Assessing the synergistic effects of artificial intelligence on pollutant and carbon emission mitigation in China

被引:18
|
作者
Zhong, Wenli [1 ]
Liu, Yang [1 ]
Dong, Kangyin [1 ]
Ni, Guohua [2 ]
机构
[1] Univ Int Business & Econ, Sch Int Trade & Econ, Beijing 100029, Peoples R China
[2] Beijing Technol & Business Univ, Sch Econ, Beijing 100048, Peoples R China
基金
中国国家自然科学基金;
关键词
Artificial intelligence; Synergistic emissions of pollutants and CO2; Mediation effects; Sustainable development; SYS-GMM technique; CO2; EMISSIONS; IMPACT; URBANIZATION; PRODUCTIVITY; INNOVATION; INDUSTRY; QUALITY;
D O I
10.1016/j.eneco.2024.107829
中图分类号
F [经济];
学科分类号
02 ;
摘要
Artificial intelligence (AI) has become a key driver in the latest wave of scientific and technological advancement, and its rapid development, proliferation, and environmental impacts cannot be ignored. China and numerous emerging economies are confronted with the dual challenges of environmental degradation and climate change. Hence, it is imperative to assess whether the advancement of AI can contribute to a synergistic reduction in pollutant and CO2 emissions. This paper utilizes the system-generalized method of moments (SYS-GMM) to study the synergistic effect of artificial intelligence on mitigating pollutant and carbon emissions. The following three main conclusions are drawn: (1) AI plays a major role in synergistically decreasing pollutant and CO2 emissions; (2) AI indirectly helps lower pollutant and CO2 emissions by fostering technological advancements and enhancing industrial structures. Although it contributes to an increase in emissions by expanding production scale, its suppression effect dominates overall; (3) The impact of AI applications is particularly vital in cities with strict environmental controls, especially in the central and eastern regions. Finally, we suggest some policy measures to augment the influence of AI in reducing emissions and attaining sustainable development.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] The effects of carbon dioxide, methane and nitrous oxide emission taxes: An empirical study in China
    Liu, Lan-Cui
    Wu, Gang
    JOURNAL OF CLEANER PRODUCTION, 2017, 142 : 1044 - 1054
  • [42] Carbon emission effects of publicly planned logistics nodes: experience from Chengdu, China
    Sun, Wenjie
    Zhang, Jin
    Li, Guoqi
    Zhu, Lulu
    He, Nannan
    ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2023, 30 (10) : 26150 - 26163
  • [43] Interregional carbon emission spillover-feedback effects in China
    Zhang, Youguo
    ENERGY POLICY, 2017, 100 : 138 - 148
  • [44] Economic cost, energy transition, and pollutant mitigation: The effect of China's different mitigation pathways toward carbon neutrality
    Liu, Xianmei
    Peng, Rui
    Bai, Caiquan
    Chi, Yuanying
    Liu, Yuxiang
    ENERGY, 2023, 275
  • [45] Research on Identification Algorithm of Pollutant Excessive Emission Users Based on Power Big Data and Artificial Intelligence
    Qin, Yu
    Zhang, Xiaohang
    2022 6TH INTERNATIONAL CONFERENCE ON POWER AND ENERGY ENGINEERING, ICPEE, 2022, : 215 - 219
  • [46] Assessing the energy productivity of China's textile industry under carbon emission constraints
    Zhao, Hongli
    Lin, Boqiang
    JOURNAL OF CLEANER PRODUCTION, 2019, 228 : 197 - 207
  • [47] Assessing the carbon mitigation impact of energy choices in China: a focus on renewable energy and thermal efficiency improvement
    Yang, Jie
    Du, Yimeng
    Ma, Teng
    APPLIED ECONOMICS, 2025, 57 (17) : 2038 - 2055
  • [48] Who takes the lead: Synergistic emission reduction effects of proactive government and efficient market in atmospheric pollution mitigation
    Tian, Pengpeng
    Pan, Zichun
    Zeng, Xuemei
    Zhu, Yuchun
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2024, 371
  • [49] The pollutant and carbon emissions reduction synergistic effect of green fiscal policy: Evidence from China
    Fan, Hongmin
    Liang, Chen
    FINANCE RESEARCH LETTERS, 2023, 58
  • [50] Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets
    Huber, Martin
    Meier, Jonas
    Wallimann, Hannes
    TRANSPORTATION RESEARCH PART B-METHODOLOGICAL, 2022, 163 : 22 - 39