Shaping China's carbon peak roadmaps: A dynamic model for provincial residential buildings DOI

Youfeng Qiao,

Jinfan Zhang, Tengfei Huo

et al.

Sustainable Production and Consumption, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Drivers and spatial patterns of carbon emissions from residential buildings: An empirical analysis of Fuzhou city (China) DOI
Xiaojuan Li, Chengxin Lin, Mingchao Lin

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 257, P. 111534 - 111534

Published: April 17, 2024

Language: Английский

Citations

24
Integrated BIM-IoT platform for carbon emission assessment and tracking in prefabricated building materialization DOI
Xiaojuan Li, Ming Jiang, Chengxin Lin

et al.

Resources Conservation and Recycling, Journal Year: 2025, Volume and Issue: 215, P. 108122 - 108122

Published: Jan. 8, 2025

Language: Английский

Citations

3
Provincial allocation of China's commercial building operational carbon toward carbon neutrality DOI Creative Commons
Yanqiao Deng,

Minda Ma,

Nan Zhou

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 384, P. 125450 - 125450

Published: Feb. 6, 2025

Language: Английский

Citations

3
Spatio-temporal distribution and peak prediction of energy consumption and carbon emissions of residential buildings in China DOI

Jiayi Tan,

Shanbi Peng, Enbin Liu

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 376, P. 124330 - 124330

Published: Aug. 28, 2024

Language: Английский

Citations

12
Machine Learning-Based Carbon Emission Predictions and Customized Reduction Strategies for 30 Chinese Provinces DOI Open Access

Soonhyun Hong,

Ting Ting Fu,

Ming Dai

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(5), P. 1786 - 1786

Published: Feb. 20, 2025

With the intensification of global climate change, discerning identification carbon emission drivers and accurate prediction emissions have emerged as critical components in addressing this urgent issue. This paper collected data from Chinese provinces 1997 to 2021. Machine learning algorithms were applied identify province characteristics determine influence provincial development types their drivers. Analysis indicated that technology energy consumption had greatest impact on low-carbon potential (LCPPs), economic growth hub (EGHPs), sustainable (SGPs), technology-driven (LCTDPs), high-carbon-dependent (HCDPs). Furthermore, a predictive framework incorporating grey model (GM) alongside tree-structured parzen estimator (TPE)-optimized support vector regression (SVR) was employed forecast for forthcoming decade. Findings demonstrated approach provided substantial improvements accuracy. Based these studies, utilized combination SHapley Additive exPlanation (SHAP) political, economic, social, technological analysis—strengths, weaknesses, opportunities, threats (PEST-SWOTs) analysis methods propose customized reduction suggestions five development, such promoting technology, transformation structure, optimizing industrial structure.

Language: Английский

Citations

1
How Vietnam can achieve net-zero carbon emissions in construction and built environment by 2050: An integrated AHP and DEMATEL approach DOI
Nguyễn Văn Tâm,

To Thi Huong Quynh,

Nguyễn Quốc Toản

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112752 - 112752

Published: Feb. 1, 2025

Language: Английский

Citations

1
Dynamic spatiotemporal evolution and spatial effect of carbon emissions in urban agglomerations based on nighttime light data DOI
Hao Wu, Yi Yang, Wen Li

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 113, P. 105712 - 105712

Published: July 27, 2024

Language: Английский

Citations

7
Evaluating resilience and enhancing strategies for old urban communities amidst epidemic challenges DOI
Chengxin Lin, Rixin Chen,

B Wang

et al.

Habitat International, Journal Year: 2024, Volume and Issue: 153, P. 103187 - 103187

Published: Sept. 27, 2024

Language: Английский

Citations

7
Investigating the impacts of the Dual Carbon Targets on energy and carbon flows in China DOI

Peng-Tao Wang,

Qiang Xu, Feiyin Wang

et al.

Energy, Journal Year: 2024, Volume and Issue: 313, P. 133778 - 133778

Published: Nov. 10, 2024

Language: Английский

Citations

7
Carbon emission causal discovery and multi-step forecasting using spatiotemporal information DOI
Xiaoyan Li, Wenting Zhan,

Peng Luo

et al.

Information Sciences, Journal Year: 2024, Volume and Issue: 665, P. 120372 - 120372

Published: Feb. 28, 2024

Language: Английский

Citations

5