Pub Date : 2025-11-28DOI: 10.1016/j.geosus.2025.100391
Xiaofan Xu , Yuxiao Kong , Jintao Zhang , Jianping Duan , Minghong Tan , Xue Yang , Hongzhou Zhu , Deliang Chen
Global warming and socioeconomic development are expected to exacerbate human exposure to heat stress, but the extent and inequality of such changes remain unclear. Here, we quantified the future population exposure to heat stress (PEHS) under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) scenarios using a novel decomposition framework that separates the contributions of climate change, population change, and their interaction. Results show that global PEHS will increase substantially during the 21st century, with low-latitude regions experiencing the largest absolute increases, and high-latitude regions facing the largest relative increases. Globally, projected increases in PEHS under SSP3–7.0 are roughly three times those under SSP1–2.6, with low latitudes contributing about 70 %–75 % of the global total. SSP1–2.6 most effectively limits future heat exposure, with the highest risks in low-latitude developing regions, underscoring the need for low-emission pathways and targeted population and urbanization management. The findings highlight the urgent need for both climate mitigation and population adaptation strategies to address the growing and uneven heat exposure risks worldwide.
{"title":"Increasing meridional disparity of population exposure to heat stress","authors":"Xiaofan Xu , Yuxiao Kong , Jintao Zhang , Jianping Duan , Minghong Tan , Xue Yang , Hongzhou Zhu , Deliang Chen","doi":"10.1016/j.geosus.2025.100391","DOIUrl":"10.1016/j.geosus.2025.100391","url":null,"abstract":"<div><div>Global warming and socioeconomic development are expected to exacerbate human exposure to heat stress, but the extent and inequality of such changes remain unclear. Here, we quantified the future population exposure to heat stress (PEHS) under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) scenarios using a novel decomposition framework that separates the contributions of climate change, population change, and their interaction. Results show that global PEHS will increase substantially during the 21st century, with low-latitude regions experiencing the largest absolute increases, and high-latitude regions facing the largest relative increases. Globally, projected increases in PEHS under SSP3–7.0 are roughly three times those under SSP1–2.6, with low latitudes contributing about 70 %–75 % of the global total. SSP1–2.6 most effectively limits future heat exposure, with the highest risks in low-latitude developing regions, underscoring the need for low-emission pathways and targeted population and urbanization management. The findings highlight the urgent need for both climate mitigation and population adaptation strategies to address the growing and uneven heat exposure risks worldwide.</div></div>","PeriodicalId":52374,"journal":{"name":"Geography and Sustainability","volume":"7 1","pages":"Article 100391"},"PeriodicalIF":8.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1016/j.geosus.2025.100398
Xiaozhen Wang , Shuai Wang , Kangying Li , Xing Wu , Chunbo Huang , Zhouping Shangguan , Kaibo Wang , Lei Deng
Understanding the complex interactions between human activities and ecosystem functions is a prerequisite for achieving sustainable development. Since the implementation of the “Grain for Green” Project in 1999, ecosystem functions in China’s Loess Plateau have significantly improved. However, intensified human activities have also exacerbated the pressures on the region’s fragile ecological environment. This study investigates the spatiotemporal variations in the human activity intensity index (HAI) and net ecosystem benefits (NEB) from 2000 to 2020, using expert-based assessments and an enhanced cost-benefit evaluation framework. Results indicate that HAI increased by 16.7 % and 16.6 % at the grid and county levels, respectively. NEB exhibited pronounced spatial heterogeneity, with a total increase of USD 36.2 trillion at the grid scale. At the county level, the average NEB rose by 75 %. The degree of trade-off was higher at the grid scale than at the county scale, while the synergistic areas initially expanded and then declined at both scales. Key areas for improvement and regions of lagging development were identified as priority zones for ecological management and spatial planning at both spatial resolutions. This study offers scientific insights and practical guidance for harmonizing ecological conservation with high-quality development in ecologically vulnerable regions.
{"title":"Linking net ecosystem benefits and human activity: Regional management implications on the China’s Loess Plateau","authors":"Xiaozhen Wang , Shuai Wang , Kangying Li , Xing Wu , Chunbo Huang , Zhouping Shangguan , Kaibo Wang , Lei Deng","doi":"10.1016/j.geosus.2025.100398","DOIUrl":"10.1016/j.geosus.2025.100398","url":null,"abstract":"<div><div>Understanding the complex interactions between human activities and ecosystem functions is a prerequisite for achieving sustainable development. Since the implementation of the “Grain for Green” Project in 1999, ecosystem functions in China’s Loess Plateau have significantly improved. However, intensified human activities have also exacerbated the pressures on the region’s fragile ecological environment. This study investigates the spatiotemporal variations in the human activity intensity index (HAI) and net ecosystem benefits (NEB) from 2000 to 2020, using expert-based assessments and an enhanced cost-benefit evaluation framework. Results indicate that HAI increased by 16.7 % and 16.6 % at the grid and county levels, respectively. NEB exhibited pronounced spatial heterogeneity, with a total increase of USD 36.2 trillion at the grid scale. At the county level, the average NEB rose by 75 %. The degree of trade-off was higher at the grid scale than at the county scale, while the synergistic areas initially expanded and then declined at both scales. Key areas for improvement and regions of lagging development were identified as priority zones for ecological management and spatial planning at both spatial resolutions. This study offers scientific insights and practical guidance for harmonizing ecological conservation with high-quality development in ecologically vulnerable regions.</div></div>","PeriodicalId":52374,"journal":{"name":"Geography and Sustainability","volume":"7 1","pages":"Article 100398"},"PeriodicalIF":8.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1016/j.geosus.2025.100393
Jinhao Liu , Zhongbao Xin
Global climate change is a pressing environmental challenge. Climate-induced migration highlights the severe impact of unsuitable climatic conditions. However, current research methods are limited in their ability to assess climate suitability for residents in high-altitude areas. In this study, we assess climate suitability across the Qinghai–Xizang Plateau from 1979 to 2018 and project future changes using four different Shared Socioeconomic Pathway (SSP) climate scenarios by constructing the Climate Suitability Index (CSI). The findings reveal a notable increase in CSI from 0.32 to 0.36 from 1979 to 2018. The primary factors contributing to the increased climate suitability are increasing annual mean precipitation (61.42 %) and decreasing solar radiation (17.22 %) from 1979 to 2018. Furthermore, the study forecasts a continued enhancement of climate suitability across all SSP scenarios, with SSP585 demonstrating the greatest improvement, followed by SSP370, SSP245, and SSP126. Although low oxygen levels at high altitudes remain a challenge, the overall improvement in climate suitability offers hope for people living at high altitudes to cope with climate change.
{"title":"The increasing climate suitability for human habitation on the Qinghai–Xizang Plateau","authors":"Jinhao Liu , Zhongbao Xin","doi":"10.1016/j.geosus.2025.100393","DOIUrl":"10.1016/j.geosus.2025.100393","url":null,"abstract":"<div><div>Global climate change is a pressing environmental challenge. Climate-induced migration highlights the severe impact of unsuitable climatic conditions. However, current research methods are limited in their ability to assess climate suitability for residents in high-altitude areas. In this study, we assess climate suitability across the Qinghai–Xizang Plateau from 1979 to 2018 and project future changes using four different Shared Socioeconomic Pathway (SSP) climate scenarios by constructing the Climate Suitability Index (CSI). The findings reveal a notable increase in CSI from 0.32 to 0.36 from 1979 to 2018. The primary factors contributing to the increased climate suitability are increasing annual mean precipitation (61.42 %) and decreasing solar radiation (17.22 %) from 1979 to 2018. Furthermore, the study forecasts a continued enhancement of climate suitability across all SSP scenarios, with SSP585 demonstrating the greatest improvement, followed by SSP370, SSP245, and SSP126. Although low oxygen levels at high altitudes remain a challenge, the overall improvement in climate suitability offers hope for people living at high altitudes to cope with climate change.</div></div>","PeriodicalId":52374,"journal":{"name":"Geography and Sustainability","volume":"7 1","pages":"Article 100393"},"PeriodicalIF":8.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1016/j.geosus.2025.100392
Chenjin An , Jianghao Wang , Chenghu Zhou
With the rapid advancement of Artificial Intelligence (AI) technologies, its applications have become increasingly widespread across various aspects of geography, offering unprecedented analytical capabilities across disciplinary boundaries. Despite this revolutionary potential, a comprehensive understanding of the current research landscape and development trajectory of AI in geographical sciences remains limited. To fill this gap, we conducted a large-scale systematic review based on 400,000 geographical publications published from 1990 to 2023. We utilized large language model (LLM) prompt engineering, topic modeling and other natural language processing techniques to analyze the publications. Our findings reveal that AI applications constitute 8.1 % of geographical research, with publication volume having increased 20-fold over three decades. Both China and the United States have been the leading contributors to AI-driven geographical studies, together accounting for 62.78 % of all publications in this field. Notably, more than half of the studies used traditional machine learning methods. Among the various geographical topics, remote sensing applications and spatial data analysis emerged as the most extensively explored areas using AI techniques, with image feature extraction being the topic with the deepest level of adoption and most significant ongoing impact of AI methods. This systematic review provides critical insights into the integration trajectory of AI within geographical sciences, establishing a foundation for identifying emerging research opportunities and enhancing our understanding of AI’s transformative role in advancing geographical knowledge.
{"title":"The evolution and current landscape of AI in geographical research: A large-scale systematic review","authors":"Chenjin An , Jianghao Wang , Chenghu Zhou","doi":"10.1016/j.geosus.2025.100392","DOIUrl":"10.1016/j.geosus.2025.100392","url":null,"abstract":"<div><div>With the rapid advancement of Artificial Intelligence (AI) technologies, its applications have become increasingly widespread across various aspects of geography, offering unprecedented analytical capabilities across disciplinary boundaries. Despite this revolutionary potential, a comprehensive understanding of the current research landscape and development trajectory of AI in geographical sciences remains limited. To fill this gap, we conducted a large-scale systematic review based on 400,000 geographical publications published from 1990 to 2023. We utilized large language model (LLM) prompt engineering, topic modeling and other natural language processing techniques to analyze the publications. Our findings reveal that AI applications constitute 8.1 % of geographical research, with publication volume having increased 20-fold over three decades. Both China and the United States have been the leading contributors to AI-driven geographical studies, together accounting for 62.78 % of all publications in this field. Notably, more than half of the studies used traditional machine learning methods. Among the various geographical topics, remote sensing applications and spatial data analysis emerged as the most extensively explored areas using AI techniques, with image feature extraction being the topic with the deepest level of adoption and most significant ongoing impact of AI methods. This systematic review provides critical insights into the integration trajectory of AI within geographical sciences, establishing a foundation for identifying emerging research opportunities and enhancing our understanding of AI’s transformative role in advancing geographical knowledge.</div></div>","PeriodicalId":52374,"journal":{"name":"Geography and Sustainability","volume":"7 1","pages":"Article 100392"},"PeriodicalIF":8.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1016/j.geosus.2025.100390
Zihua Chen , Jiaxin Li , Haiyang Cui , Xiaowei Li , Zhenbo Wang
With the global expansion of protected areas (PAs) and increasing involvement of indigenous communities, understanding their impacts on indigenous peoples is crucial. This study evaluates the extent to which China’s national cultural ecological protection areas (CEPAs) safeguard indigenous culture, using land-use disturbance as a key metric to assess impacts on cultural keystone species (CKS). We employ a multi-step evaluation framework that reclassifies land use, identifies environment-dependent CKS, and analyzes land-use dynamics by comparing disturbances before and after CEPAs establishment. Our results reveal that, despite overall improvements in land conditions, over 36 % of CEPAs are in land disturbance threat or warning status. All of these sites are indigenous CEPAs, indicating a disproportionate disturbance burden on indigenous communities. Notably, traditional medicinal practices are particularly vulnerable. These findings underscore the urgent need for policies aligning ecological diversity with cultural diversity to support the global commitment to expand PAs to over 30 % of Earth’s land and oceans by 2030.
{"title":"Land use dynamics and the fate of indigenous culture in China’s cultural ecological protection areas","authors":"Zihua Chen , Jiaxin Li , Haiyang Cui , Xiaowei Li , Zhenbo Wang","doi":"10.1016/j.geosus.2025.100390","DOIUrl":"10.1016/j.geosus.2025.100390","url":null,"abstract":"<div><div>With the global expansion of protected areas (PAs) and increasing involvement of indigenous communities, understanding their impacts on indigenous peoples is crucial. This study evaluates the extent to which China’s national cultural ecological protection areas (CEPAs) safeguard indigenous culture, using land-use disturbance as a key metric to assess impacts on cultural keystone species (CKS). We employ a multi-step evaluation framework that reclassifies land use, identifies environment-dependent CKS, and analyzes land-use dynamics by comparing disturbances before and after CEPAs establishment. Our results reveal that, despite overall improvements in land conditions, over 36 % of CEPAs are in land disturbance threat or warning status. All of these sites are indigenous CEPAs, indicating a disproportionate disturbance burden on indigenous communities. Notably, traditional medicinal practices are particularly vulnerable. These findings underscore the urgent need for policies aligning ecological diversity with cultural diversity to support the global commitment to expand PAs to over 30 % of Earth’s land and oceans by 2030.</div></div>","PeriodicalId":52374,"journal":{"name":"Geography and Sustainability","volume":"7 1","pages":"Article 100390"},"PeriodicalIF":8.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145908677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1016/j.geosus.2025.100396
Qi Zhang , Fu Chen , Zhanbin Luo , Jun Fan , Yanfeng Zhu , Jing Ma , Yongjun Yang , Xi-en Long , Alejandro Gonzalez-Ollauri , Miao Gan , Weihong Guo , Yuxiang Ma , Qiaoling Wang , Shenglu Zhou , Mingan Shao
Amid accelerating global land degradation, establishing high-efficiency ecological restoration principles and frameworks is crucial. Here, we explore the application of threshold effects in the ecological restoration process based on field experiments and globally available experimental data from 173 sites. Combining data integration analysis and meta-analysis, we collectively verified the universality of threshold effects in grasslands. The global grasslands’ average nitrogen application threshold is 3.78 g·m−2·yr−1, while the threshold value of degraded grassland (3.65 g·m−2·yr−1) is lower than that of nondegraded grassland (5.90 g·m−2·yr−1). The low nitrogen-driven thresholds are affected by degradation status, climate (precipitation and temperature), and other site conditions, but not fertilization forms. Independent experiments further demonstrated that an increase in soil moisture content can lead to the disappearance of nitrogen threshold effects, revealing that ecological threshold effects are influenced by ecosystem stress factors. Following the significant increase in plant biomass triggered by the nitrogen threshold, the ecosystem undergoes systemic improvement. Soil organic carbon, urease activity, soil microbial diversity, and other soil properties are significantly enhanced. Soil nitrogen cycle-related microbial communities and soil physicochemical attributes are significantly activated. The results indicate that a threshold response pattern may develop before nitrogen saturation is reached, and low nitrogen input can boost productivity and improve the plant-soil-microbe system. Our findings reveal a nonprogressive path of restoration in degraded ecosystems, and thus, restoration based on threshold effects can offer an efficient and safe solution to combat ecological degradation.
{"title":"Exploring the optimal nitrogen threshold for global grassland restoration","authors":"Qi Zhang , Fu Chen , Zhanbin Luo , Jun Fan , Yanfeng Zhu , Jing Ma , Yongjun Yang , Xi-en Long , Alejandro Gonzalez-Ollauri , Miao Gan , Weihong Guo , Yuxiang Ma , Qiaoling Wang , Shenglu Zhou , Mingan Shao","doi":"10.1016/j.geosus.2025.100396","DOIUrl":"10.1016/j.geosus.2025.100396","url":null,"abstract":"<div><div>Amid accelerating global land degradation, establishing high-efficiency ecological restoration principles and frameworks is crucial. Here, we explore the application of threshold effects in the ecological restoration process based on field experiments and globally available experimental data from 173 sites. Combining data integration analysis and meta-analysis, we collectively verified the universality of threshold effects in grasslands. The global grasslands’ average nitrogen application threshold is 3.78 g·m<sup>−2</sup>·yr<sup>−1</sup>, while the threshold value of degraded grassland (3.65 g·m<sup>−2</sup>·yr<sup>−1</sup>) is lower than that of nondegraded grassland (5.90 g·m<sup>−2</sup>·yr<sup>−1</sup>). The low nitrogen-driven thresholds are affected by degradation status, climate (precipitation and temperature), and other site conditions, but not fertilization forms. Independent experiments further demonstrated that an increase in soil moisture content can lead to the disappearance of nitrogen threshold effects, revealing that ecological threshold effects are influenced by ecosystem stress factors. Following the significant increase in plant biomass triggered by the nitrogen threshold, the ecosystem undergoes systemic improvement. Soil organic carbon, urease activity, soil microbial diversity, and other soil properties are significantly enhanced. Soil nitrogen cycle-related microbial communities and soil physicochemical attributes are significantly activated. The results indicate that a threshold response pattern may develop before nitrogen saturation is reached, and low nitrogen input can boost productivity and improve the plant-soil-microbe system. Our findings reveal a nonprogressive path of restoration in degraded ecosystems, and thus, restoration based on threshold effects can offer an efficient and safe solution to combat ecological degradation.</div></div>","PeriodicalId":52374,"journal":{"name":"Geography and Sustainability","volume":"7 1","pages":"Article 100396"},"PeriodicalIF":8.0,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145941334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-10DOI: 10.1016/j.geosus.2025.100378
David J Eldridge
{"title":"Dryland Social-Ecological Systems in Changing Environments: By Bojie Fu and Mark Stafford Smith, 2024, Springer, Singapore. 424 pages, Open Access. ISBN 978-981-99-9374-1","authors":"David J Eldridge","doi":"10.1016/j.geosus.2025.100378","DOIUrl":"10.1016/j.geosus.2025.100378","url":null,"abstract":"","PeriodicalId":52374,"journal":{"name":"Geography and Sustainability","volume":"6 6","pages":"Article 100378"},"PeriodicalIF":8.0,"publicationDate":"2025-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-13DOI: 10.1016/j.geosus.2025.100377
Yu Zhang , Ben Niu , Zhipeng Wang , Meng Li , Jianshuang Wu , Xianzhou Zhang
Livestock management plays a crucial role in environmental protection, food security, and sustainable livelihoods worldwide. However, comprehensive research on its microeconomic dimensions remains limited. Here, we used piecewise structural equation modeling to identify key drivers of livestock management among rural smallholders, focusing on livestock stocking rates (LSR) and livestock offtake rates (LOR). Data were collected via semi-structured questionnaires and household head interviews in 54 villages in northern Xizang between 2018 and 2020 (n = 549). Our findings revealed pronounced spatial heterogeneity in livestock management, with households in alpine meadows showing the highest LSR (2.14 standardized sheep units per hectare, SSU· ha−1) and the lowest LOR (9 %), in contrast to households in desert steppe areas (0.27 SSU· ha−1 and 15 %, respectively). Across northern Xizang, five grouped environmental factors—climatic conditions, natural resource endowment, market conditions, demographics, and household income—jointly explained 66 % and 20 % of the variance in LSR and LOR, respectively. Biophysical factors had a greater influence than socioeconomic ones, though demographic variables and market conditions were also positively correlated with LSR and LOR, respectively. Given the consistently low LOR among species (9 %–15 %), with marked differences between yaks and sheep (5 %) and goats (2 %), targeted policies are needed to encourage herders to adopt circular economy practices to balance ecological conservation with economic growth. This study highlights an underutilized livestock economy in high-altitude pastoral communities and clarifies the interplay of biophysical and socioeconomic factors in herders’ decision-making. The findings offer valuable insights for refining policy frameworks related to livestock and environmental management in rural China and beyond.
{"title":"Biophysical and socioeconomic drivers of livestock management in high-altitude Xizang, China","authors":"Yu Zhang , Ben Niu , Zhipeng Wang , Meng Li , Jianshuang Wu , Xianzhou Zhang","doi":"10.1016/j.geosus.2025.100377","DOIUrl":"10.1016/j.geosus.2025.100377","url":null,"abstract":"<div><div>Livestock management plays a crucial role in environmental protection, food security, and sustainable livelihoods worldwide. However, comprehensive research on its microeconomic dimensions remains limited. Here, we used piecewise structural equation modeling to identify key drivers of livestock management among rural smallholders, focusing on livestock stocking rates (LSR) and livestock offtake rates (LOR). Data were collected via semi-structured questionnaires and household head interviews in 54 villages in northern Xizang between 2018 and 2020 (<em>n</em> = 549). Our findings revealed pronounced spatial heterogeneity in livestock management, with households in alpine meadows showing the highest LSR (2.14 standardized sheep units per hectare, SSU· ha<sup>−1</sup>) and the lowest LOR (9 %), in contrast to households in desert steppe areas (0.27 SSU· ha<sup>−1</sup> and 15 %, respectively). Across northern Xizang, five grouped environmental factors—climatic conditions, natural resource endowment, market conditions, demographics, and household income—jointly explained 66 % and 20 % of the variance in LSR and LOR, respectively. Biophysical factors had a greater influence than socioeconomic ones, though demographic variables and market conditions were also positively correlated with LSR and LOR, respectively. Given the consistently low LOR among species (9 %–15 %), with marked differences between yaks and sheep (5 %) and goats (2 %), targeted policies are needed to encourage herders to adopt circular economy practices to balance ecological conservation with economic growth. This study highlights an underutilized livestock economy in high-altitude pastoral communities and clarifies the interplay of biophysical and socioeconomic factors in herders’ decision-making. The findings offer valuable insights for refining policy frameworks related to livestock and environmental management in rural China and beyond.</div></div>","PeriodicalId":52374,"journal":{"name":"Geography and Sustainability","volume":"6 6","pages":"Article 100377"},"PeriodicalIF":8.0,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-28DOI: 10.1016/j.geosus.2025.100376
Zizhao Ni , Wenwu Zhao , Caichun Yin , Michael E. Meadows , Paulo Pereira
Although geography’s role in advancing the Sustainable Development Goals (SDGs) is widely recognised, a comprehensive quantitative synthesis of its intellectual contributions has been absent. This study fills that critical research gap through a large-scale bibliometric analysis. Drawing from 122 core geography journals (Web of Science, 2010–2024), we employed three-level search criteria (SDGs, sustainability and SDG indicators) to identify a final corpus of 70,122 relevant articles. We then combined publication trend analysis, co-citation and collaboration networks, and keyword co-occurrence mapping to systematically delineate research foci, contributions, and future directions. Our findings reveal six major thematic research clusters: (1) climate change impacts and governance; (2) agricultural landscape and environmental sustainability; (3) resilience and adaptive capability in social-ecological systems; (4) land use change and metacoupling impacts; (5) urban growth and transport accessibility; and (6) biodiversity and ecosystem services. The SDG overlap analysis highlights strong linkages among environmental SDGs, while revealing that SDG 1 (No Poverty) and SDG 10 (Reduced Inequalities) are more isolated. Overall, geography supports the SDGs across four key dimensions: (1) providing spatial data analysis for assessment; (2) conducting regional studies for localisation; (3) applying human-environment interaction research to advance synergies; and (4) strengthening science-policy interface efforts for achievement. To maximise its future impact, this study calls for the geography community to develop a dedicated methodological framework for SDG analysis, proactively contribute to shaping the post-2030 agenda, advance holistic integrated approaches, and prudently harness the power of artificial intelligence to accelerate sustainability transitions.
尽管地理学在推进可持续发展目标（sdg）方面的作用得到了广泛认可，但尚未对其智力贡献进行全面的定量综合。本研究通过大规模文献计量分析填补了这一关键研究空白。从122种核心地理期刊（Web of Science, 2010-2024）中，我们采用了三级搜索标准（可持续发展目标、可持续性和可持续发展目标指标）来确定70,122篇相关文章的最终语料库。然后，我们结合发表趋势分析、共被引和合作网络、关键词共现映射，系统地描绘了研究重点、贡献和未来方向。研究结果揭示了六大专题研究集群：(1)气候变化影响与治理；(2)农业景观与环境可持续性；(3)社会生态系统的复原力和适应能力；(4)土地利用变化及其元耦合影响；(5)城市增长和交通可达性；(6)生物多样性和生态系统服务。可持续发展目标重叠分析强调了环境可持续发展目标之间的紧密联系，同时揭示了可持续发展目标1（消除贫困）和可持续发展目标10（减少不平等）更加孤立。总体而言，地理在四个关键维度上支持可持续发展目标：(1)为评估提供空间数据分析；（二）进行区域本土化研究；(3)应用人与环境相互作用研究促进协同效应；(4)加强科学与政策的对接努力。为了最大限度地发挥其未来影响，本研究呼吁地理学界为可持续发展目标分析制定专门的方法框架，积极参与制定2030年后议程，推进整体综合方法，并审慎利用人工智能的力量加速可持续发展转型。
{"title":"Mapping geography’s engagement with the Sustainable Development Goals: Research foci, contributions, and future directions","authors":"Zizhao Ni , Wenwu Zhao , Caichun Yin , Michael E. Meadows , Paulo Pereira","doi":"10.1016/j.geosus.2025.100376","DOIUrl":"10.1016/j.geosus.2025.100376","url":null,"abstract":"<div><div>Although geography’s role in advancing the Sustainable Development Goals (SDGs) is widely recognised, a comprehensive quantitative synthesis of its intellectual contributions has been absent. This study fills that critical research gap through a large-scale bibliometric analysis. Drawing from 122 core geography journals (Web of Science, 2010–2024), we employed three-level search criteria (SDGs, sustainability and SDG indicators) to identify a final corpus of 70,122 relevant articles. We then combined publication trend analysis, co-citation and collaboration networks, and keyword co-occurrence mapping to systematically delineate research foci, contributions, and future directions. Our findings reveal six major thematic research clusters: (1) climate change impacts and governance; (2) agricultural landscape and environmental sustainability; (3) resilience and adaptive capability in social-ecological systems; (4) land use change and metacoupling impacts; (5) urban growth and transport accessibility; and (6) biodiversity and ecosystem services. The SDG overlap analysis highlights strong linkages among environmental SDGs, while revealing that SDG 1 (No Poverty) and SDG 10 (Reduced Inequalities) are more isolated. Overall, geography supports the SDGs across four key dimensions: (1) providing spatial data analysis for assessment; (2) conducting regional studies for localisation; (3) applying human-environment interaction research to advance synergies; and (4) strengthening science-policy interface efforts for achievement. To maximise its future impact, this study calls for the geography community to develop a dedicated methodological framework for SDG analysis, proactively contribute to shaping the post-2030 agenda, advance holistic integrated approaches, and prudently harness the power of artificial intelligence to accelerate sustainability transitions.</div></div>","PeriodicalId":52374,"journal":{"name":"Geography and Sustainability","volume":"6 6","pages":"Article 100376"},"PeriodicalIF":8.0,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-20DOI: 10.1016/j.geosus.2025.100375
Fenzhen Su , Fengqin Yan , Wenzhou Wu , Dongjie Fu , Yinxia Cao , Vincent Lyne , Michael Meadows , Ling Yao , Jianghao Wang , Yuanyuan Huang , Chong Huang , Jun Qin , Shifeng Fang , An Zhang
Geography is shifting from static description to a feedback-driven, adaptive discipline integrating sensing, prediction, comparison, and continuous self-improvement. This transformation underlies Intelligent Geography (IG), where artificial intelligence (AI), big data analytics, and high-performance computing (HPC) converge to enhance spatial understanding and guide intelligent decisions in complex systems. The discipline’s historical stages—descriptive, experimental, theoretical, quantitative, GIScience, and information geography—form the foundation for an overarching adaptive framework. In this framework, diverse geospatial data streams seamlessly feed real-time models whose predicted outputs are compared with observed conditions to iteratively refine predictions. A hallmark of IG is embedding domain theory into AI workflows, producing predictive models that self-adjust to new data or control system behavior. Applications such as smart traffic management, climate-responsive urban planning, and disaster-resilient digital twins illustrate the sensing–prediction–adaptation/learning cycle in practice for complex changing systems. We examine the enabling roles of HPC, deep learning, and geographic large models in implementing feedback loops, and address persistent challenges in data integration, interpretability, and governance. We conclude with a vision of IG as an evolving socio-technical ecosystem that through adaptation and self-learning turns spatial data into adaptive, actionable knowledge that assists in intelligent decision-making, whether it is for AI systems or human ones.
{"title":"Advancing intelligent geography: Current status, innovations, and future prospects","authors":"Fenzhen Su , Fengqin Yan , Wenzhou Wu , Dongjie Fu , Yinxia Cao , Vincent Lyne , Michael Meadows , Ling Yao , Jianghao Wang , Yuanyuan Huang , Chong Huang , Jun Qin , Shifeng Fang , An Zhang","doi":"10.1016/j.geosus.2025.100375","DOIUrl":"10.1016/j.geosus.2025.100375","url":null,"abstract":"<div><div>Geography is shifting from static description to a feedback-driven, adaptive discipline integrating sensing, prediction, comparison, and continuous self-improvement. This transformation underlies Intelligent Geography (IG), where artificial intelligence (AI), big data analytics, and high-performance computing (HPC) converge to enhance spatial understanding and guide intelligent decisions in complex systems. The discipline’s historical stages—descriptive, experimental, theoretical, quantitative, GIScience, and information geography—form the foundation for an overarching adaptive framework. In this framework, diverse geospatial data streams seamlessly feed real-time models whose predicted outputs are compared with observed conditions to iteratively refine predictions. A hallmark of IG is embedding domain theory into AI workflows, producing predictive models that self-adjust to new data or control system behavior. Applications such as smart traffic management, climate-responsive urban planning, and disaster-resilient digital twins illustrate the sensing–prediction–adaptation/learning cycle in practice for complex changing systems. We examine the enabling roles of HPC, deep learning, and geographic large models in implementing feedback loops, and address persistent challenges in data integration, interpretability, and governance. We conclude with a vision of IG as an evolving socio-technical ecosystem that through adaptation and self-learning turns spatial data into adaptive, actionable knowledge that assists in intelligent decision-making, whether it is for AI systems or human ones.</div></div>","PeriodicalId":52374,"journal":{"name":"Geography and Sustainability","volume":"6 6","pages":"Article 100375"},"PeriodicalIF":8.0,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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