Pub Date : 2026-03-01Epub Date: 2026-03-04DOI: 10.1016/j.resenv.2026.100318
Peiyu Wang , Xiyan Mao , Xianjin Huang , Christina Prell
Under the global consensus on mitigating climate change and managing environmental risks, low-carbon technology (LCT) imports have emerged as a new driver of green economic growth. Although the economic and environmental benefits of LCT trade are widely recognized, the mechanisms through which imports enhance green total factor productivity (GTFP) remain insufficiently understood. This study develops a theoretical framework to explain how LCT imports promote green growth through trade spillovers and structural optimization. Using panel data covering 101 countries and 124 LCT products from 2001 to 2022, this study employs mediation and moderation models to examine the transmission mechanisms and key influencing factors. The results show that LCT imports enhance GTFP mainly through knowledge diffusion rather than technology spillovers. LCT imports facilitate energy structure optimization, which drives GTFP improvement, but they do not fundamentally change the industrial structure. Moreover, maintaining stable trade linkages with technologically advanced partners amplifies this positive effect. These findings uncover the mechanisms by which LCT trade fosters green productivity and provide scientific insights to inform LCT trade policies.
{"title":"Direct and indirect mechanisms through which low-carbon technology imports enhance green total factor productivity","authors":"Peiyu Wang , Xiyan Mao , Xianjin Huang , Christina Prell","doi":"10.1016/j.resenv.2026.100318","DOIUrl":"10.1016/j.resenv.2026.100318","url":null,"abstract":"<div><div>Under the global consensus on mitigating climate change and managing environmental risks, low-carbon technology (LCT) imports have emerged as a new driver of green economic growth. Although the economic and environmental benefits of LCT trade are widely recognized, the mechanisms through which imports enhance green total factor productivity (GTFP) remain insufficiently understood. This study develops a theoretical framework to explain how LCT imports promote green growth through trade spillovers and structural optimization. Using panel data covering 101 countries and 124 LCT products from 2001 to 2022, this study employs mediation and moderation models to examine the transmission mechanisms and key influencing factors. The results show that LCT imports enhance GTFP mainly through knowledge diffusion rather than technology spillovers. LCT imports facilitate energy structure optimization, which drives GTFP improvement, but they do not fundamentally change the industrial structure. Moreover, maintaining stable trade linkages with technologically advanced partners amplifies this positive effect. These findings uncover the mechanisms by which LCT trade fosters green productivity and provide scientific insights to inform LCT trade policies.</div></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"24 ","pages":"Article 100318"},"PeriodicalIF":7.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-10DOI: 10.1016/j.resenv.2026.100310
Fangyi Li , Qi Zhang , Wu Xie , Zhen Shao , Jingjing Li
Municipal solid waste (MSW) incineration plants play a crucial role both in waste-to-energy sector and “Zero-waste Cities” construction in China. As the number and capacity of waste incineration plants (WIPs) are increasing rapidly, their expansion process and carbon reduction effect in history and future deserve in-depth research. This study examines the historical and future expansion of WIPs and their carbon reduction potential at the county level. We employ a machine learning framework combining two models: a Bayesian neural network (BNN) model to predict the probability of new WIP allocation and an explainable machine learning model to identify key factors driving county-level carbon emissions from waste disposal. It finds that the growth of WIPs from 202 to 902 during 2015-2022 has significantly reduced carbon emissions from 129.0 Mt CO2-eq to 91.1 Mt CO2-eq. Based on the BNN model, there will be 1,408 WIPs serving 2,492 county-level administrative units in 2035. The RF model with feature analysis demonstrates that landfill ratio, incineration ratio, population, and geographic location primarily influence the carbon emissions of waste disposal at the county level. Scenario analysis shows the temporal and spatial distribution of carbon reduction potential: the north and central regions have the largest carbon reduction potential in the country due to the rapid growth of WIPs, while a shift in spatial priority for carbon reduction is identified from municipal districts to counties. The findings provide targeted insights to address regional imbalances and guide infrastructure planning towards sustainable waste management and the low-carbon transition.
{"title":"Assessing the carbon reduction potential of municipal solid waste incineration at the county level in China","authors":"Fangyi Li , Qi Zhang , Wu Xie , Zhen Shao , Jingjing Li","doi":"10.1016/j.resenv.2026.100310","DOIUrl":"10.1016/j.resenv.2026.100310","url":null,"abstract":"<div><div>Municipal solid waste (MSW) incineration plants play a crucial role both in waste-to-energy sector and “Zero-waste Cities” construction in China. As the number and capacity of waste incineration plants (WIPs) are increasing rapidly, their expansion process and carbon reduction effect in history and future deserve in-depth research. This study examines the historical and future expansion of WIPs and their carbon reduction potential at the county level. We employ a machine learning framework combining two models: a Bayesian neural network (BNN) model to predict the probability of new WIP allocation and an explainable machine learning model to identify key factors driving county-level carbon emissions from waste disposal. It finds that the growth of WIPs from 202 to 902 during 2015-2022 has significantly reduced carbon emissions from 129.0 Mt CO<sub>2</sub>-eq to 91.1 Mt CO<sub>2</sub>-eq. Based on the BNN model, there will be 1,408 WIPs serving 2,492 county-level administrative units in 2035. The RF model with feature analysis demonstrates that landfill ratio, incineration ratio, population, and geographic location primarily influence the carbon emissions of waste disposal at the county level. Scenario analysis shows the temporal and spatial distribution of carbon reduction potential: the north and central regions have the largest carbon reduction potential in the country due to the rapid growth of WIPs, while a shift in spatial priority for carbon reduction is identified from municipal districts to counties. The findings provide targeted insights to address regional imbalances and guide infrastructure planning towards sustainable waste management and the low-carbon transition.</div></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"24 ","pages":"Article 100310"},"PeriodicalIF":7.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-03-04DOI: 10.1016/j.resenv.2026.100316
Shuai Pan , Yicheng Xu , Zitian Li , Lewis M. Fulton , H. Oliver Gao
Electric vehicle (EV) adoption has been viewed widely as a promising climate mitigation strategy, whereas the pace and scale of EV transition varies significantly in worldwide regions. This study examines recent policy dynamics in the global EV transition, employing the timing and magnitude of reductions in transport non-energy costs as key policy variables. Through systematic quantitative analysis using the Global Change Analysis Model (GCAM), this study evaluates how different policy strengths shape EV transition pathways and their potential for energy savings and emission reductions. Results indicate that a uniform 20% reduction in non-energy costs for passenger electric cars worldwide from 2035 onward could increase EV penetration during the corresponding period from approximately 25–50% to around 70–85%. Such widespread adoption is expected to substantially lower future energy consumption and CO2 emissions in the U.S., Europe, and China, while also helping curb rising emission trends in India, Southeast Asia, and Western Africa. Implementing the same 20% cost reduction in China as early as 2025 can effectively replicate its recent rapid growth in EV penetration. For the European Union, a phased cost-reduction approach (10% in 2030, rising to 30% from 2035) would likely enable it to meet the clean mobility target outlined in its 2025 Automotive Package, namely a 90% reduction in tailpipe emissions from 2035 onward. The use of battery EVs increases electricity consumption, but such impact remains modest. The popularity of fuel cell EVs can push up hydrogen demand, which makes expansion of green hydrogen production necessary. Results suggest the necessity of providing support like technological assistance and financial investment from leading automotive markets (e.g., the U.S., Europe, and China) to developing countries, to ensure a global, inclusive, and equitable EV transition.
{"title":"Future changes in CO2 emissions in the shift to electric mobility in countries with varied levels of zero-emission vehicle policies","authors":"Shuai Pan , Yicheng Xu , Zitian Li , Lewis M. Fulton , H. Oliver Gao","doi":"10.1016/j.resenv.2026.100316","DOIUrl":"10.1016/j.resenv.2026.100316","url":null,"abstract":"<div><div>Electric vehicle (EV) adoption has been viewed widely as a promising climate mitigation strategy, whereas the pace and scale of EV transition varies significantly in worldwide regions. This study examines recent policy dynamics in the global EV transition, employing the timing and magnitude of reductions in transport non-energy costs as key policy variables. Through systematic quantitative analysis using the Global Change Analysis Model (GCAM), this study evaluates how different policy strengths shape EV transition pathways and their potential for energy savings and emission reductions. Results indicate that a uniform 20% reduction in non-energy costs for passenger electric cars worldwide from 2035 onward could increase EV penetration during the corresponding period from approximately 25–50% to around 70–85%. Such widespread adoption is expected to substantially lower future energy consumption and CO<sub>2</sub> emissions in the U.S., Europe, and China, while also helping curb rising emission trends in India, Southeast Asia, and Western Africa. Implementing the same 20% cost reduction in China as early as 2025 can effectively replicate its recent rapid growth in EV penetration. For the European Union, a phased cost-reduction approach (10% in 2030, rising to 30% from 2035) would likely enable it to meet the clean mobility target outlined in its 2025 Automotive Package, namely a 90% reduction in tailpipe emissions from 2035 onward. The use of battery EVs increases electricity consumption, but such impact remains modest. The popularity of fuel cell EVs can push up hydrogen demand, which makes expansion of green hydrogen production necessary. Results suggest the necessity of providing support like technological assistance and financial investment from leading automotive markets (e.g., the U.S., Europe, and China) to developing countries, to ensure a global, inclusive, and equitable EV transition.</div></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"24 ","pages":"Article 100316"},"PeriodicalIF":7.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-10DOI: 10.1016/j.resenv.2026.100303
Ke Wang , Wenxiang Wu , Haimeng Liu , Xueqin Zhang , Xinshuai Ren , Maochou Liu , Jiahui Cheng , Jing Geng , Bo Yang
Balancing ecological conservation with renewable energy development is a critical challenge in China, where the rapid expansion of renewable energy is indispensable for achieving carbon neutrality but increasingly intensifies conflicts with conservation. To balance these conflicts, we developed a multi-objective land planning framework that integrates high-resolution spatial data on biodiversity, ecosystem services, vulnerable carbon stocks, and renewable energy potentials. Within this framework, we implemented three land-use scenarios representing distinct policy preferences—Conservation-First, Energy-First, and Multi-Zones (equal weighting of conservation and energy objectives)—to allocate land optimally and quantify the trade-offs between ecological conservation and renewable energy development. Our results revealed that without ecological safeguards, renewable energy expansion would place more than 2 million km2 of land at risk and expose 901.31 Gt of vulnerable carbon, while also threatening the nation's highly irreplaceable species habitats—which, although covering only about 80 km2, are extremely sensitive to land-use change. In contrast, the Multi-Zones scenario reduces carbon exposure by ∼30 Gt, safeguards 10% more critical habitats, and preserves nearly 90% (642.5 GW) of national objective of renewable energy. The trade-offs induced by wind and solar energy are concentrated in northern and eastern Inner Mongolia and southern Xizang provinces, while hydropower-related trade-offs occur mainly in the highly elevated regions of Yunnan province, highlighting areas where adaptive conservation strategies and conflict-sensitive energy planning are most needed. Based on these findings, we proposed region-specific strategies to guide policies that align China's dual carbon commitments with Ecological Conservation Redline and support the achievement of SDG 7 and SDG 15.
{"title":"Balancing land planning priorities for ecological conservation and renewable energy development through multi-objective land planning in China","authors":"Ke Wang , Wenxiang Wu , Haimeng Liu , Xueqin Zhang , Xinshuai Ren , Maochou Liu , Jiahui Cheng , Jing Geng , Bo Yang","doi":"10.1016/j.resenv.2026.100303","DOIUrl":"10.1016/j.resenv.2026.100303","url":null,"abstract":"<div><div>Balancing ecological conservation with renewable energy development is a critical challenge in China, where the rapid expansion of renewable energy is indispensable for achieving carbon neutrality but increasingly intensifies conflicts with conservation. To balance these conflicts, we developed a multi-objective land planning framework that integrates high-resolution spatial data on biodiversity, ecosystem services, vulnerable carbon stocks, and renewable energy potentials. Within this framework, we implemented three land-use scenarios representing distinct policy preferences—Conservation-First, Energy-First, and Multi-Zones (equal weighting of conservation and energy objectives)—to allocate land optimally and quantify the trade-offs between ecological conservation and renewable energy development. Our results revealed that without ecological safeguards, renewable energy expansion would place more than 2 million km<sup>2</sup> of land at risk and expose 901.31 Gt of vulnerable carbon, while also threatening the nation's highly irreplaceable species habitats—which, although covering only about 80 km<sup>2</sup>, are extremely sensitive to land-use change. In contrast, the Multi-Zones scenario reduces carbon exposure by ∼30 Gt, safeguards 10% more critical habitats, and preserves nearly 90% (642.5 GW) of national objective of renewable energy. The trade-offs induced by wind and solar energy are concentrated in northern and eastern Inner Mongolia and southern Xizang provinces, while hydropower-related trade-offs occur mainly in the highly elevated regions of Yunnan province, highlighting areas where adaptive conservation strategies and conflict-sensitive energy planning are most needed. Based on these findings, we proposed region-specific strategies to guide policies that align China's dual carbon commitments with Ecological Conservation Redline and support the achievement of SDG 7 and SDG 15.</div></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"24 ","pages":"Article 100303"},"PeriodicalIF":7.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-11DOI: 10.1016/j.resenv.2026.100308
Zhe Li , Wei Wu , Xianggang Zhao , Shiqi Tian , Kai Li , Yufan Wu , Linjuan Li
Investigating the scale transmission mechanisms of ecological networks (ENs) resilience is critical for designing adaptive planning and policies to mitigate multi-scale anthropogenic pressures. Accordingly, an analysis framework applicable to multi-level scales was developed, taking the provincial, city cluster and city scales as a nested hierarchy, constructing structural ENs (based on Morphological Spatial Pattern Analysis) and functional ENs (based on the ecological importance and sensitivity evaluation), then evaluating their resilience and cross-scale transmission characteristics using a node attack model. Results showed structural and functional ENs were most stable at city scale, with stability weakening as scale increased. Complete removal of city scale patches caused a larger decline in provincial scale resilience (0.19 and 0.15 for structural and functional ENs) than in the city cluster scale (0.08 and 0.09 for structural and functional ENs). The provincial scale resilience only decreased 0.07 for structural ENs when city cluster scale patches were removed completely, lower than complete removal of city scale patches, but it showed extreme vulnerability at the initial stage. ENs resilience exhibited cascading fragility, removing finer scale patches can trigger asymmetric cross-scale transmission, and the re-stabilization could be achieved through sacrificial degradation of secondary nodes and reconfiguration of peripheral corridors. It emphasized the critical need for tiered conservation strategies and coordinated management across scales to enhance ENs stability. The proposed framework provides a transferable tool for multi-scale ENs optimization, supporting biodiversity conservation and sustainable development goals.
{"title":"Evaluate the resilience of ecological networks under node attack scenarios to reveal the cross-scale transmission characteristics","authors":"Zhe Li , Wei Wu , Xianggang Zhao , Shiqi Tian , Kai Li , Yufan Wu , Linjuan Li","doi":"10.1016/j.resenv.2026.100308","DOIUrl":"10.1016/j.resenv.2026.100308","url":null,"abstract":"<div><div>Investigating the scale transmission mechanisms of ecological networks (ENs) resilience is critical for designing adaptive planning and policies to mitigate multi-scale anthropogenic pressures. Accordingly, an analysis framework applicable to multi-level scales was developed, taking the provincial, city cluster and city scales as a nested hierarchy, constructing structural ENs (based on Morphological Spatial Pattern Analysis) and functional ENs (based on the ecological importance and sensitivity evaluation), then evaluating their resilience and cross-scale transmission characteristics using a node attack model. Results showed structural and functional ENs were most stable at city scale, with stability weakening as scale increased. Complete removal of city scale patches caused a larger decline in provincial scale resilience (0.19 and 0.15 for structural and functional ENs) than in the city cluster scale (0.08 and 0.09 for structural and functional ENs). The provincial scale resilience only decreased 0.07 for structural ENs when city cluster scale patches were removed completely, lower than complete removal of city scale patches, but it showed extreme vulnerability at the initial stage. ENs resilience exhibited cascading fragility, removing finer scale patches can trigger asymmetric cross-scale transmission, and the re-stabilization could be achieved through sacrificial degradation of secondary nodes and reconfiguration of peripheral corridors. It emphasized the critical need for tiered conservation strategies and coordinated management across scales to enhance ENs stability. The proposed framework provides a transferable tool for multi-scale ENs optimization, supporting biodiversity conservation and sustainable development goals.</div></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"24 ","pages":"Article 100308"},"PeriodicalIF":7.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-08DOI: 10.1016/j.resenv.2026.100305
Linlin Xu , Hu Yu , Linsheng Zhong
Rapid tourism development in ecologically fragile regions poses a risk of exceeding ecosystem self-regulation capabilities. To promote the coordination of conservation and development, this study aims to explore the nonlinear relationship between ecosystem health and tourism activities. This study established a tourism activity intensity index and employed a machine learning model to simulate ecological responses on the Qinghai-Tibet Plateau. The results reveal that the response of ecosystem health to tourism activity is characterized by distinct nonlinear features. While ecosystem health maintains relative stability under low-to-moderate disturbance, it suffers accelerated degradation once the intensity exceeds critical thresholds. Specifically, quantitative thresholds were identified at the kilometer-grid scale (0.134) and the trajectory-point scale (0.337), suggesting a scale-dependent resilience mechanism. Natural geographical conditions and tourism organization patterns jointly shape diverse nonlinear response patterns. These insights bridge the gap between theoretical carrying capacity and quantitative estimation, offering practical references for defining the safe operating space in high-altitude areas.
{"title":"Ecosystem health responses to tourism activities in the Qinghai-Tibet Plateau: Nonlinear relationships and mechanism","authors":"Linlin Xu , Hu Yu , Linsheng Zhong","doi":"10.1016/j.resenv.2026.100305","DOIUrl":"10.1016/j.resenv.2026.100305","url":null,"abstract":"<div><div>Rapid tourism development in ecologically fragile regions poses a risk of exceeding ecosystem self-regulation capabilities. To promote the coordination of conservation and development, this study aims to explore the nonlinear relationship between ecosystem health and tourism activities. This study established a tourism activity intensity index and employed a machine learning model to simulate ecological responses on the Qinghai-Tibet Plateau. The results reveal that the response of ecosystem health to tourism activity is characterized by distinct nonlinear features. While ecosystem health maintains relative stability under low-to-moderate disturbance, it suffers accelerated degradation once the intensity exceeds critical thresholds. Specifically, quantitative thresholds were identified at the kilometer-grid scale (0.134) and the trajectory-point scale (0.337), suggesting a scale-dependent resilience mechanism. Natural geographical conditions and tourism organization patterns jointly shape diverse nonlinear response patterns. These insights bridge the gap between theoretical carrying capacity and quantitative estimation, offering practical references for defining the safe operating space in high-altitude areas.</div></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"24 ","pages":"Article 100305"},"PeriodicalIF":7.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-06DOI: 10.1016/j.resenv.2026.100307
Yike Li , Jing Gao , Yunpeng Shi , Yan Wu , Heng Xu , Jia Zhou , Tao Lu
Escalating global population and economic expansion are fueling unprecedented demand for natural resources, making a robust understanding of human impacts on biomass flows critical for achieving the Sustainable Development Goals. The Human Appropriation of Net Primary Production (HANPP) is a key metric for quantifying human effects on ecosystem biomass flows; however, a lack of research on the dynamics of HANPP utilization efficiency has likely led to an overestimation of biomass resource pressure and an oversight of the separation between HANPP and socioeconomic development driven by improvements in utilization efficiency. To fill these gaps, we generated annual 1-km HANPP maps for China (1990–2022) and developed a two-dimensional conceptual framework to characterize dynamic interactions between biomass utilization and retention. Our findings show that, during the early period of socioeconomic development (1990–2000), biomass utilization and retention exhibited a trade-off, marked by a low utilization efficiency of 55.33%. Subsequently, with the growth of socio-ecological systems, strong demand fueled a steady increase in NPP utilization intensity—yet enhanced biomass utilization efficiency simultaneously mitigated this upward trend by reducing NPP losses, with utilization efficiency rising from 64.16% (2000–2010) to 73.94% (2010–2022). This efficiency-driven shift not only moved utilization-retention interactions toward coordination but also facilitated the separation of HANPP from socioeconomic development. Our findings underscore the need for strategies to enhance biomass utilization efficiency, thereby supporting sustainable resource use.
{"title":"Enhanced biomass utilization efficiency reshapes spatiotemporal shifts of China's HANPP","authors":"Yike Li , Jing Gao , Yunpeng Shi , Yan Wu , Heng Xu , Jia Zhou , Tao Lu","doi":"10.1016/j.resenv.2026.100307","DOIUrl":"10.1016/j.resenv.2026.100307","url":null,"abstract":"<div><div>Escalating global population and economic expansion are fueling unprecedented demand for natural resources, making a robust understanding of human impacts on biomass flows critical for achieving the Sustainable Development Goals. The Human Appropriation of Net Primary Production (HANPP) is a key metric for quantifying human effects on ecosystem biomass flows; however, a lack of research on the dynamics of HANPP utilization efficiency has likely led to an overestimation of biomass resource pressure and an oversight of the separation between HANPP and socioeconomic development driven by improvements in utilization efficiency. To fill these gaps, we generated annual 1-km HANPP maps for China (1990–2022) and developed a two-dimensional conceptual framework to characterize dynamic interactions between biomass utilization and retention. Our findings show that, during the early period of socioeconomic development (1990–2000), biomass utilization and retention exhibited a trade-off, marked by a low utilization efficiency of 55.33%. Subsequently, with the growth of socio-ecological systems, strong demand fueled a steady increase in NPP utilization intensity—yet enhanced biomass utilization efficiency simultaneously mitigated this upward trend by reducing NPP losses, with utilization efficiency rising from 64.16% (2000–2010) to 73.94% (2010–2022). This efficiency-driven shift not only moved utilization-retention interactions toward coordination but also facilitated the separation of HANPP from socioeconomic development. Our findings underscore the need for strategies to enhance biomass utilization efficiency, thereby supporting sustainable resource use.</div></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"24 ","pages":"Article 100307"},"PeriodicalIF":7.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-26DOI: 10.1016/j.resenv.2026.100314
Xuanru Zhou , Gengyuan Liu , Hongxiu Li , Weicen Chang , Qipeng Deng , Marco Casazza
Rare earth elements (REEs) are indispensable raw materials for green technologies, but their production process may cause significant ecological pressure. As the world's major supplier of REEs, China undertakes most of the mining and refining tasks, but it is unclear whether the Chinese rare earth supply chain has an impact on biodiversity and what part is due to foreign consumption and what part is domestic consumption. Here, we developed BLARES, a dynamic assessment framework that combines system dynamics with life cycle biodiversity impact accounting, to evaluate the impact of China's rare earth supply chain on biodiversity during the period from 2000 to 2060 under four SSP scenarios. We used mean species abundance (MSA) to measure the impact, covering land occupation and climate-driven pathways, and considering ecological time lag factors. The results show a clear ecological debt pattern. In the medium term, biodiversity pressure increases or remains high because the marginal effects brought by upstream expansion are released. Only when the used inventory of green technology products in rare earths is accumulated to stabilize the replacement of fossil energy, will the impact of the rare earth supply chain on biodiversity show a net improvement. Compared to the baseline value in 2000, the cumulative avoided biodiversity loss has exceeded 4 × 107 by 2060. The net impact turns positive around 2040, and the cumulative net impact reaches zero around 2049, indicating a significant delay in ecological compensation. The time when the carbon balance occurs is earlier than that of biodiversity compensation, suggesting that net zero carbon emissions do not necessarily mean zero ecological emissions. From the perspective of final demand attribution, for every 1% increase in overseas demand, China's annual net biodiversity loss increases by approximately 0.031%, while for every 1% increase in the use of rare earth elements for green deployment in China, it decreases by about 0.84%. Our research results do not support the simplistic claim that “the more deployment, the better”. The transition path should be evaluated using the time of ecological compensation and the peak of medium-term pressure, rather than solely based on the final carbon emission results.
{"title":"Biodiversity loss from rare earth production for green technologies in China: a global supply chain perspective","authors":"Xuanru Zhou , Gengyuan Liu , Hongxiu Li , Weicen Chang , Qipeng Deng , Marco Casazza","doi":"10.1016/j.resenv.2026.100314","DOIUrl":"10.1016/j.resenv.2026.100314","url":null,"abstract":"<div><div>Rare earth elements (REEs) are indispensable raw materials for green technologies, but their production process may cause significant ecological pressure. As the world's major supplier of REEs, China undertakes most of the mining and refining tasks, but it is unclear whether the Chinese rare earth supply chain has an impact on biodiversity and what part is due to foreign consumption and what part is domestic consumption. Here, we developed BLARES, a dynamic assessment framework that combines system dynamics with life cycle biodiversity impact accounting, to evaluate the impact of China's rare earth supply chain on biodiversity during the period from 2000 to 2060 under four SSP scenarios. We used mean species abundance (MSA) to measure the impact, covering land occupation and climate-driven pathways, and considering ecological time lag factors. The results show a clear ecological debt pattern. In the medium term, biodiversity pressure increases or remains high because the marginal effects brought by upstream expansion are released. Only when the used inventory of green technology products in rare earths is accumulated to stabilize the replacement of fossil energy, will the impact of the rare earth supply chain on biodiversity show a net improvement. Compared to the baseline value in 2000, the cumulative avoided biodiversity loss has exceeded 4 × 10<sup>7</sup> <span><math><mrow><mtext>MSA</mtext><mo>·</mo><mtext>ha</mtext><mo>·</mo><mtext>yr</mtext></mrow></math></span> by 2060. The net impact turns positive around 2040, and the cumulative net impact reaches zero around 2049, indicating a significant delay in ecological compensation. The time when the carbon balance occurs is earlier than that of biodiversity compensation, suggesting that net zero carbon emissions do not necessarily mean zero ecological emissions. From the perspective of final demand attribution, for every 1% increase in overseas demand, China's annual net biodiversity loss increases by approximately 0.031%, while for every 1% increase in the use of rare earth elements for green deployment in China, it decreases by about 0.84%. Our research results do not support the simplistic claim that “the more deployment, the better”. The transition path should be evaluated using the time of ecological compensation and the peak of medium-term pressure, rather than solely based on the final carbon emission results.</div></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"24 ","pages":"Article 100314"},"PeriodicalIF":7.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-10DOI: 10.1016/j.resenv.2026.100309
Yifan He , Wenchao Xue , Kang Xiao , Qiuju Wang , Guoren Xu
This study investigates the efficiency of biochar derived from waste activated sludge (WAS) as an electrode material in flowing-electrode capacitive deionization (FCDI), demonstrating improved desalination performance compared to commercial activated carbon (AC). The WAS-derived biochar (B-800-KH-1), synthesized via 2-h pyrolysis at 800 °C followed by KOH activation at a mass ratio of 1:1, exhibits a hierarchical micro/mesoporous architecture with an average pore diameter of 9.22 nm, a mesopore volume ratio of 76.57% and a disorder/graphite bands intensity ratio of 1.18. Moreover, the embedded silicate framework within the biochar synergistically interacts with the carbon phase, promoting ion pathways, and mitigating charge transfer resistance. Optimized operation of the FCDI system using the prepared B-800-KH-1 electrode achieved a maximum salt removal rate of 1.92 ± 0.23 μmol/cm2·min and an average removal rate of 1.24 ± 0.07 μmol/cm2·min, representing increments of approximately 56.1% and 7.8%, respectively, over commercial AC. Additionally, it demonstrated an energy-normalized salt removal of 8.35 ± 0.26 μmol/J at a 5.0 wt% carbon content and an applied voltage of 1.2 V. These findings highlight that high desalination efficiency is not solely dictated by total carbon content or specific surface area; rather, the interplay of mesoporous carbon domains with the silicate network and hierarchical pore connectivity drives enhanced electrosorption. Utilizing of WAS as a biochar precursor not only provides an effective electrode material but also offers a sustainable solution for WAS management, paving the way for advanced and environmentally cost-effective conscious desalination technologies.
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Pub Date : 2026-03-01Epub Date: 2026-02-24DOI: 10.1016/j.resenv.2026.100315
Yefeng Jiang, Xi Guo
Soil degradation is a critical global challenge, and its accurate assessment is fundamental to advancing sustainable soil management and ensuring food security. The minimum dataset (MDS) has been widely adopted in soil degradation assessments, typically constructed using various statistical and machine learning techniques. However, a lack of systematic comparisons among these methods introduces substantial uncertainty into degradation evaluations. Here, we propose a two-step approach to optimize MDS construction using 99 dryland red soil samples from Ji'an, China, and the analysis of 30 physical, chemical, and biological indicators. First, machine learning algorithms—including Decision Tree, Random Forest, Gradient Boosting Regression Tree (GBRT), and Extreme Gradient Boosting—were employed for feature selection. Subsequently, indicator weighting and scoring functions were applied to assess the degree of soil degradation. Our results demonstrate that the proposed two-step approach outperforms both conventional statistical dimensionality reduction techniques (i.e., principal component and K-means cluster analyses) and the direct application of machine learning models. Multiple model evaluation metrics—including the coefficient of determination, error metrics, statistical tests, and correlation with crop yield—consistently indicate that the MDS derived from the GBRT-based two-step approach is highly suitable for rapid characterization of dryland red soil degradation. This MDS comprises organic carbon, microaggregates, bulk density, field capacity, urease, nitrate nitrogen, available phosphorus, available potassium, and total phosphorus. The assessment further revealed that dryland soils in the study area were generally at a moderate degradation level, primarily driven by the combined effects of climatic, soil, topographic, and anthropogenic factors. This study validates the two-step approach as an effective tool for assessing dryland red soil degradation and offers new insights for the development of a global soil degradation monitoring system.
{"title":"Optimizing minimum dataset for soil degradation assessment in dryland of southern China: A two-step approach","authors":"Yefeng Jiang, Xi Guo","doi":"10.1016/j.resenv.2026.100315","DOIUrl":"10.1016/j.resenv.2026.100315","url":null,"abstract":"<div><div>Soil degradation is a critical global challenge, and its accurate assessment is fundamental to advancing sustainable soil management and ensuring food security. The minimum dataset (MDS) has been widely adopted in soil degradation assessments, typically constructed using various statistical and machine learning techniques. However, a lack of systematic comparisons among these methods introduces substantial uncertainty into degradation evaluations. Here, we propose a two-step approach to optimize MDS construction using 99 dryland red soil samples from Ji'an, China, and the analysis of 30 physical, chemical, and biological indicators. First, machine learning algorithms—including Decision Tree, Random Forest, Gradient Boosting Regression Tree (GBRT), and Extreme Gradient Boosting—were employed for feature selection. Subsequently, indicator weighting and scoring functions were applied to assess the degree of soil degradation. Our results demonstrate that the proposed two-step approach outperforms both conventional statistical dimensionality reduction techniques (i.e., principal component and <em>K</em>-means cluster analyses) and the direct application of machine learning models. Multiple model evaluation metrics—including the coefficient of determination, error metrics, statistical tests, and correlation with crop yield—consistently indicate that the MDS derived from the GBRT-based two-step approach is highly suitable for rapid characterization of dryland red soil degradation. This MDS comprises organic carbon, microaggregates, bulk density, field capacity, urease, nitrate nitrogen, available phosphorus, available potassium, and total phosphorus. The assessment further revealed that dryland soils in the study area were generally at a moderate degradation level, primarily driven by the combined effects of climatic, soil, topographic, and anthropogenic factors. This study validates the two-step approach as an effective tool for assessing dryland red soil degradation and offers new insights for the development of a global soil degradation monitoring system.</div></div>","PeriodicalId":34479,"journal":{"name":"Resources Environment and Sustainability","volume":"24 ","pages":"Article 100315"},"PeriodicalIF":7.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147405896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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