Pub Date : 2024-10-18DOI: 10.1016/j.scs.2024.105920
Hao Wu , Yi Yang , Wen Li
Land use/land cover (LULC) structure optimization can effectively increase carbon storage/carbon sequestration (CS) and help realize carbon neutrality goals1. Studying the spatial distributions of LULC and CS under climate change conditions is highly important for realizing sustainable development goals. This study is based on different climate change models, and the coordinated development of economic, water, carbon and ecological sustainability was considered to establish a comprehensive multiscale, multiscenario and multiobjective LULC optimization model. Then, different climate change scenarios were optimized, and regional CS values were predicted. The LULC simulation model provided satisfactory simulation results at different scales. Notably, the average accuracy exceeded 0.92. The optimized land expansion results exhibited heterogeneity. Forestland change accounted for the largest proportion of the total LULC change. After optimization, the CS values under the different scenarios were similar. The northwestern part of the study area served as the main carbon sink area. The aim of this study was to respond to future complex climate change by rationally planning the LULC structure, thus achieving the sustainable development of urban agglomerations.
{"title":"Spatial optimization of land use and carbon storage prediction in urban agglomerations under climate change: Different scenarios and multiscale perspectives of CMIP6","authors":"Hao Wu , Yi Yang , Wen Li","doi":"10.1016/j.scs.2024.105920","DOIUrl":"10.1016/j.scs.2024.105920","url":null,"abstract":"<div><div>Land use/land cover (LULC) structure optimization can effectively increase carbon storage/carbon sequestration (CS) and help realize carbon neutrality goals<sup>1</sup>. Studying the spatial distributions of LULC and CS under climate change conditions is highly important for realizing sustainable development goals. This study is based on different climate change models, and the coordinated development of economic, water, carbon and ecological sustainability was considered to establish a comprehensive multiscale, multiscenario and multiobjective LULC optimization model. Then, different climate change scenarios were optimized, and regional CS values were predicted. The LULC simulation model provided satisfactory simulation results at different scales. Notably, the average accuracy exceeded 0.92. The optimized land expansion results exhibited heterogeneity. Forestland change accounted for the largest proportion of the total LULC change. After optimization, the CS values under the different scenarios were similar. The northwestern part of the study area served as the main carbon sink area. The aim of this study was to respond to future complex climate change by rationally planning the LULC structure, thus achieving the sustainable development of urban agglomerations.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105920"},"PeriodicalIF":10.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.scs.2024.105918
Jialin Du , Weihao Hu , Sen Zhang , Wen Liu , Zhenyuan Zhang , Daojuan Wang , Zhe Chen
The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.
{"title":"A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties","authors":"Jialin Du , Weihao Hu , Sen Zhang , Wen Liu , Zhenyuan Zhang , Daojuan Wang , Zhe Chen","doi":"10.1016/j.scs.2024.105918","DOIUrl":"10.1016/j.scs.2024.105918","url":null,"abstract":"<div><div>The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105918"},"PeriodicalIF":10.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.scs.2024.105916
Chunguang Hu , Maomao Zhang , Gaoliu Huang , Zhuoqi Li , Yucheng Sun , Jianqing Zhao
China's rapid economic growth and urbanization have caused significant Land Cover Changes (LCC), worsened the Urban Heat Island (UHI) effect and reducing the Thermal Comfort (TC). Despite existing studies, there remains a gap in understanding the specific contributions of various LCC types to the TC, particularly in Qinhuai River Basin. This study addresses this gap by examining the LCC effects from 2013 to 2022 based on targeted metrics. We propose a novel TC classification model and introduce indices, including the Land Cover Contribution Index (LCI) and the Land Cover Classification Contribution Index (LCCI), to quantify the influence of different LCC types on the TC. Our findings reveal that farmland and woodland positively impact the TC, while the negative influence of impervious surfaces has intensified. The area of farmland in the most comfortable category has shown significant variability, while impermeable surfaces in uncomfortable and very uncomfortable categories have surged. Additionally, the Urban Water Body Contribution Index (U-WCI) consistently exceeded the Non-Urban Water Body Contribution Index (N-WCI), indicating an enhanced UHI effect within urban areas. This study concludes that changes in farmland and impervious surfaces are crucial for the TC and provides practical recommendations for land use planning against climate change.
{"title":"Tracking the impact of the land cover change on the spatial-temporal distribution of the thermal comfort: Insights from the Qinhuai River Basin, China","authors":"Chunguang Hu , Maomao Zhang , Gaoliu Huang , Zhuoqi Li , Yucheng Sun , Jianqing Zhao","doi":"10.1016/j.scs.2024.105916","DOIUrl":"10.1016/j.scs.2024.105916","url":null,"abstract":"<div><div>China's rapid economic growth and urbanization have caused significant Land Cover Changes (LCC), worsened the Urban Heat Island (UHI) effect and reducing the Thermal Comfort (TC). Despite existing studies, there remains a gap in understanding the specific contributions of various LCC types to the TC, particularly in Qinhuai River Basin. This study addresses this gap by examining the LCC effects from 2013 to 2022 based on targeted metrics. We propose a novel TC classification model and introduce indices, including the Land Cover Contribution Index (LCI) and the Land Cover Classification Contribution Index (LCCI), to quantify the influence of different LCC types on the TC. Our findings reveal that farmland and woodland positively impact the TC, while the negative influence of impervious surfaces has intensified. The area of farmland in the most comfortable category has shown significant variability, while impermeable surfaces in uncomfortable and very uncomfortable categories have surged. Additionally, the Urban Water Body Contribution Index (U-WCI) consistently exceeded the Non-Urban Water Body Contribution Index (N-WCI), indicating an enhanced UHI effect within urban areas. This study concludes that changes in farmland and impervious surfaces are crucial for the TC and provides practical recommendations for land use planning against climate change.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105916"},"PeriodicalIF":10.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.scs.2024.105903
Haolin Yang , Weijun Gao , Siqi Xu , You Li , Xindong Wei , Yafei Wang
Urban power decarbonization is essential in the fight against climate change, yet current research often neglects the financial risks faced by investors and the shifting demands of consumers in liberalized electricity markets. This study addresses these gaps by proposing a modified Markowitz Mean-Variance Portfolio (MVP) theory, integrated with the Low Emissions Analysis Platform (LEAP), and a deep learning model. On this basis, an urban energy transition framework centered on Power Purchase Agreements (PPAs) is proposed and developed. The framework is validated considering a case study in Kitakyushu, Japan, highlighting its potential in accelerating power sector decarbonization and achieving net-zero emissions by 2038. Additionally, the internal rate of return (IRR) remains stable between 14.5 % and 19.6 % across seven other cities. While the framework reduces long-term cash flow volatility, its effectiveness hinges on industrial electrification efficiency and regional energy self-sufficiency. The findings indicate that relying solely on renewable energy for low-carbon transitions is unrealistic. Furthermore, green hydrogen could emerge as a viable alternative to fossil fuels, potentially replacing batteries for long-term energy storage. Future research should explore cross-regional energy trade and establish legal frameworks for long-term energy transactions to bolster urban energy transition resilience across diverse geographic and economic contexts.
{"title":"Urban-scale power decarbonization using a modified power purchase agreements framework based on Markowitz mean-variance theory","authors":"Haolin Yang , Weijun Gao , Siqi Xu , You Li , Xindong Wei , Yafei Wang","doi":"10.1016/j.scs.2024.105903","DOIUrl":"10.1016/j.scs.2024.105903","url":null,"abstract":"<div><div>Urban power decarbonization is essential in the fight against climate change, yet current research often neglects the financial risks faced by investors and the shifting demands of consumers in liberalized electricity markets. This study addresses these gaps by proposing a modified Markowitz Mean-Variance Portfolio (MVP) theory, integrated with the Low Emissions Analysis Platform (LEAP), and a deep learning model. On this basis, an urban energy transition framework centered on Power Purchase Agreements (PPAs) is proposed and developed. The framework is validated considering a case study in Kitakyushu, Japan, highlighting its potential in accelerating power sector decarbonization and achieving net-zero emissions by 2038. Additionally, the internal rate of return (IRR) remains stable between 14.5 % and 19.6 % across seven other cities. While the framework reduces long-term cash flow volatility, its effectiveness hinges on industrial electrification efficiency and regional energy self-sufficiency. The findings indicate that relying solely on renewable energy for low-carbon transitions is unrealistic. Furthermore, green hydrogen could emerge as a viable alternative to fossil fuels, potentially replacing batteries for long-term energy storage. Future research should explore cross-regional energy trade and establish legal frameworks for long-term energy transactions to bolster urban energy transition resilience across diverse geographic and economic contexts.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105903"},"PeriodicalIF":10.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1016/j.scs.2024.105904
Pingtao Yi , Ruxue Shi , Weiwei Li , Qiankun Dong
The sustainable development of urban agglomerations represents a significant driving force in national and global development. This study establishes an indicator system comprising factors associated with the economy, society, and environment, in accordance with the Triple Bottom Line, to assess the sustainability of 12 urban agglomerations in China. A novel framework is proposed, including a dynamic probability weighting method based on sufficient stochastic simulations and a coordination-difference-driven aggregation approach that considers the coordination degree and differences between evaluated objects. The evaluation revealed significant regional disparities in urban agglomeration sustainability from 2012 to 2021. The eastern region's Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei region, and Shandong Peninsula exhibit above-average sustainability performance. Conversely, the western region's Guangzhong, Guangxi Beibu Gulf, Chengyu, and Ningxia Yellow River regions exhibit below-average performance. Moreover, the growth rate of sustainability values for the 12 urban agglomerations followed a downward trajectory. Furthermore, the environmental dimension is the primary driver of sustainable development in urban agglomerations, while the economic dimension represents the main obstacle. These findings offer policymakers a scientific and practical framework to guide sustainability-related decisions.
{"title":"Evaluation of the coordination-difference-driven sustainability of 12 urban agglomerations in China based on the dynamic probability weighting method","authors":"Pingtao Yi , Ruxue Shi , Weiwei Li , Qiankun Dong","doi":"10.1016/j.scs.2024.105904","DOIUrl":"10.1016/j.scs.2024.105904","url":null,"abstract":"<div><div>The sustainable development of urban agglomerations represents a significant driving force in national and global development. This study establishes an indicator system comprising factors associated with the economy, society, and environment, in accordance with the Triple Bottom Line, to assess the sustainability of 12 urban agglomerations in China. A novel framework is proposed, including a dynamic probability weighting method based on sufficient stochastic simulations and a coordination-difference-driven aggregation approach that considers the coordination degree and differences between evaluated objects. The evaluation revealed significant regional disparities in urban agglomeration sustainability from 2012 to 2021. The eastern region's Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei region, and Shandong Peninsula exhibit above-average sustainability performance. Conversely, the western region's Guangzhong, Guangxi Beibu Gulf, Chengyu, and Ningxia Yellow River regions exhibit below-average performance. Moreover, the growth rate of sustainability values for the 12 urban agglomerations followed a downward trajectory. Furthermore, the environmental dimension is the primary driver of sustainable development in urban agglomerations, while the economic dimension represents the main obstacle. These findings offer policymakers a scientific and practical framework to guide sustainability-related decisions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105904"},"PeriodicalIF":10.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1016/j.scs.2024.105910
Jinting Zhang , Kui Yang , Jingdong Wu , Ying Duan , Yanni Ma , Jingzhi Ren , Zenan Yang
Under the "dual carbon" goals, targeting issues such as the difficulty in changing the high-carbon economic development model in pilot cities and the inability of previous prediction models to meet current needs, this paper provides an in-depth analysis of carbon stocks and emissions in a peak pilot City spanning from 2000 to 2020. Utilizing the PLUS model, this study forecasts land use/cover data under diverse future scenarios, encompassing natural development (ND) as well as ecological protection (EP). Moreover, the Bi-LSTM deep learning model is developed using six influencing factors to simulate carbon emissions. The research also examined the spatiotemporal changes in carbon budget and balance. The findings of the study reveal several significant conclusions:(1) The PLUS model demonstrated high predictive accuracy in forecasting future land-use types, achieving an average overall accuracy exceeding 0.89 and a Kappa value of 0.8568; The Bi-LSTM model achieved the highest accuracy among all competing models, with an score reaching 0.864. (2) Under the EP scenario from 2020 to 2030, the rate of decline in carbon storage has slowed down ( of carbon storage have been avoided from disappearing), and land use efficiency has significantly improved. Due to the protection of ecological land, a certain carbon sink effect has been generated, resulting in lower regional carbon emissions compared to the ND scenario, emphasizing the importance and necessity of setting ecological red lines for carbon stock optimization. (3) Carbon payment areas are primarily concentrated in urban centers, and over time, these areas and carbon compensation zones each account half of the total area. (4) Under different scenarios, the carbon balance of built land has been partially mitigated, and the overall trend is developing favorably.
{"title":"Scenario simulation of carbon balance in carbon peak pilot cities under the background of the \"dual carbon\" goals","authors":"Jinting Zhang , Kui Yang , Jingdong Wu , Ying Duan , Yanni Ma , Jingzhi Ren , Zenan Yang","doi":"10.1016/j.scs.2024.105910","DOIUrl":"10.1016/j.scs.2024.105910","url":null,"abstract":"<div><div>Under the \"dual carbon\" goals, targeting issues such as the difficulty in changing the high-carbon economic development model in pilot cities and the inability of previous prediction models to meet current needs, this paper provides an in-depth analysis of carbon stocks and emissions in a peak pilot City spanning from 2000 to 2020. Utilizing the PLUS model, this study forecasts land use/cover data under diverse future scenarios, encompassing natural development (ND) as well as ecological protection (EP). Moreover, the Bi-LSTM deep learning model is developed using six influencing factors to simulate carbon emissions. The research also examined the spatiotemporal changes in carbon budget and balance. The findings of the study reveal several significant conclusions:(1) The PLUS model demonstrated high predictive accuracy in forecasting future land-use types, achieving an average overall accuracy exceeding 0.89 and a Kappa value of 0.8568; The Bi-LSTM model achieved the highest accuracy among all competing models, with an <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> score reaching 0.864. (2) Under the EP scenario from 2020 to 2030, the rate of decline in carbon storage has slowed down (<span><math><mrow><mn>6.44</mn><mspace></mspace><mo>×</mo><mspace></mspace><msup><mrow><mn>10</mn></mrow><mn>6</mn></msup><mspace></mspace><mi>t</mi></mrow></math></span> of carbon storage have been avoided from disappearing), and land use efficiency has significantly improved. Due to the protection of ecological land, a certain carbon sink effect has been generated, resulting in lower regional carbon emissions compared to the ND scenario, emphasizing the importance and necessity of setting ecological red lines for carbon stock optimization. (3) Carbon payment areas are primarily concentrated in urban centers, and over time, these areas and carbon compensation zones each account half of the total area. (4) Under different scenarios, the carbon balance of built land has been partially mitigated, and the overall trend is developing favorably.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105910"},"PeriodicalIF":10.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1016/j.scs.2024.105913
Feng-Wen Shan , Xuan Liu , Ming-Kun Sun , Zhengmin Qian , Michael G. Vaughn , Niraj R. Chavan , Shu-Li Xu , He-Hai Huang , Zhao-Huan Gui , Ru-Qing Liu , Li-Wen Hu , Li-Zi Lin , Zhong Lin , Qin-Tai Yang , Guang-Hui Dong
Background
Anthropogenic heat (AH) is defined as the significant release of waste heat into the environment due to human activities, serving as a controllable heat source contributing to global climate change. However, epidemiological evidence establishing a clear association between AH and childhood asthma is currently lacking.
Objectives
To explore the relationship between children's exposure to AH and asthma, as well as its related symptoms.
Methods
This population-based cross-sectional study, part of the National Chinese Children Health Study from 2012 to 2018, involved 188,145 children aged 6 to 18 years. We used multisource remote sensing images and ancillary data to estimate AH exposure. Data on asthma symptoms were collected through validated self-reported questionnaires. A generalized linear mixed model was applied to determine the associations.
Results
Our findings indicate a positive correlation between AH exposure and asthma risk in children. An interquartile range (IQR) increase in total AH was linked to higher odds of current asthma (OR: 1.15, 95 % CI: 1.10, 1.20) after adjusting for covariates. Categorizing AH by source, industrial AH exhibited the strongest effect, with an increased risk of current asthma (OR: 1.16, 95 % CI: 1.11, 1.22). Notably, younger children exhibited stronger associations between AH exposure and asthma-related symptoms, with boys showing heightened susceptibility, particularly for persistent cough.
Conclusion
This study suggests that exposure to AH may elevate the risk of asthma and related symptoms, particularly in boys and younger children. Providing a foundation for developing practical strategies to mitigate the adverse impacts of global warming on respiratory health, while also guiding the formulation and evaluation of climate action and public health policies, and supporting sustainable urban development.
{"title":"Association of anthropogenic heat with asthma and related symptoms among children in China: A novel index reflecting climate change","authors":"Feng-Wen Shan , Xuan Liu , Ming-Kun Sun , Zhengmin Qian , Michael G. Vaughn , Niraj R. Chavan , Shu-Li Xu , He-Hai Huang , Zhao-Huan Gui , Ru-Qing Liu , Li-Wen Hu , Li-Zi Lin , Zhong Lin , Qin-Tai Yang , Guang-Hui Dong","doi":"10.1016/j.scs.2024.105913","DOIUrl":"10.1016/j.scs.2024.105913","url":null,"abstract":"<div><h3>Background</h3><div>Anthropogenic heat (AH) is defined as the significant release of waste heat into the environment due to human activities, serving as a controllable heat source contributing to global climate change. However, epidemiological evidence establishing a clear association between AH and childhood asthma is currently lacking.</div></div><div><h3>Objectives</h3><div>To explore the relationship between children's exposure to AH and asthma, as well as its related symptoms.</div></div><div><h3>Methods</h3><div>This population-based cross-sectional study, part of the National Chinese Children Health Study from 2012 to 2018, involved 188,145 children aged 6 to 18 years. We used multisource remote sensing images and ancillary data to estimate AH exposure. Data on asthma symptoms were collected through validated self-reported questionnaires. A generalized linear mixed model was applied to determine the associations.</div></div><div><h3>Results</h3><div>Our findings indicate a positive correlation between AH exposure and asthma risk in children. An interquartile range (IQR) increase in total AH was linked to higher odds of current asthma (OR: 1.15, 95 % CI: 1.10, 1.20) after adjusting for covariates. Categorizing AH by source, industrial AH exhibited the strongest effect, with an increased risk of current asthma (OR: 1.16, 95 % CI: 1.11, 1.22). Notably, younger children exhibited stronger associations between AH exposure and asthma-related symptoms, with boys showing heightened susceptibility, particularly for persistent cough.</div></div><div><h3>Conclusion</h3><div>This study suggests that exposure to AH may elevate the risk of asthma and related symptoms, particularly in boys and younger children. Providing a foundation for developing practical strategies to mitigate the adverse impacts of global warming on respiratory health, while also guiding the formulation and evaluation of climate action and public health policies, and supporting sustainable urban development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105913"},"PeriodicalIF":10.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Energy flexibility and energy resilience are now becoming new key features of building energy systems under the context of climate change and energy transition. During the system operation phase, these two performance indexes might be contradictory and require tradeoff. The main contribution of this study is to propose a two-stage mixed-integer linear programming (MILP) model to optimally tradeoff between flexibility and resilience. Its main idea is to improve the resilience of building energy system with minimum constraints on system flexibility using the outage risk information provided by smart grid. Two new concepts are considered in the proposed method, including self-sufficient requirement and continuous outage probability. The insight is to add additional penalty for the time step in which its battery state of charge (SOC) is far from self-sufficient requirement while the corresponding continuous outage probability is high. To validate our proposed method, a probabilistic outage simulation model is developed using sigmoid function and Markov Chain. Comprehensive numerical studies are conducted to compare the proposed method with traditional economic mode and backup mode under two outage patterns. The results demonstrate that the proposed method only uses 6.7 % additional operation cost such that 78.3 % of baseload curtailment and 81.1 % of user discomfort are reduced. The proposed MILP model can provide practical guideline for the flexibility and resilience tradeoff of distributed energy resources.
{"title":"Two-stage optimal scheduling for flexibility and resilience tradeoff of PV-battery building via smart grid communication","authors":"Xinbin Liang, Wei Ge, Zheming Zhang, Fei Zheng, Xinqiao Jin, Zhimin Du","doi":"10.1016/j.scs.2024.105919","DOIUrl":"10.1016/j.scs.2024.105919","url":null,"abstract":"<div><div>Energy flexibility and energy resilience are now becoming new key features of building energy systems under the context of climate change and energy transition. During the system operation phase, these two performance indexes might be contradictory and require tradeoff. The main contribution of this study is to propose a two-stage mixed-integer linear programming (MILP) model to optimally tradeoff between flexibility and resilience. Its main idea is to improve the resilience of building energy system with minimum constraints on system flexibility using the outage risk information provided by smart grid. Two new concepts are considered in the proposed method, including self-sufficient requirement and continuous outage probability. The insight is to add additional penalty for the time step in which its battery state of charge (SOC) is far from self-sufficient requirement while the corresponding continuous outage probability is high. To validate our proposed method, a probabilistic outage simulation model is developed using sigmoid function and Markov Chain. Comprehensive numerical studies are conducted to compare the proposed method with traditional economic mode and backup mode under two outage patterns. The results demonstrate that the proposed method only uses 6.7 % additional operation cost such that 78.3 % of baseload curtailment and 81.1 % of user discomfort are reduced. The proposed MILP model can provide practical guideline for the flexibility and resilience tradeoff of distributed energy resources.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105919"},"PeriodicalIF":10.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1016/j.scs.2024.105909
Pengcheng Li , Yun Chen , Haifeng Niu , Lu Zhang , Yu Tang , Guang Zhu , Zhongyuan Zhang , Yizhe Ma , Wen Wu
Urban parks have been widely proved to be effective in reducing particulate matter pollution, but there is still a knowledge gap in quantitatively evaluating their reduction effects. The purpose of this study is to develop a new method to quantify the reduction effect of PM2.5 in urban parks through high-precision spatio-temporal monitoring experiments in 22 typical urban parks in Shenyang, China, so as to fill this gap. In this study, the cubic polynomial function model was used for the first time to establish the relationship curve between PM2.5 concentration inside and outside the park at different distances. The results showed that the park PM2.5 reduction magnitude and distance were about 5.04–10.14 ug/m3 and 149.47–150.19 m, respectively. Partial correlation analysis revealed that the relationship between the reduction evaluation indexes and the environmental factors had time heterogeneity. The park's internal characteristics and surrounding building environment was the key factor affecting the park PM2.5 reduction effect. In addition, parks smaller than 4.71 hm2 demonstrated better PM2.5 reduction efficiency. In conclusion, this study provides a new quantitative approach to evaluating the park PM2.5 reduction effect and offers data-driven insights for optimizing park planning to enhance the permeability of these effects beyond park boundaries.
{"title":"How to evaluate the reduction effect of the park on PM2.5? Exploratory application of the maximum and cumulative perspective","authors":"Pengcheng Li , Yun Chen , Haifeng Niu , Lu Zhang , Yu Tang , Guang Zhu , Zhongyuan Zhang , Yizhe Ma , Wen Wu","doi":"10.1016/j.scs.2024.105909","DOIUrl":"10.1016/j.scs.2024.105909","url":null,"abstract":"<div><div>Urban parks have been widely proved to be effective in reducing particulate matter pollution, but there is still a knowledge gap in quantitatively evaluating their reduction effects. The purpose of this study is to develop a new method to quantify the reduction effect of PM<sub>2.5</sub> in urban parks through high-precision spatio-temporal monitoring experiments in 22 typical urban parks in Shenyang, China, so as to fill this gap. In this study, the cubic polynomial function model was used for the first time to establish the relationship curve between PM<sub>2.5</sub> concentration inside and outside the park at different distances. The results showed that the park PM<sub>2.5</sub> reduction magnitude and distance were about 5.04–10.14 ug/m<sup>3</sup> and 149.47–150.19 m, respectively. Partial correlation analysis revealed that the relationship between the reduction evaluation indexes and the environmental factors had time heterogeneity. The park's internal characteristics and surrounding building environment was the key factor affecting the park PM<sub>2.5</sub> reduction effect. In addition, parks smaller than 4.71 hm<sup>2</sup> demonstrated better PM<sub>2.5</sub> reduction efficiency. In conclusion, this study provides a new quantitative approach to evaluating the park PM<sub>2.5</sub> reduction effect and offers data-driven insights for optimizing park planning to enhance the permeability of these effects beyond park boundaries.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105909"},"PeriodicalIF":10.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1016/j.scs.2024.105914
Kai-yang Fu , Yu-zhe Liu , Xin-yu Lu , Bin Chen , You-hua Chen
This study examines the health impacts of Climate Resilient City (CRC) policies using a difference-in-differences methodology. Our findings demonstrate that CRC policies significantly improve public health, particularly benefiting vulnerable populations and residents in regions with extreme temperatures. Mechanism analysis reveals that these policies enhance urban climate resilience through improved water management, air pollution reduction, energy conservation, and strengthened social capital. Moreover, our results show that CRC policies help reduce health disparities linked to differences in medical resources and climate conditions. This study provides crucial insights for policymakers in designing effective climate and public health strategies, emphasizing the importance of climate-resilient urban development.
{"title":"Health impacts of climate resilient city development: Evidence from China","authors":"Kai-yang Fu , Yu-zhe Liu , Xin-yu Lu , Bin Chen , You-hua Chen","doi":"10.1016/j.scs.2024.105914","DOIUrl":"10.1016/j.scs.2024.105914","url":null,"abstract":"<div><div>This study examines the health impacts of Climate Resilient City (<em>CRC</em>) policies using a difference-in-differences methodology. Our findings demonstrate that <em>CRC</em> policies significantly improve public health, particularly benefiting vulnerable populations and residents in regions with extreme temperatures. Mechanism analysis reveals that these policies enhance urban climate resilience through improved water management, air pollution reduction, energy conservation, and strengthened social capital. Moreover, our results show that <em>CRC</em> policies help reduce health disparities linked to differences in medical resources and climate conditions. This study provides crucial insights for policymakers in designing effective climate and public health strategies, emphasizing the importance of climate-resilient urban development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105914"},"PeriodicalIF":10.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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