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

Acemoglu, 2018, Low-skill and high-skill automation, J. Hum. Cap., 12, 204, 10.1086/697242 Acemoglu, 2020, Robots and jobs: evidence from US labor markets, J. Polit. Econ., 128, 2188, 10.1086/705716 Aghion, 2019 Agovino, 2019, Modelling the relationship between energy intensity and GDP for European countries: an historical perspective (1800–2000), Energy Econ., 82, 114, 10.1016/j.eneco.2018.02.017 Agrawal, 2019, Artificial intelligence: the ambiguous labor market impact of automating prediction, J. Econ. Perspect., 33, 31, 10.1257/jep.33.2.31 Ajayi, 2020, European industrial energy intensity: innovation, environmental regulation, and price effects, EJ, 41 Apergis, 2009, Energy consumption and economic growth in Central America: evidence from a panel cointegration and error correction model, Energy Econ., 31, 211, 10.1016/j.eneco.2008.09.002 Apergis, 2010, On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth, Ecol. Econ., 69, 2255, 10.1016/j.ecolecon.2010.06.014 Arellano, 1991, Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations, Rev. Econ. Stud., 58, 277, 10.2307/2297968 Arellano, 1995, Another look at the instrumental variable estimation of error-components models, J. Econ., 68, 29, 10.1016/0304-4076(94)01642-D Autor, 2013, The growth of low-skill service jobs and the polarization of the US labor market, Am. Econ. Rev., 103, 1553, 10.1257/aer.103.5.1553 Ballestar, 2020, Knowledge, robots and productivity in SMEs: explaining the second digital wave, J. Bus. Res., 108, 119, 10.1016/j.jbusres.2019.11.017 Berg, 2018, Should we fear the robot revolution? (the correct answer is yes), J. Monet. Econ., 97, 117, 10.1016/j.jmoneco.2018.05.014 Blundell, 1998, Initial conditions and moment restrictions in dynamic panel data models, J. Econ., 87, 115, 10.1016/S0304-4076(98)00009-8 Brossog, 2015, Reducing the energy consumption of industrial robots in manufacturing systems, Int. J. Adv. Manuf. Technol., 78, 1315, 10.1007/s00170-014-6737-z Buer, 2018, The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda, International Journal of Production Research, 56, 2924, 10.1080/00207543.2018.1442945 Burnett, 2017, The convergence of U.S state-level energy intensity, Energy Econ., 62, 357, 10.1016/j.eneco.2016.03.029 Conti, 2018, Transition towards a green economy in Europe: innovation and knowledge integration in the renewable energy sector, Res. Policy, 47, 1996, 10.1016/j.respol.2018.07.007 Corsatea, T.D., Lindner, S., Arto, I., Roman, M.V., Rueda-Cantuche, J.M., Velazquez Afonso, A., Amores, A.F., Neuwahl, F., World input-output database environmental accounts, Update 2000-2016, 2019, 10.2760/ 024036,JRC116234. Díaz, 2019, Economic growth, energy intensity and the energy mix, Energy Econ., 81, 1056, 10.1016/j.eneco.2019.05.022 Farajzadeh, 2018, Energy intensity and its components in Iran: determinants and trends, Energy Econ., 73, 161, 10.1016/j.eneco.2018.05.021 Fisher-Vanden, 2016, Factors influencing energy intensity in four Chinese industries, Energy J., 37, 153, 10.5547/01956574.37.SI1.kfis Gadaleta, 2019, Optimization of the energy consumption of industrial robots for automatic code generation, Robot. Comput. Integr. Manuf., 57, 452, 10.1016/j.rcim.2018.12.020 Ghobakhloo, 2021, Industry 4.0 and opportunities for energy sustainability, J. Clean. Prod., 295, 10.1016/j.jclepro.2021.126427 Goos, 2014, Explaining job polarization: routine-biased technological change and offshoring, Am. Econ. Rev., 104, 2509, 10.1257/aer.104.8.2509 Graetz, 2018, Robots at work, Rev. Econ. Stat., 100, 753, 10.1162/rest_a_00754 Greening, 1997, Comparison of six decomposition methods: application to aggregate energy intensity for manufacturing in 10 OECD countries, Energy Econ., 19, 375, 10.1016/S0140-9883(96)01028-6 Hansen, 1982, Large sample properties of generalized method of moments estimators, Econometrica, 50, 1029, 10.2307/1912775 Herrendorf, 2013, Two perspectives on preferences and structural transformation, Am. Econ. Rev., 103, 2752, 10.1257/aer.103.7.2752 Howarth, 1991, Manufacturing energy use in eight OECD countries: decomposing the impacts of changes in output, industry structure and energy intensity, Energy Econ., 13, 135, 10.1016/0140-9883(91)90046-3 Huang, 2020, Domestic R&D activities, technology absorption ability, and energy intensity in China, Energy Policy, 138, 10.1016/j.enpol.2019.111184 Jimenez, 2014, Energy intensity: a decomposition and counterfactual exercise for Latin American countries, Energy Econ., 42, 161, 10.1016/j.eneco.2013.12.015 Jung, 2020, Industrial robots, employment growth, and labor cost: a simultaneous equation analysis, Technol. Forecast. Soc. Chang., 159, 10.1016/j.techfore.2020.120202 Kagermann Karimu, 2017, Energy intensity and convergence in Swedish industry: a combined econometric and decomposition analysis, Energy Econ., 62, 347, 10.1016/j.eneco.2016.07.017 Keynes, 1931, Economic possibilities for our grandchildren, 358 Kromann, 2020, Automation and productivity—a cross-country, cross-industry comparison, Ind. Corp. Chang., 29, 265 Kumaresan, 1999, An integrated network approach to systems of innovation—the case of robotics in Japan, Res. Policy, 28, 563, 10.1016/S0048-7333(98)00128-0 Lee, 2005, Energy consumption and GDP in developing countries: a cointegrated panel analysis, Energy Econ., 27, 415, 10.1016/j.eneco.2005.03.003 Lee, 2019, Oil price shocks and Chinese banking performance: do country risks matter?, Energy Econ., 77, 46, 10.1016/j.eneco.2018.01.010 Lee, 2020, The impact of natural disaster on energy consumption: international evidence, Energy Econ., 105021 Lee, 2021, Oil price shocks, geopolitical risks, and green bond market dynamics, The North Am. J. Econ. Fin., 55 Lee, 2021, The impact of natural disaster on energy consumption: international evidence, Energy Econ., 97, 10.1016/j.eneco.2020.105021 Lescaroux, 2008, Decomposition of US manufacturing energy intensity and elasticities of components with respect to energy prices, Energy Econ., 30, 1068, 10.1016/j.eneco.2007.11.002 Li, 2017, Review of methodologies and polices for evaluation of energy efficiency in high energy-consuming industry, Appl. Energy, 187, 203, 10.1016/j.apenergy.2016.11.039 Li, 2020, Transition from factor-driven to innovation-driven urbanization in China: a study of manufacturing industry automation in Dongguan City, China Econ. Rev., 59, 10.1016/j.chieco.2019.101382 Liu, 2020, Influence of artificial intelligence on technological innovation: Evidence from the panel data of china’s manufacturing sectors, Technological Forecasting and Social Change, 158, 120142, 10.1016/j.techfore.2020.120142 Liu, 2020, Convergence of the World’s energy use, Resour. Energy Econ., 62, 10.1016/j.reseneeco.2020.101199 Liu, 2020, Influence of artificial intelligence on technological innovation: evidence from the panel data of China’s manufacturing sectors, Technol. Forecast. Soc. Chang., 158, 10.1016/j.techfore.2020.120142 Lv, 2021, Green technology innovation and financial development: do environmental regulation and innovation output matter?, Energy Econ., 98, 10.1016/j.eneco.2021.105237 Meike, 2011, Energy efficient use of robotics in the automobile industry, 507 Michaels, 2014, Has ICT polarized skill demand? Evidence from eleven countries over twenty-five years, Rev. Econ. Stat., 96, 60, 10.1162/REST_a_00366 Mishra, 2009, The energy-GDP Nexus: evidence from a panel of Pacific Island countries, Resour. Energy Econ., 31, 210, 10.1016/j.reseneeco.2009.04.002 Mulder, 2014, Dynamics and determinants of energy intensity in the service sector: A cross-country analysis, 1980–2005, Ecological Economics, 100, 1, 10.1016/j.ecolecon.2014.01.016 Moshiri, 2016, Changes in energy intensity in Canada, Energy J., 37, 10.5547/01956574.37.4.smos Mulder, 2015, International specialization, structural change and the evolution of manufacturing energy intensity in OECD countries, Energy J., 36, 111, 10.5547/01956574.36.3.pmul Müller, 2018, Fortune favors the prepared: how SMEs approach business model innovations in industry 4.0, Technol. Forecast. Soc. Chang., 132, 2, 10.1016/j.techfore.2017.12.019 Nascimento, 2018, Exploring industry 4.0 technologies to enable circular economy practices in a manufacturing context: a business model proposal, J. Manuf. Technol. Manag., 30, 607, 10.1108/JMTM-03-2018-0071 Pan, 2019, Dynamics of financial development, trade openness, technological innovation and energy intensity: evidence from Bangladesh, Energy, 171, 456, 10.1016/j.energy.2018.12.200 Parker, 2016, Energy efficiency in the manufacturing sector of the OECD: analysis of Price elasticities, Energy Econ., 58, 38, 10.1016/j.eneco.2016.06.003 Rafiq, 2016, Urbanization, openness, emissions, and energy intensity: a study of increasingly urbanized emerging economies, Energy Econ., 56, 20, 10.1016/j.eneco.2016.02.007 Roodman, 2009, How to do Xtabond2: an introduction to difference and system GMM in Stata, Stata J., 9, 86, 10.1177/1536867X0900900106 Scalera, 2020, Enhancing energy efficiency of a 4-DOF parallel robot through task-related analysis, Machines, 8, 10, 10.3390/machines8010010 Schmidt, 2019, Measuring the temporal dynamics of policy mixes – an empirical analysis of renewable energy policy Mixes’ balance and design features in nine countries, Res. Policy, 48, 10.1016/j.respol.2018.03.012 Shapiro, 1984, Equilibrium unemployment as a worker discipline device, Am. Econ. Rev., 74, 433 Sherwani, 2020, Collaborative robots and industrial revolution 4.0 (IR 4.0), 1 Stucki, 2019, Which firms Benefit from Investments in Green Energy Technologies? – the effect of energy costs, Res. Policy, 48, 546, 10.1016/j.respol.2018.09.010 Tajudeen, 2021, The underlying drivers of economy-wide energy efficiency and asymmetric energy Price responses, Energy Econ., 98, 10.1016/j.eneco.2021.105222 Timma, 2016, Life after the financial crisis. Energy intensity and energy use decomposition on sectorial level in Latvia, Appl. Energy, 162, 1586, 10.1016/j.apenergy.2015.04.021 Voigt, 2014, Energy intensity developments in 40 major economies: structural change or technology improvement?, Energy Econ., 41, 47, 10.1016/j.eneco.2013.10.015 Wang, 2015, A performance evaluation of the energy, environmental, and economic efficiency and productivity in China: an application of global data envelopment analysis, Appl. Energy, 147, 617, 10.1016/j.apenergy.2015.01.108 Windmeijer, 2005, A finite sample correction for the variance of linear efficient two-step GMM estimators, J. Econ., 126, 25, 10.1016/j.jeconom.2004.02.005 Wu, 2018, Distribution dynamics of energy intensity in Chinese cities, Applied Energy, 211, 875, 10.1016/j.apenergy.2017.10.097 Wurlod, 2018, The impact of green innovation on energy intensity: an empirical analysis for 14 industrial sectors in OECD countries, Energy Econ., 71, 47, 10.1016/j.eneco.2017.12.012 Yun, 2016, The relationship between technology, business model, and market in autonomous car and intelligent robot industries, Technol. Forecast. Soc. Chang., 103, 142, 10.1016/j.techfore.2015.11.016 Zeira, 1998, Workers, machines, and economic growth, Q. J. Econ., 113, 1091, 10.1162/003355398555847