 2021, there are nearly 200 million people infected with the virus and over 4 million deaths worldwide. Unfortunately, these numbers are still on the rise.

While the COVID-19 pandemic continues to ravage both the health and economy of nations around the world, we are also contending with one of the greatest crises in human history, i.e., global environmental change. The change is mainly due to human behaviours and is a major threat to over half of the world’s population. Unfortunately, the world’s poorest communities, those with the least responsibility for producing environmental decay, are the most affected (Fust, 2010 ). If the human population continues to contribute to climate change, rising global temperatures and more natural disasters will plague the world. We would likely encounter more flooding and more droughts with rising sea levels and higher temperatures (Huckelba & Van Lange, 2020 ). Climate scientists have estimated that the Arctic Ocean will be free of ice by the summer of 2050 (Screen & Deser, 2019 ). As the ice melts, ancient viruses and bacteria from the permafrost soils that are potentially harmful to human beings may be released (Tsangaras & Greenwood, 2018 ). This could cause more outbreaks of infectious diseases in addition to the diseases of zoonotic origin that we are already experiencing.

To date, there have been several studies regarding the environmental impact of the COVID-19 pandemic. Helm et al. presented a comprehensive research agenda from the environmental and economics perspective with five sets of research questions including (1) the short-term effects, (2) the longer-term economic effects and some of their potential environmental effects, (3) deglobalisation, trade and environmental effects, (4) environmental aspects of intergenerational equity, the balance sheet approach, and natural capital, and (5) the permanent effects on behavioural shifts (Helm, 2020 ). Another article provided four underlying research clusters based on a systematic content analysis of the studies, namely (1) COVID-19 and environmental degradation, (2) COVID-19 and air pollution, (3) COVID-19 and climate or meteorological influences, and (4) COVID-19 and temperature (Shakil et al., 2020 ). Lopez-Feldman et al. provided an insight on air pollution exposure from the COVID-19 pandemic (López-Feldman et al., 2021 ). There was also a review that focused on the negative impact of the pandemic on socio-economic factors and the positive environmental impact on air and water quality (Saadat et al., 2020 ). Straka et al. discussed improved air quality during the pandemic in the United States (Straka et al., 2021 ). Another study which examined the effect of meteorological factors on COVID-19 health outcomes reported that high temperature and high humidity minimise the transmission of COVID-19 but when the temperature, wind speed, dew or frost point, precipitation, and surface pressure are low, the transmission is enhanced (Sarkodie & Owusu, 2020 ). In the literature on the subject that we reviewed, there was no comprehensive narrative summarizing the positive environmental impacts of the COVID-19 pandemic lockdown globally.

Naturally, there has been a lot of research on COVID-19 and most of it has been focused rightly on the negative impact of the pandemic. Disease, death, and economic devastation certainly warrant such attention. However, even though the outbreak has adversely altered the everyday lives of millions of people around the world since its onset, there have also been some benefits. With a non-normative approach, this paper considers various unexpected positive aspects of the COVID-19 pandemic lockdown on planetary health. Even if we will not continue “lockdowns” after the COVID-19 pandemic abates, the positive environmental impacts we witnessed during the pandemic show that it is still possible to make great and rapid positive change. This fact should persuade citizens and governments around the world who doubt that we can or should act in environmental and climate change matters that it is necessary, urgent and, above all, possible to effect change. For the sake of all life on earth, authorities everywhere must press for new policies that will address global environmental change including climate change, environmental degradation, and the looming threat of more zoonotic pandemics. The objective of this paper is to identify many of the positive environmental changes that occurred during the COVID-19 pandemic and point to those as examples for policymakers and citizens around the world to show that it is still possible to make great and rapid positive change.

Materials and methods

In this paper, we discussed the positive impacts of the COVID-19 pandemic lockdown on four environmental domains: air, water, soil, and noise. The positive effects were defined as reduction of air pollutants; improved water quality and increases in aquatic life; reduction of solid waste, wastewater and sewage sludge; and decreases in noise pollution. We searched through two major citation databases, namely PubMed, and Google Scholar using the terms ‘COVID-19 pandemic’, ‘environment’, ‘air’, ‘water’, ‘soil’, ‘noise’, ‘aquatic life’, ‘land use’, ‘wildlife’, ‘zoonotic disease’ for articles dated between 1st January 2020 and 1st February 2021. We also used reverse-forward citation (hand searches) from the included studies to search for relevant studies. We limited our search to English language articles only.

Environmental impacts of the COVID-19 pandemic lockdown

The outbreak of COVID-19 was declared a pandemic by the World Health Organization (WHO) on 11th March 2020 (Mitra et al., 2020 ). Quarantine, isolation, and lockdown measures were implemented in much of the world as per public health epidemiology practices in order to mitigate human-to-human transmission of COVID-19 (Shah et al., 2020 ). The measures taken by countries to tackle this crisis have led to an enormous economic meltdown and disruptions in social behavioural patterns (Hart & Halden, 2020 ). However, this once-in-a-century phenomenon paradoxically resulted in some positive impacts on planetary health. By taking into account the impact of industry shutdowns, lockdowns, and travel bans, there were multiple environmental benefits from the COVID-19 outbreak, primarily due to less anthropogenic pollution.

Air quality and atmospheric particles

According to the WHO, 4.6 million people die annually from diseases and illnesses directly linked to unhealthy air quality (Cohen et al., 2017 ). Air pollution has been known to be associated with a major prevalence of respiratory diseases such as pneumonia, chronic obstructive pulmonary disease, asthma, and others (Van Donkelaar et al., 2010 ). Air pollutants such as nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ), and particulate matter 2.5 (PM 2.5 ) are present in our atmosphere in alarming amounts and data show more people are dying each year due to bad air quality. Before the pandemic, in the period between 2016 and 2018, nearly 10,000 additional premature deaths in the US were associated with the increase in PM 2.5 . For the year 2016, these deaths, apart from the tragedy they represent, also resulted in $89 billion in economic damages (Clay & Muller, 2019 ). The 2019 World Air Quality Report showed that South Korea was among 37 member countries of the Organisation for Economic Co-operation and Development with the highest PM 2.5 level in the air. In China, 98% of the cities surpassed the targets established by WHO guidelines. Of the top 30 most polluted cities, 21 are located in India while five are located in Pakistan (IQAir, 2019 ).

During the COVID-19 pandemic, many places across the globe implemented lockdown measures. The mobility index report based on Google tracking data from February to April 2020 across the region that includes Spain, Italy and France showed a reduction of up to 90% in mobility (Muhammad et al., 2020 ). There were travel restrictions for tourism, entertainment, and personal purposes. The restrictions caused many flights to be grounded resulting in a significant lessening of emissions since planes are known for their intensive use of fossil fuels. Industrial production decreased and there was a reduction in the use of vehicles, both of which lowered demand for oil and reduced extraction and refining activity. The world also saw a decline in coal consumption by power plants. While several studies found an increase in ozone levels during the pandemic (Grange et al., 2020 ; Huang et al., 2021 ; Lovrić et al., 2021 ), overall, the unprecedented circumstances of the pandemic lockdown brought about a major reduction in air pollution, resulting in positive effects concerning air quality worldwide (Lenzen et al., 2020 ).

Globally, atmospheric emissions were reduced by 0.6 Mt of PM 2.5 , and 5.1 Mt of SO 2 and NO 2 . It is noteworthy that in the 32-year history of intergovernmental climate policy, none of the attempts by any government or any international agreement has had such a drastic mitigation impact on air pollution (Lenzen et al., 2020 ). Data collected on the level of concentration of aerosol optical depth (AOD) by Moderate Resolution Imaging Spectroradiometer onboard National Aeronautics and Space Administration’s (NASA) satellites indicated a massive reduction by 50% of AOD in the India region’s air quality. This is due to the substantial change in aerosol levels, the lowest in 20 years, because of COVID-19 lockdowns in India (Gautam, 2020b ). The pandemic also exerted a major downward effect on China’s energy consumption and air pollutant emissions (Wang & Su, 2020 ). During the quarantine in China, there was a significant reduction in NO 2 concentrations based on data collected by the TROPOspheric Monitoring Instrument onboard the Copernicus Sentinel-5 Precursor satellite. The ozone monitoring instrument aboard the NASA Aura spacecraft also reported similar atmospheric changes. There was as much as 30% NO 2 emission reduction just in Central China (Dutheil et al., 2020 ). A similar trend was also observed with emissions of carbon dioxide (CO 2 ) associated with the use of fossil fuels like coal and crude oils. The CO 2 emissions decreased by 18.7% (182 Mt CO 2 ) compared to the first quarter in 2019, including decreases of 12.2% (92 Mt CO 2 ) in the industrial sector, 61.9% (62 Mt CO 2 ) in transport, and 23.9% (28 Mt CO 2 ) in construction (Wang et al., 2020 ). Some data showed that there was a reduction of nearly 1 million tons of carbon emissions during the lockdown, which is equivalent to 6% of global emissions. The PM 2.5 index in China across 367 cities also showed a reduction of 18.9 μg/m 3 (Zambrano-Monserrate et al., 2020 ). In Turkey, the PM 2.5 index was reduced by 34.5% by the end of April 2020 (Aydın et al., 2020 ). According to the European Space Agency (ESA) and NASA, European countries such as Spain, Italy, and, France as well as the US also showed a 20–30% reduction of NO 2 during the pandemic (Gautam, 2020a ).

The enduring global COVID-19 pandemic lockdown is revealing the direct link between the level of air pollution and the amount of economic activity, including manufacturing, transportation, energy generation, etc. This suggests that in order to fight air pollution, a renewable and green energy-based system will be required and should be widely adopted whenever possible in all areas, particularly at industrial sites. For instance, encouragement from governments, including tax reduction, subsidies, or monetary rewards to encourage the use of solar panels and windmills for both the residential and industrial sectors. This pandemic also provides an excellent chance for us to study and invent ways to monitor urban traffic and transportation in order to cut fuel consumption and maintain a healthy environment. Reduced road travel and fewer air flights throughout the world resulted in a significant reduction in fuel use. One way to maintain reduced road travel is to encourage public transit. Travelling via public transit consumes less energy and causes less pollution than travelling by private vehicles. In addition, providing financial incentives to promote the use of electric vehicles or bike riding are also pivotal steps towards a greener environment.

Water quality and aquatic life

Air pollutants in the atmosphere could cause nutrient pollution in waterways as well. For instance, NO 2 can react with other chemicals to form acid rain, which specifically causes harm to freshwater ecosystems including streams, rivers, lakes, and watersheds. Water acidification, along with the concomitant reduction in acid neutralization capacity, has led to deleterious changes in water quality (Akimoto, 2003 ; Clair & Hindar, 2005 ). With regards to the air pollution reduction mentioned earlier, there would be an expected reduction in acid rain formation thereby producing higher availability of freshwater globally.

Water ecosystems showed signs of recuperation in many parts of the world during the pandemic. There was an improvement in the water quality of the Ganga River, the ‘National River’ of India, during the lockdown especially around industrial clusters and urban areas. The effect could be seen in terms of increased dissolved oxygen and decreased biological oxygen demand, faecal coliform, total coliform, and nitrate concentration. With improved water quality, the river’s self-cleaning properties were enhanced. Typically, there are vast quantities of effluent from domestic and industrial wastewater that reach the river untreated or partially treated and this results in severe deterioration of water quality. Over the last two decades, several government programs cost the government millions of dollars without much success. During the lockdown period, there was a reduction of approximately 1300–1340 million litres per day of industrial wastewater. Other human activities like social and religious functions, waterways transport, fishing, and so forth were also prohibited, leading to less dumping of solid waste and less littering along its banks by residents and tourists. Recent research by the Indian Institute of Technology, Roorkee, showed that the water of the Ganga River became fit for drinking purposes during the pandemic. As defined by the Central Pollution Control Board, the river was listed as category “A” and its waters were classified as fit for drinking with conventional treatment (Dutta et al., 2020 ; Upadhyay, 2020 ). Improvement was also observed in the Yamuna River, near Delhi. Measurements of the dissolved oxygen in the river water were as high as about 5 mg/L during the pandemic. After two months of COVID-19 lockdown, the water in Venice, Italy, also appeared cleaner and aquatic life became more noticeable, something not seen in the city for many years. As the number of tourists dropped, activity on the waterways, sediment churning, and other causes of water pollution dropped substantially causing the waters in Venice’s canals to be much cleaner compared to the time before COVID-19 (Saadat et al., 2020 ).

Another unexpected environmental impact was observed in aquatic life. Due to the migratory nature of fishermen and the frequency of international visitors, it was feared that fishing communities and ports could potentially become hotspots for COVID-19 infection (FAO, 2020 ). Therefore, many fisheries across the world experienced partial or complete shutdown resulting in a reduction of fishing activities during the pandemic. In addition to port shutdowns, there was a sharp decline in demand for seafood, loss of access to cold storage, and termination of shipping and air cargo services (Orlowski, 2020 ). The reduction in fishing and its related industries allowed marine ecosystems some time to recover. According to Dr. Rainer Froese of the GEOMAR Helmholtz Centre for Ocean Research in Germany, a decline in fishing caused by the pandemic would lead to a rise in fish biomass. European fish stocks such as whitefish, flatfish, and herring would nearly double their biomass if there were no fishing for one year. China also reported that tuna, which originally followed the Kuroshio Current to Japanese fishing grounds via the China Sea, appears to have stopped to feed in the China Sea (Korten, 2020 ). Furthermore, Carlos Quarte, research chair at the Red Sea Research Center in Saudi Arabia, stated that the pandemic accelerated the regeneration of fish populations allowing the Center to meet conservation targets faster. The COVID-19 pandemic lockdown was a de facto moratorium on heavily fished stocks which created an effect similar to the virtual cessation of commercial fishing during World Wars I and II.

There were also reports showing increases in sea mammals such as killer whales, dolphins, and seals in regions where they have not been seen in decades (Lombrana, 2020 ). In addition, endangered turtles are experiencing a resurgence. In Thailand, the nests for leatherback sea turtles are at their highest numbers in two decades. A similar trend was seen in Florida, US, with a significant increase of 76 leatherback sea turtle nests compared to 2019 (Geggel, 2020 ). The practices of pharmaceutical firms along with other overharvesting activities led to a decline in the number of horseshoe crabs over the decades. During the pandemic, there was a sign of recovery with hundreds of thousands of horseshoe crabs spawning on the shores of the Delaware Bay in the Eastern US. This in return assisted a rebound in red knot birds which rely on the eggs of the crabs for food (Nishan, 2020 ). A global mass coral bleaching has been occurring since 2014 due to global warming. During the pandemic, there were signs of rebound for the coral reef ecosystems. For instance, in Hawaii, there was an increase in the coral reef especially among baby corals when compared to 2019 (Tianna, 2020 ). Both the reduction in fisheries activities and the restoration of aquatic life during the pandemic may have caused a decline in the extraction of blue carbon that further reduced atmospheric CO 2 emissions (Mariani et al., 2020 ).

Although there have been some good environmental results from the COVID-19 pandemic lockdown, a few weeks or months will not be sufficient to undo or repair the environmental damage that has been done over many years. Governments need to build proper sewage treatment plants and ensure that businesses and industries dispose of their waste in accordance with strict rules and regulations, imposing heavy fines on violators. In the context of fisheries, better enforcement of national rules over fishing would likely improve fisheries management. International and regional agreements for the monitoring, control, and surveillance of local and international waters may help prevent overfishing. Researchers should also focus on action-oriented studies to identify the consequences of long-term food security as well as environmental and resource management implications of current fishing activities and seek alternatives for seafood-based food and other products.

Soil quality, land use, and wildlife sightings

The soil is an essential component of the ecosystem that regulates not only the surrounding environment but also affects water, climate, and food production (Mishra et al., 2020 ). Soil contamination from anthropogenic activities, specifically urbanisation, will reduce crop yield and its quality. It will also change soil organic matter and its biodiversity as well as the quality of groundwater (Singh & Singh, 2020 ). During the pandemic, there were reports of a significant reduction of solid waste. A survey from Tunisia showed that 85% of respondents indicated positive changes in food waste prevention by setting up a strategy of saving, storing, and eating leftovers (Jribi et al., 2020 ). In Morocco, two cities, Khenifra and Tighassaline, showed a reduction in municipal solid waste in March 2020 compared to March 2019 (2572 tons versus 2456 tons; 136 tons versus 126 tons, respectively) (Ouhsine et al., 2020 ). In Shanghai, China, there was a reduction of approximately 23% in household waste (Fan et al., 2020 ). Solid waste from industrial and construction activities was also reduced during the crisis (Qarani & Development, 2020 ).

Wastewater and sewage sludge contribute to soil pollution as well. Several reports indicated a decrease in municipal, industrial, commercial, and grey water waste during the pandemic. Increased green areas and lesser soil erosion were observed as well (Qarani & Development, 2020 ). One study indicated that every 1% increase in urban vegetation would result in a 2.6% decrease in cumulative COVID‐19 cases (You & Pan, 2020 ). Moreover, a notable change in the appearance of many beaches across the world could be seen during the pandemic lockdown. Waste generated by tourists was significantly reduced resulting in beaches such as Acapulco in Mexico, Barcelona in Spain, and Salinas in Ecuador now looking cleaner with clearer waters (Zambrano-Monserrate et al., 2020 ). A survey from observations on Salinas, Manta, and Galápagos beaches indicated that the lockdown also led to a temporary improvement of environmental conditions similar to marine protected areas. Approximately 45% of the Respondents agreed that the beaches are cleaner with lesser plastic (Ormaza-González & Castro-Rodas, 2020 ). As stated earlier, better air and water quality result in less acid contamination of the ground and thereby causing a drop in soil acidification. For all these reasons, soil contamination declined significantly and this led to improved soil quality.

There was also a surge in wildlife sightings during the COVID-19 pandemic lockdown, particularly in urban environments. These sightings included a wild puma in downtown Santiago, Chile, during a night-time curfew, and a herd of plundering goats on the streets of Llandudno, Wales (Child, 2020 ). These incidences are mainly due to a significant reduction in human mobility during the crisis. The COVID-19 “anthropause” reduced the destructive effects of our increasingly expansive lifestyles on the movement of animals (Rutz et al., 2020 ). This phenomenon may provide some insights for further research to better understand how we might improve coexistence between humans and wildlife, particularly in designing a more sustainable and wildlife-friendly environment.

Consumer involvement is vital in reducing domestic waste and creating a healthier environment. Using eco-friendly products and implementing the concept of “reduce, reuse, and recycle” would have a big impact. Governments and businesses need to reduce deforestation and plant more trees to increase green areas and reduce soil erosion. On top of that, an increase in urban vegetation will help restore some balance in habitats that were destroyed during urbanization. Seeing a resurgence of some species and a return to certain habitats of others during the lockdown is a persuasive argument for better township planning and more protected areas and wildlife conservation zones. Finally, more research to gather data that can be used to develop better environmental regulations for wildlife is needed.

Reduction of noise pollution and impact on co-inhabitants

Automobile traffic, ships on the sea, aircraft, industry, and other human activities are all sources of seismic noise. In order to flatten the contagion curve, the COVID-19 pandemic lockdown created a widespread reduction in human activity, leading to a months-long reduction of up to 50% in seismic noise. This is the longest and most prominent recorded reduction in global anthropogenic seismic noise throughout history (Lecocq et al., 2020 ).

One study found significant noise reduction in China in late January 2020 and then Italy, the whole of Europe, and the rest of the world between March and April 2020. The lockdown measures in mainland China contributed to a marked decrease of roughly 4–12 dB in cultural noise (seismic noise with frequencies above 1 Hz). There was, on the other hand, a smaller decrease in seismic noise of around 1–6 dB in Italy after the country was locked down since traffic in Italy did not decrease as much as in China (Xiao et al., 2020 ). A 33% reduction in seismic noise was found after a lockdown in Brussels, Belgium (Lecocq et al., 2020 ). In the City of Madrid, there was a reduction of 4 to 6 dBA captured by the monitoring network during the lockdown (Asensio et al., 2020 ). In the island nation of Barbados in the Caribbean, due to a reduction in tourism, the seismic noise from March to April 2020 decreased by approximately 45% compared to observations made for the same period in 2019. A permanent seismic station in Sri Lanka indicated a reduction of nearly 50%, the highest recorded since July 2013 (Lecocq et al., 2020 ). A study also showed that the pandemic lockdown led to a 5–10 dB reduction of the anthropogenic noise level in Shillong, India (Somala, 2020 ).

More than 200 million people around the world are affected by noise pollution (Mirzaei et al., 2012 ). Nearly 40% of the population in the countries of the European Union were subjected to road traffic noise with an equivalent sound pressure level of more than 55 dBA. Most of those people live in areas that do not provide residents with acoustic comfort. In cities in developing countries, the noise pollution problem is also severe and is primarily caused by traffic-induced noise (Berglund et al., 1999 ). The Environmental Expert Council of Germany stated that the persistence of extreme irritation from noise over extended periods of time may lead to distress (Ising & Kruppa, 2004 ). Many epidemiological studies in the past have identified the correlation between exposure to noise pollution and numerous medical conditions such as myocardial infarction, cardiovascular disease, hypertension, sleep disorders, psychiatric disorders, weakened immune systems, and birth defects (Geravandi et al., 2015 ). The COVID-19 pandemic lockdown had altered the soundscape and led to a significant reduction in noise pollution. This could result in fewer noise-induced health impairments.

Noise pollution is also a threat to animals with a considerable impact on communication, use of space, and their reproduction rate (Sordello et al., 2020 ). A research was conducted in the San Francisco Bay Area, before and after the recent state-wide confinement, to determine whether a common songbird (white-crowned sparrow) would responsively utilize the newly emptied acoustic space. The birds reacted by generating higher performance songs at lower amplitudes, effectively optimizing the distance and salience of communication (Derryberry et al., 2020 ). Omnipresent anthropogenic noise is also one of the most hazardous forms of pollution in aquatic ecosystems. This includes sounds produced by human activity such as commercial shipping, oil exploration, construction activities, military and mapping sonars (Hildebrand, 2009 ). There was a significant reduction in ocean ambient noise during the pandemic. A study was conducted during the first quarter of 2020 in both the deep ocean and inland waters of Canada’s Pacific coast using the near real-time ocean sensing networks North-East Pacific Time-series Undersea Networked Experiments (NEPTUNE) and Victoria Experimental Network Under the Sea (VENUS). The NEPTUNE observatory reported an average reduction of 1.5 dB in the mean weekly noise power spectral density at 100 Hz year-over-year. The overall change in power at 100 Hz showed a drop of 2.7–7.1 dB on both the Central and East nodes of the VENUS observatory, respectively, using the time series of the difference in median weekly power (Thomson & Barclay, 2020 ). Many sea creatures like Baleens whales use sounds in the water to navigate, hunt for food, and communicate (Tyack & Clark, 2000 ). A study showed that there was an association between noise reduction and decreased baseline levels of stress-related faecal hormone metabolites (glucocorticoids) in North Atlantic right whales ( Eubalaena glacialis ). This might result in a recovery of the endangered right whale population due to less noise pollution (Rolland et al., 2012 ). Zooplankton and tens of thousands of fish species, which are sensitive to noises, may also have had higher chances to go through their spawning cycle and flourish due to the decline of underwater ocean noise during the COVID-19 pandemic.

Companies and vehicle manufacturers should increase their efforts to design automobiles that emit less noise. Noise barriers can also be installed since they are effective at blocking the direct flow of sound waves from the highway or other busy regions to houses, businesses, and natural habitats. Much can be done to lessen the noise produced from shipping and transport. Reducing nighttime traffic, proper terminal allocation, reducing the airflow of fans, lowering the energy consumption of ships, and utilizing an onshore power supply, particularly at night, appear to be viable options (Čurović et al., 2021 ). Additional metrics defining human activities that generate noise need to be developed so that people have a better understanding of their area’s noise environment. Similar to weather stations established to gather data for weather forecasts, additional noise indicators may need to be set up at various places to measure the level of noise pollution in order to provide information to the public about noise pollution, an invisible danger that is often neglected.

Planetary health and zoonotic disease

Of note, there were enormous environmental benefits as a result of the COVID-19 pandemic lockdown that could have paradoxically reduced the number of morbidities and mortalities due to anthropogenic pollution. Unfortunately, the resumption of large industrial activities will likely reverse all these environmental improvements.

The Economist Intelligence Unit measured the readiness to confront climate change of the world’s 82 largest economies. Due to the impact of higher temperatures and more severe weather events, the climate change Resilience Index showed that there would be a 3% drop in the global gross domestic product by 2050 with developing nations having poorer resilience than richer ones. By mid-century, climate change could result in direct costs to the world economy of $7.9 trillion as intensified droughts, floods, and crop failures impede development and endanger infrastructure (Economist Intelligence Unit, 2019 ). According to the WHO, the social and environmental determinants of health, like clean air and water, adequate food, and secure shelter are affected by climate change. An increase of about 250,000 additional deaths are expected each year from malnutrition, malaria, diarrhoea, and heat stress between 2030 and 2050 (World Health Organization, 2018 ). Therefore, no party should ignore climate change or downplay the consequences of it.

Anthrax, another zoonotic disease, severely affected reindeer herds in Siberia and led to an outbreak in 2016. It was reported that the pathogen was released from infected carcasses in permafrost soil as the ice thawed (Stella et al., 2020 ). Anthrax may not be the only infectious disease lurking in the ice. There is speculation that there are tens of thousands of carcasses with infectious disease preserved in the frozen soil. For instance, victims of the 1918 Spanish flu were found in the permafrost with ground-penetrating radar (Davis et al., 2000 ). As global warming continues to increase the temperature of the planet, we may experience the re-emergence of pathogens that were already eradicated in the past or that are new to us and which could serve as serious health threats to both animals and human beings. Regrettably, the positive changes from the COVID-19 pandemic lockdown will not be permanent and will not mitigate climate change and environmental issues. However, it surely serves as a practical lesson to the world that environmental degradation and climate change are still reversible and that it is possible to take drastic measures to achieve the Sustainable Development Goals of the United Nations for the long-term environmental health of the planet. This includes measures like extensive utilisation of clean energy and changing our sources and means of producing food. Food production accounts for approximately 26% of global greenhouse gas emissions and the largest contribution to that is from livestock and fisheries (Poore & Nemecek, 2018 ). The WHO Director-General, Dr. Tedros Adhanom Ghebreyesus, stated that the COVID-19 outbreak will not be the last pandemic and without addressing climate change and animal welfare, efforts to improve human health are hopeless (Millard, 2020 ). While there is still time for us to repair much of the environmental damage that has already been done, world leaders should revisit and revise their national policies to emphasize the urgency of environmental matters. With that and other measures, such as addressing animal welfare, hopefully, another COVID-19-like pandemic can be averted.

Limitation of the study and recommendation

We acknowledge that since the study only focused on the positive global environmental impacts of the COVID-19 pandemic lockdown, this may result in some inherent bias. Therefore, regardless of the positive impacts on the environment discussed above, we recognise that there were also negative consequences on planetary health during the COVID-19 lockdown. There is mounting evidence that indoor air pollution is a severe threat to human health (Mannucci & Franchini, 2017 ). Indoor activities from household or workstation tasks all contribute to indoor pollution (Amoatey et al., 2020 ). These emissions must remain within acceptable levels and should be monitored on a regular basis in order to implement effective pollution mitigation strategies (Agarwal et al., 2021 ). There were also studies that showed a positive association of air pollutants with increasing cases of COVID-19 (Coker et al., 2020 ; Wu et al., 2020 ). This is particularly important because, during the pandemic lockdown, people were forced to stay indoors. Therefore, measures like ensuring proper ventilation are critical to offering a healthy indoor environment for the safety of inhabitants and at the same time, to reduce the spread of the disease (Agarwal et al., 2021 ). While many places experienced improved water quality in natural resources, wastewater treatment plants reported a higher level of organic load and chemical contaminants due to the increased use of sanitisers, disinfectants, and antibiotics (Elsaid et al., 2021 ). Wastewater had to be properly treated before being discharged into biological treatment facilities and subsequently into water bodies (Leonhauser et al., 2014 ). In addition, wastewater can also capture viruses like COVID-19 that shed during personal hygiene and are present in the excretion or discharge from human beings, such as from the mouth cavity, upper respiratory tract, faeces, and urine (Cheung et al., 2020 ). Wastewater analysis was shown to be sensitive to viral testing and cost-effective and thus, could be employed as a surveillance tool for epidemiological studies. When compared to individual COVID-19 testing, wastewater analysis is clearly less invasive, simpler, and less expensive. It is an important component in effectively combatting COVID-19 and in improving our preparation in the case of viral re-emergence (Randazzo et al., 2020 ). On the other hand, there was an unavoidable increase in the use of personal protective equipment, such as gloves, masks, etc., that directly increased the amount of medical waste. For instance, in Wuhan city, China, the epicentre of COVID-19, there was an extra 200 tons of medical waste in a single day on 24th February 2020 (Bashir et al., 2020 ). Hence, effective and stringent waste management measures must be implemented before another major source of environmental pollution arises.

In return, the beneficial environmental effects also resulted in some feedback loop effects on the COVID-19. For instance, it was hypothesized that the COVID-19 virus can bind to PM and increase the chance of survival of the virus in the atmosphere under conditions of atmospheric stability (McNeill, 2020 ). If proven, the spread of the coronavirus could be curbed with improved air quality. There is also a possibility that healthy or active soil might serve as a medium to halt the spread of COVID-19. Viruses, when in contact with soil and its clay fractions or particles (either kaolinite or most preferably bentonite), will be weakened and controlled naturally. This may help to slow down the rapid spread of COVID-19. A further systematic investigation is required to study and validate the association (Mishra et al., 2020 ).

Conclusions

While the negative impacts from the pandemic cannot be ignored, the COVID-19 crisis has presented an extraordinary situation with substantial environmental gain. The positive impacts on global environmental and planetary health that the COVID-19 pandemic lockdown brought about are noteworthy, even if they will be quickly erased as the world goes from lockdowns to a gradual return to normalcy. Therefore, behavioural changes such as adopting a lifestyle that reduces carbon footprint are needed to make a positive impact on the environment. Even though environmental health is improving, efforts to mitigate the long-lasting and ongoing environmental pollution remains a herculean task. Ideally, measures should be taken to lengthen the positive impacts we experienced from the pandemic in the hope that the earth’s self-recuperative properties will carry on for the long term. At the same time, observations and knowledge gained of the positive environmental impacts of the pandemic should be documented and used to help make comprehensive evidence-based public policies for the survival of humankind. World leaders should consider the national policy changes necessary to ensure continuity of as many of the positive environmental impacts from the COVID-19 pandemic lockdown as possible. Those changes would also serve to lessen the likelihood of another zoonotic calamity.

Acknowledgements

We would like to thank the Director-General of Health Malaysia for his permission to publish this article.

Authors’ contributions

Conceptualization, H.C.L., I.L., A.S.H.C.; methodology, H.C.L., I.L., A.S.H.C., and K.H.A.; validation, H.C.L., L.C.M., and K.H.A..; formal analysis, H.C.L.,K.W.G., L.C.M., and K.H.A.; investigation, H.C.L., I.L., A.S.H.C., K.W.G., L.C.M., and K.H.A.; resources, H.C.L., I.L., A.S.H.C., K.W.G., L.C.M., and K.H.A.; data curation, H.C.L., and K.H.A.; writing—original draft preparation, H.C.L., I.L., L.C.M., and K.H.A.; writing—review and editing, H.C.L., I.L., A.S.H.C., K.W.G., L.C.M., and K.H.A.; supervision, I.L., A.S.H.C., L.C.M., and K.H.A.; project administration, H.C.L., I.L., and A.S.H.C.; funding acquisition, K.H.A.

No funding was received for conducting this study.

Declarations

The authors have no conflicts of interest to declare that are relevant to the content of this article.

All authors have read and agreed to the published version of the manuscript.

Medical Review & Ethics Committee (MREC), Ministry of Health Malaysia granted the exemption.

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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.

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The Macroeconomic Impact of Climate Change: Global vs. Local Temperature

This paper estimates that the macroeconomic damages from climate change are six times larger than previously thought. We exploit natural variability in global temperature and rely on time-series variation. A 1°C increase in global temperature leads to a 12% decline in world GDP. Global temperature shocks correlate much more strongly with extreme climatic events than the country-level temperature shocks commonly used in the panel literature, explaining why our estimate is substantially larger. We use our reduced-form evidence to estimate structural damage functions in a standard neoclassical growth model. Our results imply a Social Cost of Carbon of $1,056 per ton of carbon dioxide. A business-as-usual warming scenario leads to a present value welfare loss of 31%. Both are multiple orders of magnitude above previous estimates and imply that unilateral decarbonization policy is cost-effective for large countries such as the United States.

Adrien Bilal gratefully acknowledges support from the Chae Family Economics Research Fund at Harvard University. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Student Essay 2 – Impact of Lockdown on Our Planet

We received some fantastic essay submission by our students during Eco Week. Today, we would like to share another winning essay on the topic of “Impact of Lockdown on Our Planet”, by Jia Xuan in Form 5.

Read the full essay below.

            The last decade was the warmest ever recorded. The oceans are acidifying, ice caps are melting and forests are burning. It would require tremendous efforts to slow down climate change, let alone reverse it. Thankfully, the COVID-19 pandemic is a blessing for the environment.

            Countries worldwide have commenced “lockdowns”, commonly known in the media. Specifically in Malaysia, the Movement Control Order (MCO) began on the 18th of March 2020 as ordered by the federal government. Scientists predicted that due to the severe lifestyle changes forced onto the population, this pandemic will reduce the most anthropogenic carbon emissions since World War II.

            There has been a reduction of 71% of flights worldwide, reducing the carbon dioxide emissions significantly. Alongside, all forms of transportation have been decreased, allowing the percentage of “good” air in Malaysia to double in the past month! The drop in carbon dioxide, carbon monoxide, methane and nitrous oxides is extremely effective in slowing down climate change, since the emissions from vehicles were one of the major contributors. Human induced air pollution is one of the main reasons that biodiversity is disappearing at 1000 times the normal rate of extinction. With stores, schools, factories and virtually everything closed, there has been a large drop in energy consumption worldwide. Burning of fossil fuels as the world’s main energy source is the largest contributor to greenhouse gas production. Habitat destruction and deforestation for provisions of fossil fuels reduces photosynthesis, narrows diversity of species and pollutes land, air and water. The drop in energy use will save animal lives and allow carbon dioxide to be cleared at a faster rate, reducing our GHG and slowing the thinning of our ozone layer. The reduction of acid rain will also save the lives of many marine animals, plants and much more. Not to mention, the toxic byproducts of power plants are the largest contributors to water pollution in the US so the reduction of poisonous waste, often containing arsenic, mercury and lead will prevent extinction and mass death of marine life.

Even the tragic deaths of more than 300000 people, as of the 16th of May 2020, has brought improvements to our surroundings. The world’s average carbon footprint per capita is 4.8 tonnes as of 2017, leading to a reduction of almost 1.5 million tonnes just this year! The reduction in greenhouse emissions due to these lockdowns worldwide allows the ozone layer to repair or slow the depletion of it ; fighting climate change, reducing intensities of natural disasters, lowering sea levels, reducing acid rain and much more if kept up. For the first time in 30 years, the Himalaya Mountains can be seen from New Delhi due to decreasing air pollution. In Venice, the canal waters are finally clear enough to see fishes through. NASA even released satellite images of a drastic reduction in pollution over some of the cities with the worst air quality in the world : Beijing, Shanghai and Chengdu.

            With COVID-19 originating from Wuhan, China and the USA having the most severe experience with the pandemic, it is a stroke of luck. China and the US are the top two countries, for most, ranked by energy consumption, greenhouse gas emission, population and much more. Hence, having tighter restrictions in those countries will improve the environment the most.

            I find that an important improvement to come from this pandemic is the changes of how society will function after. Is it really necessary to take a flight all the way to another country when a business meeting can be held through Zoom? Or that we drive 10km away for one cup of bubble tea? The lockdowns have rationalised people, reducing the use of transportation and resorting to our advancing technology for simple yet practical replacements. The effect of this mentality will multiply and benefit the environment significantly in the future.

Thankfully, influencers on social media have also been advising concepts like it. There have even been promotions for healthy eating. Diets such as being vegan or cutting out meat products have been very popular in hopes to “glow up” this quarantine, as advertised in the media. Temporary or long term, the reduction in dairy and meat products will benefit the environment tremendously. Meat such as beef produces 60kg of CO2 from 1kg of product. Even products such as cheese produce 21kg of CO2 per 1kg of substance! 14.5% of the world’s greenhouse gas emissions are from the food and agriculture industry, with most GHG being methane and nitrous oxides. Methane has 28 times more of an effect on global warming in comparison to carbon dioxide! Cutting out or reducing the consumption of meat or dairy will have a significant positive effect on a person’s carbon footprint and will aid in our fight against climate change. Even the decrease in consumption of food will aid not only the environment but a person’s health, reducing obesity, diabetes and such alongside allowing the overall agriculture industry to reduce in size.

            However, there are many downsides to the pandemic : creating mountains of waste through single use items and medical supplies, the fear of public transportation and the stunted economy foreshadowing a steep increase in greenhouse emissions in the near future. Though most of the positive impacts of COVID-19 on the environment are temporary, I believe that these small gifts will allow more people to see what we are capable of in our fight against climate change and motivate them to take part in improving the future of our environment. That is the biggest impact of it all.