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The effect of manufacturing intelligence on green innovation performance in China

Author

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  • Yang, Haochang
  • Li, Lianshui
  • Liu, Yaobin

Abstract

Accelerating the intelligent transformation of manufacturing industry is an important strategic choice to realize the green innovation transformation. Based on the perspectives of static efficiency and dynamic productivity, this paper analyzes the effect of manufacturing intelligence on green innovation performance and its internal mechanism from theoretical and empirical levels by using the dynamic spatial lag model (DSAR), mediating effect model and moderating effect model. The results show that: In the whole country, manufacturing intelligence has a significant promotion effect on green innovation performance; The reason why manufacturing intelligence can promote the improvement of green innovation performance is that manufacturing intelligence is conducive to the production of "technology promotion effect" and "cost reduction effect", so as to promote green technology innovation, then effectively increase the desirable outputs and significantly decrease the undesirable outputs; The effect of manufacturing intelligence on green innovation performance has obvious regional heterogeneity: the improvement effect of manufacturing intelligence on green innovation performance in the eastern region is significantly higher than that in the central and western regions. In addition, further analysis shows that green technological progress rather than green technical efficiency is the main driving force for manufacturing intelligence to improve the dynamic green innovation performance.

Suggested Citation

  • Yang, Haochang & Li, Lianshui & Liu, Yaobin, 2022. "The effect of manufacturing intelligence on green innovation performance in China," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:tefoso:v:178:y:2022:i:c:s0040162522001019
    DOI: 10.1016/j.techfore.2022.121569
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    1. Fare, R. & Grosskopf, S. & Hernandez-Sancho, F., 2004. "Environmental performance: an index number approach," Resource and Energy Economics, Elsevier, vol. 26(4), pages 343-352, December.
    2. Michael Beenstock & Daniel Felsenstein, 2007. "Spatial Vector Autoregressions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 2(2), pages 167-196.
    3. Bernard Fingleton & Julie Le Gallo, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances: Finite sample properties," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 319-339, August.
    4. repec:zbw:bofrdp:2015_027 is not listed on IDEAS
    5. Sally Gee & Andrew McMeekin, 2011. "Eco-Innovation Systems and Problem Sequences: The Contrasting Cases of US and Brazilian Biofuels," Industry and Innovation, Taylor & Francis Journals, vol. 18(3), pages 301-315.
    6. Gao, Yuning & Zhang, Meichen, 2019. "The measure of technical efficiency of China’s provinces with carbon emission factor and the analysis of the influence of structural variables," Structural Change and Economic Dynamics, Elsevier, vol. 49(C), pages 120-129.
    7. Carlino, Gerald & Kerr, William R., 2015. "Agglomeration and Innovation," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 349-404, Elsevier.
    8. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    9. Julie Le Gallo & Bernard Fingleton, 2008. "Estimating spatial models with endogenous variables, a spatial lag and spatially dependent disturbances : finite sample properties," Post-Print hal-00485035, HAL.
    10. Eiadat, Yousef & Kelly, Aidan & Roche, Frank & Eyadat, Hussein, 2008. "Green and competitive? An empirical test of the mediating role of environmental innovation strategy," Journal of World Business, Elsevier, vol. 43(2), pages 131-145, March.
    11. repec:zbw:bofrdp:urn:nbn:fi:bof-201512111472 is not listed on IDEAS
    12. Haochang Yang & Faming Zhang & Yixin He, 2021. "Exploring the effect of producer services and manufacturing industrial co-agglomeration on the ecological environment pollution control in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(11), pages 16119-16144, November.
    13. Sarkodie, Samuel Asumadu & Ozturk, Ilhan, 2020. "Investigating the Environmental Kuznets Curve hypothesis in Kenya: A multivariate analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    14. Philippe Aghion & Benjamin F. Jones & Charles I. Jones, 2018. "Artificial Intelligence and Economic Growth," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 237-282, National Bureau of Economic Research, Inc.
    15. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    16. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    17. Miller, Stephen M. & Upadhyay, Mukti P., 2000. "The effects of openness, trade orientation, and human capital on total factor productivity," Journal of Development Economics, Elsevier, vol. 63(2), pages 399-423, December.
    18. Shujing Yue & Yang Yang & Yaoyu Hu, 2016. "Does Foreign Direct Investment Affect Green Growth? Evidence from China’s Experience," Sustainability, MDPI, vol. 8(2), pages 1-14, February.
    19. Miao, Chenglin & Fang, Debin & Sun, Liyan & Luo, Qiaoling, 2017. "Natural resources utilization efficiency under the influence of green technological innovation," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 153-161.
    20. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
    21. Malin Song & Jun Tao & Shuhong Wang, 2015. "FDI, technology spillovers and green innovation in China: analysis based on Data Envelopment Analysis," Annals of Operations Research, Springer, vol. 228(1), pages 47-64, May.
    22. Michael E. Porter & Claas van der Linde, 1995. "Toward a New Conception of the Environment-Competitiveness Relationship," Journal of Economic Perspectives, American Economic Association, vol. 9(4), pages 97-118, Fall.
    23. Sueyoshi, Toshiyuki & Goto, Mika, 2012. "Returns to scale and damages to scale under natural and managerial disposability: Strategy, efficiency and competitiveness of petroleum firms," Energy Economics, Elsevier, vol. 34(3), pages 645-662.
    24. Song, Malin & Wang, Shuhong & Sun, Jing, 2018. "Environmental regulations, staff quality, green technology, R&D efficiency, and profit in manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 133(C), pages 1-14.
    25. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
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