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ORIGINAL RESEARCH
Intelligent Manufacturing, Man-Machine
Matching Degree and Urban Green
Total Factor Productivity
1
Institute of Quantitative Economics and Statistics, Huaqiao University, Xiamen 361021, China
2
School of Economics, Xiamen University, Xiamen 361005, China
3
School of Economies and Business Administration, Chongqing University, Chongqing 400044, China
Submission date: 2023-12-24
Final revision date: 2024-02-21
Acceptance date: 2024-04-08
Online publication date: 2024-09-04
Publication date: 2025-01-28
Corresponding author
Yang Shen
Institute of Quantitative Economics and Statistics, Huaqiao University, Xiamen 361021, China
Institute of Quantitative Economics and Statistics, Huaqiao University, Xiamen 361021, China
Pol. J. Environ. Stud. 2025;34(3):2157-2174
KEYWORDS
intelligent manufacturinggreen total factor productivitypanel smooth transformation regression modelman-machine matching degreesustainable development
TOPICS
ABSTRACT
With the deep integration and development of artificial intelligence technology in the economy
and society, intelligent manufacturing provides a new opportunity for “overtaking in corners” to
improve green total factor productivity. In order to clarify the relationship between the application
of intelligent manufacturing and local green development, based on the panel data of 262 cities at or
above the prefecture level in China from 2008 to 2019, this paper analyzes the impact of intelligent
manufacturing on the total factor productivity of urban green and investigates the role of man-machine
matching in it by using the panel smooth transformation regression model. The research results show
that the development of intelligent manufacturing can obviously promote the urban green total factor
productivity, but this promotion effect will show an invisible slowdown with the continuous improvement
of the application level of intelligent manufacturing. At the same time, intelligent manufacturing can
significantly improve the green total factor productivity of China’s non-resource cities, cities with
a high level of digital economy development, and eastern regional cities. Further research found that
when the man-machine matching degree crossed the threshold, intelligent manufacturing could fully
release the promotion of green total factor productivity. The research conclusions and suggested
measures are of great significance for China to grasp the technical characteristics and advantages of
intelligent manufacturing and promote low-carbon economic transformation.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
REFERENCES (81)
1.
AECMOGLU D., AGHION P., BURSZTYN L., HEMOUS D. The environment and directed technical change. American Economic Review, 102 (1), 131, 2012. https://doi.org/10.1257/aer.10... PMid:26719595 PMCid:PMC4692283.
2.
ZHENG H., FENG C., YANG J. Examining the internal-structural effects of internet development on China's urban green total factor productivity. Emerging Markets Finance and Trade, 59 (15), 4174, 2023. https://doi.org/10.1080/154049....
3.
VINUESA R., AZIZPOUR H., LEITE I., BALAAM M., DIGNUMU V., DOMISCH S., FELLllANDER A., LANGHANS S.D., TEGMARK M., NERINI F.F. The role of artificial intelligence in achieving the sustainable development goals. Nature Communication, 11 (1), 233, 2020. https://doi.org/10.1038/s41467... PMid:31932590 PMCid:PMC6957485.
4.
ZHOU J.Q., CHEN D., XIA N. The green development effect of artificial intelligence: technology empowerment and structure optimization. Journal of Contemporary Economic Science, 45 (5), 30, 2023.
5.
SURAJIT B., MUMHAMMAD R.S., SNEHA G., SANTOSH K.S., RAJESH K.S., RUCHI M. Unveiling the impact of carbon-neutral policies on vital resources in Industry 4.0 driven smart manufacturing: A data-driven investigation. Computers and Industrial Engineering, 187, 109798, 2023. https://doi.org/10.1016/j.cie.....
6.
HUANG J., WEI J. Impact of intelligent development on the total factor productivity of firms - Based on the evidence from listed Chinese manufacturing firms. Journal of Advanced Computational Intelligence and Intelligent Informatics, 26 (4), 555, 2022. https://doi.org/10.20965/jacii....
7.
YANG Z., SHEN Y. The impact of intelligent manufacturing on industrial green total factor productivity and its multiple mechanisms. Frontiers in Environmental Science, 10, 1058664, 2023. https://doi.org/10.3389/fenvs.....
8.
WANG S., XUE Z. Has the development of the digital economy improved green total factor productivity in China? - A study based on data at the provincial level in China. Frontiers in Environmental Science, 11, 1073997, 2023. https://doi.org/10.3389/fenvs.....
9.
YANG H., LI L., LIU Y. The effect of manufacturing intelligence on green innovation performance in China. Technological Forecasting and Social Change, 178, 121569, 2022. https://doi.org/10.1016/j.tech... PMCid:PMC10881008.
10.
LYU H., SHI B., LI N., KANG R. Intelligent manufacturing and carbon emissions reduction: evidence from the use of industrial robots in China. International Journal of Environmental Research and Public Health, 19 (23), 15538, 2022. https://doi.org/10.3390/ijerph... PMid:36497614 PMCid:PMC9737826.
11.
AN K., SHAN Y., SHI S. Impact of industrial intelligence on total factor productivity. Sustainability, 14 (21), 14535, 2022. https://doi.org/10.3390/su1421....
12.
LYU Y., XIAO X., ZHANG J. Does the digital economy enhance green total factor productivity in China? The evidence from a national big data comprehensive pilot zone. Structural Change and Economic Dynamics, 69, 183, 2023. https://doi.org/10.1016/j.stru....
13.
SUN D., WU X., GU J., XU L., WANG H. Calculation of green total factor productivity of industrial enterprises in Yangtze River Delta urban agglomeration and its influencing factors. Science and Technology Forum of China, 12, 91, 2021.
14.
GUO J., ZHANG K., LIU K. Exploring the mechanism of the impact of green finance and digital economy on China's green total factor productivity. International Journal of Environmental Research and Public Health, 19 (23), 16303, 2022. https://doi.org/10.3390/ijerph... PMid:36498376 PMCid:PMC9739410.
15.
ZHAO C. Is low-carbon energy technology a catalyst for driving green total factor productivity development? The case of China. Journal of Cleaner Production, 428 (20), 139507, 2023. https://doi.org/10.1016/j.jcle....
16.
TIAN Y., PANG J. The role of internet development on green total-factor productivity - An empirical analysis based on 109 cities in Yangtze River economic belt. Journal of Cleaner Production, 378 (10), 134415, 2022. https://doi.org/10.1016/j.jcle....
17.
CHENG C., YU X., HU H., SU Z., ZHANG S. Measurement of China's green total factor productivity introducing human capital composition. International Journal of Environmental Research and Public Health, 19 (20), 13563, 2022. https://doi.org/10.3390/ijerph... PMid:36294143 PMCid:PMC9602896.
18.
SUN Y., YANG M. Research on the club convergence of green TFP and the source of regional disparity in China. Journal of Quantitative and Technical Economics, 37 (6), 47, 2020.
19.
MENG X., TANG M., KONG F., LI S. The effect of environmental information disclosure on green total factor productivity: evidence from quasi-natural experiments on cities in China. Sustainability, 14 (20), 13079, 2022. https://doi.org/10.3390/su1420....
20.
ZHAO X., NAKONIECZNY J., JABEEN F., SHAHZAD U., JIA, W. Does green innovation induce green total factor productivity? Novel findings from Chinese city level data. Technological Forecasting and Social Change, 185, 122021, 2022. https://doi.org/10.1016/j.tech....
21.
YE A., XIAO Z. Study on the influence of industrial structure diversification on urban green total factor productivity - An empirical analysis based on spatial dobbin model. Journal of Industrial Technical Economy, 42 (3), 33, 2023.
22.
LUO Y., NYARKO M., LU Z., WU C. Environmental regulation and green total factor productivity in China: A perspective of Porter's and Compliance Hypothesis. Ecological Indicators, 145, 109744, 2022. https://doi.org/10.1016/j.ecol....
23.
TONG L., CHIAPPETTA J.C.J., BELGACERN S.B., NAJAM H., ABBAS J. Role of environmental regulations, green finance, and investment in green technologies in green total factor productivity: Empirical evidence from Asian region. Journal of Cleaner Production, 380, 134930, 2022. https://doi.org/10.1016/j.jcle....
24.
TANG D., SHAN Z., HE J., ZHAO Z. How do environmental regulations and outward foreign direct investment impact the green total factor productivity in China? A mediating effect test based on provincial panel data. Journal of Environmental Research and Public Health, 19 (23), 15717, 2022. https://doi.org/10.3390/ijerph... PMid:36497791 PMCid:PMC9740457.
25.
JIANG X., LU X.H., GONG M. Assessing the effects of land transfer marketization on green total factor productivity from the perspective of resource allocation: Evidence from China. Frontiers in Environmental Science, 10, 975282, 2022. https://doi.org/10.3389/fenvs.....
26.
ACEMOGLU D., RESTREPO P. Automation and new tasks: how technology displaces and reinstates labor. Journal of Economic Perspectives, 33 (2), 3, 2021. https://doi.org/10.1257/jep.33....
27.
LI H., TIAN X., WU H. Artificial intelligence infiltration, labor productivity and export toughness improvement of China manufacturing industry. Journal of Northwest Population, 44 (6), 29, 2023.
28.
ZHAO P., GAO Y., SUN X. How does artificial intelligence affect green economic growth? - Evidence from China. Science of the Total Environment, 834, 155306, 2023. https://doi.org/10.1016/j.scit... PMid:35461946.
29.
CHANG L., TAGHIZADEH H.F., MOHSIN M. Role of artificial intelligence on green economic development: Joint determinates of natural resources and green total factor productivity. Resources Policy, 82, 103508, 2023. https://doi.org/10.1016/j.reso... PMCid:PMC12244555.
30.
HE B., BAI K. Digital twin-based sustainable intelligent manufacturing: A review. Advances In Manufacturing, 9, 1, 2021. https://doi.org/10.1007/s40436....
31.
HAO X.L., WANG X.H., WU H.T., HAO Y. Path to sustainable development: Does digital economy matter in manufacturing green total factor productivity? Sustainable Development, 31 (1), 360, 2022. https://doi.org/10.1002/sd.239....
32.
LIU X.M., ZHANG Y.Q. Study on the impact of intelligent city pilot on green and low-carbon development. Environmental Science and Pollution Research, 30 (20), 57882, 2023. https://doi.org/10.1007/s11356... PMid:36973616.
33.
YU D.H., LI Y.H. Innovation of industrial organization in the era of digital economy - Taking the industrial chain ecosystem driven by digital technology as an example. Reform, 329, 24, 2021.
34.
MENG F.S., ZHAO Y. How does digital economy affect green total factor productivity at the industry level in China: from a perspective of global value chain. Environmental Science and Pollution Research, 29 (52), 79497, 2022. https://doi.org/10.1007/s11356... PMid:35713830 PMCid:PMC10868804.
35.
WANG H., ZHAN S.K. Intelligent manufacturing, labor substitution and non-collective extrusion - sociological research on the influence of "machine substitution" on workers' employment. Journal of Anhui Normal University (Humanities and Social Sciences Edition), 49 (4), 85, 2021.
36.
LI J., AN Z.D., WANG Y. On the substitution and complementarity between robots and labor: Evidence from advanced and emerging economies. Sustainability, 15 (12), 9790, 2023. https://doi.org/10.3390/su1512....
37.
ZHANG J.N., MA X.Y., LIU J.M. How can the digital economy and human capital improve city. Sustainability, 14 (23), 15617, 2022. https://doi.org/10.3390/su1423....
38.
YAO H., GU X.L., YU Q. Impact of graduate student expansion and innovative human capital on green total factor productivity. Sustainability, 15 (2), 1721, 2023. https://doi.org/10.3390/su1502....
39.
HE X., ZHU G., FENG D. Industrial robot application and labor income share - evidence from China industrial enterprises. China Industrial Economics, 421, 98, 2023.
40.
YU L., WANG Y., WEI X., ZENG C. Towards low-carbon development: The role of industrial robots in decarbonization in Chinese cities. Journal of Environmental Management, 330, 117216, 2023. https://doi.org/10.1016/j.jenv... PMid:36621317.
41.
ZHANG Y., WU Z. Intelligence and green total factor productivity based on China's province-level manufacturing data. Sustainability, 13 (9), 4989, 2021. https://doi.org/10.3390/su1309....
42.
JIANG H., JIANG P., WANG D., WU J. Can smart city construction facilitate green total factor productivity? A quasi-natural experiment based on China's pilot smart city. Sustainable Cities And Society, 69, 102809, 2021. https://doi.org/10.1016/j.scs.....
43.
LIU Q., LIU Z., XU W., TANG Q., ZHOU Z., PHAM D.T. Human-robot collaboration in disassembly for sustainable manufacturing. International Journal of Production Research, 57 (12), 4027, 2019. https://doi.org/10.1080/002075....
44.
WON P., JUNJIRO S. Sustainable human-machine collaborations in digital transformation technologies adoption: A comparative case study of Japan and Germany. Sustainability, 14 (17), 10583, 2022. https://doi.org/10.3390/su1417....
45.
WANG H.R., CUI H.R., ZHAO Q.Z. Effect of green technology innovation on green total factor productivity in China: Evidence from spatial durbin model analysis. Journal of Cleaner Production, 288, 125624, 2020. https://doi.org/10.1016/j.jcle....
46.
LI X., SHU Y. JIN X. Environmental regulation, carbon emissions and green total factor productivity: A case study of China. Environment, Development and Sustainability, 24 (3), 1, 2021. https://doi.org/10.1007/s10668....
47.
LI, Y., CHEN Y. Development of an SBM-ML model for the measurement of green total factor productivity: The case of pearl river delta urban agglomeration. Renewable and Sustainable Energy Reviews, 145, 111131, 2021. https://doi.org/10.1016/j.rser....
48.
XU M., TAN R. Digital economy as a catalyst for low-carbon transformation in China: new analytical insights. Humanities and Social Sciences Communications, 11, 841, 2024. https://doi.org/10.1057/s41599....
49.
GUAN Y., WANG H., GUAN R., DING L. Measuring inclusive green total factor productivity from urban level in China. Frontiers in Environmental Science, 10, 966246, 2022. https://doi.org/10.3389/fenvs.....
50.
PENG Y., CHEN Z., LEE J. Dynamic convergence of green total factor productivity in Chinese cities. Sustainability, 12 (12), 4883, 2020. https://doi.org/10.3390/su1212....
51.
SHEN Y., ZHANG X. Digital economy, intelligent manufacturing, and labor mismatch. Journal of Advanced Computational Intelligence and Intelligent Informatics, 26 (4), 655, 2022. https://doi.org/10.20965/jacii....
52.
LU N., ZHOU W., DOU Z. Can intelligent manufacturing empower manufacturing? - an empirical study considering ambidextrous capabilities. Industrial Management and Data Systems, 123 (1), 188-203, 2023. https://doi.org/10.1108/IMDS-1....
53.
CHEN Y., CHENG L., LEE C. How does the use of industrial robots affect the ecological footprint? International evidence. Ecological Economics, 198, 107483, 2022. https://doi.org/10.1016/j.ecol....
54.
WANG J., LIU Y., WANG W., WU H. The effects of "machine replacing human" on carbon emissions in the context of population aging - Evidence from China. Urban Climate, 49, 101519, 2023. https://doi.org/10.1016/j.ucli....
55.
ACEMOGLU D., RESTREPO P. Robots and Jobs: Evidence from US labor markets. Journal of Political Economy, 128 (6), 2188, 2020. https://doi.org/10.1086/705716 PMCid:PMC11006199.
56.
SHEN Y., ZHANG X. The impact of artificial intelligence on employment: the role of virtual agglomeration. Humanities and Social Sciences Communications, 11, 1122, 2024. https://doi.org/10.1057/s41599....
57.
SHEN Y., ZHANG X. Intelligent manufacturing, green technological innovation and environmental pollution. Journal of Innovation and Knowledge, 8 (3), 100384, 2023. https://doi.org/10.1016/j.jik.....
58.
LEE C., QIN S., LI Y. Does industrial robot application promote green technology innovation in the manufacturing industry? Technological Forecasting and Social Change, 183, 121893, 2022. https://doi.org/10.1016/j.tech....
59.
HUANG Y., JIANG P. Digital low carbon road: Industrial robots and urban industrial carbon emissions. Journal of Finance and Economics, 49 (10), 34, 2023.
60.
XIAO Y., HUANG H., QIAN X., ZHANG L., AN B. Can new-type urbanization reduce urban building carbon emissions? New evidence from China. Sustainable Cities and Society, 90, 104410, 2023. https://doi.org/10.1016/j.scs.....
61.
DONG Z., WANG H. Urban wealth and the choice of green technology. Economic Research Journal, 56 (4), 143, 2021.
62.
YANG C., ZHAO S. Scaling of Chinese urban CO2 emissions and multiple dimensions of city size. Science of the Total Environment, 857 (2), 159502, 2022. https://doi.org/10.1016/j.scit... PMid:36265639.
63.
XU S., ZHOU Y. OFDI, Industrial structure upgrading and green development - spatial effect based on China's evidence. Sustainability, 15 (3), 2810, 2023. https://doi.org/10.3390/su1503....
64.
CHIU Y., ZHANG W. Moderating effect of financial development on the relationship between renewable energy and carbon emissions. Energies, 16, 1467, 2023. https://doi.org/10.3390/en1603....
65.
LYU K., YANG S., ZHENG K., ZHANG Y. How does the digital economy affect carbon emission efficiency? Evidence from energy consumption and industrial value chain. Energies, 16 (2), 761, 2023. https://doi.org/10.3390/en1602....
66.
HE X., LIANG Y., LIANG D., DENG H. The impact of China's information infrastructure construction policy on green total factor productivity: moving towards a green world. Environmental Science and Pollution Research, 30 (46), 103017, 2023. https://doi.org/10.1007/s11356... PMid:37676455.
67.
WANG Z., LUO J. Evaluating the tourism green productivity and its driving factors in the context of climate change. Tourism Economics, 30 (3), 655, 2024. https://doi.org/10.1177/135481....
68.
ZHAN X., LI R. Y.M., LIU X., HE F., WANG M., QIN Y., HE F., WANG M., QIN Y., XIA J., LIAO W. Fiscal decentralisation and green total factor productivity in China: SBM-GML and IV model approaches. Frontiers in Environmental Science, 10, 989194, 2022. https://doi.org/10.3389/fenvs.....
69.
WU L., ZHU C., SONG X., HE J. Impact of environmental regulation on carbon emissions in countries along the belt and road - An empirical study based on PSTR model. International Journal of Environmental Research and Public Health, 20, 2164, 2023. https://doi.org/10.3390/ijerph... PMid:36767531 PMCid:PMC9915267.
70.
KUSIAK A. Smart manufacturing must embrace big data. Nature, 544 (7648), 23, 2019. https://doi.org/10.1038/544023... PMid:28383012.
71.
LI G., LIAO F. Input digitalization and green total factor productivity under the constraint of carbon emissions. Journal of Cleaner Production, 377, 134403, 2022. https://doi.org/10.1016/j.jcle....
72.
GRAETZ G., MICHAELS G. Robots at Work. The Review of Economics and Statistics, 100 (5), 753-768, 2018. https://doi.org/10.1162/rest_a....
73.
SHENG D., BU W. The use of robots and the pollution emission of enterprises in China. Journal of Quantitative and Technical Economics, 39 (9), 157, 2022.
74.
ZHENG Z., ZHU Y., WANG Y., YANG Y., FANG Z. Spatio-temporal heterogeneity of the coupling between digital economy and green total factor productivity and its influencing factors in China. Environmental Science and Pollution Research, 30, 82326, 2023. https://doi.org/10.1007/s11356... PMid:37328720.
75.
ZHAO T., ZHANG Z., LIANG S. Digital economy, entrepreneurial activity and high-quality development - Empirical evidence from China city. Journal of Management World, 36 (10), 65, 2020.
76.
ZHAO J. Impact of green finance on low-carbon transformation: Spatial spillover effects in China. The North American Journal of Economics and Finance, 74, 102202, 2024. https://doi.org/10.1016/j.naje....
77.
ZHENG Q., WANG G. Application of artificial intelligence technology and productivity of manufacturing enterprises in China - Re-examination of "productivity paradox". Journal of Learning and Practice, 11, 59, 2021.
78.
YIN S., ZHANG N., ULLAH K., GAO S. Enhancing digital innovation for the sustainable transformation of manufacturing industry: A pressure-state-response system framework to perceptions of digital green innovation and its performance for green and intelligent manufacturing. Systems, 10, 72, 2022. https://doi.org/10.3390/system....
79.
DONG T., YIN S. New energy-driven construction industry: Digital green innovation investment project selection of photovoltaic building materials enterprises using an integrated fuzzy decision approach. Systems, 11, 11, 2023. https://doi.org/10.3390/system....
80.
JIANG R., YANG S., LIAN S., JEFFERSON G. The impact of internet development on green total factor productivity in China's prefectural cities. Information Technology for Development, 29 (4), 462, 2023. https://doi.org/10.1080/026811....
81.
YANG S., LIU F. Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system. Ecological Economics, 216, 108021, 2024. https://doi.org/10.1016/j.ecol....
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