Do industrial robots reduce carbon intensity? The role of natural resource rents and corruption control.
Carbon emission intensity
Corruption control
Linear and nonlinear analysis
Natural resource rents
Robots
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
Oct 2023
Oct 2023
Historique:
received:
26
07
2023
accepted:
03
09
2023
medline:
30
10
2023
pubmed:
22
9
2023
entrez:
22
9
2023
Statut:
ppublish
Résumé
Although the research on the impact of robotics on carbon emissions is increasing, there are still relatively few studies on the impact of robots on carbon intensity from the perspective of natural resources and corruption. In order to fill in the research gaps, panel data from 66 countries between 1993 and 2018 are collected, and linear and nonlinear panel regression approaches are developed. Natural resource rent and corruption control are used as threshold variables, robot penetration is used as explanatory variables, and carbon emission intensity is the explained variable. The results of the linear model show that robot penetration is negatively correlated with carbon emission intensity, which means that robot penetration reduces carbon emission intensity. The results of the nonlinear model show that when natural resource rents and corruption control are used as thresholds, the relationship between robot penetration and carbon emission intensity presents a U shape and an inverted U shape, respectively. Specifically, the threshold for natural resource rents is 4.7%. When the natural resource rent is lower than this threshold, the robot penetration rate reduces the carbon emission intensity, but when the natural resource rent is higher than this threshold, the robot penetration rate increases the carbon emission intensity. The threshold value of corruption control is -0.4349. When the corruption control is lower than this threshold, the robot penetration rate increases the carbon emission intensity. If the corruption control is higher than this threshold, the robot reduces the carbon emission intensity. Finally, policy recommendations for better use of robotics to reduce carbon emission intensity are put forward from the perspective of natural resource rent and corruption control.
Identifiants
pubmed: 37737944
doi: 10.1007/s11356-023-29760-7
pii: 10.1007/s11356-023-29760-7
doi:
Substances chimiques
Carbon
7440-44-0
Carbon Dioxide
142M471B3J
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
107549-107567Subventions
Organisme : National Natural Science Foundation of China
ID : 72104246
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Acemoglu D, Restrepo P (2020) Robots and jobs: evidence from US labor markets. J Polit Econ 128(6):2188–2244
doi: 10.1086/705716
Acemoglu D, Restrepo P (2022) Tasks, automation, and the rise in US wage inequality. Econometrica 90(5):1973–2016
doi: 10.3982/ECTA19815
Anokhin S, Schulze WS (2009) Entrepreneurship, innovation, and corruption. J Bus Ventur 24(5):465–476. https://doi.org/10.1016/j.jbusvent.2008.06.001
doi: 10.1016/j.jbusvent.2008.06.001
Auty RM (1994) Industrial policy reform in six large newly industrializing countries: The resource curse thesis. World Dev 22(1):11–26
doi: 10.1016/0305-750X(94)90165-1
Bardhan P (1997) Corruption and development: a review of issues. J Econ Lit 35(3):1320–1346
Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econ 87(1):115–143
doi: 10.1016/S0304-4076(98)00009-8
Brougham D, Haar J (2018) Smart technology, artificial intelligence, robotics, and algorithms (STARA): employees’ perceptions of our future workplace. J Manag Organ 24(2):239–257
doi: 10.1017/jmo.2016.55
Cette G, Devillard A, Spiezia V (2022) Growth factors in developed countries: A 1960–2019 growth accounting decomposition. Comp Econ Stud 64:159–185. https://doi.org/10.1057/s41294-021-00170-3
Chen P, Gao J, Ji Z, Liang H, Peng Y (2022a) Do artificial intelligence applications affect carbon emission performance?—Evidence from panel data analysis of Chinese cities. Energies 15(15):5730. https://doi.org/10.3390/en15155730
Chen Y, Cheng L, Lee C-C (2022b) How does the use of industrial robots affect the ecological footprint? International evidence. Ecol Econ 198:107483. https://doi.org/10.1016/j.ecolecon.2022.107483
Corden WM, Neary JP (1982) Booming sector and de-industrialisation in a small open economy. Econ J 92(368):825–848
doi: 10.2307/2232670
Dong F, Li Y, Qin C, Sun J (2021a) How industrial convergence affects regional green development efficiency: a spatial conditional process analysis. J Environ Manage 300:113738
doi: 10.1016/j.jenvman.2021.113738
Dong F, Li Y, Zhang X, Zhu J, Zheng L (2021b) How does industrial convergence affect the energy efficiency of manufacturing in newly industrialized countries? Fresh evidence from China. J Clean Prod 316:128316
doi: 10.1016/j.jclepro.2021.128316
Du K, Cheng Y, Yao X (2021) Environmental regulation, green technology innovation, and industrial structure upgrading: the road to the green transformation of Chinese cities. Energy Econ 98:105247
doi: 10.1016/j.eneco.2021.105247
Du L, Lin W (2022) Does the application of industrial robots overcome the Solow paradox? Evidence from China. Technol Soc 68:101932. https://doi.org/10.1016/j.techsoc.2022.101932
Gan J, Liu L, Qiao G, Zhang Q (2023) The role of robot adoption in green innovation: evidence from China. Econ Model 119:101932. https://doi.org/10.1016/j.econmod.2022.106128
Graetz G, Michaels G (2018) Robots at work. Rev Econ Stat 100(5):753–768. https://doi.org/10.1162/rest_a_00754
doi: 10.1162/rest_a_00754
Granger CW, Newbold P (1974) Spurious regressions in econometrics. J Econ 2(2):111–120
doi: 10.1016/0304-4076(74)90034-7
Gylfason T (2001) Natural resources, education, and economic development. Eur Econ Rev 45(4–6):847–859
doi: 10.1016/S0014-2921(01)00127-1
Hansen BE (1999) Threshold effects in non-dynamic panels: estimation, testing, and inference. J Econ 93(2):345–368
doi: 10.1016/S0304-4076(99)00025-1
Höök M, Tang X (2013) Depletion of fossil fuels and anthropogenic climate change—a review. Energy Policy 52:797–809
doi: 10.1016/j.enpol.2012.10.046
IFR (2022) International federal of robotics. World Robotics - Industrial Robots. https://ifr.org/
James A, Aadland D (2011) The curse of natural resources: an empirical investigation of US counties. Resour Energy Econ 33(2):440–453
doi: 10.1016/j.reseneeco.2010.05.006
Jiang W, Gong SH, Li XT (2022) Robot shock, capital-embodied technological progress and carbon reduction of manufacturing sector—theoretical analysis and empirical evidence from China. China Ind Econ 2022(10):24–42. https://doi.org/10.19581/j.cnki.ciejournal.2022.10.002
Jungmittag A, Pesole A (2019) The impact of robots on labour productivity: A panel data approach covering 9 industries and 12 countries. In: JRC Working papers Series on Labour, Education and Technology 2019/08 https://www.econstor.eu/handle/10419/231332
Kashyap A, Yadav AK, Vatsa ON, Chandaka TN, Shukla OJ (2023) Investigation of the critical success factors in the implementation of the lean industry 4.0 in manufacturing supply chain: an ISM approach. Manag Environ Qual: Int J 34(4):981–996
doi: 10.1108/MEQ-04-2022-0109
Koch M, Manuylov I, Smolka M (2021) Robots and firms. Econ J 131(638):2553–2584. https://doi.org/10.1093/ej/ueab009
doi: 10.1093/ej/ueab009
Lee C-C, Qin S, Li Y (2022) Does industrial robot application promote green technology innovation in the manufacturing industry? Technol Forecast Soc Chang 183:121893. https://doi.org/10.1016/j.techfore.2022.121893
Lee H, Calvin K, Dasgupta D, Krinner G, Mukherji A, Thorne P et al (2023) AR6 synthesis report: Climate change 2023. Intergovernmental Panel on Climate Change (IPCC) 2023
Li R, Wang Q, Liu Y, Jiang R (2021) Per-capita carbon emissions in 147 countries: the effect of economic, energy, social, and trade structural changes. Sustain Prod Consum 27:1149–1164. https://doi.org/10.1016/j.spc.2021.02.031
doi: 10.1016/j.spc.2021.02.031
Li R, Li L, Wang Q (2022a) The impact of energy efficiency on carbon emissions: evidence from the transportation sector in Chinese 30 provinces. Sustain Cities Soc 82:103880. https://doi.org/10.1016/j.scs.2022.103880
doi: 10.1016/j.scs.2022.103880
Li R, Wang X, Wang Q (2022b) Does renewable energy reduce ecological footprint at the expense of economic growth? An empirical analysis of 120 countries. J Clean Prod 346:131207
doi: 10.1016/j.jclepro.2022.131207
Li Y, Zhang Y, Pan A, Han M, Veglianti E (2022c) Carbon emission reduction effects of industrial robot applications: heterogeneity characteristics and influencing mechanisms. Technol Soc 70:102034. https://doi.org/10.1016/j.techsoc.2022.102034
Li X, Tian Q (2023) How does usage of robot affect corporate carbon emissions?—evidence from China’s manufacturing sector. Sustainability 15(2):1198. https://doi.org/10.3390/su15021198
Liu H, Pata UK, Zafar MW, Kartal MT, Karlilar S, Caglar AE (2023) Do oil and natural gas prices affect carbon efficiency? Daily evidence from China by wavelet transform-based approaches. Resour Policy 85:104039. https://doi.org/10.1016/j.resourpol.2023.104039
doi: 10.1016/j.resourpol.2023.104039
Liu J, Liu L, Qian Y, Song S (2022) The effect of artificial intelligence on carbon intensity: evidence from China's industrial sector. Socio-Econ Plan Sci 83:101002. https://doi.org/10.1016/j.seps.2020.101002
Luan F, Yang X, Chen Y, Regis PJ (2022) Industrial robots and air environment: a moderated mediation model of population density and energy consumption. Sustain Prod Consum 30:870–888. https://doi.org/10.1016/j.spc.2022.01.015
doi: 10.1016/j.spc.2022.01.015
Lv H, Shi B, Li N, Kang R (2022) Intelligent manufacturing and carbon emissions reduction: evidence from the use of industrial robots in China. Int J Environ Res Public Health 19(23):15538. https://doi.org/10.3390/ijerph192315538
Mora C, Spirandelli D, Franklin EC, Lynham J, Kantar MB, Miles W et al (2018) Broad threat to humanity from cumulative climate hazards intensified by greenhouse gas emissions. Nat Clim Chang 8(12):1062–1071
doi: 10.1038/s41558-018-0315-6
Nie Y, Zhou Y, Wang H, Zeng L, Bao W (2023) How does the use of robots promote carbon reduction?——spatial correlation and heterogeneity analysis
Nižetić S, Djilali N, Papadopoulos A, Rodrigues JJPC (2019) Smart technologies for promotion of energy efficiency, utilization of sustainable resources and waste management. J Clean Prod 231:565–591. https://doi.org/10.1016/j.jclepro.2019.04.397
doi: 10.1016/j.jclepro.2019.04.397
Oliva P (2015) Environmental regulations and corruption: automobile emissions in Mexico City. J Polit Econ 123(3):686–724
doi: 10.1086/680936
Ozkan-Ozen YD, Sezer D, Ozbiltekin-Pala M, Kazancoglu Y (2023) Risks of data-driven technologies in sustainable supply chain management. Manag Environ Qual: Int J 34(4):926–942
doi: 10.1108/MEQ-03-2022-0051
Ozturk I, Acaravci A (2010) CO2 emissions, energy consumption and economic growth in Turkey. Renew Sustain Energy Rev 14(9):3220–3225
doi: 10.1016/j.rser.2010.07.005
Pan Y, Dong F (2023) Factor substitution and development path of the new energy market in the BRICS countries under carbon neutrality: inspirations from developed European countries. Appl Energy 331:120442
doi: 10.1016/j.apenergy.2022.120442
Pesaran MH (2007) A simple panel unit root test in the presence of cross-section dependence. J Appl Economet 22(2):265–312
doi: 10.1002/jae.951
Pesaran MH (2020) General diagnostic tests for cross-sectional dependence in panels. Empir Econ 60(1):13–50. https://doi.org/10.1007/s00181-020-01875-7
doi: 10.1007/s00181-020-01875-7
Pesaran MH, Shin Y, Smith RP (1999) Pooled mean group estimation of dynamic heterogeneous panels. J Am Stat Assoc 94(446):621–634
doi: 10.1080/01621459.1999.10474156
Quah JS (2006) Curbing Asian corruption: an impossible dream? Curr Hist 105(690):176–179
doi: 10.1525/curh.2006.105.690.176
Sachs JD, Warner A (1995) Natural resource abundance and economic growth, no. 5398. National bureau of economic research, Cambridge. https://doi.org/10.3386/w5398
Sachs JD, Warner AM (1997) Fundamental sources of long-run growth. Am Econ Rev 87(2):184–188
Sachs JD, Warner AM (2001) The curse of natural resources. Eur Econ Rev 45(4–6):827–838
doi: 10.1016/S0014-2921(01)00125-8
Sala-i-Martin X, Subramanian A (2013) Addressing the natural resource curse: an illustration from Nigeria. J Afr Econ 22(4):570–615
doi: 10.1093/jae/ejs033
Seo MH, Kim S, Kim Y-J (2019) Estimation of dynamic panel threshold model using Stata. Stand Genomic Sci 19(3):685–697
Sharif A, Kartal MT, Bekun FV, Pata UK, Foon CL, Kılıç Depren S (2023) Role of green technology, environmental taxes, and green energy towards sustainable environment: Insights from sovereign Nordic countries by CS-ARDL approach. Gondwana Res 117:194–206. https://doi.org/10.1016/j.gr.2023.01.009
doi: 10.1016/j.gr.2023.01.009
Sims RE, Rogner H-H, Gregory K (2003) Carbon emission and mitigation cost comparisons between fossil fuel, nuclear and renewable energy resources for electricity generation. Energy Policy 31(13):1315–1326
doi: 10.1016/S0301-4215(02)00192-1
Song J, Chen Y, Luan F (2023) Air pollution, water pollution, and robots: is technology the panacea. J Environ Manage 330:117170. https://doi.org/10.1016/j.jenvman.2022.117170
doi: 10.1016/j.jenvman.2022.117170
Sultana N, Rahman MM, Khanam R, Kabir Z (2022) Environmental quality and its nexus with informal economy, corruption control, energy use, and socioeconomic aspects: the perspective of emerging economies. Heliyon 8(6):09569. https://doi.org/10.1016/j.heliyon.2022.e09569
Sun J, Dong F (2022) Decomposition of carbon emission reduction efficiency and potential for clean energy power: evidence from 58 countries. J Clean Prod 363:132312
doi: 10.1016/j.jclepro.2022.132312
Tang C, Irfan M, Razzaq A, Dagar V (2022) Natural resources and financial development: role of business regulations in testing the resource-curse hypothesis in ASEAN countries. Resour Policy 76:102612
doi: 10.1016/j.resourpol.2022.102612
Torvik R (2002) Natural resources, rent seeking and welfare. J Dev Econ 67(2):455–470
doi: 10.1016/S0304-3878(01)00195-X
Vishwakarma A, Dangayach G, Meena M, Gupta S, Luthra S (2023) Adoption of blockchain technology enabled healthcare sustainable supply chain to improve healthcare supply chain performance. Manag Environ Qual: Int J 34(4):1111–1128
doi: 10.1108/MEQ-02-2022-0025
Wang Q, Zhang F, Li R (2023) Free trade and carbon emissions revisited: the asymmetric impacts of trade diversification and trade openness. Sustain Dev https://doi.org/10.1002/sd.2703
Wang L, Wang H, Cao Z, He Y, Dong Z, Wang S (2022a) Can industrial intellectualization reduce carbon emissions? — empirical evidence from the perspective of carbon total factor productivity in China. Technol Forecast Soc Chang 184:121969. https://doi.org/10.1016/j.techfore.2022.121969
Wang Q, Wang X, Li R (2022b) Does urbanization redefine the environmental Kuznets curve? An empirical analysis of 134 countries. Sustain Cities Soc 76:103382. https://doi.org/10.1016/j.scs.2021.103382
doi: 10.1016/j.scs.2021.103382
Wang J, Wang W, Liu Y, Wu H (2023a) Can industrial robots reduce carbon emissions? Based on the perspective of energy rebound effect and labor factor flow in China. Technol Soc 72. https://doi.org/10.1016/j.techsoc.2023.102208
Wang Q, Sun J, Li R, Korkut Pata U (2023b) Linking trade openness to load capacity factor: the threshold effects of natural resource rent and corruption control. Gondwana Res. https://doi.org/10.1016/j.gr.2023.05.016
doi: 10.1016/j.gr.2023.05.016
Wang Q, Sun J, Pata UK, Li R, Kartal MT (2023c) Digital economy and carbon dioxide emissions: examining the role of threshold variables. Geosci Front 101644. https://doi.org/10.1016/j.gsf.2023.101644
WDI (2022) World development indicators. WorldBank. https://data.worldbank.org
Westerlund J (2005) New simple tests for panel cointegration. Economet Rev 24(3):297–316
doi: 10.1080/07474930500243019
WGI (2022) Worldwide Governance Indicators. World Bank. https://info.worldbank.org/governance/wgi/
Wilson M, Paschen J, Pitt L (2022) The circular economy meets artificial intelligence (AI): understanding the opportunities of AI for reverse logistics. Manag Environ Qual: Int J 33(1):9–25
doi: 10.1108/MEQ-10-2020-0222
Wu Q (2023) Sustainable growth through industrial robot diffusion: quasi-experimental evidence from a Bartik shift-share design. Econ Transit Inst Change. https://doi.org/10.1111/ecot.12367
doi: 10.1111/ecot.12367
York R, Rosa EA, Dietz T (2003) STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol Econ 46(3):351–365
doi: 10.1016/S0921-8009(03)00188-5
Yu L, Zeng C, Wei X (2022) The impact of industrial robots application on air pollution in China: mechanisms of energy use efficiency and green technological innovation. Sci Prog 105(4):368504221144093. https://doi.org/10.1177/00368504221144093
doi: 10.1177/00368504221144093
Zhang C, Liu C (2015) The impact of ICT industry on CO2 emissions: a regional analysis in China. Renew Sustain Energy Rev 44:12–19. https://doi.org/10.1016/j.rser.2014.12.011
doi: 10.1016/j.rser.2014.12.011
Zhang Q, Zhang F, Mai Q (2022a) Robot adoption and green productivity: curse or boon. Sustain Prod Consum 34:1–11. https://doi.org/10.1016/j.spc.2022.08.025
doi: 10.1016/j.spc.2022.08.025
Zhang X, Liu P, Zhu H (2022b) The impact of industrial intelligence on energy intensity: Evidence from China. Sustainability 14(12):7219. https://doi.org/10.3390/su14127219