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Factors affecting the community acceptance of onshore wind farms: a case study of the zhongying wind farm in eastern china.

onshore wind farm case study

1. Introduction

  • What factors influence community acceptance of wind farms?
  • How do these factors interact and form the background of community acceptance?
  • What are the possible recommendations for improving community acceptance of wind farms through the optimization of planning procedures?

2. Methodology

3.1. category of factors, 3.2. variance analysis, 3.3. regression analysis, 3.3.1. information, 3.3.2. compensation, 3.3.3. visual angle, 3.3.4. noise, 3.4. environmental impacts and accompanied annoyances, 4. discussion, 4.1. significant variables in the regression model, 4.2. location-related factors, 4.3. demographic factors, 4.4. environmental impact factors, 4.5. public participation factors, 4.6. limitation in this research, 5. conclusions and recommendations, author contributions, acknowledgments, conflicts of interest, appendix a. questionnaire for inhabitants around zhongying wind farm in ningbo, china.

  • Your age: (1) <18 (2) 18–30 (3) 30–40 (4) 40–50 (5) 50–60 (6) >60
  • Your gender: (1) Male (2) Female
  • What is your marital status? (1) Single (2) Married (3) Divorced (4) Widowed (5) Other
  • Your educational background: (1) Primary school or lower (2) Middle school (3) High school (4) Professional academy (5) University or higher (6) Other
  • Your job:____________________________________________________________________________
  • How long do you live here? (1) <1 year (2) 1–5 years (3) 5–10 years (4) 10–20 years (5) >20 years
  • Do you know about Zhongying Wind Farm Project? (1) I know clearly (2) I know about it (3) Not very familiar (4) I don’t know it at all
  • The distance from your residence to the nearest wind turbine: (1) <500 m (2) 500–1000 m (3) 1000–3000 m (4) >3000 m
  • Visibility of the wind turbines from your residence: (1) Invisible (2) Small part visible (3) Most visible (4) Fully exposed
  • Does the environmental impact (noise, flicker) of the wind farm affect your life? (1) Not at all (2) It has a certain impact (3) Medium impact (4) Unbearable impact (5) Other thoughts ____________________________________________________________________________________
  • How long you are affected by the wind farm each day? (1) None at all (2) <1 h (3) 1–3 h (4) 3–5 h (5) 5–8 h (6) >8 h
  • What is your opinion on the visual impact of the wind farm? (1) No impact (2) Positive impact (3) Have negative effects (4) Not clear
  • Does the wind farm project have compensation measures for surrounding residents? (1) No compensation (2) Yes ____________________________________________________ (3) Unknown
  • Your opinions and suggestions on wind farm planning: ________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
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Click here to enlarge figure

FactorsDescription of Factors
DistanceDirect distance from the settlement to the nearest wind turbine
Visual angleThe angle between the front views wind turbines and the sight of people
Original environmentOriginal environment in the locality
AgeThe age of interviewees
GenderThe gender of interviewees
FamilyThe marital status of interviewees
EducationReceived educational level of interviewees
Length of residenceHow many years interviewees live in the community
OccupationThe job of the interviewees
VisibilityThe proportion of visible wind turbines
NoiseWhether perceived noise or not
Soil & water pollutionSoil and water pollution in the locality of the wind turbines
InformationAre the interviewees well informed before the project approved?
CompensationWhether the interviewees are satisfied with the compensation or not?
GroupsDistance to the Nearest Wind TurbineSource of RespondentsDistance
(m)
Number of IntervieweesPopulation
1. Very near<1 kmShangwang Village710497379
Xiayang Village79010293
Daotou Village5707776
Aokou Village670660
Taipingao Village600812
Aodi Village9001155
2. Near1–2 kmShanglongquan Village1300466339
Wangjialu Village150010118
Dongshan Village1200492
Shangchemen Village15003421
Kunting Village17007850
Ganao Village16507327
Caojiatang Village16805466
Dongyuan Village16004254
3. Mid-distance2–4 kmShangliu Village2200–28003714590
Guichi Village2300–320015595
Xiawan Village2800–38008162
4. Remote distance>4 kmNearby residents-376-
Passers-23-
Tourists-8-
VariablesCategoriesF (ANOVA)P (Significance)
Distance1: < 1 km, 2: 1–2 km, 3: 2–4 km, 4: > 4 km.40.740 **0.000
Visual angle1: < 30°, 2: 30–45°, 3: 45–75°, 4: > 75°.10.482 **0.000
Original environment1: positive, 2: normal, 3: negative.0.1120.894
Age1: < 18, 2: 18–40, 3: 40–60, 4: > 60.13.364 **0.000
GenderMale, female0.4480.504
Family1: single, 2: married, 3: widowed/divorced.6.283 *0.002
Education1: primary school, 2: secondary school, 3: high school, 4: college/university.11.378 **0.000
Length of residence1: < 5, 2: 5–10, 3: 10–20, 4: > 20.10.876 **0.000
Occupation1: primary industry, 2: secondary industry, 3: tertiary industry, 4: civil servant.9.191 **0.000
Visibility1: invisible, 2: partly visible, 3: most visible, 4: totally visible.27.331 **0.000
Noise1: yes, 0: no.41.478 **0.000
Soil & water pollution1: yes, 0: no.2.3060.103
Information1: no, 2: little information, 3: well informed, 4: positively participated.39.671 **0.000
Compensation1: not enough compensation, 0: satisfied with/no care about compensation.26.897 **0.000
Independent VariableModel 1Model 2Model 3Model 4
Distance0.469 **0.316 **0.1490.095
Visual angle−0.149−0.191 *−0.189 *−0.150 *
Age −0.130−0.138−0.142
Family −0.0140.0040.001
Education 0.156 *0.1170.117
Length of residence −0.215 **−0.173 *−0.091
Occupation 0.0820.0800.053
Visibility −0.185 *−0.140
Noise −0.137 *−0.123 *
Information −0.213 **
Compensation −0.160 **
Constant 3.0525.6577.9258.438
R 0.3340.4960.5250.576
N169169169169

Share and Cite

Guan, J.; Zepp, H. Factors Affecting the Community Acceptance of Onshore Wind Farms: A Case Study of the Zhongying Wind Farm in Eastern China. Sustainability 2020 , 12 , 6894. https://doi.org/10.3390/su12176894

Guan J, Zepp H. Factors Affecting the Community Acceptance of Onshore Wind Farms: A Case Study of the Zhongying Wind Farm in Eastern China. Sustainability . 2020; 12(17):6894. https://doi.org/10.3390/su12176894

Guan, Jinjin, and Harald Zepp. 2020. "Factors Affecting the Community Acceptance of Onshore Wind Farms: A Case Study of the Zhongying Wind Farm in Eastern China" Sustainability 12, no. 17: 6894. https://doi.org/10.3390/su12176894

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Developing onshore wind farms in Aotearoa New Zealand: carbon and energy footprints

  • Cite this article
  • https://doi.org/10.1080/03036758.2024.2344785

Introduction

Life cycle assessment of onshore wind farms: an overview, results and discussion, conclusion and prospects.

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In recognition of deeper insights into the implications of wind farm deployments, this paper addresses the need for an updated Life Cycle Assessment (LCA) for onshore wind generation systems, using 4.3 MW wind turbines and direct drive permanent magnet synchronous generators. The environmental and energy performances were estimated through an LCA for an onshore wind plant under construction in Aotearoa New Zealand with a total nameplate capacity of 176 MW. This study used real construction data showing literature data overestimates civil works and underestimates transportation contributions in the wind farm footprint. Further, different end-of-life management alternatives for turbine blades are analysed: landfill, mechanical recycling, and chemical recycling. The results indicate a carbon footprint of 10.8–9.7 gCO 2eq /kWh, a greenhouse gas payback time of 1.5–1.7 years for avoided combined cycle gas turbines, and an energy payback time of 0.4–0.5 years, in which the chemical recycling of the blades is the lower emission solution overall. The outcomes underscore the environmental efficiency of onshore wind farms and their important role in the energy transition. Notably, the manufacturing of wind turbines is the primary contributor to the carbon and energy footprints, highlighting a critical area for targeted environmental mitigation strategies.

  • Wind energy
  • permanent magnet
  • blade recycling
  • greenhouse gas emissions
  • energy return on investment
  • life cycle assessment

Energy systems are going through profound changes, as significant efforts are dedicated to reducing reliance on fossil fuels, increasing energy efficiency, and rapid deployment of renewable energy technologies such as solar and wind generation. Aotearoa New Zealand is committed to the energy transition, with a target of 100% renewable electricity generation by 2030 (Minister of Climate Change Citation 2022 ), and net zero greenhouse gas emissions by 2050, except for biogenic methane (New Zealand Government Citation 2019 ). Wind energy is expected to be an essential technology for the expansion of electrification and deployment of renewables in Aotearoa New Zealand. Indeed, all future energy scenarios modelled by different organisations foresee a steep increase in the installed capacity of wind energy generation (Pincelli et al. Citation 2024 ).

In Aotearoa New Zealand, the deployment of onshore wind plants started in 1993 (Ministry of Business Innovation & Employment Citation 2022 ), but the wind industry deployment accelerated from 2004 with the adoption of wind turbines with nominal capacities larger than 1 MW (Zhang et al. Citation 2023 ). By 2023 the onshore wind installed capacity reached 1 GW. The capacity factor of wind generation in the country is very high averaging 41%, almost twice the global average (Zhang et al. Citation 2023 ), making the country very suitable for wind generation. Indeed, there are several ongoing projects in different stages of execution for new onshore wind plants in Aotearoa New Zealand that could add 2.2 GW to the installed capacity (New Zealand Wind Energy Association Citation n.d. ). By 2050, it is estimated the onshore wind installed capacity could reach nearly 6 GW (Pincelli et al. Citation 2024 ).

The energy transition is further accelerated by the continued decreasing costs of wind generation and other renewable technologies (He et al. Citation 2020 ). The increase in rotor sizes is a crucial contributor, as larger rotors harness more energy while saving expenses during the operational and management phase and balance of the system (Johnson et al. Citation 2019 ). Wind turbine technology designs are characterised as either gearbox or direct drive; in the latter, the generator is directly connected to the hub without a gearbox (Das and Nandi Citation 2022 ). The two technical designs differ significantly in mass and material composition (Carrara et al. Citation 2020 ). The permanent magnet synchronous generator is the most common direct drive technology (PMS-DD), as it requires low maintenance and has high efficiency. Moreover, the overall weight of wind PMS-DD turbines is lighter, as they do not require gearboxes (Carrara et al. Citation 2020 ). However, they are more expensive (Moghadam and Nejad Citation 2020 ). For onshore applications, PMS-DD turbines have been securing a larger market presence, yet their adoption is still not widespread, and the traditional gearbox configuration prevails (Carrara et al. Citation 2020 ).

Wind energy generates minimal environmental impacts during the operational phase (Das and Nandi Citation 2022 ). However, wind turbines and other components of an onshore wind plant have embedded environmental footprints associated with their production, as well as plant construction and decommission. Life Cycle Assessment (LCA), a widely used environmental impact assessment method, offers a comprehensive approach to estimate the environmental impacts of products or services throughout their entire life cycle. The carbon footprint and energy requirements of the life cycles of onshore wind plants have been substantially studied within the scientific community. Manufacturing wind turbines has been reported as the major contributor to greenhouse gas (GHG) emissions. However, the results in the literature for the carbon footprint of onshore wind plants vary from 3.3 (Tahtah et al. Citation 2022 ) to 70 (Nassar et al. Citation 2024 ) gCO 2eq /kWh, influenced by different factors such as technology employed, power plant capacity, turbine nominal capacity, site location, and temporal analysis resolution. These considerable variations in reported GHG emissions of onshore wind systems contribute to a lack of consensus on the environmental burdens and benefits of wind systems. It has been previously noticed that, in general, the median values of GHG emissions decrease with the rise in the nominal capacity of wind turbines (Mendecka and Lombardi Citation 2019 ). However, most LCA studies for onshore wind plants consider wind turbines with a low nominal capacity of up to 2.5 MW, and fewer studies have analysed larger wind turbines (Mendecka and Lombardi Citation 2019 ).

As the deployment of wind energy rises, the amount of generated turbine waste increases. Thus, ensuring sustainable end-of-life management is crucial. While recycling metals and rare earth elements is feasible, blades, on the other hand, are made of materials that are difficult to recycle, often leading to their disposal in landfills or incineration (Nagle et al. Citation 2020 ). Technologies for recycling blades have been developed (Yang et al. Citation 2023 ), but are not feasible yet because of their level of maturity, high costs, or lack of a market for secondary materials (Jensen Citation 2019 ; Yang et al. Citation 2023 ). Therefore, assessing the environmental performance of end-of-life management strategies for turbine blades has become relevant, with recent advances in the life cycle benefits of recycling blades (Diez-Cañamero and Mendonza Citation 2023 ; Sproul et al. Citation 2023 ; Yang et al. Citation 2023 ). However, most LCA studies for onshore wind farms have overlooked mechanical and chemical recycling solutions for the blades, considering only a linear production system in which they are disposed of (Das and Nandi Citation 2022 ; Elmariami et al. Citation 2023 ; Nassar et al. Citation 2024 ).

Objectives of the study

This paper provides a comprehensive LCA of an onshore wind farm under development in Aotearoa New Zealand, and more specifically contributes to updating the environmental performance of onshore wind systems by considering the PMS-DD technology and a nominal capacity of 4.3 MW for the individual wind turbines, as most the literature focuses only on small turbines. Another significant contribution of the study is the utilisation of original real data for the construction phase, including transportation activities. The study enhances the literature by exploring different end-of-life management strategies for blades. Given the predominant linear production system for blades, with recycling efforts only recently emerging, it becomes crucial to assess their environmental performance thoroughly.

Table 1. Review of relevant and recent LCA studies for onshore wind farms.

Most studies considered a system boundary from cradle-to-grave, including material extraction and processing, component manufacture, transportation, installation and construction, operation and maintenance, dismantling, and end-of-life management. However, few studies comprehensively included the impacts of wind farm infrastructure construction and installation, such as civil and electrical works and project management (Alsaleh and Sattler Citation 2019 ; Li et al. Citation 2021 ). Other studies rely on the Ecoinvent database for modelling the wind farm components and phases. However, that might lead to a potential overestimation of certain impacts because of the rapid evolution of the technologies (Cassoret et al. Citation 2023 ).

The literature review shows that despite industry advancements in increasing wind turbine sizes, most LCA studies have considered turbines up to 2 MW of nominal generation capacity (see Table 1 ). The environmental performance of different wind turbine technology designs has been previously analysed (Schreiber et al. Citation 2019 ). However, apart from the reported research of Das and Nandi ( Citation 2022 ) and Şentürk et al. ( Citation 2021 ), most reviewed LCA studies for onshore wind farms only considered wind turbines with gearboxes or overlooked the design technology.

Specific within the Aotearoa New Zealand context, Rule et al. ( Citation 2009 ) estimated the life cycle GHG emissions and energy demand for wind energy systems. More recently, a simplified LCA model has been proposed to generate energy and carbon indicators, which is based on correlations of the dimensions and weights of the components of turbines rather than a comprehensive process-based LCA (Walmsley et al. Citation 2017 ).

The Siemens-Gamesa and Vestas wind turbine manufacturers offer LCA or Environmental Product Declaration (EPD) analyses for their wind turbines, encompassing various turbine sizes, including larger ones. However, for onshore applications, their considerations are limited to the gearbox technology design only.

Another limitation observed in prior LCA studies conducted for onshore wind farms is the exclusion of alternative end-of-life management strategies for wind turbine blades beyond landfilling and incineration (see Table 1 ). Basosi et al. ( Citation 2020 ) and Alsaleh and Sattler ( Citation 2019 ) assumed a recycling rate for blades, yet notably omitted substantial information regarding the recycling process technologies employed, the underlying assumptions and data utilised, nor did they elaborate on the results and discussion about blade recycling. Thus, those previous studies lack crucial insights into the frameworks and implications of recycling blades.

Recent efforts have been made to understand the environmental performance of alternatives for the end-of-life management of blades through specialised LCA studies focusing specifically on this aspect (Diez-Cañamero and Mendonza Citation 2023 ; Sproul et al. Citation 2023 ; Yang et al. Citation 2023 ). Recycling technologies for post-consumer blades, such as mechanical and chemical recycling, remain infeasible due to varying levels of maturity, cost competitiveness, and market availability (Yang et al. Citation 2023 ). Nevertheless, it is anticipated that these technologies will advance over time. Therefore, it is crucial to incorporate them into LCA studies for onshore wind farms to accurately evaluate their potential environmental benefits and inform decisions.

Summarising, the conducted literature review reveals gaps that limit a comprehensive understanding of the environmental impacts of wind farms. First, studies tend to overlook the implications of larger turbines, which are becoming increasingly prevalent in the wind energy sector. Second, the choice of turbine technology is also commonly disregarded, and greater attention needs to be paid to permanent magnet direct drive generators. Another significant oversight is the lack of investigation into blade recycling routes, despite the growing importance of sustainable end-of-life management within the wind energy sector. This study addresses these gaps by contributing to the development of a more comprehensive LCA for wind farms.

Review of results for life cycle greenhouse emissions

Figure 1. Results from the literature for overall life cycle GHG emissions of onshore wind farms and the contributors to the emissions. Some values were approximated.

Figure 1. Results from the literature for overall life cycle GHG emissions of onshore wind farms and the contributors to the emissions. Some values were approximated.

The manufacturing phase has been reported as the primary contributor to GHG emissions ( Figure 1 ). Materials extraction and processing, and the production of wind turbines encompass approximately 90% of the life cycle GHG emissions of onshore wind farms (Kadiyala et al. Citation 2017 ).

Figure 2. Life cycle assessment (LCA) framework, based on ISO 14040 (2006).

Figure 2. Life cycle assessment (LCA) framework, based on ISO 14040 (2006).

The functional unit is 1 kWh of electricity generated (assuming a 30-year operational life).

System description

Figure 3. Location of the developed wind farm pinpointed, in Hastings District, in light blue, 35 km northwest of Napier City, in red.

Figure 3. Location of the developed wind farm pinpointed, in Hastings District, in light blue, 35 km northwest of Napier City, in red.

Table 2. Key parameters of the onshore wind plant.

Table 3. specifications of the components for the onshore wind plant., table 4. technical specification for the wind turbines..

Figure 4. LCA system boundary adopted for the onshore wind farm study.

Figure 4. LCA system boundary adopted for the onshore wind farm study.

Table 5. Inventory for PMSG-DD wind turbine manufacturing.

Inventory data for the material composition of the cables were taken from product specifications. The data for the transformer was approximated with the transformer used in the study of Vélez-Henao and Vivanco ( Citation 2021 ). Support information 2 presents the data utilised for manufacturing cables and transformers.

The foundation is made of concrete and steel. The concrete is produced on-site, assuming that steel and cement are sourced mainly in the Hawkes Bay region. The wind farm project reduced the concrete and steel requirement for the foundations (Batters Citation 2023 ). The data for the foundation were collected from the farm developers and aggregated within civil works during the construction phase.

The site construction work constitutes soil removal for the tower installation, mounting mobile cranes, concrete foundations, access road construction, laying and installation of cables, and usage of energy and electricity for other purposes. Detailed data were obtained directly from the wind farm developer for civil works, electrical works, project management, and turbine works. The data specified the material demand, fuel consumption in equipment, electricity usage, and waste management.

For the operational and maintenance (O&M) phase, the following activities were considered: change of lubricants and motor oils, and turbine inspections. Turbine inspections were assumed to occur twice a year per turbine in a passenger vehicle (Vélez-Henao and Vivanco Citation 2021 ). It is assumed that 33% of the blades, and 15% of the generator, nacelle, and hub system are replaced over the lifetime (Vélez-Henao and Vivanco Citation 2021 ).

Wind turbines are decommissioned when the onshore wind farm reaches the end-of-life. In the decommissioning phase, foundations and access roads are assumed to remain on site. It was assumed that decommissioning the farm requires the same amount of electricity and mounting mobile crane usage as for constructing it.

The components are dismantled, the recyclable materials are recycled, and the non-recyclable waste is disposed of in landfills. Recycling some of the turbine components and materials is relatively simple because the recycling processes for those materials are already established. Metals and bulk materials, such as steel, copper, and aluminium, are assumed to be recycled with a recycling rate of 90%. Recycling rare earth elements has been minimal across different industry sectors (Jensen Citation 2019 ). However, recycling rare earth elements from permanent magnets in wind turbine generators is more feasible because of their large size, accessibility, and disassembly procedures (Jensen Citation 2019 ). A recycling rate of 81% is assumed for neodymium, dysprosium, and boron, as it is economically viable to recycle large amounts of permanent magnets (Reimer et al. Citation 2018 ). The composite materials used for making the blades are the most difficult to recycle.

Table 6. Parameters for the end-of-life management solutions of the blades.

For the background data, the Ecoinvent v.3.9.1 database was utilised. The electricity grid mix and industrial heat specific for the regions where the components are manufactured were used.

Impact assessment

The climate change impact category was selected to analyse the environmental impacts of the onshore wind farm, for which the indicator global warming potential (GWP100) was used. The GWP measures lifecycle GHG emissions in kg CO 2eq , using the IPCC (GTP100) method. The cumulative energy demand (CED) was also included as part of the lifecycle energy analyses.

onshore wind farm case study

The GPBT was calculated considering avoided emissions from consuming electricity from the grid (Fonseca and Carvalho Citation 2022 ), as well as from gas turbines, as in the Aotearoa New Zealand context the generated electricity of the onshore wind system potentially displaces electricity dispatched from combined-cycle gas turbine power plants.

EROI represents how much energy is obtained from a system compared to how much of that energy is required to create and implement the system. It is described by a unitless ratio of the energy returned to society to the energy required to make that energy source (Fthenakis Citation 2017 ).

Figure 5. Overall life cycle GHG emissions of the onshore wind farm in Aotearoa New Zealand, compared to previous studies in the literature.

Figure 5. Overall life cycle GHG emissions of the onshore wind farm in Aotearoa New Zealand, compared to previous studies in the literature.

Comparing lifecycle GHG emissions of onshore wind farms must be approached with some caution, because of different assumptions, methodological choices, modelled technologies, and site specifications. Moreover, the variability for onshore wind farms is significantly high. For instance, the GHG emissions for another wind farm deployed in Aotearoa New Zealand were estimated as 3 gCO 2eq /kWh (Rule et al. Citation 2009 ), much lower than this study’s wind farm case. According to the review of Mendecka and Lombardi ( Citation 2019 ), the overall median value for onshore wind farms is 9.7 gCO 2eq /kWh. The life cycle emissions result of this study is, therefore, aligned with the median carbon footprint reported in the literature.

The environmental payback time, in terms of GPBT, for the onshore wind farm is 3.1 years (avoiding the national grid) and 1.7 years (avoiding combined cycle gas turbines). The electricity grid in Aotearoa New Zealand is already of low carbon intensity, and its mean emission factor over the last 5 years is 103 gCO 2eq /kWh (Ministry for the Environment Citation 2022 ). Nevertheless, the onshore wind system emissions are 89.5% lower compared to the country’s grid. Deploying onshore wind plants in Aotearoa New Zealand therefore helps to reduce the emissions associated with the energy system as fossil fuels are phased out and electrification expands.

This study has some methodological limitations. First, it focuses only on the energy intensity and GHG emissions throughout the life cycle of the wind farm, even though there are other environmental impacts, such as ozone depletion, human toxicity, acidification, eutrophication, and resource depletion. Social, wildlife, or economic impacts were not considered. In addition, this LCA study estimates potential impacts, not measuring real impacts on the field. The results are limited by available inventory data, although data were selected representing the specific technology (4.3 MW permanent magnet direct drive turbine), and original data were utilised for construction and transportation phases.

Energy indicators

The findings indicate that the system is highly energy efficient. The onshore wind plant recoups the energy used in its production in 0.5 years, providing a quick net energy benefit. The low EPBT value indicates that the system enhances environmental benefits, as it generates much more energy than was consumed during its production. The EROI result is 66, which shows a high energy gain compared to the energy invested to manufacture the onshore wind plant. The EROI of onshore wind plants in Aotearoa New Zealand has been previously estimated, reaching up to 62.3, depending on the plant (Walmsley et al. Citation 2017 ). The findings show that onshore wind plants are very efficient providing significant net energy gain and contributing to long-term sustainability.

Contribution analysis

Figure 6. Life cycle GHG emissions and CED for the analysed wind plant in Aotearoa New Zealand.

Figure 6. Life cycle GHG emissions and CED for the analysed wind plant in Aotearoa New Zealand.

Within the turbine’s manufacturing process, the usage of steel was identified as the main source responsible for nearly half of the emissions. Steel production is energy-intensive, coming from non-renewable heating sources, such as coal furnaces, and electricity mix. A fifth of the emissions associated with turbine manufacturing stems from the blades, primarily due to the production processes of carbon and glass fibres. Wind farms can enhance their environmental performance by minimising the steel requirement for towers through advancements in design and incorporating recycled secondary steel materials in manufacturing (Vélez-Henao and Vivanco Citation 2021 ). Additionally, the wind energy sector has been developing new technologies for blade production, including utilising organic materials (Li et al. Citation 2022 ). Support Information 3 presents a sensitivity analysis for the demand for steel, glass and carbon fibres, as well as for the required electricity for assembly.

Increasing the usage of renewable energy sources in the manufacturing phase can significantly enhance overall emissions reduction for future wind farm developments. Given that most components are manufactured in China, the country’s ongoing energy transition holds the potential to reduce the embodied emissions associated with wind turbines.

The GHG emissions credit for recycling metals in the end-of-life management phase of turbines and substations contributes significantly to the reduction of the overall life cycle emissions of the farm by 28% (−3.0 gCO 2eq /kWh). Other studies have highlighted that recycling materials at the wind farm end-of-life phase can yield GHG emissions savings ranging from 20 (Bonou et al. Citation 2016 ) to 40% (Atilgan and Azapagic Citation 2016b ). Recycling credits recognise that recycling materials avoid the extraction of raw materials and their associated environmental impacts. Incorporating recycling routes, therefore, increases the environmental performance of the wind farm.

The life cycle phases of a wind farm hold different levels of uncertainty. The manufacturing, installation and transportation phases hold more certainty, the former because of well-established manufacturing processes, and the latter because of the implementation of original data, which is discussed in the following subsection. However, uncertainties arise in the end-of-life phase. Wind farms have long lifetimes, and extensive experience with end-of-life management for wind farm components is still lacking. The effectiveness of recycling routes and their associated environmental impacts might change over time with more experience in managing the end-of-life of wind farms.

Construction and transportation phases

Figure 7. Life cycle GHG emissions for the analysed wind plant in Aotearoa New Zealand, comparing data for construction and transportation phases.

Figure 7. Life cycle GHG emissions for the analysed wind plant in Aotearoa New Zealand, comparing data for construction and transportation phases.

When employing original data sourced directly from the wind farm developer, the construction phase exhibits lower emissions, attributed to reduced concrete usage and optimised construction methods. On the other hand, the transportation phase demonstrates higher emissions, reflecting the inclusion of all contractors’ transportation activities. This highlights the significance of comprehensive data collection to assess environmental impacts accurately. The scenario reliant solely on literature values may underestimate transportation and overestimate construction emissions due to the variability in practices across projects.

LCA using original data for the installation and services of wind farms has also indicated that environmental impacts were largely associated with transportation, as in the Bonou et al. ( Citation 2016 ) study. Strategies for achieving environmental improvements for the transportation phase include the adoption of electrified vehicles, incorporation of renewable fuels, and optimisation of logistic processes. Optimising logistics can reduce travel and fuel consumption through route planning, load consolidation, and vehicle sharing.

Other LCA studies have reported a higher emission share for the installation phase attributed to the production of cement for the concrete foundation (Gomaa et al. Citation 2019 ). In this study, during the construction phase, cement, steel, and gravel production are the main contributors to GHG emissions. Strategies to enhance the environmental performance of the installation phase include increasing the utilisation of recycled crushed materials on roads, increasing the fuel efficiency of heavy machinery (Rajaei and Tinjum Citation 2013 ), and incorporating alternative cementitious materials.

Blade end-of-life management scenarios

Figure 8. Life cycle GHG emissions for the analysed wind plant in Aotearoa New Zealand, considering different end-of-life management strategy for the blades.

Figure 8. Life cycle GHG emissions for the analysed wind plant in Aotearoa New Zealand, considering different end-of-life management strategy for the blades.

Although LCA studies for wind farms overlook the recycling of blades, this study results align with the LCA findings of Diez-Cañamero and Mendonza ( Citation 2023 ) specifically for end-of-life management of turbine blades, in which chemical recycling potentially generates higher carbon credits. However, there remains no consensus on the environmental impacts and benefits of chemical recycling, as other studies have yielded higher GHG emissions values due to increased energy consumption and potential sensitivity to thermal energy sources (Sproul et al. Citation 2023 ). Chemical recycling is currently in a laboratory demonstration phase, suggesting that its GHG emissions may change as the process transitions to larger-scale implementation.

In this study, the mechanical recycling process is inherently more energy-intensive compared to the other end-of-life management solutions. The environmental impact intensity of each end-of-life management route is notably sensitive to changes in the energy mix. Thus, shifting towards renewable electricity generation can significantly reduce the emissions associated with waste recovery methods such as mechanical recycling. Given that mechanical recycling is a more mature technology, it holds promise for effective end-of-life management for the post-consumer blades, moving away from conventional disposal in landfills.

Developing recycling routes aimed at reducing the environmental impacts of post-consumer blades at their end-of-life stage is therefore imperative. Feasible, large-scale recycling routes are required for turbine blades, aligning with the objectives of the electricity transition and the circular economy. As these technologies mature, become cost-competitive, and attain commercial availability, they hold the potential to enhance the environmental performances of wind farms.

Other options for mitigating the impacts at the end-of-life stage of turbine blades can involve exploring secondary uses for the blade, such as pedestrian bridges and transmission towers. However, they are less demanding applications not capable of addressing all the potential post-consumer blades (Yang et al. Citation 2023 ). Other end-of-life management options, such as incineration, pyrolysis, and co-generation in cement kilns, were not assessed in this study and could be subject to further investigation in subsequent research endeavours.

As Aotearoa New Zealand undergoes a profound shift towards low-carbon energy solutions, with the expansion of electrification and deployment of wind systems, it becomes increasingly important to adapt and update LCA for these systems to comprehensively evaluate their life cycle energy demand and GHG emissions. This study has shown that despite energy investments and GHG emissions in the production phase, the onshore wind plant offsets its emissions over its lifespan, making it a suitable option for the energy transition in Aotearoa New Zealand, and elsewhere. This underscores that onshore wind plants are aligned with the principles of sustainable development. Nevertheless, it remains crucial to continue implementing improvements aimed at limiting negative environmental impacts while maximising positive contributions throughout the supply chain of onshore wind plants.

Recycling post-consumer turbine blades is emerging as an alternative to address the challenge of blade waste, which is currently predominantly disposed of in landfills. Both mechanical and chemical recycling methods show potential environmental benefits, reducing the overall wind farm GHG emissions from 10.8 (landfill) to 10.3 and 9.7 gCO 2eq , respectively. However, it is crucial to note that these recycling technologies are not yet commercially feasible, and mechanical recycling presents the most mature option. As recycling processes continue to evolve, their potential environmental impacts and benefits are subject to change, particularly as they become more efficient and utilise renewable electricity sources in their operations. Therefore, ongoing research and development efforts are essential to analysing the potential benefits of recycling turbine blades and integrating them into LCA for onshore wind farms.

The prospects for further LCA studies for wind farms rely on accounting for ongoing changes in the energy supply, end-of-life management, and technology advances. As manufacturing countries undergo an energy transition and increasingly rely on renewable sources, understanding the implications of these shifts on the environmental footprints of wind turbines is crucial. As the wind energy sector gains more expertise in end-of-life management, adjusting LCA databases to accurately reflect these developments is required. Finally, because of the rapid advancements of technologies, regular updates to LCA studies are necessary to ensure they remain reflective of current practices and accurately inform decision-making processes.

Supplemental Material

The Doctoral Scholarship programme of Te Herenga Waka Victoria University of Wellington provided financial support to undertake the research. Meridian Energy Ltd. is acknowledged for providing valuable information and data pertaining to the construction and operation of their Harapaki Wind Farm.

No potential conflict of interest was reported by the author(s).

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Factors Affecting the Community Acceptance of Onshore Wind Farms: A Case Study of the Zhongying Wind Farm in Eastern China

  • August 2020
  • Sustainability 12(17):6894

Jinjin Guan at East China University of Science and Technology

  • East China University of Science and Technology

Harald Zepp at Ruhr-Universität Bochum

  • Ruhr-Universität Bochum

Abstract and Figures

The factors influencing community acceptance of wind farms.

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COMMENTS

  1. Landscape Visual Impact Evaluation for Onshore Wind Farm: A ...

    In line with the target of optimizing the onshore wind farm planning procedures, the LVIE model has decomposed the visual impact into three dimensions: landscape sensitivity, the visual impact of WTs, and viewer exposure.

  2. Planning for the Future of Onshore Wind Farms through ...

    This paper draws upon detailed empirical data from four onshore wind farm case studies to investigate the range and impact of changes that have occurred over their operational life, including the different temporalities of the changes and the range of actors involved and impacted by the changes.

  3. Factors Affecting the Community Acceptance of Onshore Wind ...

    This study clarified the factors influencing community acceptance of onshore wind farms in eastern China and the interaction between various factors in the case study of Zhongying Wind Farm. The on-site investigation and questionnaires ensured the reliability of the results.

  4. Hybrid life cycle assessment of an onshore wind farm ...

    This paper carried out a hybrid LCA including services of an onshore wind farm of 19.5 MW of capacity installed, located in the high Guajira in Colombia. This wind farm is the first renewable energy project connected to the national grid.

  5. Onshore versus offshore wind power trends and recent study ...

    A greater deal of wind energy has been harvested with onshore wind farm industries for several decades, and it is a well-matured alternative for maximizing wind power generation.

  6. Full article: Developing onshore wind farms in Aotearoa New ...

    This paper provides a comprehensive LCA of an onshore wind farm under development in Aotearoa New Zealand, and more specifically contributes to updating the environmental performance of onshore wind systems by considering the PMS-DD technology and a nominal capacity of 4.3 MW for the individual wind turbines, as most the literature focuses only ...

  7. Landscape Visual Impact Evaluation for Onshore Wind Farm: A ...

    This paper proposed a landscape visual impact evaluation (LVIE) model that combines the theoretical framework and practical solutions and optimizes the onshore wind farm planning procedures.

  8. End-of-life decision making for onshore wind and solar farms in

    MW onshore wind and 578, 553 MW solar photovoltaic worldwide, considering the long-term future of our existing onshore renewable energy sites is vitally important. In Great Britain, onshore wind and solar farms are often granted a time-limited planning consent of 25 years. Onshore wind farms are now starting to reach the end of these

  9. Factors Affecting the Community Acceptance of Onshore Wind ...

    This study clarified the factors influencing community acceptance of onshore wind farms in eastern China and the interaction between various factors in the case study of Zhongying Wind...

  10. Introducing a group spatial decision support system for use ...

    The method is used in a study case on planning of onshore wind energy in Mexico, which has been developed through a collaborative Geoweb application, and is functioning in a distributed and asynchronous real-time way, so-called Geospatial System of Collective Intelligence (SIGIC).