Impact of Digitization and Artificial Intelligence on Carbon Emissions Considering Variable Interaction and Heterogeneity: An Interpretable Deep Learning Modeling Framework

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106333 - 106333

Опубликована: Март 1, 2025

Язык: Английский

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Unleashing the Power of Artificial Intelligence: A Game Changer for Urban Energy Efficiency in China

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106372 - 106372

Опубликована: Апрель 1, 2025

Язык: Английский

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Development of data-driven estimation models of village carbon emissions by built form factors: The study in Huaihe River Basin, China

Building and Environment, Год журнала: 2025, Номер unknown, С. 112846 - 112846

Опубликована: Март 1, 2025

Язык: Английский

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Scenario forecasting of carbon neutrality by combining the LEAP model and future land-use simulation: An empirical study of Shenzhen, China

Sustainable Cities and Society, Год журнала: 2025, Номер unknown, С. 106367 - 106367

Опубликована: Апрель 1, 2025

Язык: Английский

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Carbon Balance Matching Relationships and Spatiotemporal Evolution Patterns in China’s National-Level Metropolitan Areas

Land, Год журнала: 2025, Номер 14(4), С. 800 - 800

Опубликована: Апрель 8, 2025

In the urgent context of global climate change and carbon neutrality goals, effective balance regulation is critical for achieving temperature control targets. Metropolitan areas encounter significant challenges in emission reduction, energy transition advancement, enhancement sequestration capabilities. However, traditional analysis methods have limitations capturing dynamic changes guiding precise regulation. Therefore, this study developed a dynamic–static classification system based on Ecological Support Coefficient (ESC) Economic Contributive (ECC). This examined emissions China’s 14 national-level metropolitan from 2000 to 2020. The results showed that: (1) Carbon an increasing trend, exhibiting spatial distribution with higher levels north, moderate central region, lowest southeast. contrast, exhibited pattern east, lower west. (2) Static revealed that ECC ESC northern regions were relatively weaker than those other regions. Dynamic further upward trend economic ecological capabilities these areas. along coast within Yangtze River Belt needed optimize their economic–ecological coordination efficiency. Although southern coastal demonstrated robust vitality, they encountered support pressures. (3) development level environmental quality predominant factors area classification. Regions tended exhibit enhanced ESC, while stronger prioritized growth. provided solid scientific basis formulating differentiated low-carbon transformation strategies, thereby supporting high-quality maintaining between ecologic objectives. Moreover, it offered both theoretical foundations practical guidance optimizing sustainable pathways similar globally.

Язык: Английский

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Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections

Accident Analysis & Prevention, Год журнала: 2024, Номер 211, С. 107890 - 107890

Опубликована: Дек. 19, 2024

Язык: Английский

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