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Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries

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  • Wang, En-Ze
  • Lee, Chien-Chiang
  • Li, Yaya

Abstract

Considering the continuing slowdown of the improvement in energy intensity around the world, it is essential to seek a more effective measure to address the dilemma of energy and sustainable development. To this end, this research attempts to provide fresh insight into the determinants of energy intensity from the perspective of industrial robots and an industry-based view. By applying the dynamic panel GMM estimate methodology to a new data panel that includes 38 countries and 17 manufacturing sectors, this study provides the first comprehensive assessment of the use of industrial robots on manufacturing energy intensity. We found that industrial robots could significantly improve manufacturing energy intensity, and our hypotheses passed a series of robustness tests. Moreover, this improvement effect works through the technology improvement effect and technological complement effect between industrial robots and labor. Finally, we found a heterogeneous nexus exists between industrial robots and manufacturing energy intensity. Specifically, industrial robots can exert influence on non-renewable energy intensity rather than renewable energy intensity. Compared to capital-intensive sectors, we found that the use of industrial robots mainly affected labor-intensive sectors. We also found that Industry 4.0 could promote the improvement effects of industrial robots on manufacturing energy intensity.

Suggested Citation

  • Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    • Handle: RePEc:eee:eneeco:v:105:y:2022:i:c:s0140988321005934
    DOI: 10.1016/j.eneco.2021.105748
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    1. Joseph Zeira, 1998. "Workers, Machines, and Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1091-1117.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Lee, Chi-Chuan & Lee, Chien-Chiang, 2019. "Oil price shocks and Chinese banking performance: Do country risks matter?," Energy Economics, Elsevier, vol. 77(C), pages 46-53.
    4. Lescaroux, François, 2008. "Decomposition of US manufacturing energy intensity and elasticities of components with respect to energy prices," Energy Economics, Elsevier, vol. 30(3), pages 1068-1080, May.
    5. Victor Ajayi and David Reiner, 2020. "European Industrial Energy Intensity: Innovation, Environmental Regulation, and Price Effects," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 105-128.
    6. Rafiq, Shuddhasattwa & Salim, Ruhul & Nielsen, Ingrid, 2016. "Urbanization, openness, emissions, and energy intensity: A study of increasingly urbanized emerging economies," Energy Economics, Elsevier, vol. 56(C), pages 20-28.
    7. Karen Fisher-Vanden, Yong Hu, Gary Jefferson, Michael Rock and Michael Toman, 2016. "Factors influencing energy intensity in four Chinese industries," The Energy Journal, International Association for Energy Economics, vol. 0(China Spe).
    8. Georg Graetz & Guy Michaels, 2018. "Robots at Work," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 753-768, December.
    9. Wesley Burnett, J. & Madariaga, Jessica, 2017. "The convergence of U.S. state-level energy intensity," Energy Economics, Elsevier, vol. 62(C), pages 357-370.
    10. 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).
    11. Lene Kromann & Nikolaj Malchow-Møller & Jan Rose Skaksen & Anders Sørensen, 2020. "Automation and productivity—a cross-country, cross-industry comparison [Computing inequality: have computers changed the labor market?]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 29(2), pages 265-287.
    12. Daron Acemoglu & Pascual Restrepo, 2018. "Low-Skill and High-Skill Automation," Journal of Human Capital, University of Chicago Press, vol. 12(2), pages 204-232.
    13. Müller, Julian Marius & Buliga, Oana & Voigt, Kai-Ingo, 2018. "Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 2-17.
    14. Maarten Goos & Alan Manning & Anna Salomons, 2014. "Explaining Job Polarization: Routine-Biased Technological Change and Offshoring," American Economic Review, American Economic Association, vol. 104(8), pages 2509-2526, August.
    15. Lee, Chien-Chiang, 2005. "Energy consumption and GDP in developing countries: A cointegrated panel analysis," Energy Economics, Elsevier, vol. 27(3), pages 415-427, May.
    16. Conti, C. & Mancusi, M.L. & Sanna-Randaccio, F. & Sestini, R. & Verdolini, E., 2018. "Transition towards a green economy in Europe: Innovation and knowledge integration in the renewable energy sector," Research Policy, Elsevier, vol. 47(10), pages 1996-2009.
    17. Mulder, Peter & de Groot, Henri L.F. & Pfeiffer, Birte, 2014. "Dynamics and determinants of energy intensity in the service sector: A cross-country analysis, 1980–2005," Ecological Economics, Elsevier, vol. 100(C), pages 1-15.
    18. David H. Autor & David Dorn, 2013. "The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market," American Economic Review, American Economic Association, vol. 103(5), pages 1553-1597, August.
    19. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    20. Voigt, Sebastian & De Cian, Enrica & Schymura, Michael & Verdolini, Elena, 2014. "Energy intensity developments in 40 major economies: Structural change or technology improvement?," Energy Economics, Elsevier, vol. 41(C), pages 47-62.
    21. Jimenez, Raul & Mercado, Jorge, 2014. "Energy intensity: A decomposition and counterfactual exercise for Latin American countries," Energy Economics, Elsevier, vol. 42(C), pages 161-171.
    22. Li, Xun & Hui, Eddie Chi-man & Lang, Wei & Zheng, Shali & Qin, Xiaozhen, 2020. "Transition from factor-driven to innovation-driven urbanization in China: A study of manufacturing industry automation in Dongguan City," China Economic Review, Elsevier, vol. 59(C).
    23. Berg, Andrew & Buffie, Edward F. & Zanna, Luis-Felipe, 2018. "Should we fear the robot revolution? (The correct answer is yes)," Journal of Monetary Economics, Elsevier, vol. 97(C), pages 117-148.
    24. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    25. Ewing, Bradley T. & Payne, James E. & Caporin, Massimilano, 2022. "The Asymmetric Impact of Oil Prices and Production on Drilling Rig Trajectory: A correction," Resources Policy, Elsevier, vol. 79(C).
    26. Sven-Vegard Buer & Jan Ola Strandhagen & Felix T. S. Chan, 2018. "The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda," International Journal of Production Research, Taylor & Francis Journals, vol. 56(8), pages 2924-2940, April.
    27. Mishra, Vinod & Smyth, Russell & Sharma, Susan, 2009. "The energy-GDP nexus: Evidence from a panel of Pacific Island countries," Resource and Energy Economics, Elsevier, vol. 31(3), pages 210-220, August.
    28. Timma, Lelde & Zoss, Toms & Blumberga, Dagnija, 2016. "Life after the financial crisis. Energy intensity and energy use decomposition on sectorial level in Latvia," Applied Energy, Elsevier, vol. 162(C), pages 1586-1592.
    29. Díaz, Antonia & Marrero, Gustavo A. & Puch, Luis A. & Rodríguez, Jesús, 2019. "Economic growth, energy intensity and the energy mix," Energy Economics, Elsevier, vol. 81(C), pages 1056-1077.
    30. Stucki, Tobias, 2019. "Which firms benefit from investments in green energy technologies? – The effect of energy costs," Research Policy, Elsevier, vol. 48(3), pages 546-555.
    31. Pan, Xiongfeng & Uddin, Md. Kamal & Han, Cuicui & Pan, Xianyou, 2019. "Dynamics of financial development, trade openness, technological innovation and energy intensity: Evidence from Bangladesh," Energy, Elsevier, vol. 171(C), pages 456-464.
    32. Ajay Agrawal & Joshua S. Gans & Avi Goldfarb, 2019. "Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 31-50, Spring.
    33. Shapiro, Carl & Stiglitz, Joseph E, 1984. "Equilibrium Unemployment as a Worker Discipline Device," American Economic Review, American Economic Association, vol. 74(3), pages 433-444, June.
    34. Kumaresan, Nageswaran & Miyazaki, Kumiko, 1999. "An integrated network approach to systems of innovation--the case of robotics in Japan," Research Policy, Elsevier, vol. 28(6), pages 563-585, August.
    35. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    36. Wang, Zhaohua & Feng, Chao, 2015. "A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: An application of global data envelopment analysis," Applied Energy, Elsevier, vol. 147(C), pages 617-626.
    37. Saeed Moshiri and Nana Duah, 2016. "Changes in Energy Intensity in Canada," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    38. Wu, Jianxin & Wu, Yanrui & Se Cheong, Tsun & Yu, Yanni, 2018. "Distribution dynamics of energy intensity in Chinese cities," Applied Energy, Elsevier, vol. 211(C), pages 875-889.
    39. Li, Ming-Jia & Tao, Wen-Quan, 2017. "Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry," Applied Energy, Elsevier, vol. 187(C), pages 203-215.
    40. Guy Michaels & Ashwini Natraj & John Van Reenen, 2010. "Has ICT Polarized Skill Demand? Evidence from Eleven Countries over 25 Years," CEP Discussion Papers dp0987, Centre for Economic Performance, LSE.
    41. Agovino, Massimiliano & Bartoletto, Silvana & Garofalo, Antonio, 2019. "Modelling the relationship between energy intensity and GDP for European countries: An historical perspective (1800–2000)," Energy Economics, Elsevier, vol. 82(C), pages 114-134.
    42. Yun, JinHyo Joseph & Won, DongKyu & Jeong, EuiSeob & Park, KyungBae & Yang, JeongHo & Park, JiYoung, 2016. "The relationship between technology, business model, and market in autonomous car and intelligent robot industries," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 142-155.
    43. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    44. Greening, Lorna A. & Davis, William B. & Schipper, Lee & Khrushch, Marta, 1997. "Comparison of six decomposition methods: application to aggregate energy intensity for manufacturing in 10 OECD countries," Energy Economics, Elsevier, vol. 19(3), pages 375-390, July.
    45. Berthold Herrendorf & Richard Rogerson & ?kos Valentinyi, 2013. "Two Perspectives on Preferences and Structural Transformation," American Economic Review, American Economic Association, vol. 103(7), pages 2752-2789, December.
    46. Huang, Junbing & Chen, Xiang, 2020. "Domestic R&D activities, technology absorption ability, and energy intensity in China," Energy Policy, Elsevier, vol. 138(C).
    47. Schmidt, Tobias S. & Sewerin, Sebastian, 2019. "Measuring the temporal dynamics of policy mixes – An empirical analysis of renewable energy policy mixes’ balance and design features in nine countries," Research Policy, Elsevier, vol. 48(10).
    48. Ballestar, María Teresa & Díaz-Chao, Ángel & Sainz, Jorge & Torrent-Sellens, Joan, 2020. "Knowledge, robots and productivity in SMEs: Explaining the second digital wave," Journal of Business Research, Elsevier, vol. 108(C), pages 119-131.
    49. Howarth, Richard B. & Schipper, Lee & Duerr, Peter A. & Strøm, Steinar, 1991. "Manufacturing energy use in eight OECD countries : Decomposing the impacts of changes in output, industry structure and energy intensity," Energy Economics, Elsevier, vol. 13(2), pages 135-142, April.
    50. Lv, Chengchao & Shao, Changhua & Lee, Chien-Chiang, 2021. "Green technology innovation and financial development: Do environmental regulation and innovation output matter?," Energy Economics, Elsevier, vol. 98(C).
    51. Parker, Steven & Liddle, Brantley, 2016. "Energy efficiency in the manufacturing sector of the OECD: Analysis of price elasticities," Energy Economics, Elsevier, vol. 58(C), pages 38-45.
    52. Jung, Jin Hwa & Lim, Dong-Geon, 2020. "Industrial robots, employment growth, and labor cost: A simultaneous equation analysis," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    53. Peter Mulder, 2015. "International Specialization, Structural Change and the Evolution of Manufacturing Energy Intensity in OECD Countries," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    54. Wurlod, Jules-Daniel & Noailly, Joëlle, 2018. "The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries," Energy Economics, Elsevier, vol. 71(C), pages 47-61.
    55. Lee, Chien-Chiang & Wang, Chih-Wei & Ho, Shan-Ju & Wu, Ting-Pin, 2021. "The impact of natural disaster on energy consumption: International evidence," Energy Economics, Elsevier, vol. 97(C).
    56. Farajzadeh, Zakariya & Nematollahi, Mohammad Amin, 2018. "Energy intensity and its components in Iran: Determinants and trends," Energy Economics, Elsevier, vol. 73(C), pages 161-177.
    57. Karimu, Amin & Brännlund, Runar & Lundgren, Tommy & Söderholm, Patrik, 2017. "Energy intensity and convergence in Swedish industry: A combined econometric and decomposition analysis," Energy Economics, Elsevier, vol. 62(C), pages 347-356.
    58. Guy Michaels & Ashwini Natraj & John Van Reenen, 2014. "Has ICT Polarized Skill Demand? Evidence from Eleven Countries over Twenty-Five Years," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 60-77, March.
    59. Tajudeen, Ibrahim A., 2021. "The underlying drivers of economy-wide energy efficiency and asymmetric energy price responses," Energy Economics, Elsevier, vol. 98(C).
    60. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    61. Lee, Chi-Chuan & Lee, Chien-Chiang & Li, Yong-Yi, 2021. "Oil price shocks, geopolitical risks, and green bond market dynamics," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    62. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    63. Apergis, Nicholas & Payne, James E. & Menyah, Kojo & Wolde-Rufael, Yemane, 2010. "On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth," Ecological Economics, Elsevier, vol. 69(11), pages 2255-2260, September.
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    More about this item

    Keywords

    Energy intensity; Industrial robots; Manufacturing sectors; Technology improvement effect and complement effect; Industry 4.0;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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