Pub Date : 2025-04-14DOI: 10.1016/j.eiar.2025.107949
Tianpeng Wang , Xin Su , Yichuan Mei , Wei Xiong
China's power system is increasingly challenged in its pursuit of low-carbon transition under the carbon neutrality target. However, current studies often overlook the comprehensive impacts of climate change on the supply-demand balance and decarbonization pathways of China's power system, especially at the provincial level. This study addresses this gap by integrating the dual effects of climate change on electricity demand and supply into a provincial power system optimization model. The model achieves supply-demand balance across provinces through a cost-minimization approach. Findings reveal that climate change substantially intensifies the pressure on China's low-carbon power transition, leading to an estimated 20 % increase in total system costs by 2050. This cost escalation results from the combined effects of rising electricity demand, declining thermal generation efficiency, and renewable output variability. The analysis also reveals notable regional disparities, with southern and eastern provinces more affected by climate change than northwestern provinces. Incorporating climate change impacts into low-carbon power system planning provides theoretical support for regional strategies, emphasizing the need for targeted measures, grid flexibility, and climate-resilient infrastructure to achieve China's sustainable energy goals.
{"title":"Climate change intensifies low-carbon transition pressure in China's power system","authors":"Tianpeng Wang , Xin Su , Yichuan Mei , Wei Xiong","doi":"10.1016/j.eiar.2025.107949","DOIUrl":"10.1016/j.eiar.2025.107949","url":null,"abstract":"<div><div>China's power system is increasingly challenged in its pursuit of low-carbon transition under the carbon neutrality target. However, current studies often overlook the comprehensive impacts of climate change on the supply-demand balance and decarbonization pathways of China's power system, especially at the provincial level. This study addresses this gap by integrating the dual effects of climate change on electricity demand and supply into a provincial power system optimization model. The model achieves supply-demand balance across provinces through a cost-minimization approach. Findings reveal that climate change substantially intensifies the pressure on China's low-carbon power transition, leading to an estimated 20 % increase in total system costs by 2050. This cost escalation results from the combined effects of rising electricity demand, declining thermal generation efficiency, and renewable output variability. The analysis also reveals notable regional disparities, with southern and eastern provinces more affected by climate change than northwestern provinces. Incorporating climate change impacts into low-carbon power system planning provides theoretical support for regional strategies, emphasizing the need for targeted measures, grid flexibility, and climate-resilient infrastructure to achieve China's sustainable energy goals.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"114 ","pages":"Article 107949"},"PeriodicalIF":9.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-14DOI: 10.1016/j.eiar.2025.107943
Xian Wang , Junfeng Liu , Ying Liu , Xiurong Hu , Huihuang Wu , Yuhan Zhou , Wendong Ge , Jianmin Ma , Shu Tao
A carbon tax is widely recognized as a critical tool for promoting low-carbon transitions, yet its regional disparities in effectiveness have not been extensively studied. This study introduces a multi-regional Computable General Equilibrium (CGE) model with an endogenous freight module, allowing for a detailed representation of domestic trade flows and a comprehensive evaluation of carbon tax impacts on provincial production, trade and emission efficiency. Overall, a carbon tax of 50 CNY/ton is estimated to reduce CO2 emissions by 652 million tons from production and 12.3 million tons from transportation, resulting in a 4 % decline in the national average carbon intensity. However, the effectiveness of the carbon tax varies significantly across regions. Fossil fuel-dependent provinces, such as Shanxi and Hebei, and regions experiencing an influx of production capacity exhibit limited reductions in production carbon intensity averaging as low as 1 %. Additionally, nearly half of the provinces report increased freight carbon intensity, primarily attributable to a shift toward heavier goods exports and longer shipping distances, with export distances increasing by 0.5 % to 1.5 % across most regions. While a moderate carbon tax rate supports low-carbon transition goals for most provinces, coal-reliant regions and oil-dependent freight sectors may require higher tax rates to achieve deeper decarbonization. These findings highlight the importance of tailored, region-specific policy interventions to address diverse economic and energy profiles.
{"title":"Regional disparities in carbon tax effectiveness: A multi-regional CGE analysis of provincial production and freight emissions in China","authors":"Xian Wang , Junfeng Liu , Ying Liu , Xiurong Hu , Huihuang Wu , Yuhan Zhou , Wendong Ge , Jianmin Ma , Shu Tao","doi":"10.1016/j.eiar.2025.107943","DOIUrl":"10.1016/j.eiar.2025.107943","url":null,"abstract":"<div><div>A carbon tax is widely recognized as a critical tool for promoting low-carbon transitions, yet its regional disparities in effectiveness have not been extensively studied. This study introduces a multi-regional Computable General Equilibrium (CGE) model with an endogenous freight module, allowing for a detailed representation of domestic trade flows and a comprehensive evaluation of carbon tax impacts on provincial production, trade and emission efficiency. Overall, a carbon tax of 50 CNY/ton is estimated to reduce CO<sub>2</sub> emissions by 652 million tons from production and 12.3 million tons from transportation, resulting in a 4 % decline in the national average carbon intensity. However, the effectiveness of the carbon tax varies significantly across regions. Fossil fuel-dependent provinces, such as Shanxi and Hebei, and regions experiencing an influx of production capacity exhibit limited reductions in production carbon intensity averaging as low as 1 %. Additionally, nearly half of the provinces report increased freight carbon intensity, primarily attributable to a shift toward heavier goods exports and longer shipping distances, with export distances increasing by 0.5 % to 1.5 % across most regions. While a moderate carbon tax rate supports low-carbon transition goals for most provinces, coal-reliant regions and oil-dependent freight sectors may require higher tax rates to achieve deeper decarbonization. These findings highlight the importance of tailored, region-specific policy interventions to address diverse economic and energy profiles.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"114 ","pages":"Article 107943"},"PeriodicalIF":9.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Food security is essential to the sustainability of any society. China's food security not only matters to itself, but also to the rest of the world. However, it remains unclear whether China, especially its internal provinces, has achieved or will achieve grain self-sufficiency – a central component of its national food security. This study assessed China's grain self-sufficiency ratio (GSSR) from 2000 to 2030 by quantifying grain production and demand at national, regional, and provincial levels. Combining machine learning and modeling methods, the impacts of climate change and geospatial heterogeneity in biophysical and demographic conditions on China's grain self-sufficiency were assessed. The results show that from 2000 to 2020, China's grain production increased faster than its demand, with GSSR exceeding 95 % after 2012. This trend is projected to continue, reaching 118 % by 2030. However, regional disparities in grain production will widen, with China's grain self-sufficiency becoming highly dependent on the northeast and a few key provinces. This divergence is driven by rising yields in major grain-producing provinces, where population decline and aging reduce consumption. In contrast, eastern provinces face growing demand due to population increases, while local production continues to fall. Farmland loss and climate change further increase future food security uncertainty. While farmland protection is necessary but not sufficient. Strengthening agricultural technology and implementing climate adaptation strategies are also necessary. This study offered a comprehensive, multi-scale evaluation and prediction of China's GSSR, incorporating future climate, land-use changes, and population dynamics. The findings highlighted growing regional disparities as a key uncertainty in China's future food security.
{"title":"Rising disparities in grain self-sufficiency across China: Provincial divergence amidst overall national improvement","authors":"Xiaoyu Yu , Rui Xiao , Zhonghao Zhang , Feng Zhou , Fei Mo , Zhenci Xu , Feng Tian , Zhi Gao , Yansheng Li , Peng Zhu , Kailiang Yu , Lumeng Liu , Chen Xu , Jianguo Wu","doi":"10.1016/j.eiar.2025.107942","DOIUrl":"10.1016/j.eiar.2025.107942","url":null,"abstract":"<div><div>Food security is essential to the sustainability of any society. China's food security not only matters to itself, but also to the rest of the world. However, it remains unclear whether China, especially its internal provinces, has achieved or will achieve grain self-sufficiency – a central component of its national food security. This study assessed China's grain self-sufficiency ratio (GSSR) from 2000 to 2030 by quantifying grain production and demand at national, regional, and provincial levels. Combining machine learning and modeling methods, the impacts of climate change and geospatial heterogeneity in biophysical and demographic conditions on China's grain self-sufficiency were assessed. The results show that from 2000 to 2020, China's grain production increased faster than its demand, with GSSR exceeding 95 % after 2012. This trend is projected to continue, reaching 118 % by 2030. However, regional disparities in grain production will widen, with China's grain self-sufficiency becoming highly dependent on the northeast and a few key provinces. This divergence is driven by rising yields in major grain-producing provinces, where population decline and aging reduce consumption. In contrast, eastern provinces face growing demand due to population increases, while local production continues to fall. Farmland loss and climate change further increase future food security uncertainty. While farmland protection is necessary but not sufficient. Strengthening agricultural technology and implementing climate adaptation strategies are also necessary. This study offered a comprehensive, multi-scale evaluation and prediction of China's GSSR, incorporating future climate, land-use changes, and population dynamics. The findings highlighted growing regional disparities as a key uncertainty in China's future food security.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"114 ","pages":"Article 107942"},"PeriodicalIF":9.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-10DOI: 10.1016/j.eiar.2025.107946
Shufei Fu , Tiangui Lv , Guangdong Wu , Hongyi Li , Limeng Zhu , Xinmin Zhang
Dynamic changes in the carbon emission intensity of cultivated land utilization (CEICLU) have important impacts on food security and “dual carbon” goals. We used benchmark regression models, Moran's index, and spatial Durbin's model to explore the impact and spatial effects of agricultural green transformation (AGT) on China's major grain-producing areas (MGPA), major grain-selling areas (MGSA), and production and sales balance areas (PASBA) on CEICLU from 2006 to 2022. The motivation-opportunity-ability (MOA) analysis framework provides a comprehensive approach to explain the mechanism and construct indicators from the perspective of cultivated land production entities (CLPEs). Our results showed that (1) from 2006 to 2022, China's AGTL increased from 0.168 to 0.336. The spatial differences in AGTL are significant, with those in the MGSA being the greatest, followed by those in the MGPA and PASBA. (2) The increase in carbon emissions from cultivated land use in China slowed, while that in the CEICLU decreased from 2.345 t/104 yuan to 0.717 t/104 yuan. The MGPA, MGSA and PASBA showed a downward trend in the CEICLU district. (3) The increase in AGTL in China significantly inhibited CEICLU. The inhibitory effect shows a decreasing pattern of MGSA, MGPA, and PASBA between regions. (4) There was a significant spatial spillover effect of Chinese AGTL on the inhibitory effect of CEICLU. This spillover effect is crucial in the MGPA and PASBA but not in the MGSA. Based on the results, a policy reference is provided for the differentiated regulation of the low-carbon utilization behavior of the CLPE in different food production areas.
{"title":"Discerning changes in carbon emission intensity of cultivated land utilization since agricultural green transformation: Based on the motivation-opportunity-ability (MOA) framework","authors":"Shufei Fu , Tiangui Lv , Guangdong Wu , Hongyi Li , Limeng Zhu , Xinmin Zhang","doi":"10.1016/j.eiar.2025.107946","DOIUrl":"10.1016/j.eiar.2025.107946","url":null,"abstract":"<div><div>Dynamic changes in the carbon emission intensity of cultivated land utilization (CEICLU) have important impacts on food security and “dual carbon” goals. We used benchmark regression models, Moran's index, and spatial Durbin's model to explore the impact and spatial effects of agricultural green transformation (AGT) on China's major grain-producing areas (MGPA), major grain-selling areas (MGSA), and production and sales balance areas (PASBA) on CEICLU from 2006 to 2022. The motivation-opportunity-ability (MOA) analysis framework provides a comprehensive approach to explain the mechanism and construct indicators from the perspective of cultivated land production entities (CLPEs). Our results showed that (1) from 2006 to 2022, China's AGTL increased from 0.168 to 0.336. The spatial differences in AGTL are significant, with those in the MGSA being the greatest, followed by those in the MGPA and PASBA. (2) The increase in carbon emissions from cultivated land use in China slowed, while that in the CEICLU decreased from 2.345 t/10<sup>4</sup> yuan to 0.717 t/10<sup>4</sup> yuan. The MGPA, MGSA and PASBA showed a downward trend in the CEICLU district. (3) The increase in AGTL in China significantly inhibited CEICLU. The inhibitory effect shows a decreasing pattern of MGSA, MGPA, and PASBA between regions. (4) There was a significant spatial spillover effect of Chinese AGTL on the inhibitory effect of CEICLU. This spillover effect is crucial in the MGPA and PASBA but not in the MGSA. Based on the results, a policy reference is provided for the differentiated regulation of the low-carbon utilization behavior of the CLPE in different food production areas.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"114 ","pages":"Article 107946"},"PeriodicalIF":9.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-10DOI: 10.1016/j.eiar.2025.107945
Yang Guan , Bin Li , Yang Xiao , Ye Qiang , Nannan Zhang , Rongguang Shi
China has proposed the Beautiful China Goals (BCGs), outlining a long-term, comprehensive environmental strategy and a localized pathway for achieving environmentally sustainable development. The present study establishes a research framework integrating an evaluation indicator system, interaction identification methods, and a city-level case study for the BCGs, providing the evaluation of BCG progress, the identification of interactions between BCGs, and an in-depth examination of the dynamics between BCG progress and interaction. The results indicate a positive yet significantly north-south differentiated progress in developing a Beautiful China. BCG interaction identification based on document interpretation, correlation analysis, and city-level information mining reveals the differentiation among BCGs at global and local levels. The decoupling-recoupling process observed between the BCGs throughout the overall development of Beautiful China deepens our understanding of China's path toward environmentally sustainable development, which may inform policymaking for comprehensively and synergistically developing a Beautiful China.
{"title":"Identifying the interactions between the Beautiful China Goals: An analytical localization of the Sustainable Development Goals","authors":"Yang Guan , Bin Li , Yang Xiao , Ye Qiang , Nannan Zhang , Rongguang Shi","doi":"10.1016/j.eiar.2025.107945","DOIUrl":"10.1016/j.eiar.2025.107945","url":null,"abstract":"<div><div>China has proposed the Beautiful China Goals (BCGs), outlining a long-term, comprehensive environmental strategy and a localized pathway for achieving environmentally sustainable development. The present study establishes a research framework integrating an evaluation indicator system, interaction identification methods, and a city-level case study for the BCGs, providing the evaluation of BCG progress, the identification of interactions between BCGs, and an in-depth examination of the dynamics between BCG progress and interaction. The results indicate a positive yet significantly north-south differentiated progress in developing a Beautiful China. BCG interaction identification based on document interpretation, correlation analysis, and city-level information mining reveals the differentiation among BCGs at global and local levels. The decoupling-recoupling process observed between the BCGs throughout the overall development of Beautiful China deepens our understanding of China's path toward environmentally sustainable development, which may inform policymaking for comprehensively and synergistically developing a Beautiful China.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"114 ","pages":"Article 107945"},"PeriodicalIF":9.8,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-09DOI: 10.1016/j.eiar.2025.107940
Qiang Wang , Shitong Ye , Xinhao Du , Kun Luo , Jianren Fan
Wind power has emerged as a prominent source of renewable energy, eliciting mounting apprehensions regarding its potential atmospheric ramifications. In this study, we employed the mesoscale Weather Research and Forecasting and Community Multiscale Air Quality modeling system, coupled with wind farm parameterization, to explore the effects of wind farm clusters on the diurnal variation of meteorological elements and air pollutants distributions at Zhangbei County in China. The results show that the extraction of wind power from the airflow has a greater impact on the atmosphere at night than during the day. At night, the atmosphere is relatively stable, wind farm clusters cause significant speed deficit, warming, and evaporation, making the atmospheric particulates sink into the lifted atmospheric flow mass and leading to an increase in the PM2.5 concentration of 1.31 μg/m3. During the daytime, the clusters increase the planetary boundary layer, facilitating the vertical diffusion of pollutants, and a decrease in PM2.5 of approximately 0.55 μg/m3 is still observed, though the diffusion weakened by the unstable atmosphere. Importantly, wind farm clusters redistribute air pollutants. The pollutant concentration in the wind farm's dense area decreases during the day and increases at night, while its spatial variation is negligible. It provides a feasible method to evaluate the environmental effects and a practical dataset for the diurnal redistribution of air pollutants caused by wind farms.
{"title":"Day-night redistribution of air pollutants induced by onshore wind farm clusters in winter","authors":"Qiang Wang , Shitong Ye , Xinhao Du , Kun Luo , Jianren Fan","doi":"10.1016/j.eiar.2025.107940","DOIUrl":"10.1016/j.eiar.2025.107940","url":null,"abstract":"<div><div>Wind power has emerged as a prominent source of renewable energy, eliciting mounting apprehensions regarding its potential atmospheric ramifications. In this study, we employed the mesoscale Weather Research and Forecasting and Community Multiscale Air Quality modeling system, coupled with wind farm parameterization, to explore the effects of wind farm clusters on the diurnal variation of meteorological elements and air pollutants distributions at Zhangbei County in China. The results show that the extraction of wind power from the airflow has a greater impact on the atmosphere at night than during the day. At night, the atmosphere is relatively stable, wind farm clusters cause significant speed deficit, warming, and evaporation, making the atmospheric particulates sink into the lifted atmospheric flow mass and leading to an increase in the PM<sub>2.5</sub> concentration of 1.31 μg/m<sup>3</sup>. During the daytime, the clusters increase the planetary boundary layer, facilitating the vertical diffusion of pollutants, and a decrease in PM<sub>2.5</sub> of approximately 0.55 μg/m<sup>3</sup> is still observed, though the diffusion weakened by the unstable atmosphere. Importantly, wind farm clusters redistribute air pollutants. The pollutant concentration in the wind farm's dense area decreases during the day and increases at night, while its spatial variation is negligible. It provides a feasible method to evaluate the environmental effects and a practical dataset for the diurnal redistribution of air pollutants caused by wind farms.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"114 ","pages":"Article 107940"},"PeriodicalIF":9.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-08DOI: 10.1016/j.eiar.2025.107937
Gaoyuan Wang , Liuying Wang , Mengyuan Jia , Tian Chen , Chye Kiang Heng
The dynamic mechanisms by which water constraints (WC) regulate socio-ecological system (SES) adaptation remain underexplored, hindering effective water governance strategies. This study fills that gap by examining how WC modulates SES adaptability throughout the adaptive cycle—revealing its regulatory role, feedback pathways, and changing influence on SES resilience over time. By integrating adaptive cycle theory, catastrophe progression models, the DPSIR framework, and cloud-based assessment techniques, we develop a dynamic analytical framework that captures the nonlinear, multi-scale interactions between WC and SES evolution in river basins. Our results show that WC exerts negative pressures during conservation (K) and release (Ω) phases while triggering adaptive shifts during reorganization (α) and exploitation (γ) phases. In 70 % of cities, HSS and EES exhibit a synergistic adaptation pattern, although the strength and direction of their coupling vary with the cycle stage. Moreover, WC has asymmetrical effects by stimulating HSS growth while constraining EES, creating a competition-cooperation dynamic between socio-economic and ecological functions. We also identify a unidirectional causality from WC to SES, in contrast to the bidirectional relationships between WC and both HSS and EES, indicating reciprocal adaptation processes. Impulse response analysis further reveals that while SES resilience to WC improves over time, excessive WC stress can lead to long-term ecological trade-offs and instability. Overall, our findings challenge the traditional view of WC as a static constraint and instead position it as a dynamic driver of socio-ecological restructuring, offering a predictive framework for assessing SES resilience and providing actionable insights for adaptive water governance and sustainable urban planning in resource-scarce regions globally.
{"title":"Dynamic evolution and interaction mechanisms of socio-ecological systems and water constraints within an adaptive cycle framework: A case study of the Lower Yangtze River Basin","authors":"Gaoyuan Wang , Liuying Wang , Mengyuan Jia , Tian Chen , Chye Kiang Heng","doi":"10.1016/j.eiar.2025.107937","DOIUrl":"10.1016/j.eiar.2025.107937","url":null,"abstract":"<div><div>The dynamic mechanisms by which water constraints (WC) regulate socio-ecological system (SES) adaptation remain underexplored, hindering effective water governance strategies. This study fills that gap by examining how WC modulates SES adaptability throughout the adaptive cycle—revealing its regulatory role, feedback pathways, and changing influence on SES resilience over time. By integrating adaptive cycle theory, catastrophe progression models, the DPSIR framework, and cloud-based assessment techniques, we develop a dynamic analytical framework that captures the nonlinear, multi-scale interactions between WC and SES evolution in river basins. Our results show that WC exerts negative pressures during conservation (K) and release (Ω) phases while triggering adaptive shifts during reorganization (α) and exploitation (γ) phases. In 70 % of cities, HSS and EES exhibit a synergistic adaptation pattern, although the strength and direction of their coupling vary with the cycle stage. Moreover, WC has asymmetrical effects by stimulating HSS growth while constraining EES, creating a competition-cooperation dynamic between socio-economic and ecological functions. We also identify a unidirectional causality from WC to SES, in contrast to the bidirectional relationships between WC and both HSS and EES, indicating reciprocal adaptation processes. Impulse response analysis further reveals that while SES resilience to WC improves over time, excessive WC stress can lead to long-term ecological trade-offs and instability. Overall, our findings challenge the traditional view of WC as a static constraint and instead position it as a dynamic driver of socio-ecological restructuring, offering a predictive framework for assessing SES resilience and providing actionable insights for adaptive water governance and sustainable urban planning in resource-scarce regions globally.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"114 ","pages":"Article 107937"},"PeriodicalIF":9.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-08DOI: 10.1016/j.eiar.2025.107939
Xingxing Liu , Yongjie Meng , Qing Yang , Jinmei Wang , Ling He
The Yangtze River Delta (YRD) region is a microcosm of China's high-quality socio-economic development, highlighting the influential role of transportation and the issue of carbon emission reduction. Given the complex relationship between thriving freight transportation and the carbon emission crisis, this study assesses the impact of freight structure adjustments on carbon emission reduction in the YRD using a crisis resolution model that combines bi-objective optimization (BOP) and logistic chaotic mapping (LCM). The dynamic Crisis Resolution Degree (CRD) metric evaluates carbon emission crisis resolution under different orientations. Results show that LCM improves model adaptability, aligning with real-world transportation dynamics. Freight adjustments and carbon emissions have non-linear interactions, with railway and air transportation being more flexible than road transportation. The YRD's future freight structure will shift to railway and waterway transportation, but road transportation will still play a key role. Efficiency-oriented solutions, combined with risk management, have high crisis resolution potential, while carbon reduction-oriented solutions, though environmentally beneficial, are less efficient. Recommendations include developing multimodal infrastructure, improving data monitoring, and implementing region-specific strategies.
{"title":"Evaluation of freight restructure to reduce carbon emissions in the Yangtze River Delta: A crisis-resolution model","authors":"Xingxing Liu , Yongjie Meng , Qing Yang , Jinmei Wang , Ling He","doi":"10.1016/j.eiar.2025.107939","DOIUrl":"10.1016/j.eiar.2025.107939","url":null,"abstract":"<div><div>The Yangtze River Delta (YRD) region is a microcosm of China's high-quality socio-economic development, highlighting the influential role of transportation and the issue of carbon emission reduction. Given the complex relationship between thriving freight transportation and the carbon emission crisis, this study assesses the impact of freight structure adjustments on carbon emission reduction in the YRD using a crisis resolution model that combines bi-objective optimization (BOP) and logistic chaotic mapping (LCM). The dynamic Crisis Resolution Degree (CRD) metric evaluates carbon emission crisis resolution under different orientations. Results show that LCM improves model adaptability, aligning with real-world transportation dynamics. Freight adjustments and carbon emissions have non-linear interactions, with railway and air transportation being more flexible than road transportation. The YRD's future freight structure will shift to railway and waterway transportation, but road transportation will still play a key role. Efficiency-oriented solutions, combined with risk management, have high crisis resolution potential, while carbon reduction-oriented solutions, though environmentally beneficial, are less efficient. Recommendations include developing multimodal infrastructure, improving data monitoring, and implementing region-specific strategies.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"114 ","pages":"Article 107939"},"PeriodicalIF":9.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Coal-fired power plants remain a major contributor to global CO₂ emissions, necessitating the urgent deployment of carbon capture technologies to mitigate climate impacts. This study evaluated four post-combustion carbon capture (PCC) systems – monoethanolamine (MEA)-based chemical absorption, ammonia-based absorption, membrane separation, and calcium looping (CaL) – through life cycle environmental and economic assessments at the power plant level, to assess the trade-offs between emission reductions and cost-effectiveness across technologies. The results showed that, compared with the baseline plant, PCC technologies reduced the global warming potential and acidification potential by 61.3–77.6 % and 66.2–83.5 %, respectively. Other environmental impact categories increased to varying degrees, in particular Abiotic Depletion Potential fossil (by 3.8–49.3 %) and the marine aquatic ecotoxicity potential (by 10.8–66.8 %), which account for more than 80 % of the total environmental impact. The total life cycle costs of PCC plants increased by 35–66 % compared with that of the baseline plant, with external costs decreasing by 66.5–78.1 % and internal costs increasing by 62.6–100.9 %. The CaL power plant had the lowest environmental impact of the PCC plants except for Abiotic Depletion Potential elements, which had the largest reduction in external costs (78.1 %) and an increase in internal costs (77.7 %). Membrane separation PCC plants had the lowest total life cycle cost, reducing external costs by 70.0 %, while increasing internal costs by only 62.6 % compared. Thus, we recommend deploying membrane separation as a PCC technology, which combines environmental and economic factors. Our findings provide a reference for companies to select and deploy carbon capture technology.
{"title":"Life cycle environmental impact and economic analysis of post-combustion carbon capture technologies in supercritical coal-fired power plants","authors":"Yue Wang, Shuai Shao, Qiufeng Gao, Yun Zhang, Xiaomeng Wang, Xinran Gao","doi":"10.1016/j.eiar.2025.107933","DOIUrl":"10.1016/j.eiar.2025.107933","url":null,"abstract":"<div><div>Coal-fired power plants remain a major contributor to global CO₂ emissions, necessitating the urgent deployment of carbon capture technologies to mitigate climate impacts. This study evaluated four post-combustion carbon capture (PCC) systems – monoethanolamine (MEA)-based chemical absorption, ammonia-based absorption, membrane separation, and calcium looping (CaL) – through life cycle environmental and economic assessments at the power plant level, to assess the trade-offs between emission reductions and cost-effectiveness across technologies. The results showed that, compared with the baseline plant, PCC technologies reduced the global warming potential and acidification potential by 61.3–77.6 % and 66.2–83.5 %, respectively. Other environmental impact categories increased to varying degrees, in particular Abiotic Depletion Potential fossil (by 3.8–49.3 %) and the marine aquatic ecotoxicity potential (by 10.8–66.8 %), which account for more than 80 % of the total environmental impact. The total life cycle costs of PCC plants increased by 35–66 % compared with that of the baseline plant, with external costs decreasing by 66.5–78.1 % and internal costs increasing by 62.6–100.9 %. The CaL power plant had the lowest environmental impact of the PCC plants except for Abiotic Depletion Potential elements, which had the largest reduction in external costs (78.1 %) and an increase in internal costs (77.7 %). Membrane separation PCC plants had the lowest total life cycle cost, reducing external costs by 70.0 %, while increasing internal costs by only 62.6 % compared. Thus, we recommend deploying membrane separation as a PCC technology, which combines environmental and economic factors. Our findings provide a reference for companies to select and deploy carbon capture technology.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"114 ","pages":"Article 107933"},"PeriodicalIF":9.8,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-07DOI: 10.1016/j.eiar.2025.107941
Xiaobo Shen , Boqiang Lin , Zhicheng Wang
Since data is increasingly recognized as a vital component in production, studying its role in low-carbon energy transition (LCET) is crucial for promoting sustainable urban development. This research focuses on Chinese cities and utilizes a Difference-in-Differences (DID) approach to empirically investigate the effects of data factor development (DFD) on LCET. The results indicate that DFD facilitates urban LCET, with green innovation and high-tech industry agglomeration as essential channels. Heterogeneity analysis reveals that non-central cities, old industrial bases, new energy demonstration cities, and cities with high digital industry activity show more substantial impacts from DFD on LCET. Further analysis reveals that the progress of green financing and the heightened government attention to the low-carbon environment substitute the beneficial effects of DFD on LCET. These findings provide policy insights for urban management authorities to steer economic digital transformation and advance the clean development of energy systems.
{"title":"Assessing the impact of data factor development on low-carbon energy transition: Insights from Chinese cities","authors":"Xiaobo Shen , Boqiang Lin , Zhicheng Wang","doi":"10.1016/j.eiar.2025.107941","DOIUrl":"10.1016/j.eiar.2025.107941","url":null,"abstract":"<div><div>Since data is increasingly recognized as a vital component in production, studying its role in low-carbon energy transition (LCET) is crucial for promoting sustainable urban development. This research focuses on Chinese cities and utilizes a Difference-in-Differences (DID) approach to empirically investigate the effects of data factor development (DFD) on LCET. The results indicate that DFD facilitates urban LCET, with green innovation and high-tech industry agglomeration as essential channels. Heterogeneity analysis reveals that non-central cities, old industrial bases, new energy demonstration cities, and cities with high digital industry activity show more substantial impacts from DFD on LCET. Further analysis reveals that the progress of green financing and the heightened government attention to the low-carbon environment substitute the beneficial effects of DFD on LCET. These findings provide policy insights for urban management authorities to steer economic digital transformation and advance the clean development of energy systems.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"114 ","pages":"Article 107941"},"PeriodicalIF":9.8,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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