Support Vector Machine Based Spatiotemporal Variations and Influencing Factors of Carbon Storage in Typical Lake Basins of Xinjiang, China DOI

Feiying Xia,

C. Cao,

Yaling Chang

и другие.

Опубликована: Янв. 1, 2024

Under the influence of anthropogenic and climate, lake basins have undergone varying degrees degradation (VDD), primarily manifested in transformation land use type fluctuations carbon storage. However, accurate assessment effects VDD on storage remains a challenge. In this study, four with were selected based community characteristics vegetation coverage. Based method support vector machine (SVM) to classify basins. The cover changes (LUCC) quantified, their between 2000 2020 evaluated using Integrated Valuation Ecosystem Services Tradeoffs (InVEST) model. results showed that ecological environment Kanas Sayram Lake, which less degraded, was still being compromised, leading reductions forest grassland area. On contrary, Bosten Ulungur Lake characterized by more severe degradation, signs stabilization, improvements From 2020, Bosten, Kanas, Basin increased 12515.13 tC, 73184.50 80244.10 457130.55 respectively. Grassland, forest, cropland main pools. density strong spatial correlation Normalized Difference Vegetation Index (NDVI) VDD. This study contributed deeper comprehension intricate interplay LUCC within affected VDD, offering valuable insights for estimation broader scale.

Язык: Английский

How do local governments respond to national urban containment policies? Evidence from China DOI
Shihao Zhu, Longfei Zheng,

Daquan Huang

и другие.

Habitat International, Год журнала: 2025, Номер 157, С. 103320 - 103320

Опубликована: Фев. 16, 2025

Язык: Английский

Процитировано

0

Identifying the determinants of natural, anthropogenic factors and precursors on PM1 pollution in urban agglomerations in China: Insights from optimal parameter-based geographic detector and robust geographic weighted regression models DOI
Ping Zhang, Yong Wang, Wenjie Ma

и другие.

Environmental Research, Год журнала: 2025, Номер unknown, С. 121817 - 121817

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0

Urban Spatial Blessing: Effect of Land Use Intensity on Human Development Index DOI Creative Commons
Xiang Luo,

Shuchen Niu,

Xin Li

и другие.

Land, Год журнала: 2025, Номер 14(5), С. 1085 - 1085

Опубликована: Май 16, 2025

Urban land use is characterized by pronounced externalities. In most developing countries, economic welfare considerations drive the changes in intensity, leading to spatial reallocation of resources and thereby affecting enhancement urban welfare. This study combined multi-source data construct a panel dataset 284 prefectural-level above cities China from 2011 2022, employed Durbin model, heterogeneity mechanism model systematically analyze spillover effects intensity (ULUI) on (Human Development Index, HDI), its heterogeneity, underlying influencing mechanisms. The concluded that: (i) Both HDI ULUI have shown certain improvement despite some distinct regional heterogeneity; (ii) significantly contributes local welfare, yet exerts negative effect neighboring cities, effective boundary this 400 km. (iii) Spatial analysis revealed that for non-eastern non-megacities, whereas it positive eastern megacities, though estimated coefficients are relatively small. (iv) terms mechanism, industrial rationalization, advancement, agglomeration market dimension, as well expenditure scaling, structuring, public serviceability non-market essential channels affect both cities. results indicate current “land-based” not conducive there an urgent need better understanding principles factor allocation agglomeration, establishing cross-regional synergistic mechanisms, fully leveraging comparative advantages geographic conditions scale across different so improve space

Язык: Английский

Процитировано

0

Does urban green space form influence the spatial pattern of noise complaints? DOI
Xin-Chen Hong,

Dan-Yin Zhang,

Fangbing Hu

и другие.

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106506 - 106506

Опубликована: Июнь 1, 2025

Язык: Английский

Процитировано

0

Evaluating the Spatial Heterogeneity and Driving Factors of Sustainable Development Level in Chengdu with Point of Interest Data and Geographic Detector Model DOI Creative Commons
Yantao Ling,

Yilang Zhao,

Qingzhong Ren

и другие.

Land, Год журнала: 2024, Номер 13(7), С. 1018 - 1018

Опубликована: Июль 8, 2024

Over the past few decades, China has undergone largest and fastest urbanization process in world history. By 2023, Chengdu’s rate had reached 80.5%, significantly higher than national average of 66.16%. Studying experience Chengdu is great significance for optimizing urban planning policies other cities China. Although much literature explored from macro micro perspectives, studies using a top-down approach to examine fringe expansion are relatively scarce. This study first applies entropy weight method analyze spatial-temporal evolution trends development, identifying areas imbalanced development prominent issues. Secondly, K-means machine learning algorithm nightlight data used reconstruct classify regions, comparative analysis conducted with administrative divisions further identify unreasonable spatial distribution structure. Finally, POI geographical detector micro-driving forces major limiting factors solutions. The found that gap between rural narrowing during process, but there severe differentiation second circle Chengdu, where economic accelerating residents’ happiness declining. Moreover, based on land-use reveals urban-rural mainly concentrated Chengdu. Micro-level driving factor western region many small settlements, dense road network scattered functional areas. eastern inefficient extensive use construction land. Additionally, mismatch student status household registration resulted lagging educational resource high entry barriers have hindered progress urbanization, leading low per capita welfare expenditure. These reasons main causing decline happiness, this impact shows significant differences at different temporal scales. Encouraging innovation research or education can serve as long-term effective force promoting sustainable urbanization. provides valuable insights scientifically process.

Язык: Английский

Процитировано

1

Spatial Characteristics and Influencing Factors of People's Livelihood Issues Based on Urban Online Governance Platforms: A Case of Chengdu, China DOI
Sha Peng, Run Liu, Ya Sun

и другие.

Journal of Urban Planning and Development, Год журнала: 2024, Номер 150(4)

Опубликована: Июль 17, 2024

Язык: Английский

Процитировано

1

Exploring urban and agricultural land use planning DOI Creative Commons

Zexu Chen,

Huachun Dong

Results in Engineering, Год журнала: 2024, Номер 24, С. 103093 - 103093

Опубликована: Окт. 10, 2024

Язык: Английский

Процитировано

1

Analysis of the spatial characteristics and driving forces of underground consumer service space in Chinese megacities based on multi-source data DOI Creative Commons
Yuxiao Tang, Yudi Tang

Sustainable Cities and Society, Год журнала: 2024, Номер 116, С. 105924 - 105924

Опубликована: Окт. 18, 2024

Язык: Английский

Процитировано

1

Support Vector Machine Based Spatiotemporal Variations and Influencing Factors of Carbon Storage in Typical Lake Basins of Xinjiang, China DOI

Feiying Xia,

C. Cao,

Yaling Chang

и другие.

Опубликована: Янв. 1, 2024

Under the influence of anthropogenic and climate, lake basins have undergone varying degrees degradation (VDD), primarily manifested in transformation land use type fluctuations carbon storage. However, accurate assessment effects VDD on storage remains a challenge. In this study, four with were selected based community characteristics vegetation coverage. Based method support vector machine (SVM) to classify basins. The cover changes (LUCC) quantified, their between 2000 2020 evaluated using Integrated Valuation Ecosystem Services Tradeoffs (InVEST) model. results showed that ecological environment Kanas Sayram Lake, which less degraded, was still being compromised, leading reductions forest grassland area. On contrary, Bosten Ulungur Lake characterized by more severe degradation, signs stabilization, improvements From 2020, Bosten, Kanas, Basin increased 12515.13 tC, 73184.50 80244.10 457130.55 respectively. Grassland, forest, cropland main pools. density strong spatial correlation Normalized Difference Vegetation Index (NDVI) VDD. This study contributed deeper comprehension intricate interplay LUCC within affected VDD, offering valuable insights for estimation broader scale.

Язык: Английский

Процитировано

0