Advancing Regional–Scale Spatio–Temporal Dynamics of FFCO2 Emissions in Great Bay Area DOI Creative Commons

Jing Zhao,

Qunqun Zhao,

Wenjiang Huang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(13), P. 2354 - 2354

Published: June 27, 2024

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple and fusion method, we developed an optimal (3 km) FFCO2 emission product that incorporates quantified uncertainties connects global–regional–city scales. Our findings reveal following: (1) post–reconstruction reduces for 2000–2014 2015–2021 ±9.77% ±11.39%, respectively, outperforming other improving accuracy 73% compared ODIAC EDGAR (57%, 65%). (2) Long–term trends Greater Bay Area (GBA) show upward trajectory, with 2.8% rise during global financial crisis −0.19% decline COVID-19 pandemic. Spatial analysis uncovers “core–subcore–periphery” pattern. (3) The core city GZ consistently contributes largest emissions, followed by DG as second–largest emitter, HK seventh–highest emitter. Factors influencing center–shift pattern include urban form cities, population migration, GDP contribution, but not electricity consumption. reconstructed method offer reliable solution lack directly observed enhancing decision–making policymakers.

Language: Английский

Exploring the spatial association characteristics of carbon emission efficiency in China’s construction industry: A network perspective DOI
Fangliang Wang, Qi Zhang

Energy and Buildings, Journal Year: 2025, Volume and Issue: 329, P. 115289 - 115289

Published: Jan. 11, 2025

Language: Английский

Citations

0
Spatial network analysis and driving forces of urban carbon emission performance: Insights from Guangdong Province DOI
Xuewei Zhang,

Jiabei Zhou,

Rong Wu

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 951, P. 175538 - 175538

Published: Aug. 14, 2024

Language: Английский

Citations

3
Analysis of the Driving Mechanism of Urban Carbon Emission Correlation Network in Shandong Province Based on TERGM DOI Open Access
Jiekun Song, Huisheng Xiao, Zhicheng Liu

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(10), P. 4233 - 4233

Published: May 17, 2024

Analyzing the driving factors and mechanisms of urban carbon emission correlation networks can provide effective reduction decision-making support for Shandong Province other regions with similar industrial characteristics. Based on data from various cities in 2013 to 2021, spatial network was established by using a modified gravity model. The characteristics were explored Social Network Analysis (SNA) method, significant affecting identified through Quadratic Assignment Procedure (QAP) analysis motif analysis. mechanism analyzed Temporal Exponential Random Graph Models (TERGMs). results show that: (1) exhibits multi-threaded complex correlations relatively stable structure, overcoming geographical distance limitations. (2) Qingdao, Jinan, Rizhao have high degree centrality, betweenness closeness centrality network, Qingdao Jinan being central. (3) be spatially clustered into four regions, each distinct roles, displaying certain “neighboring clustering” phenomenon. (4) Endogenous structures such as Mutual, Ctriple, Gwesp significantly impact formation evolution while Twopath does not expected impact; FDI promote generation reception relationships network; IR spillover GS, differences GDP, EI, similarities organic within temporal level, has shown stability during study period.

Language: Английский

Citations

2
Advancing Regional–Scale Spatio–Temporal Dynamics of FFCO2 Emissions in Great Bay Area DOI Creative Commons

Jing Zhao,

Qunqun Zhao,

Wenjiang Huang

et al.

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(13), P. 2354 - 2354

Published: June 27, 2024

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple and fusion method, we developed an optimal (3 km) FFCO2 emission product that incorporates quantified uncertainties connects global–regional–city scales. Our findings reveal following: (1) post–reconstruction reduces for 2000–2014 2015–2021 ±9.77% ±11.39%, respectively, outperforming other improving accuracy 73% compared ODIAC EDGAR (57%, 65%). (2) Long–term trends Greater Bay Area (GBA) show upward trajectory, with 2.8% rise during global financial crisis −0.19% decline COVID-19 pandemic. Spatial analysis uncovers “core–subcore–periphery” pattern. (3) The core city GZ consistently contributes largest emissions, followed by DG as second–largest emitter, HK seventh–highest emitter. Factors influencing center–shift pattern include urban form cities, population migration, GDP contribution, but not electricity consumption. reconstructed method offer reliable solution lack directly observed enhancing decision–making policymakers.

Language: Английский

Citations

0