|
|
The Effect of Artificial Intelligence Technology on Carbon Emissions |
Xue Fei1,Liu Jiaqi2,Fu Yamei2,3 |
(1.School of Applied Economics, University of Chinese Academy of Social Sciences, Beijing 102488, China;2.School of Economics & Management, Northwest University, Xi′an 710127, China;3.School of Statistics, Xi'an University of Finance and Economics, Xi′an 710100, China) |
|
|
Abstract To actively address the issue of climate change, China has proposed a vision of "emission peak and carbon neutrality". Achieving "emission peak and carbon neutrality" is a comprehensive and profound economic and social systemic change that requires the joint efforts of the whole society. As a strategic technology for technological revolution and industrial change, Artificial Intelligence (AI) technologies will play an important role in addressing climate change and bring significant opportunities for low-carbon development. The process of achieving the "double carbon goal" is essentially a path of transformation to technology-intensive industry. However, the relationship between AI technologies and carbon emissions has been relatively under-discussed in academia compared to the much attention at the policy level and the flourishing development at the practical level. In particular, AI technologies may exhibit a more complex dual effect on carbon emissions. In view of this, this paper answers the question of whether AI technology can enable carbon neutrality and help reduce CO2 emissions. What are the mechanisms of AI technologies affecting carbon emissions? Are there any regional differences in the effect of AI technologies on carbon emissions? The purpose of this paper is to provide empirical evidence for the carbon reduction effect of AI technologies and to provide useful policy insights for achieving the goal of "double carbon".#br#In terms of theoretical research, this paper believes that the impact of AI technologies on carbon emissions may have a dual effect.On one hand, AI can increase total carbon emissions through direct energy consumption.At the same time, AI technology on carbon emissions may have a rebound effect.In addition, AI technologies has scale effect on economic growth, which will lead to the increase of total regional carbon emissions. On the other hand, AI technology can reduce carbon emissions by assisting decision-making, reshaping production and life styles, and facilitating low-carbon technological innovation.First, it can help reduce carbon emissions by assisting decision-making.Second, it can drive changes in production and consumption patterns to reduce carbon emissions.Third, AI reduces carbon emissions by enabling low-carbon technology innovation.To sum up, the relationship between AI technologies and carbon emissions is not a simple linear relationship, but depends on the combined effect of the above dual effects.In terms of influence mechanism, the effect of AI technologies on carbon emissions is mainly reflected in energy utilization efficiency.#br#This paper analyzes the effects, mechanisms and differences of AI technology on carbon emissions by manually collating AI patent data as a measure of AI technology, using panel data from 30 provinces in China from 2006 to 2019 as a research sample. The empirical results show that the effect of AI technology on carbon emissions shows an "inverted U-shaped" relationship at the national level as a whole. It means when the level of AI technology reaches a certain threshold, its carbon emission reduction effect gradually becomes prominent, and the robustness test results also reinforce this conclusion. The results of the intermediate effect test show that the effect of AI technology on carbon emissions is mainly achieved by affecting energy use efficiency. The heterogeneity analysis shows that there is a significant regional difference in the impact of AI technology on carbon emissions, with a significant "inverted U-shaped" relationship between AI technology and carbon emissions in the eastern and western regions. In the central region, AI technology has not achieved carbon emission reduction, but had a significant contribution to carbon emissions.#br#The marginal contributions of this paper are as follows. First, from the perspective of research, this paper examines the effect of AI technology on carbon emissions in a more systematic way from the perspective of AI technology for the first time. Second, in terms of the research methodology, AI patent data are collected manually to measure the level of AI technology in each region, and a panel semi-parametric model and a nonlinear mediated effects model are used. Third, from the research findings, it is found that there is an "inverted U-shaped" relationship between AI technology and carbon emissions, and there is regional heterogeneity in the effect of AI. This provides empirical support for the development of differentiated AI carbon emission reduction strategies.#br#
|
Received: 07 March 2022
|
|
|
|
|
[1] ACEMOGLU D, RESTREPO P. The race between man and machine: implications of technology for growth, factor shares, and employment[J]. American Economic Review, 2018, 108(6): 1488-1542.[2] 魏巍贤,杨芳.技术进步对中国二氧化碳排放的影响[J].统计研究,2010,27(7):36-44.[3] 鄢哲明,杨志明,杜克锐.低碳技术创新的测算及其对碳强度影响研究[J].财贸经济,2017,38(8):112-128.[4] WANG Z, YANG Z, ZHANG Y, et al. Energy technology patents——CO2, emissions nexus: an empirical analysis from China[J]. Energy Policy, 2012, 42(2):248-260.[5] 卢娜,王为东,王淼,等.突破性低碳技术创新与碳排放:直接影响与空间溢出[J].中国人口·资源与环境, 2019, 29(5): 30-39.[6] 邵帅,张可,豆建民.经济集聚的节能减排效应:理论与中国经验[J].管理世界,2019,35(1):36-60,226.[7] 冯烽,叶阿忠.技术溢出视角下技术进步对能源消费的回弹效应研究——基于空间面板数据模型[J].财经研究, 2012, 38(9): 123-133.[8] 杨莉莎,朱俊鹏,贾智杰.中国碳减排实现的影响因素和当前挑战——基于技术进步的视角[J].经济研究,2019,54(11):118-132.[9] 王道平,杜克锐,鄢哲明.低碳技术创新有效抑制了碳排放吗——基于PSTR模型的实证分析[J].南京财经大学学报, 2018(6): 1-14.[10] 张华,魏晓平,吕涛.能源节约型技术进步、边际效用弹性与中国能源消耗[J]. 中国地质大学学报(社会科学版), 2015, 15(2): 11-22.[11] 谢云飞.数字经济对区域碳排放强度的影响效应及作用机制[J].当代经济管理,2022,44(2):68-78.[12] 徐维祥,周建平,刘程军.数字经济发展对城市碳排放影响的空间效应[J].地理研究,2022,41(1):111-129.[13] 缪陆军,陈静,范天正,等.数字经济发展对碳排放的影响——基于278个地级市的面板数据分析[J]. 南方金融,2022(2):45-57.[14] 黄海燕,刘叶,彭刚.工业智能化对碳排放的影响——基于我国细分行业的实证[J].统计与决策,2021,37(17):80-84.[15] 曹静,周亚林.人工智能对经济的影响研究进展[J].经济学动态,2018,59(1):103-115.[16] 朱巧玲,李敏.人工智能、技术进步与劳动力结构优化对策研究[J].科技进步与对策,2018,35(6):36-41.[17] ACEMOGLU D, RESTREPO P. Robots and jobs: evidence from US labor markets[J]. Journal of Political Economy, 2020, 128(6): 2188-2244.[18] 杨光,侯钰.工业机器人的使用、技术升级与经济增长[J].中国工业经济,2020,38(10):138-156.[19] 隆云滔,刘海波,蔡跃洲.人工智能技术对劳动力就业的影响——基于文献综述的视角[J].中国软科学,2020,35(12):56-64.[20] 张文博,周冯琦.人工智能背景下的环境治理变革及应对策略分析[J].社会科学,2019,34(7):23-30.[21] 张伟,李国祥.环境分权体制下人工智能对环境污染治理的影响[J].陕西师范大学学报(哲学社会科学版), 2021, 50(3): 121-129.[22] HENDERSON P, HU J, ROMOFF J, et al. Towards the systematic reporting of the energy and carbon footprints of machine learning[J]. Journal of Machine Learning Research, 2020, 21: 1-43.[23] STRUBELL E, GANESH A, MCCALLUM A. Energy and policy considerations for deep learning in NLP[C]. 57th Annual Meeting of The Association For Computational Linguistics, 2019.[24] 陈诗一.中国碳排放强度的波动下降模式及经济解释[J].世界经济,2011,34(4):124-143.[25] HAYES A F, PREACHER K J. Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear[J]. Multivariate Behavioral Research, 2010, 45(4): 627-660.[26] 许和连, 成丽红, 孙天阳. 制造业投入服务化对企业出口国内增加值的提升效应——基于中国制造业微观企业的经验研究[J]. 中国工业经济, 2017(10): 62-80.[27] 韩民春,韩青江.机器人技术进步对劳动力市场的冲击——基于动态随机一般均衡模型的分析[J].当代财经,2020,41(4):3-16.[28] 彭代彦,李亚诚,彭旭辉.人工智能对流动人口工资收入的影响及其作用机理[J].经济体制改革,2021,38(3):32-38.[29] 徐斌,陈宇芳,沈小波.清洁能源发展、CO2减排与区域经济增长[J].经济研究,2019,54(7):188-202.[30] WAGNER U J, TIMMINS C D. Agglomeration effects in foreign direct investment and the pollution haven hypothesis[J]. Environmental & Resource Economics, 2009, 43(2):231-256.[31] ANTWEILER W, COPELAND B R, TAYLOR M S. Is free trade good for the environment[J]. American Economic Review, 2001, 91(4):877-908.[32] LIND J, MEHLUM H. With or without U-the appropriate test for a U shaped relationship[J]. Oxford Bulletin of Economics and Statistics, 2010, 72(1): 1-12. |
|
|
|