Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China

Applied Energy, Год журнала: 2024, Номер 360, С. 122819 - 122819

Опубликована: Фев. 10, 2024

Язык: Английский

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Agri-food evolution and carbon emissions in Chinese residential consumption: A life cycle analysis of urban-rural disparities and socioeconomic influences

Environmental Impact Assessment Review, Год журнала: 2023, Номер 105, С. 107387 - 107387

Опубликована: Дек. 6, 2023

Язык: Английский

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Exploring the key influencing factors of low-carbon innovation from urban characteristics in China using interpretable machine learning

Environmental Impact Assessment Review, Год журнала: 2024, Номер 107, С. 107573 - 107573

Опубликована: Июнь 7, 2024

Язык: Английский

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Exploring provincial carbon-pollutant emission efficiency in China: An integrated approach with social network analysis and spatial econometrics

Ecological Indicators, Год журнала: 2024, Номер 159, С. 111662 - 111662

Опубликована: Янв. 31, 2024

Carbon emissions and air pollutant in China share common sources. A thorough examination of carbon is imperative for a comprehensive understanding. This study aims to assess carbon-pollutant efficiency (CPEE) 30 Chinese provinces from 2006 2020, focusing on the integration emissions. Previous research CPEE limited, this article addresses gap by investigating its spatiotemporal characteristics identifying potential influencing factors. Our employs super-efficiency data envelopment analysis model, social network analysis, spatial econometric models. The results indicate that: (1) During period, provincial follows "W-shaped" trend exhibits "bimodal" asymmetric distribution, indicating notable regional heterogeneity. (2) Although correlations are unstable do not adhere strict hierarchical structure, positive spillover effects evident CPEE. (3) correlation displays various characteristics, including Matthew effect, external preference, siphoning altruistic tendency. (4) Apart technical input, explanatory variables affecting exert significantly negative direct impact. Furthermore, indirect effect energy intensity contradicts when economic distance considered weight setting. These findings provide valuable theoretical insights practical guidance improving synergistic effects.

Язык: Английский

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The correlation between water‑carbon and urban spatial form in built-up areas: Evidence from Shenzhen City, China

Urban Climate, Год журнала: 2025, Номер 59, С. 102292 - 102292

Опубликована: Янв. 21, 2025

Язык: Английский

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Spatiotemporal prediction of carbon emissions using a hybrid deep learning model considering temporal and spatial correlations

Environmental Modelling & Software, Год журнала: 2023, Номер 172, С. 105937 - 105937

Опубликована: Дек. 23, 2023

Язык: Английский

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High-resolution carbon emission mapping and spatial-temporal analysis based on multi-source geographic data: A case study in Xi’an City, China

Environmental Pollution, Год журнала: 2024, Номер 361, С. 124879 - 124879

Опубликована: Сен. 1, 2024

Язык: Английский

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The Impact of Agglomeration on CO2 Emissions in China's Transport Sector: A Spatial Econometric Analysis

Sustainable Cities and Society, Год журнала: 2024, Номер 117, С. 105966 - 105966

Опубликована: Ноя. 6, 2024

Язык: Английский

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Exploring the spatial association characteristics of carbon emission efficiency in China’s construction industry: A network perspective

Energy and Buildings, Год журнала: 2025, Номер 329, С. 115289 - 115289

Опубликована: Янв. 11, 2025

Язык: Английский

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Toward a low-carbon economy: Insights from low- carbon complexity index

Environmental Impact Assessment Review, Год журнала: 2025, Номер 112, С. 107856 - 107856

Опубликована: Фев. 11, 2025

Язык: Английский

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