Spatio-Temporal Variation and Drivers of Land-Use Net Carbon Emissions in Chengyu Urban Agglomeration, China DOI Creative Commons
Wen Wang, Xianwei Wang, Wang Li

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2160 - 2160

Published: Dec. 11, 2024

Land-use change is an important cause of carbon emissions (CEs). In the context achieving peaking and neutrality goals, understanding coupling mechanisms between land-use CEs great significance for fostering regional low-carbon sustainable development. this study, net (LCN) calculation evaluation model was built based on perspective change. The variation matrix, standard deviation ellipse, spatial autocorrelation analysis were used to analyze spatio-temporal evolution LCN in Chengyu urban agglomeration (CUA) from 2000 2020. Meanwhile, economic contribution coefficient ecological support applied evaluate alignment among CEs, socio-economic development, environment. addition, modified Kaya Logarithmic Mean Divisia Index (LMDI) models quantitatively drivers underlying influence LCN. results showed following: (1) area built-up land forest expanded rapidly, mainly transforming grassland farmland CUA during study period. main source CEs. changes led migration center variations clustering. (2) growth rate decreased after 2010, disparities productivity compensation cities gradually narrowed environmental governance effectively improved. (3) development level energy consumption intensity primary facilitator inhibitor LCN, respectively. could offer valuable references insights formulating reduction strategies policies.

Language: Английский

The Internal Heterogeneity of Carbon Emissions in Megacities: A Case Study of Beijing, China DOI Creative Commons
Zheng Wang, Kangkang Gu, Hu Yu

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(1), P. 80 - 80

Published: Jan. 14, 2025

Cities are of wide concern to scholars due their major share global carbon emissions. Energy-related emissions differ significantly among cities, especially megacities, regional heterogeneity in socioeconomic conditions. To analyze the differences influencing factors on within and further target emission reductions, measures were developed. Beijing was selected investigate factor core zones, developing zones ecological using STIRPAT model county level. The results show following: (1) Regional existed changes from 2010 2022. grew steadily demonstrated as a part Beijing. (2) There variations Population size driving while driven primarily by GDP per capita. Notably, urbanization promoted increase but had negative influence zones. energy intensity primary force three (3) population, economic scale, industrial structure technological level lead should formulate targeted reduction based functional positioning.

Language: Английский

Citations

0
Impact Factors and Structural Pathways of Carbon Emissions in the Power Sector of the Beijing–Tianjin–Hebei Region Using MRIO Analysis DOI Creative Commons
Hao Yue,

Bingqing Wu,

Jiali Duan

et al.

Atmosphere, Journal Year: 2025, Volume and Issue: 16(2), P. 177 - 177

Published: Feb. 5, 2025

The accelerated growth of the global economy has given rise to a multitude environmental concerns that demand immediate attention. At this juncture, total carbon emissions are exhibiting gradual increase. China, United States, India, Russia, and Japan represent top five countries in terms emissions, collectively accounting for approximately 60% total. Of these, China’s highest world, representing over 30% As urbanization accelerates, from urban agglomerations constitute substantial share nation’s rendering clusters critical issue. In context agglomerations, Beijing–Tianjin–Hebei region, due factors such as industrial structure, accounts relatively high proportion 11% national future trajectory region will significantly impact high-quality development entire cluster. Consequently, research on is vital importance. This paper takes power industry subject, analyzes its status, builds multi-regional input–output model based tables data each province. study explores key influencing 2012 2017 transfer structural evolution perspective clarify reduction responsibilities provide references recommendations formulation regional collaborative emission policies. results show direct account higher compared indirect it generates by driving other industries. Industries with path include coal mining selection, equipment manufacturing, transportation, services, etc. capital input process Tianjin Hebei Beijing accompanied transfer. Promoting widespread adoption technologies have an effective suppressive effect especially Hebei; should pay attention stimulating increased final emissions; between regions industries shows downward trend sector undergoes transformation.

Language: Английский

Citations

0
Construction of Long-Term Grid-Scale Decoupling Model: A Case Study of Beijing-Tianjin-Hebei Region DOI Creative Commons

Xvlu Wang,

Minrui Zheng, Dongya Liu

et al.

Land, Journal Year: 2024, Volume and Issue: 13(11), P. 1853 - 1853

Published: Nov. 6, 2024

Against the backdrop of rapid global economic development, Beijing-Tianjin-Hebei (BTH) region, a pivotal hub and environmentally sensitive area in China, faces significant challenges sustaining its landscape ecosystem. Given region’s strategic importance vulnerability to environmental pressures, this study investigated intricate relationships between ecological risk, urban expansion, growth (EG) BTH region. Utilizing as focal point, we constructed decoupling model at grid scale explore relationship risk index (ERI), construction (CAG), EG. The results showed that (1) distinct stages regional disparities were observed trends ERI, CAG, EG within hot cold spot patterns for these factors did not align consistently. (2) From 1995 2019, coupling region underwent fluctuating transition, initially moving from an undesirable state ideal state, subsequently reverting state. Although overall some convergence, there notable spatial distribution differences. (3) heterogeneity two was relatively poor. Further analysis revealed evolution closely intertwined with policy shifts adjustments.

Language: Английский

Citations

1
Spatiotemporal analysis of carbon emissions in the Yangtze River Delta Urban Agglomeration: Insights from nighttime light data (1992–2019) DOI Creative Commons
Jing Gao, Shenlong Zhao, Lucang Wang

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102831 - 102831

Published: Sept. 1, 2024

Language: Английский

Citations

0
Unveiling the dynamic flows and spatial inequalities arising from agricultural methane and nitrous oxide emissions DOI Creative Commons
Fan Zhang, Yuping Bai,

Xin Xuan

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 84, P. 102863 - 102863

Published: Oct. 24, 2024

Language: Английский

Citations

0
Spatio-Temporal Variation and Drivers of Land-Use Net Carbon Emissions in Chengyu Urban Agglomeration, China DOI Creative Commons
Wen Wang, Xianwei Wang, Wang Li

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2160 - 2160

Published: Dec. 11, 2024

Land-use change is an important cause of carbon emissions (CEs). In the context achieving peaking and neutrality goals, understanding coupling mechanisms between land-use CEs great significance for fostering regional low-carbon sustainable development. this study, net (LCN) calculation evaluation model was built based on perspective change. The variation matrix, standard deviation ellipse, spatial autocorrelation analysis were used to analyze spatio-temporal evolution LCN in Chengyu urban agglomeration (CUA) from 2000 2020. Meanwhile, economic contribution coefficient ecological support applied evaluate alignment among CEs, socio-economic development, environment. addition, modified Kaya Logarithmic Mean Divisia Index (LMDI) models quantitatively drivers underlying influence LCN. results showed following: (1) area built-up land forest expanded rapidly, mainly transforming grassland farmland CUA during study period. main source CEs. changes led migration center variations clustering. (2) growth rate decreased after 2010, disparities productivity compensation cities gradually narrowed environmental governance effectively improved. (3) development level energy consumption intensity primary facilitator inhibitor LCN, respectively. could offer valuable references insights formulating reduction strategies policies.

Language: Английский

Citations

0