Are environment-related technologies key to unlock the path towards sustainable development: An econometric analysis

Abstract

The objective of this study is to investigate the correlation between energy intensity and environment-related technology in industrialized countries. By utilizing panel data from 23 countries over a span of 32 years (1990–2021), this research aims to contribute to the comprehension of the role of green innovation in sustainable development. The study employs the Stochastic Impacts by Regression on Population, Affluence, and Technology model while controlling for variables including population growth, gross domestic product in purchasing power parity, Information and Communication Technology capital deepening, renewable energy consumption, and green innovation represented by research and development expenditure on environment-related technology. The results of the analysis, employing panel unit root tests, cross-sectional dependence tests, and a Method of Moments quantile regression, reveal that green innovation has a positive influence on diminishing energy intensity, with a more substantial impact at higher quantiles. Moreover, ICT capital deepening is determined to have a positive and noteworthy effect on reducing energy intensity. The findings of this study offer valuable insights for policymakers in their endeavours to accomplish sustainable development goals.

Keywords

Technology / Sustainable development / Energy intensity / Quantile regression

References

M. Ahmad, P. Jiang, A. Majeed, M. Umar, Z. Khan, S. Muhammad. The dynamic impact of natural resources, technological innovations and economic growth on ecological footprint: An advanced panel data estimation. Resour. Policy, 69 (2020), Article 101817,

M. Ahmad, Z.-Y. Zhao. Empirics on linkages among industrialization, urbanization, energy consumption, CO2 emissions and economic growth: A heterogeneous panel study of China. Environ. Sci. Pollut. Res., 25 (30) (2018), pp. 30617-30632,

U. Ali, Q. Guo, M.T. Kartal, Z. Nurgazina, Z.A. Khan, A. Sharif. The impact of renewable and non-renewable energy consumption on carbon emission intensity in China: Fresh evidence from novel dynamic ARDL simulations. J. Environ.  Manag., 320 (2022), p. 115782,

A. Aslan, B. Ozsolak, N. Doğanalp. Environmental quality and renewable energy consumption with different quality indicators: Evidence from robust result with panel quantile approach. Environ. Sci. Pollut. Res., 28 (44) (2021), pp. 62398-62406,

G.C. Aye, P.E. Edoja. Effect of economic growth on CO2 emission in developing countries: Evidence from a dynamic panel threshold model. Cogent Econ. Finance, 5 (1) (2017), p. 1379239,

M.F. Bashir, B. MA, M. Shahbaz, U. Shahzad, X.V. Vo. Unveiling the heterogeneous impacts of environmental taxes on energy consumption and energy intensity: empirical evidence from OECD countries. Energy, 226 (2021), Article 120366,

M.A. Bashir, B. Sheng, B. Doğan, S. Sarwar, U. Shahzad. Export product diversification and energy efficiency: empirical evidence from OECD countries. Struct. Change Econ. Dyn., 55 (2020), pp. 232-243,

F. Bilgili, E. Koçak, Ü. Bulut, A. Kuloğlu. The impact of urbanization on energy intensity: panel data evidence considering cross-sectional dependence and heterogeneity. Energy, 133 (2017), pp. 242-256

M. Binder, A. Coad. From Average Joe’s happiness to Miserable Jane and Cheerful John: using quantile regressions to analyze the full subjective well-being distribution. J. Econ. Behav. Organ., 79 (3) (2011), pp. 275-290

J. Blomquist, J. Westerlund. Testing slope homogeneity in large panels with serial correlation. Econ. Lett., 121 (3) (2013), pp. 374-378

T.S. Breusch, A.R. Pagan. The Lagrange multiplier test and its applications to model specification in econometrics. Rev. Econ. Stud., 47 (1) (1980), pp. 239-253

I.A. Canay. A simple approach to quantile regression for panel data. Econ. J., 14 (3) (2011), pp. 368-386,

Y. Chen, B. Han, W. Liu. Green technology innovation and energy intensity in China. Nat. Hazards, 84 (S1) (2016), pp. 317-332,

U. Deichmann, A. Reuter, S. Vollmer, F. Zhang. The relationship between energy intensity and economic growth: New evidence from a multi-country multi-sectorial dataset. World Develop., 124 (2019), Article 104664,

Diaz Lopez, T. Bastein, A. Tukker. Business Model Innovation for Resource-efficiency, Circularity and Cleaner Production: What 143 Cases Tell Us. Ecol.  Econ., 155 (2019), pp. 20-35,

A. Dogan, U.K. Pata. The role of ICT, R&D spending and renewable energy consumption on environmental quality: Testing the LCC hypothesis for G7 countries. J. Cleaner Prod., 380 (2022), p. 135038,

J.C. Driscoll, A.C. Kraay. Consistent covariance matrix estimation with spatially dependent panel data. Rev. Econ. Stat., 80 (4) (1998), pp. 549-560,

J.A. Duro, V. Alcántara, E. Padilla. International inequality in energy intensity levels and the role of production composition and energy efficiency: An analysis of OECD countries. Ecolog. Econ., 69 (12) (2010), pp. 2468-2474,

S. Farhani, M. Shahbaz. What role of renewable and non-renewable electricity consumption and output is needed to initially mitigate CO2 emissions in MENA region?. Renew. Sust. Energy Rev., 40 (2014), pp. 80-90,

S. Filipović, N. Lior, M. Radovanović. The green deal–just transition and sustainable development goals Nexus. Renew. Sust. Energy Rev., 168 (2022), Article 112759

F. Fuso Nerini, B. Sovacool, N. Hughes, L. Cozzi, E. Cosgrave, M. Howells, M. Tavoni, J. Tomei, H. Zerriffi, B. Milligan. Connecting climate action with other Sustainable Development Goals. Nat. Sustain., 2 (8) (2019), pp. 674-680

C. Ghisetti, A. Marzucchi, S. Montresor. The open eco-innovation mode. An empirical investigation of eleven European countries. Res. Policy, 44 (5) (2015), pp. 1080-1093,

J. Horbach, C. Rammer, K. Rennings. Determinants of eco-innovations by type of environmental impact—The role of regulatory push/pull, technology push and market pull. Ecol. Econ., 78 (2012), pp. 112-122,

J. Hu, X. Pan, Q. Huang. Quantity or quality? The impacts of environmental regulation on firms’ innovation–Quasi-natural experiment based on China’s carbon emissions trading pilot. Technol. Forecast. Soc. Change, 158 (2020), p. 120122,

S.K. Hundie, M.D. Daksa. Does energy-environmental Kuznets curve hold for Ethiopia? The relationship between energy intensity and economic growth. J. Econ. Struct., 8 (1) (2019), p. 21,

A. Juodis, Y. Karavias, V. Sarafidis. A homogeneous approach to testing for Granger non-causality in heterogeneous panels. Empir. Econ., 60 (1) (2021), pp. 93-112,

IEA, 2019. World Energy Outlook 2019, IEA, Paris https://www.iea.org/reports/world-energy-outlook-2019, License: CC BY 4.0.

M.T. Kartal, A. Samour, T.S. Adebayo, S. Kılıç Depren. Do nuclear energy and renewable energy surge environmental quality in the United States? New insights from novel bootstrap Fourier Granger causality in quantiles approach. Prog. Nucl. Energy, 155 (2023), Article 104509,

I. Khan, F. Hou, H.P. Le. The impact of natural resources, energy consumption, and population growth on environmental quality: Fresh evidence from the United States of America. Sci. Total Environ., 754 (2021), Article 142222

M.K. Khan, M.I. Khan, M. Rehan. The relationship between energy consumption, economic growth and carbon dioxide emissions in Pakistan. Financ. Innov., 6 (1) (2020), p. 1,

D. Kirikkaleli, E. Sofuoğlu, O. Ojekemi. Does patents on environmental technologies matter for the ecological footprint in the USA? Evidence from the novel Fourier ARDL approach. Geosci. Front., 14 (4) (2023), Article 101564,

R. Koenker. Quantile regression for longitudinal data. J. Multivariate Anal., 91 (1) (2004), pp. 74-89

R. Koenker, G. Bassett. Regression quantiles. Economics, 46 (1) (1978), pp. 33-50

R. Larsson, J. Lyhagen, M. Löthgren. Likelihood-based cointegration tests in heterogeneous panels. Econ. J., 4 (1) (2001), pp. 109-142

B. Lin, K. Du. Understanding the change of energy intensity in China: A comprehensive decomposition frame. World Econ., 4 (2014), pp. 69-87

J. Lluís Carrion-i-Silvestre, T. Del Barrio-Castro, E. López-Bazo. Breaking the panels: an application to the GDP per capita. Econ. J., 8 (2) (2005), pp. 159-175

B. Luan, J. Huang, H. Zou, C. Huang. Determining the factors driving China’s industrial energy intensity: evidence from technological innovation sources and structural change. Sci. Total Environ., 737 (2020), Article 139767,

J.A.F. Machado, J.M.C. Santos Silva. Quantiles via moments. J. Econ., 213 (1) (2019), pp. 145-173,

J.A. Machado, J.S. Silva. Quantiles via moments. J. Econ., 213 (1) (2019), pp. 145-173

T. Mahmood, E. Ahmad. The relationship of energy intensity with economic growth: evidence for European economies. Energy Strategy Rev., 20 (2018), pp. 90-98

Mahmood, T., 2021. Dependence of energy intensity on economic growth: Panel data analysis of South Asian economies. 670216917.

J. Markard, R. Raven, B. Truffer. Sustainability transitions: An emerging field of research and its prospects. Res. Policy, 41 (6) (2012), pp. 955-967,

N.K. Mavi, R.K. Mavi. Energy and environmental efficiency of OECD countries in the context of the circular economy: Common weight analysis for malmquist productivity index. J. Environ. Manage., 247 (2019), pp. 651-661,

S. McCoskey, C. Kao. A residual-based test of the null of cointegration in panel data. Econ. Rev., 17 (1) (1998), pp. 57-84,

M.A.M. Moustapha, Q. Yu, B.A. Danqauh. Does renewable energy policy increase energy intensity? Evidence from the ECOWAS region. Int. J. Energy Sect. Manage., 16 (4) (2022), pp. 728-746,

Z. Nurgazina, Q. Guo, U. Ali, M.T. Kartal, A. Ullah, Z.A. Khan. Retesting the influences on CO2 emissions in China: evidence from dynamic ARDL approach. Front. Environ. Sci., 575 (2022),

S. Okushima, M. Tamura. What causes the change in energy demand in the economy? The role of technological change. Energy Econ., 32 (2010), pp. S41-S46

A. Omri, S. Daly, C. Rault, A. Chaibi. Financial development, environmental quality, trade and economic growth: what causes what in MENA countries. Energy Econ., 48 (2015), pp. 242-252,

A. Otsuka, M. Goto. Regional determinants of energy intensity in Japan: The impact of population density. Asia-Pacific J. Reg. Sci., 2 (2) (2018), pp. 257-278,

P.A. Owusu, S. Asumadu-Sarkodie. A review of renewable energy sources, sustainability issues and climate change mitigation. Cogent Eng., 3 (1) (2016), p. 1167990,

S.C. Pal, I. Chowdhuri, A. Saha, M. Ghosh, P. Roy, B. Das, R. Chakrabortty, M. Shit. COVID-19 strict lockdown impact on urban air quality and atmospheric temperature in four megacities of India. Geosci. Front., 13 (6) (2022), Article 101368,

S.R. Paramati, U. Shahzad, B. Doğan. The role of environmental technology for energy demand and energy efficiency: evidence from OECD countries. Renew. Sustain. Energy Rev., 153 (2022), Article 111735,

U.K. Pata, M.T. Kartal, T.S. Adebayo, S. Ullah. Enhancing environmental quality in the United States by linking biomass energy consumption and load capacity factor. Geosci. Front., 14 (3) (2023), Article 101531,

P. Pedroni. Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econ. Theor., 20 (3) (2004), pp. 597-625,

M.H. Pesaran, T. Yamagata. Testing slope homogeneity in large panels. J. Econ., 142 (1) (2008), pp. 50-93

M.H. Pesaran, A. Ullah, T. Yamagata. A bias-adjusted LM test of error cross-section independence. Econ. J., 11 (1) (2008), pp. 105-127

Pesaran, M. H., 2004. General diagnostic tests for cross section dependence in panels (IZA Discussion Paper No. 1240). Institute for the Study of Labor (IZA).

A. Qazi, F. Hussain, N.A. Rahim, G. Hardaker, D. Alghazzawi, K. Shaban, K. Haruna. Towards sustainable energy: a systematic review of renewable energy sources, technologies, and public opinions. IEEE Access, 7 (2019), pp. 63837-63851

M.A. Qureshi, J.A. Qureshi, A. Ahmed, S. Qaiser, R. Ali, A. Sharif. The Dynamic Relationship Between Technology Innovation and Human Development in Technologically Advanced Countries: Fresh Insights from Quantiles-on-Quantile Approach. Soc.  Indic. Res., 152 (2) (2020), pp. 555-580,

S. Rafiq, R. Salim, I. Nielsen. Urbanization, openness, emissions, and energy intensity: a study of increasingly urbanized emerging economies. Energy Econ., 56 (2016), pp. 20-28,

K. Rennings, C. Rammer. Increasing Energy and Resource Efficiency Through Innovation—An Explorative Analysis Using Innovation Survey Data. ZEW-Centre for European Economic Research Discussion; No. 09-056, ZEW, Mannheim, Germany (2009)

K.W. Robert, T.M. Parris, A.A. Leiserowitz. What is sustainable development?Goals, Indicators, Values, and Practice. Environ. Sci. Policy Sustain. Dev., 47 (3) (2005), pp. 8-21

T. Rokicki, R. Jadczak, A. Kucharski, P. Bórawski, A. Bełdycka-Bórawska, A. Szeberényi, A. Perkowska. Changes in Energy Consumption and Energy Intensity in EU Countries as a Result of the COVID-19 Pandemic by Sector and Area Economy. Energies, 15 (17) (2022), p. 6243,

S.A. Sarkodie, V. Strezov. Effect of foreign direct investments, economic development and energy consumption on greenhouse gas emissions in developing countries. Sci. Total Environ., 646 (2019), pp. 862-871

S.A. Sarkodie, A.N. Ajmi, F.F. Adedoyin, P.A. Owusu. Econometrics of Anthropogenic Emissions, Green Energy-Based Innovations, and Energy Intensity across OECD Countries. Sustainability, 13 (8) (2021), p. 4118,

H. Saunders, J. Roy, I. Azevedo, D. Chakravarty, S. Dasgupta, A. Druckman, R. Fouquet, M. Grubb, B. Lin, R. Lowe. Energy Efficiency: What has it Delivered in the Last 40 Years?. Social Sci. Electr. Publ. (2020),

R. Schmalensee, T.M. Stoker, R.A. Judson. World Carbon Dioxide Emissions: 1950–2050. Rev. Econ. Stat., 80 (1) (1998), pp. 15-27,

A. Sharif, S. Kocak, H.H.A. Khan, G. Uzuner, S. Tiwari. Demystifying the links between green technology innovation, economic growth, and environmental tax in ASEAN-6 countries: The dynamic role of green energy and green investment. Gondwana Res., 115 (2023), pp. 98-106,

R. Tan, B. Lin. What factors lead to the decline of energy intensity in China’s energy intensive industries?. Energy Econ., 71 (2018), pp. 213-221

D.D. Thomakos, T.A. Alexopoulos. Carbon intensity as a proxy for environmental performance and the informational content of the EPI. Energy Policy, 94 (2016), pp. 179-190,

M. Tvaronavičienė, D. Prakapienė, K. Garškaitė-Milvydienė, R. Prakapas, Ł. Nawrot. Energy Efficiency in the Long-Run in the Selected European Countries. Econ. Sociol., 11 (1) (2018), pp. 245-254, 10.14254/2071-789X.2018/11-1/16

S. Voigt, E. De Cian, M. Schymura, E. Verdolini. Energy intensity developments in 40 major economies: Structural change or technology improvement?. Energy Econ., 41 (2014), pp. 47-62,

D. Wang, B. Han. The impact of ICT investment on energy intensity across different regions of China. J. Renew. Sustain. Ener., 8 (5) (2016), Article 055901,

J. Westerlund. New Simple Tests for Panel Cointegration. Econ. Rev., 24 (3) (2005), pp. 297-316,

J. Westerlund, D.L. Edgerton. A simple test for cointegration in dependent panels with structural breaks. Oxf. Bull. Econ. Stat., 70 (5) (2008), pp. 665-704

J. Wu, Y. Wu, T. Se Cheong, Y. Yu. Distribution dynamics of energy intensity in Chinese cities. Appl. Energy, 211 (2018), pp. 875-889,

J.-D. Wurlod, J. Noailly. The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries. Energy Econ., 71 (2018), pp. 47-61,

L.S. Yang, J.P. Zhu, Z.J. Jia. Influencing factors and current challenges of China’s carbon emission reduction: Based on the perspective of technological progress. Econ. Res., 11 (2019), pp. 118-132

Y. Yuan, L. Guo, J. Sun. Structure, technology, management and energy using efficiency: Analysis based on 2000–2010 provincial panel data in China. China Ind. Econ., 28 (2012), pp. 18-30

P. Zhang, H. Wang. Do provincial energy policies and energy intensity targets help reduce CO2 emissions? Evidence from China. Energy, 245 (2022), Article 123275,

H. Zhang, Y. Wang, R. Li, H. Si, W. Liu. Can green finance promote urban green development? Evidence from green finance reform and innovation pilot zone in China. Environ. Sci. Pollut. Res., 30 (2023), pp. 12041-12058,

Zhou et al., 2021b Q. Zhou, C. Fu, H. Ni, L. Gong. What are the main factors that influence China’s energy intensity? —Based on aggregate and firm-level data. Energy Rep., 7 (2021), pp. 2737-2750,

Zhou et al., 2021a J. Zhou, Z. Ma, T. Wei, C. Li. Threshold Effect of Economic Growth on Energy Intensity—Evidence from 21 Developed Countries. Energies, 14 (14) (2021), p. 4199,