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

被引:32
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 90 条
[51]   USING ARTIFICIAL INTELLIGENCE TO IMPROVE REAL-TIME DECISION-MAKING FOR HIGH-IMPACT WEATHER [J].
McGovern, Amy ;
Elmore, Kimberly L. ;
Gagne, David John, II ;
Haupt, Sue Ellen ;
Karstens, Christopher D. ;
Lagerquist, Ryan ;
Smith, Travis ;
Williams, John K. .
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY, 2017, 98 (10) :2073-2090
[52]   Factor substitution and development path of the new energy market in the BRICS countries under carbon neutrality: Inspirations from developed European countries [J].
Pan, Yuling ;
Dong, Feng .
APPLIED ENERGY, 2023, 331
[53]   Artificial intelligence and productivity: global evidence from AI patent and bibliometric data [J].
Parteka, Aleksandra ;
Kordalska, Aleksandra .
TECHNOVATION, 2023, 125
[54]   Co-drivers of Air Pollutant and CO2 Emissions from On-Road Transportation in China 2010-2020 [J].
Qi, Zhulin ;
Zheng, Yixuan ;
Feng, Yueyi ;
Chen, Chuchu ;
Lei, Yu ;
Xue, Wenbo ;
Xu, Yanling ;
Liu, Zeyuan ;
Ni, Xiufeng ;
Zhang, Qingyu ;
Yan, Gang ;
Wang, Jinnan .
ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2023, 57 (50) :20992-21004
[55]   The impact of international trade on China's industrial carbon emissions since its entry into WTO [J].
Ren, Shenggang ;
Yuan, Baolong ;
Ma, Xie ;
Chen, Xiaohong .
ENERGY POLICY, 2014, 69 :624-634
[56]   Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries [J].
Rezaei, Mohammad Hossein ;
Sadeghzadeh, Milad ;
Nazari, Mohammad Alhuyi ;
Ahmadi, Mohammad Hossein ;
Astaraei, Fatemeh Razi .
INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES, 2018, 13 (03) :266-271
[57]   Application of robust intelligent schemes for accurate modelling interfacial tension of CO2 brine systems: Implications for structural CO2 trapping [J].
Safaei-Farouji, Majid ;
Thanh, Hung Vo ;
Dashtgoli, Danial Sheini ;
Yasin, Qamar ;
Radwan, Ahmed E. ;
Ashraf, Umar ;
Lee, Kang-Kun .
FUEL, 2022, 319
[58]   ENVIRONMENTAL-QUALITY AND DEVELOPMENT - IS THERE A KUZNETS CURVE FOR AIR-POLLUTION EMISSIONS [J].
SELDEN, TM ;
SONG, DQ .
JOURNAL OF ENVIRONMENTAL ECONOMICS AND MANAGEMENT, 1994, 27 (02) :147-162
[59]   How urbanization affects CO2 emissions of STIRPAT model in Malaysia? The application [J].
Shahbaz, Muhammad ;
Loganathan, Nanthakumar ;
Muzaffar, Ahmed Taneem ;
Ahmed, Khalid ;
Jabran, Muhammad Ali .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2016, 57 :83-93
[60]   Drivers in carbon dioxide, air pollutants emissions and health benefits of China's clean vehicle fleet 2019-2035 [J].
Shi, Xurong ;
Lei, Yu ;
Xue, Wenbo ;
Liu, Xin ;
Li, Shengyue ;
Xu, Yanling ;
Lv, Chen ;
Wang, Shuxiao ;
Wang, Jinnan ;
Yan, Gang .
JOURNAL OF CLEANER PRODUCTION, 2023, 391