Pub Date : 2026-06-01Epub Date: 2026-01-28DOI: 10.1016/j.eiar.2026.108352
Zitong Zhang, Nan Zhang
Urban areas play a critical role in reconciling carbon reduction with welfare enhancement under global carbon neutrality goals. This study evaluates the Carbon Welfare Performance (CWP) of 284 Chinese cities (2000−2023) using a novel two-stage Super-Network Slacks-Based Measure (Super-NSBM) model. By decomposing CWP into green production efficiency (GPE) and welfare transformation efficiency (WTE), we identify significant structural imbalances: while national CWP showed modest growth, this was solely driven by a 38.2% increase in WTE, whereas GPE declined by 31.5%. Spatially, CWP evolved from fragmented clusters to a multi-polar pattern. Dagum Gini decomposition reveals that regional disparities are primarily attributable to cross-regional overlaps rather than simple east-west divisions, with GPE being the main source of inequality. Slack analysis further identifies three inefficiency patterns, which are high redundancy–high emissions, capital inefficiency–structural mismatch, and high output–environmental shortfall, providing a basis for targeted policy interventions. These findings integrate welfare dimensions into carbon efficiency evaluation and offer valuable insights for fostering low-carbon, welfare-enhancing urban transitions in China and other developing economies.
{"title":"Spatiotemporal dynamics of carbon welfare performance: Evidence from a two-stage super-NSBM analysis of Chinese cities","authors":"Zitong Zhang, Nan Zhang","doi":"10.1016/j.eiar.2026.108352","DOIUrl":"10.1016/j.eiar.2026.108352","url":null,"abstract":"<div><div>Urban areas play a critical role in reconciling carbon reduction with welfare enhancement under global carbon neutrality goals. This study evaluates the Carbon Welfare Performance (CWP) of 284 Chinese cities (2000−2023) using a novel two-stage Super-Network Slacks-Based Measure (Super-NSBM) model. By decomposing CWP into green production efficiency (GPE) and welfare transformation efficiency (WTE), we identify significant structural imbalances: while national CWP showed modest growth, this was solely driven by a 38.2% increase in WTE, whereas GPE declined by 31.5%. Spatially, CWP evolved from fragmented clusters to a multi-polar pattern. Dagum Gini decomposition reveals that regional disparities are primarily attributable to cross-regional overlaps rather than simple east-west divisions, with GPE being the main source of inequality. Slack analysis further identifies three inefficiency patterns, which are high redundancy–high emissions, capital inefficiency–structural mismatch, and high output–environmental shortfall, providing a basis for targeted policy interventions. These findings integrate welfare dimensions into carbon efficiency evaluation and offer valuable insights for fostering low-carbon, welfare-enhancing urban transitions in China and other developing economies.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"119 ","pages":"Article 108352"},"PeriodicalIF":11.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146075172","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 : 2026-06-01Epub Date: 2026-01-19DOI: 10.1016/j.eiar.2026.108349
Yunsheng Bai , Gengyuan Liu , Yang Guo , Nan Zhang , Frederick Kwame Yeboah , Pei Wang , Zhaobo Liu , Liang Dong
This paper explores the conceptual and practical expansion of industrial symbiosis (IS) to the realm of industry-city integration (ICI), and further proposes industry-city co-prosperity as a new paradigm for sustainable urban-industrial development. A systematic literature review was conducted. This review analyzed 132 selected studies to trace the evolution from firm-centered IS network to multi-scalar urban-industrial synergies. The results indicate that traditional IS, while successful in enabling resource-sharing among firms, remains limited by spatial confinement and administrative boundaries. ICI bridges this gap by redefining the city as a functional node of industrial and talent chains, extending symbiotic practices to cross-regional metabolic flows and social integration. A typology of three distinct governance models for ICI (Top-Down Planning, Self-Organizing, and Government-Promotion) is developed and a multi-dimensional comparative analysis of international cases under each model is conducted. The findings reveal that no single model is sufficient; instead, a hybrid governance architecture is essential to overcome administrative silos and foster emerging circular service industries. The paper concludes that aligning industrial development with urban sustainability requires moving beyond resource efficiency toward a state of dynamic, functional synergy and inclusive governance.
{"title":"From symbiosis to co-prosperity: Redefining industry-city integration for urban resilience","authors":"Yunsheng Bai , Gengyuan Liu , Yang Guo , Nan Zhang , Frederick Kwame Yeboah , Pei Wang , Zhaobo Liu , Liang Dong","doi":"10.1016/j.eiar.2026.108349","DOIUrl":"10.1016/j.eiar.2026.108349","url":null,"abstract":"<div><div>This paper explores the conceptual and practical expansion of industrial symbiosis (IS) to the realm of industry-city integration (ICI), and further proposes industry-city co-prosperity as a new paradigm for sustainable urban-industrial development. A systematic literature review was conducted. This review analyzed 132 selected studies to trace the evolution from firm-centered IS network to multi-scalar urban-industrial synergies. The results indicate that traditional IS, while successful in enabling resource-sharing among firms, remains limited by spatial confinement and administrative boundaries. ICI bridges this gap by redefining the city as a functional node of industrial and talent chains, extending symbiotic practices to cross-regional metabolic flows and social integration. A typology of three distinct governance models for ICI (Top-Down Planning, Self-Organizing, and Government-Promotion) is developed and a multi-dimensional comparative analysis of international cases under each model is conducted. The findings reveal that no single model is sufficient; instead, a hybrid governance architecture is essential to overcome administrative silos and foster emerging circular service industries. The paper concludes that aligning industrial development with urban sustainability requires moving beyond resource efficiency toward a state of dynamic, functional synergy and inclusive governance.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"119 ","pages":"Article 108349"},"PeriodicalIF":11.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025612","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 : 2026-06-01Epub Date: 2026-02-14DOI: 10.1016/j.eiar.2026.108392
N.C.R. Ferreira , D.R. Rosa , L.N. Ferreira , D.C. Rodrigues , M. Barbari , S.C. Chou , R.R. Andrade
Rising temperatures and humidity threaten global livestock production and food security. This study assesses the future impacts of heat stress in regions with high livestock production in Brazil by analyzing high-resolution (5 km) climate projections from the Eta Regional Climate Model under the RCP4.5 and RCP8.5 scenarios. Using the Temperature-Humidity Index (THI), we evaluated future heat stress conditions for dairy and beef cattle, goats, sheep, pigs, broilers, and layers. The results indicate a substantial increase in both the frequency and intensity of heat stress events across Brazil during the 21st century, especially under the RCP8.5 scenario. The number of days reaching extreme heat-stress levels (ndaysTHI) is projected to increase significantly, particularly in the western and central regions of the study area, and for more vulnerable species such as pigs and poultry. For instance, the number of extreme THI days for dairy cattle and goats is projected to increase by up to 244 days by the end of the century (long-term period compared to historical) under RCP8.5. This research provides essential data for developing effective and sustainable adaptation strategies to mitigate the effects of climate change on Brazil's livestock sector, offering insights applicable to other countries facing similar challenges. It addresses a significant knowledge gap by providing high-resolution, localized projections of future heat stress across Brazil's major production regions.
{"title":"Regional impacts of heat stress on livestock in Brazil under climate change scenarios","authors":"N.C.R. Ferreira , D.R. Rosa , L.N. Ferreira , D.C. Rodrigues , M. Barbari , S.C. Chou , R.R. Andrade","doi":"10.1016/j.eiar.2026.108392","DOIUrl":"10.1016/j.eiar.2026.108392","url":null,"abstract":"<div><div>Rising temperatures and humidity threaten global livestock production and food security. This study assesses the future impacts of heat stress in regions with high livestock production in Brazil by analyzing high-resolution (5 km) climate projections from the Eta Regional Climate Model under the RCP4.5 and RCP8.5 scenarios. Using the Temperature-Humidity Index (THI), we evaluated future heat stress conditions for dairy and beef cattle, goats, sheep, pigs, broilers, and layers. The results indicate a substantial increase in both the frequency and intensity of heat stress events across Brazil during the 21st century, especially under the RCP8.5 scenario. The number of days reaching extreme heat-stress levels (<em>ndaysTHI</em>) is projected to increase significantly, particularly in the western and central regions of the study area, and for more vulnerable species such as pigs and poultry. For instance, the number of extreme THI days for dairy cattle and goats is projected to increase by up to 244 days by the end of the century (long-term period compared to historical) under RCP8.5. This research provides essential data for developing effective and sustainable adaptation strategies to mitigate the effects of climate change on Brazil's livestock sector, offering insights applicable to other countries facing similar challenges. It addresses a significant knowledge gap by providing high-resolution, localized projections of future heat stress across Brazil's major production regions.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"119 ","pages":"Article 108392"},"PeriodicalIF":11.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170828","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 : 2026-06-01Epub Date: 2026-01-21DOI: 10.1016/j.eiar.2026.108346
Zhixiu Li , Yifei Sun , Jiajing Song , Yihan Wang , Yangyang Wei
With the increasing prominence of mental health issues among humans, the restorative benefits of natural environments have garnered widespread attention. As a typical high-restorative living environment, the forest village plays a significant role in generating positive restorative effects. Previous studies have mainly explored the simple correlations between environmental characteristics and psychological or physiological indicators, while the differences in restorative benefits between virtual and real environments have not yet been systematically quantified within a unified experimental framework. This study employs electroencephalography (EEG) technology through a dual-modal experiment of virtual reality (VR) observation and real-world experience to quantify the neurophysiological impacts of forest village environments on psychological restoration. Based on structural equation modeling analysis, it reveals the causal relationships between environmental characteristics and brainwave activity. Using national forest villages as case examples, EEG data were collected from participants with the eego™ mylab device. Combining restorative evaluation and environmental preference scales, the study comprehensively analyzes the “psychological–physiological” response mechanisms underlying the restorative benefits of typical sample environments. The results show that the forest village environment significantly enhances α wave power (real-world group: 0.351; VR group: 0.314; p < 0.05) and suppresses excessive β wave activity (real-world group: −0.242; VR group: −0.213; p < 0.05), confirming its neural mechanisms in stress alleviation and relaxation promotion. Environmental preference indirectly regulates brainwave activity through restorative evaluation, with “mystery” showing the highest explanatory power (real-world group standardized factor loading λ = 0.847, explanatory power λ2 = 71.7%; VR group λ = 0.821, λ2 = 67.4%). This study proposes an interdisciplinary framework and dynamic feedback pathway of “environmental preference–psychological evaluation–neural response.” It not only provides neuroscientific evidence for the restorative benefits of forest village environments and promotes a data-driven transformation in environmental psychology, but also offers new insights into the design of ecological wellness scenarios and the development of remote environmental healing systems.
{"title":"Neurophysiological assessment of restorative benefits in forest-rural landscapes: EEG responses to real-world and virtual environments","authors":"Zhixiu Li , Yifei Sun , Jiajing Song , Yihan Wang , Yangyang Wei","doi":"10.1016/j.eiar.2026.108346","DOIUrl":"10.1016/j.eiar.2026.108346","url":null,"abstract":"<div><div>With the increasing prominence of mental health issues among humans, the restorative benefits of natural environments have garnered widespread attention. As a typical high-restorative living environment, the forest village plays a significant role in generating positive restorative effects. Previous studies have mainly explored the simple correlations between environmental characteristics and psychological or physiological indicators, while the differences in restorative benefits between virtual and real environments have not yet been systematically quantified within a unified experimental framework. This study employs electroencephalography (EEG) technology through a dual-modal experiment of virtual reality (VR) observation and real-world experience to quantify the neurophysiological impacts of forest village environments on psychological restoration. Based on structural equation modeling analysis, it reveals the causal relationships between environmental characteristics and brainwave activity. Using national forest villages as case examples, EEG data were collected from participants with the eego™ mylab device. Combining restorative evaluation and environmental preference scales, the study comprehensively analyzes the “psychological–physiological” response mechanisms underlying the restorative benefits of typical sample environments. The results show that the forest village environment significantly enhances α wave power (real-world group: 0.351; VR group: 0.314; <em>p</em> < 0.05) and suppresses excessive β wave activity (real-world group: −0.242; VR group: −0.213; p < 0.05), confirming its neural mechanisms in stress alleviation and relaxation promotion. Environmental preference indirectly regulates brainwave activity through restorative evaluation, with “mystery” showing the highest explanatory power (real-world group standardized factor loading λ = 0.847, explanatory power λ<sup>2</sup> = 71.7%; VR group λ = 0.821, λ<sup>2</sup> = 67.4%). This study proposes an interdisciplinary framework and dynamic feedback pathway of “environmental preference–psychological evaluation–neural response.” It not only provides neuroscientific evidence for the restorative benefits of forest village environments and promotes a data-driven transformation in environmental psychology, but also offers new insights into the design of ecological wellness scenarios and the development of remote environmental healing systems.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"119 ","pages":"Article 108346"},"PeriodicalIF":11.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025615","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 : 2026-06-01Epub Date: 2026-01-19DOI: 10.1016/j.eiar.2026.108342
Luming Yan , Ruilian Zhang , Ming Ji , Yujian Li
This study investigates the impact of China's Social Stability Risk Assessment (SSRA) policy on the effectiveness of social governance. By analyzing policy implementation across various administrative regions and evaluating governance outcomes using a comprehensive index framework, the paper assesses whether SSRA contributes to proactive conflict resolution, enhanced public participation, and improved administrative coordination. Empirical evidence from regional case studies and statistical analyses suggests that the SSRA policy positively correlates with improvements in social governance, particularly in regions with strong institutional capacities and transparent risk evaluation mechanisms. However, the policy's effectiveness is uneven across jurisdictions, highlighting the importance of local governance conditions and policy enforcement quality. The findings offer insights into the role of preventive governance tools in maintaining social stability and enhancing state-society relations in transitional governance contexts.
{"title":"Does social stability risk assessment improve social governance level in China?","authors":"Luming Yan , Ruilian Zhang , Ming Ji , Yujian Li","doi":"10.1016/j.eiar.2026.108342","DOIUrl":"10.1016/j.eiar.2026.108342","url":null,"abstract":"<div><div>This study investigates the impact of China's Social Stability Risk Assessment (SSRA) policy on the effectiveness of social governance. By analyzing policy implementation across various administrative regions and evaluating governance outcomes using a comprehensive index framework, the paper assesses whether SSRA contributes to proactive conflict resolution, enhanced public participation, and improved administrative coordination. Empirical evidence from regional case studies and statistical analyses suggests that the SSRA policy positively correlates with improvements in social governance, particularly in regions with strong institutional capacities and transparent risk evaluation mechanisms. However, the policy's effectiveness is uneven across jurisdictions, highlighting the importance of local governance conditions and policy enforcement quality. The findings offer insights into the role of preventive governance tools in maintaining social stability and enhancing state-society relations in transitional governance contexts.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"119 ","pages":"Article 108342"},"PeriodicalIF":11.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025611","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 : 2026-06-01Epub Date: 2026-02-06DOI: 10.1016/j.eiar.2026.108364
Israel Carreira-Barral , Ana García-Moral , María Emilia Iñigo-Martínez , Julieta Díez-Hernández , Jesús Ibáñez , Mario Alonso-Terán , Patricia De La Fuente , Rocío Barros , Sonia Martel-Martín
This work proposes a straightforward methodology for integrating the environmental, economic, social, criticality and circularity dimensions as normalised indicators into a single equation, yielding a sustainability index, and demonstrates its applicability in the context of the steelmaking industry. This new approach, designed for early development stages and based on the elemental composition of the input materials (metal scraps and raw materials), allows the identification of those within a dataset that are of greatest concern according to their sustainability index and facilitates decision-making regarding their use in alloy production. A sensitivity analysis, with 11 studied scenarios, was conducted to evaluate the influence of the five indicators on the outcome, assigning different weights to them. The developed strategy, compatible with the Safe and Sustainable by Design framework, was successfully applied to a family of 207 materials of varying qualities. A set of raw materials, including both ferroalloys and pure elements, was identified as the most worrying group from the sustainability viewpoint, in line with previous works (e.g., ferroniobium, ferrotungsten, pure cobalt and pure copper), thereby validating the described framework. However, metal scraps should, whenever feasible, be prioritised, as their recovery would reduce the reliance on mineral resources. Consequently, a number of them are presented as alternatives to the least sustainable raw materials according to their sustainability indexes. The application of this methodology provides a holistic view of sustainability and enables rapid decisions regarding which products from a given set are more suitable for use, based on their index values and stakeholder needs.
{"title":"A five-indicator methodology for early-stage sustainable selection of metal scraps and raw materials: Application in the steel industry","authors":"Israel Carreira-Barral , Ana García-Moral , María Emilia Iñigo-Martínez , Julieta Díez-Hernández , Jesús Ibáñez , Mario Alonso-Terán , Patricia De La Fuente , Rocío Barros , Sonia Martel-Martín","doi":"10.1016/j.eiar.2026.108364","DOIUrl":"10.1016/j.eiar.2026.108364","url":null,"abstract":"<div><div>This work proposes a straightforward methodology for integrating the environmental, economic, social, criticality and circularity dimensions as normalised indicators into a single equation, yielding a sustainability index, and demonstrates its applicability in the context of the steelmaking industry. This new approach, designed for early development stages and based on the elemental composition of the input materials (metal scraps and raw materials), allows the identification of those within a dataset that are of greatest concern according to their sustainability index and facilitates decision-making regarding their use in alloy production. A sensitivity analysis, with 11 studied scenarios, was conducted to evaluate the influence of the five indicators on the outcome, assigning different weights to them. The developed strategy, compatible with the Safe and Sustainable by Design framework, was successfully applied to a family of 207 materials of varying qualities. A set of raw materials, including both ferroalloys and pure elements, was identified as the most worrying group from the sustainability viewpoint, in line with previous works (<em>e.g.</em>, ferroniobium, ferrotungsten, pure cobalt and pure copper), thereby validating the described framework. However, metal scraps should, whenever feasible, be prioritised, as their recovery would reduce the reliance on mineral resources. Consequently, a number of them are presented as alternatives to the least sustainable raw materials according to their sustainability indexes. The application of this methodology provides a holistic view of sustainability and enables rapid decisions regarding which products from a given set are more suitable for use, based on their index values and stakeholder needs.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"119 ","pages":"Article 108364"},"PeriodicalIF":11.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170883","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 : 2026-06-01Epub Date: 2026-02-18DOI: 10.1016/j.eiar.2026.108393
Chenyi Song , Yuting Huang , Rui Wang , Daqiang Yin
As global urbanization accelerates, air pollution has become a critical environmental challenge affecting urban public health. Although previous studies under the population-exposure paradigm have revealed the relationship between human activity and air pollution, few have examined how the spatiotemporal variations and inequalities of PM2.5 exposure risk (PER) relate to environmental characteristics. In particular, the influence of intraday population dynamics on PER has received limited attention. To address these gaps, we developed an environment-spatiotemporal-behavior-exposure analytical framework using big data and interpretable machine learning. Hourly-scale assessments of PER dynamics, identification of PER-evolving hotspots, spatial inequality analysis associated with multiple vulnerable groups, and systematic investigation of environmental factors impacting PER were performed. Using Shanghai as a case study, we found that spatial disparities in PER were primarily driven by resident population, whereas its temporal fluctuations were dominated by PM2.5 concentration. Persistent PER hotspots were concentrated in high-density urban centers, sub-centers, and job-housing integrated zones within the western industrial cluster, with fluctuating PER hotspots surrounding them. Low-income and migrant groups were subject to higher PER, while spatial inequalities associated with the elderly and children covered broader spatial extents. Environmental factors exerted nonlinear effects on PER, including positive-negative conversion threshold effects, marginal saturation, non-monotonicity, and temporal sensitivity. Their interaction effects further revealed multi-phase fluctuation patterns. Based on these findings, targeted strategies emphasizing spatiotemporal collaborative governance were proposed. These strategies incorporated targeted interventions by time, location, and population group to mitigate PER and its inequality, thereby supporting healthy urban planning and air quality management.
{"title":"Toward equitable air environment: Hourly spatiotemporal hotspot evolution and impact mechanisms of urban PM2.5 exposure risk","authors":"Chenyi Song , Yuting Huang , Rui Wang , Daqiang Yin","doi":"10.1016/j.eiar.2026.108393","DOIUrl":"10.1016/j.eiar.2026.108393","url":null,"abstract":"<div><div>As global urbanization accelerates, air pollution has become a critical environmental challenge affecting urban public health. Although previous studies under the population-exposure paradigm have revealed the relationship between human activity and air pollution, few have examined how the spatiotemporal variations and inequalities of PM<sub>2.5</sub> exposure risk (PER) relate to environmental characteristics. In particular, the influence of intraday population dynamics on PER has received limited attention. To address these gaps, we developed an environment-spatiotemporal-behavior-exposure analytical framework using big data and interpretable machine learning. Hourly-scale assessments of PER dynamics, identification of PER-evolving hotspots, spatial inequality analysis associated with multiple vulnerable groups, and systematic investigation of environmental factors impacting PER were performed. Using Shanghai as a case study, we found that spatial disparities in PER were primarily driven by resident population, whereas its temporal fluctuations were dominated by PM<sub>2.5</sub> concentration. Persistent PER hotspots were concentrated in high-density urban centers, sub-centers, and job-housing integrated zones within the western industrial cluster, with fluctuating PER hotspots surrounding them. Low-income and migrant groups were subject to higher PER, while spatial inequalities associated with the elderly and children covered broader spatial extents. Environmental factors exerted nonlinear effects on PER, including positive-negative conversion threshold effects, marginal saturation, non-monotonicity, and temporal sensitivity. Their interaction effects further revealed multi-phase fluctuation patterns. Based on these findings, targeted strategies emphasizing spatiotemporal collaborative governance were proposed. These strategies incorporated targeted interventions by time, location, and population group to mitigate PER and its inequality, thereby supporting healthy urban planning and air quality management.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"119 ","pages":"Article 108393"},"PeriodicalIF":11.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147384874","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 : 2026-06-01Epub Date: 2026-02-06DOI: 10.1016/j.eiar.2026.108374
Qingwei Shi , Zaiwen Jia , Weiguang Cai
The building sector, which contributes significantly to carbon emissions, faces increasing carbon lock-in risks. This has led the Chinese government to implement several energy-saving and emission-reduction policies aimed at overcoming high‑carbon path dependencies at the urban level. However, the mechanisms by which policy combinations affect carbon lock-ins in the building sector remain unclear. To address this limitation, this study used data from 284 prefecture-level cities in China and employed the Generalized Divisia Index Method to develop a framework for evaluating building carbon lock-ins. The Real-coded Accelerating Genetic Algorithm-Projection Pursuit model, dual machine learning, and Monte Carlo simulations were then used to systematically predict the mitigating effects of policy combinations on building carbon lock-ins. The results revealed that: (1) approximately one-third of Chinese cities exhibited significant building sector carbon lock-in issues; (2) economic expansion, energy consumption, and building area growth were the primary drivers of building sector carbon emissions, contributing over 85%; and (3) optimal policy combinations varied significantly across different cities, with the Low-Carbon City Pilot Program and New Energy Demonstration City Program demonstrating notable synergistic effects. Appropriate combinations were found to shorten the time required for urban building carbon unlocking by 3–4 years. This study scientifically simulated city-tailored policy combinations, providing evidence-based insights and decision support for optimizing emission-reduction policy combinations and addressing carbon lock-ins in the building sector.
{"title":"Diversified policy mix selection for building carbon lock-in risk mitigation","authors":"Qingwei Shi , Zaiwen Jia , Weiguang Cai","doi":"10.1016/j.eiar.2026.108374","DOIUrl":"10.1016/j.eiar.2026.108374","url":null,"abstract":"<div><div>The building sector, which contributes significantly to carbon emissions, faces increasing carbon lock-in risks. This has led the Chinese government to implement several energy-saving and emission-reduction policies aimed at overcoming high‑carbon path dependencies at the urban level. However, the mechanisms by which policy combinations affect carbon lock-ins in the building sector remain unclear. To address this limitation, this study used data from 284 prefecture-level cities in China and employed the Generalized Divisia Index Method to develop a framework for evaluating building carbon lock-ins. The Real-coded Accelerating Genetic Algorithm-Projection Pursuit model, dual machine learning, and Monte Carlo simulations were then used to systematically predict the mitigating effects of policy combinations on building carbon lock-ins. The results revealed that: (1) approximately one-third of Chinese cities exhibited significant building sector carbon lock-in issues; (2) economic expansion, energy consumption, and building area growth were the primary drivers of building sector carbon emissions, contributing over 85%; and (3) optimal policy combinations varied significantly across different cities, with the Low-Carbon City Pilot Program and New Energy Demonstration City Program demonstrating notable synergistic effects. Appropriate combinations were found to shorten the time required for urban building carbon unlocking by 3–4 years. This study scientifically simulated city-tailored policy combinations, providing evidence-based insights and decision support for optimizing emission-reduction policy combinations and addressing carbon lock-ins in the building sector.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"119 ","pages":"Article 108374"},"PeriodicalIF":11.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170854","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 : 2026-06-01Epub Date: 2026-02-06DOI: 10.1016/j.eiar.2026.108376
Charles Joseph , Qingxia Jenny Wang , Shahbaz Mushtaq , Yan Li , Jonathan Barratt , Tim Barratt
Weather index insurance (WII) is a promising climate risk management tool, offering a robust mechanism to enhance agricultural resilience and mitigate the impacts of weather extremes driven by increasing climate variability. The continuous evolution of computational modelling techniques presents significant opportunities to improve the development, accuracy, and reliability of WII schemes. This systematic review, conducted following PRISMA guidelines, meticulously analyzes 87 peer-reviewed studies (2008–2025). The primary focus of the study is on advanced modelling approaches for WII design, evaluation, and optimization, along with an in-depth examination of data sources and their integration. The review categorizes modelling techniques into traditional statistical methods and advanced machine learning and deep learning, highlighting their roles in hazard identification, vulnerability assessment, and insurance pricing. Furthermore, emerging technologies like blockchain and the Internet of Things (IoT) are explored for their potential to support transparent, automated, and scalable insurance delivery. Special attention is given to integrating multi-source climate data (ground-based, gridded, satellite) and addressing critical challenges such as basis risk, model validation, and spatiotemporal alignment. We identify 49 unique indices for quantifying climate indicators across various hazards and evaluate modelling frameworks for capturing complex climate-agriculture interactions. The study provides a comprehensive roadmap by reviewing modelling innovations, data integration practices, index design strategies, and policy frameworks for strengthening WII as a climate adaptation mechanism, supporting sustainability indicators aligned with global resilience goals for vulnerable agricultural systems facing rising climate risks.
{"title":"Advancing weather index insurance for climate risk management: A review of modelling techniques and implementation strategies","authors":"Charles Joseph , Qingxia Jenny Wang , Shahbaz Mushtaq , Yan Li , Jonathan Barratt , Tim Barratt","doi":"10.1016/j.eiar.2026.108376","DOIUrl":"10.1016/j.eiar.2026.108376","url":null,"abstract":"<div><div>Weather index insurance (WII) is a promising climate risk management tool, offering a robust mechanism to enhance agricultural resilience and mitigate the impacts of weather extremes driven by increasing climate variability. The continuous evolution of computational modelling techniques presents significant opportunities to improve the development, accuracy, and reliability of WII schemes. This systematic review, conducted following PRISMA guidelines, meticulously analyzes 87 peer-reviewed studies (2008–2025). The primary focus of the study is on advanced modelling approaches for WII design, evaluation, and optimization, along with an in-depth examination of data sources and their integration. The review categorizes modelling techniques into traditional statistical methods and advanced machine learning and deep learning, highlighting their roles in hazard identification, vulnerability assessment, and insurance pricing. Furthermore, emerging technologies like blockchain and the Internet of Things (IoT) are explored for their potential to support transparent, automated, and scalable insurance delivery. Special attention is given to integrating multi-source climate data (ground-based, gridded, satellite) and addressing critical challenges such as basis risk, model validation, and spatiotemporal alignment. We identify 49 unique indices for quantifying climate indicators across various hazards and evaluate modelling frameworks for capturing complex climate-agriculture interactions. The study provides a comprehensive roadmap by reviewing modelling innovations, data integration practices, index design strategies, and policy frameworks for strengthening WII as a climate adaptation mechanism, supporting sustainability indicators aligned with global resilience goals for vulnerable agricultural systems facing rising climate risks.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"119 ","pages":"Article 108376"},"PeriodicalIF":11.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146170856","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 : 2026-06-01Epub Date: 2026-01-20DOI: 10.1016/j.eiar.2026.108344
Xiangying Kong , Shengquan Lu , Baoqing Hu , Yurou Liang , Jiaxin Li
Topography critically shapes the distribution of Rural Settlements (RS). However, previous studies have often neglected the systematic role of topographic gradients, typically focusing on macro scales, which obscures the nuanced patterns and underlying mechanisms at the village level. To address this, we developed a two-dimensional elevation-slope framework to reconstruct the 40-year evolution of China's RS at the administrative village scale. We then quantified its morphological changes at the village level and employed a Geographically Weighted Machine Learning (GWML) framework, which integrates geographically weighted principles with machine learning capabilities to capture the spatial heterogeneity and non-linear effects of the driving factors. Our findings reveal a highly uneven RS distribution. By 2020, 78.49% of the settlement area was concentrated in Low elevation-Low slope (L-L) regions, comprising just 21.74% of China's landmass. Over the past four decades, expansion has trended towards higher elevations and steeper slopes, though patterns and land sources varied significantly by terrain. Plains expansion was dominated by edge-expansion onto Cultivated Land, whereas in topographically complex regions, it was more dispersed with diverse sources. Furthermore, settlement density in L-L villages was over a hundredfold greater than in High elevation-High slope (HH) villages. The optimal Geographically Weighted Random Forest (GWRF) model shows that expansion in plains is driven by land use intensity and village scale, while in complex terrains, it is governed by ecological constraints or economic density. This study systematically dissects the dynamic patterns and morphological differentiation of rural settlements under topographic constraints, offering scientific insights for rural revitalisation and regional planning.
{"title":"Understanding the village-scale expansion of rural settlements in China from a topographic perspective","authors":"Xiangying Kong , Shengquan Lu , Baoqing Hu , Yurou Liang , Jiaxin Li","doi":"10.1016/j.eiar.2026.108344","DOIUrl":"10.1016/j.eiar.2026.108344","url":null,"abstract":"<div><div>Topography critically shapes the distribution of Rural Settlements (RS). However, previous studies have often neglected the systematic role of topographic gradients, typically focusing on macro scales, which obscures the nuanced patterns and underlying mechanisms at the village level. To address this, we developed a two-dimensional elevation-slope framework to reconstruct the 40-year evolution of China's RS at the administrative village scale. We then quantified its morphological changes at the village level and employed a Geographically Weighted Machine Learning (GWML) framework, which integrates geographically weighted principles with machine learning capabilities to capture the spatial heterogeneity and non-linear effects of the driving factors. Our findings reveal a highly uneven RS distribution. By 2020, 78.49% of the settlement area was concentrated in Low elevation-Low slope (L-L) regions, comprising just 21.74% of China's landmass. Over the past four decades, expansion has trended towards higher elevations and steeper slopes, though patterns and land sources varied significantly by terrain. Plains expansion was dominated by edge-expansion onto Cultivated Land, whereas in topographically complex regions, it was more dispersed with diverse sources. Furthermore, settlement density in L-L villages was over a hundredfold greater than in High elevation-High slope (H<img>H) villages. The optimal Geographically Weighted Random Forest (GWRF) model shows that expansion in plains is driven by land use intensity and village scale, while in complex terrains, it is governed by ecological constraints or economic density. This study systematically dissects the dynamic patterns and morphological differentiation of rural settlements under topographic constraints, offering scientific insights for rural revitalisation and regional planning.</div></div>","PeriodicalId":309,"journal":{"name":"Environmental Impact Assessment Review","volume":"119 ","pages":"Article 108344"},"PeriodicalIF":11.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025613","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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