Digital intelligence and synergy of pollution reduction and carbon reduction: “Dividend” or “gap”? DOI Creative Commons
Jiajia Li,

Xianfeng Han,

Tonglei Zhang

и другие.

Chinese Journal of Population Resources and Environment, Год журнала: 2024, Номер 22(4), С. 389 - 398

Опубликована: Дек. 1, 2024

Язык: Английский

Revolutionizing energy practices: Unleashing the power of artificial intelligence in corporate energy transition DOI
Zhongzhu Chu, Zihan Zhang, Weijie Tan

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 357, С. 120806 - 120806

Опубликована: Апрель 1, 2024

Язык: Английский

Процитировано

21

The influence of digital intelligence transformation on carbon emission reduction in manufacturing firms DOI
Bin Cao, Lianqing Li, Kai Zhang

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 367, С. 121987 - 121987

Опубликована: Июль 26, 2024

Язык: Английский

Процитировано

17

Exploring the Dynamic Evolution and Drivers of the Coupled Coordination Relationship of Carbon Emission Efficiency and Economic Benefits in Construction Land Development DOI Creative Commons

Peixing Zhang,

Tianlu Jin,

Yuqi Wang

и другие.

Buildings, Год журнала: 2025, Номер 15(5), С. 759 - 759

Опубликована: Фев. 26, 2025

In the pursuit of sustainable urban development, construction land development (CLD) not only carries important mission promoting economic growth but also needs to actively respond environmental requirements reducing carbon emissions. However, there is a tension and balance between these two objectives. This study explores evolution characteristics influencing mechanisms synergistic relationship emission efficiency benefits CLD based on undesirable slacks-based measurement, coupling coordination degree (CCD) model, Tapio decoupling spatial convergence interpretable machine learning techniques. The main conclusions are as follows: (1) CCR CEE in China shows characteristic “improvement-stability-local decline”, it higher eastern region than central western regions. (2) (CEE) 2003 2023 diverse trends different provinces time scales China. (3) China’s consistent with σ-convergence β-convergence, gap level inter-regional co-ordination has narrowed. On contrary, regions do pass σ β-convergence tests, regional equilibrium be improved. (4) descending order influence CCR, they ownership structure, per capita, energy consumption unit gross domestic product, industrial foreign trade investment intensity.

Язык: Английский

Процитировано

1

The role of artificial intelligence in reducing carbon emissions: evidence from Chinese manufacturing firms DOI
Nanxu Chen, Jin-Feng Huang, Yuling Hu

и другие.

Applied Economics, Год журнала: 2025, Номер unknown, С. 1 - 18

Опубликована: Март 3, 2025

Язык: Английский

Процитировано

1

Does Income Inequality Undermine the Carbon Abatement Benefits of Artificial Intelligence? DOI
Zequn Dong,

Lingran Zhang,

C. P.-P. Tan

и другие.

Journal of Cleaner Production, Год журнала: 2024, Номер 472, С. 143437 - 143437

Опубликована: Авг. 18, 2024

Язык: Английский

Процитировано

7

Does New Digital Infrastructure Contribute to Energy-Carbon Performance?The Path Analysis of Heterogeneous Technological Progress DOI
X. Zeng, Jian Tang, Ming Ji

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Does AI elevate corporate ESG performance? A supply chain perspective DOI
Boqiang Lin, Yitong Zhu

Business Strategy and the Environment, Год журнала: 2024, Номер unknown

Опубликована: Окт. 16, 2024

Abstract As a pivotal catalyst for sustainable development, the evolution and integration of AI are propelling both companies society toward more efficient trajectory. Utilizing multi‐period difference‐in‐difference (DID) model, study assesses impact 2019 China Artificial Intelligence Pilot (AIP) policy on corporate environmental, social, governance (ESG). The study's findings following: (1) Optimizing through artificial intelligence (AI), AIP has significantly bolstered ESG performance in pilot areas. (2) Mechanistic analysis demonstrates that elevates by bolstering efficiency supply chains. (3) Heterogeneity testing shows exerts pronounced effect non‐state‐owned companies, with high energy consumption, those new sector. This manuscript furnishes empirical insights evaluating implications development initiatives.

Язык: Английский

Процитировано

3

The Spatial Spillover Impact of Artificial Intelligence on Energy Efficiency: Empirical Evidence from 278 Chinese Cities DOI
Yong Wang, Wenhao Zhao,

Xuejiao Ma

и другие.

Energy, Год журнала: 2024, Номер unknown, С. 133497 - 133497

Опубликована: Окт. 1, 2024

Язык: Английский

Процитировано

3

Optimizing Urban Pollution: Impact of Intelligent Connected Vehicles in Smart Mobility DOI
Zhao Liu,

Chengxinge Yang,

Yi‐Shuai Ren

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Artificial intelligence of robotics and green transformation: evidence from Chinese manufacturing firms DOI
Lei Sun, Jing Wang, Shanyong Wang

и другие.

Environment Development and Sustainability, Год журнала: 2025, Номер unknown

Опубликована: Март 20, 2025

Язык: Английский

Процитировано

0