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| The Mechanism and Enabling Effect of Artificial Intelligence on Green Economic Growth: The Perspective of Industrial Structure Optimization |
| Zhou Jieqi1,Chen Da1,Xia Nanxin2 |
| (1.School of Economics, Guangdong University of Finance and Economics, Guangzhou 510320, China;2.Linnan College, Sun Yat-Sen University, Guangzhou 510970, China) |
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Abstract As innovation is the key of fostering green development which can meet people's aspirations for a "beautiful home" and a "beautiful China". Innovation in green technology is especially of great importance to achieve win-win results in economy, ecology and society in global green competition. At this stage, artificial intelligence (AI) which has the attributes of a new generation of general technology, strong spillover effect and high development potential, is undoubtedly the core force and important support to realize green technology innovation. The new generation of AI technology uses big data combined with strong algorithm power to gradually realize autonomous learning, rule identification and judgment decision, providing a new way to promote energy saving and consumption reduction. Therefore, the “14th Five-Year Plan” of the Chinese government focuses on strengthening fundamental research and original innovation,and improving the supply system of common basic technologies, and aims at the construction of a number of forward-looking and strategic national major science and technology projects such as AI.#br#The following questions remain unanswered in this context: does China's AI promote green economic growth? what is the transmission mechanism? what variances exist in the release of AI green impacts when diverse heterogeneous components are present? Furthermore, how can the government shape its AI development policies in order to fully capitalize on AI’s technological and structural benefits in green and low-carbon transformation?#br#To address these issues, this study builds an indicator system based on Chinese provincial panel data from 2010 to 2020 and implements modeling approaches such as the panel fixed-effects model, mediated-effects model, the moderated mediating effect model, spatial autoregressive model, and employs the instrumental variablesto empirically test the enabling effect, path mechanism, heterogeneous impact and spatial spillover effect of AI on China's green economic growth. Meanwhile according to the empirical test results, targeted industrial policies for the development of AI are put forward, so as to improve the quality and efficiency of the economy and achieve green growth.#br#The major conclusion is that AI drives green economic growth, and its green growth effect remains significant after accounting for endogeneity bias due to reverse causality and omitted variables. AI has a driving effect on green economic growth through an advanced and rationalized industrial structure,that is, AI releases technological dividends through direct effects and generates structural dividends by promoting industrial structure optimization. Moreover, in the context of China's economic transformation, the structural dividend of AI is more obvious in regions with lower distortion of factor markets, a higher level of innovative human capital, and a better institutional environment, and its empowering effect on green growth can thus be stronger. In addition, AI has a significant positive spatial spillover effect, and its development will drive green economic growth in economically connected regions.#br#This paper has the following highlights. First, a unified theoretical logical framework of "AI-industrial structure optimization-green economic growth" is established, and the industrial structure optimization mechanism of AI affecting green economic growth is revealed from two dimensions of industrial structure upgrading and rationalization. Second, a comprehensive indicator system is used to reflect the whole picture of AI development, which effectively alleviates the problem of variable measurement errors and uses two different instrumental variables to strengthen the control of endogeneity problems. Third, the heterogeneous effects of AI industrial structure optimization effects are captured in terms of factor market distortions, innovative human capital, and the market institutional environment. Fourth, given the adoption of the geographic distance weight matrix and the economic distance weight matrix, the actual effect of AI-enabled green economic growth is further revealed from the perspective of spatial spillover, which provides useful decision-making references for the authorities to comprehensively measure the characteristics of AI development, formulate forward-looking industrial policies and promote inter-regional green synergistic development.#br#
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Received: 13 July 2022
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[1] 杜龙政,赵云辉,陶克涛,等. 环境规制、治理转型对绿色竞争力提升的复合效应——基于中国工业的经验证据[J]. 经济研究,2019,54(10):106-120.
[2] 郭凯明. 人工智能发展、产业结构转型升级与劳动收入份额变动[J]. 管理世界,2019,35(7):60-77,202-203.
[3] 顾国达,马文景. 人工智能综合发展指数的构建及应用[J]. 数量经济技术经济研究,2021,38(1):117-134.
[4] ACEMOGLU D,P RESTREPO. Automation and new tasks:how technology displaces and reinstates labor[J].Journal of Economic Perspectives,2019,33(2):3-30.
[5] 林晨,陈小亮,陈伟泽,等. 人工智能、经济增长与居民消费改善：资本结构优化的视角[J]. 中国工业经济,2020,37(2):61-83.
[6] 邵帅,范美婷,杨莉莉. 经济结构调整、绿色技术进步与中国低碳转型发展——基于总体技术前沿和空间溢出效应视角的经验考察[J]. 管理世界,2022,38(2):46-69,4-10.
[7] 金环,于立宏,徐远彬. 绿色产业政策与制造业绿色技术创新[J]. 中国人口·资源与环境,2022,32(6):136-146.
[8] 胡安军,郭爱君,钟方雷,等. 高新技术产业集聚能够提高地区绿色经济效率吗[J]. 中国人口·资源与环境,2018,28(9):93-101.
[9] 蔡跃洲,陈楠. 新技术革命下人工智能与高质量增长、高质量就业[J]. 数量经济技术经济研究,2019,36(5):3-22.
[10] 陈彦斌,林晨,陈小亮. 人工智能、老龄化与经济增长[J]. 经济研究,2019,54(7):47-63.
[11] AGHION P, JONES B F, JONES C I. Artificial intelligence and economic growth[J]. NBER Chapters, 2017(10):237-282.
[12] PRETTNER K. A note on the implications of automation for economic growth and the labor share [J].Macroeconomic Dynamics,2019,23(2)：1294-1301.
[13] GRAETZ G, G MICHAELS. Robots at work[J]. Review of Economics and Statistics,2018, 100(5):753-768.
[14] 王文,牛泽东,孙早. 工业机器人冲击下的服务业:结构升级还是低端锁定[J]. 统计研究,2020,37(7):54-65.
[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] CLAUDIA, NYARKO, MENSAH. Technological innovation and green growth in the organization for economic cooperation and development economies[J]. Journal of Cleaner Production, 2019, 240: 118204.
[17] 唐晓华,迟子茗. 工业智能化提升工业绿色发展效率的实证研究[J]. 经济学家,2022,34(2):43-52.
[18] 赵伟,赵嘉华. 离岸与技术进步的要素偏向:生产率效应与结构效应[J]. 国际贸易问题,2020,46(6):75-92.
[19] 陈喆,郑江淮. 产业互动下高技术产业集聚的环境治理效应[J]. 财经科学,2021,65(8):78-92.
[20] 徐盈之,张瑞婕,孙文远. 绿色技术创新、要素市场扭曲与产业结构升级[J]. 研究与发展管理,2021,33(6):75-86.
[21] 邹涛,李沙沙. 要素价格扭曲阻碍了企业有效市场退出吗——来自中国制造业企业的微观证据[J]. 产业经济研究,2021,20(6):87-100.
[22] CINNIRELLA F,STREB J. The role of human capital and innovation in economic development: evidence from post-Malthusian Prussia[J]. Journal of Economic Growth, 2017, 22(2):1-35.
[23] ISHISE H,Y SAWADA. Aggregate returns to social capital:estimates based on the augmented augmented-solow model[J]. Journal of Macroeconomics,2009,31(3):376-393.
[24] YIAN C . Misallocation of human capital and productivity: evidence from China[J]. Economic Research-Ekonomska Istra?ivanja, 2019, 32(1):3342-3359.
[25] 王林辉,姜昊,董直庆. 工业智能化会重塑企业地理格局吗[J]. 中国工业经济,2022,39(2):137-155.
[26] 叶祥松,刘敬. 政府支持与市场化程度对制造业科技进步的影响[J]. 经济研究,2020,55(5):83-98.
[27] 温忠麟,叶宝娟. 中介效应分析:方法和模型发展[J]. 心理科学进展,2014,22(5):731-745.
[28] HOLLAND S J, SHORE D B, CORTINA J M. Review and recommendations for integrating mediation and moderation[J].Organizational Research Methods, 2017,20(4):686-720.
[29] 周杰琦,徐国祥. 全球化对绿色经济增长是“诅咒”还是“福音”——基于要素市场扭曲视角的分析[J]. 财贸研究,2020,31(8):14-27.
[30] HAYES A F, SCHARKOW M. The relative trustworthiness of inferential tests of the indirect effect in statistical mediationanalysis: does method really matter[J].Psychological Science, 2013,24(10):1918-1927.
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