Achieving pollution abatement and carbon reduction synergistically: How can industrial robots play a role? DOI
Chongchong Xu,

Helen Lv Zhang,

Boqiang Lin

и другие.

Journal of Environmental Management, Год журнала: 2024, Номер 373, С. 123816 - 123816

Опубликована: Дек. 24, 2024

Язык: Английский

How does Energy Quota Trading Affect the Corporate Pollution Gap? Evidence from China DOI
Zhaoxuan Qiu, Jincheng Li, Bei Liu

и другие.

Economic Modelling, Год журнала: 2025, Номер unknown, С. 107025 - 107025

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1

Achieving the synergy of pollution and carbon emission reductions: Can artificial intelligence applications work? DOI
Jie Dian, Shanmin Li,

Song Tian

и другие.

China Economic Review, Год журнала: 2025, Номер unknown, С. 102389 - 102389

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

1

How public environmental appeals affect the collaborative governance in pollution and carbon reduction: Evidence from spatial effects across Chinese cities DOI
Ning Zhao,

Meilin Jin,

Zhaoxuan Qiu

и другие.

Environmental Research, Год журнала: 2024, Номер 256, С. 119249 - 119249

Опубликована: Май 27, 2024

Язык: Английский

Процитировано

8

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

6

How does financial openness affect pollution emission of industrial enterprises?—Empirical evidence from the entry of foreign banks in China DOI
Bei Liu,

Xing Bao,

Zhaoxuan Qiu

и другие.

Sustainable Development, Год журнала: 2023, Номер 32(4), С. 2910 - 2930

Опубликована: Ноя. 13, 2023

Abstract Currently, the effects of financial openness (FO) on environment have not been assessed at micro level enterprises. This article uses difference‐in‐differences method to explore pollution abatement effect FO. The results show that FO can effectively promote with a significant environmental performance enhancement effect. In addition, is stronger in large, heavy industrial, and state‐owned Besides, significantly alleviates financing constraints promotes by driving R&D investment strengthening intensity end‐of‐pipe treatment. Policy recommendations are given steadily expand pattern, improve disclosure system small medium‐sized enterprises, optimize subsidies for increased stimulate abatement. A reference other emerging economies, especially transitioning provided fully utilize under construction or improvement realize value control.

Язык: Английский

Процитировано

13

Information consumption city and carbon emission efficiency: Evidence from China's quasi-natural experiment DOI
Xujun Liu, Yuanqing Luo,

Shengtie Guo

и другие.

Environmental Research, Год журнала: 2024, Номер 255, С. 119182 - 119182

Опубликована: Май 19, 2024

Язык: Английский

Процитировано

5

The Non-linear Impact of Digital Trade Development on Carbon Emissions: Evidence from Chinese Cities DOI Creative Commons

Xiangxiang Zhou,

Hui Guo

Energy Nexus, Год журнала: 2025, Номер unknown, С. 100390 - 100390

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0

Industrial Robotics, Resource Efficiency, Energy Transition, and Environmental Quality: Designing a Sustainable Development Goals Framework for G7 Countries in the Presence of Geopolitical Risk DOI Open Access

Yuhan Xia,

Mahmood Ahmad

Sustainability, Год журнала: 2025, Номер 17(5), С. 1960 - 1960

Опубликована: Фев. 25, 2025

In recent years, the integration of industrial robotics has emerged as a powerful tool in reshaping industries by enhancing production efficiency, reducing waste generation, and optimizing resource utilization. However, robotics, particularly manufacturing production, require significant energy that can potentially impact on environmental quality. Despite growing adoption artificial intelligence (AI)-based there is paucity literature ecological footprint (EF), context advanced economies. this context, study aims to investigate transition, geopolitical risk EF G7 countries from 1993 2021. The employed econometric techniques, including Kernel-based Regularized Least Squares (KRLS) Artificial Neural Network (ANN) machine learning methods. results unveiled significantly curtail degradation impeding EF. Resource efficiency transition posed negative Geopolitical risks economic growth exacerbate Based results, proposes important policy implications for achieving sustainable development.

Язык: Английский

Процитировано

0

Artificial intelligence and enterprise pollution emissions: From the perspective of energy transition DOI

Youcai Yang,

Xiaotong Niu,

Changgui Lin

и другие.

Energy Economics, Год журнала: 2025, Номер unknown, С. 108349 - 108349

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Reducing carbon emission at the corporate level: Does artificial intelligence matter? DOI
Yanchao Feng, Yuying Yan, Ke Shi

и другие.

Environmental Impact Assessment Review, Год журнала: 2025, Номер 114, С. 107911 - 107911

Опубликована: Март 11, 2025

Язык: Английский

Процитировано

0