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Testing the role of economic complexity on the ecological footprint in China: a nonparametric causality-in-quantiles approach

Author

Listed:
  • Seyi Saint Akadiri
  • Tomiwa Sunday Adebayo
  • Obioma Chinenyenwa Asuzu
  • Ijeoma Christina Onuogu
  • Izuchukwu Oji-Okoro

Abstract

China is known for its large industrial sector and diversified energy mix, which could contribute to environmental pollution, as fossil fuels remain China's main source of energy. With the recent drive by the Chinese government to achieve low carbon emissions and further reduce greenhouse gases, this study adds to the existing literature by combining the quantile-on-quantile (QQ) regression and non-parametric techniques to examine the role of economic complexity, nonrenewables energy and renewable energy consumption on the ecological footprint in China over the period 1985Q1–2019Q4. Overall, results show that renewable energy, non-renewable energy use, economic growth and economic complexity affects ecological footprint positively. In addition, the nonparametric causality outcomes revealed that renewable energy, non-renewable energy use, economic growth and economic complexity can significantly predict variations in ecological footprint at different quantiles. We are of the opinion that policymakers in this region should work on the pro-growth mentality of China, which is majorly fossil fuel-driven. This requires an immediate replacement with more eco-friendly sources and energy-saving technologies for economic activities. Otherwise, fulfilling the SDG 13 goals in China will be challenging. For a sustainable renewable energy investment, China should shift to ancillary and spot markets, where the low energy storage and low marginal cost of renewable energy could facilitate higher reduction in electricity cost and encourage higher trading of electricity.

Suggested Citation

  • Seyi Saint Akadiri & Tomiwa Sunday Adebayo & Obioma Chinenyenwa Asuzu & Ijeoma Christina Onuogu & Izuchukwu Oji-Okoro, 2023. "Testing the role of economic complexity on the ecological footprint in China: a nonparametric causality-in-quantiles approach," Energy & Environment, , vol. 34(7), pages 2290-2316, November.
    • Handle: RePEc:sae:engenv:v:34:y:2023:i:7:p:2290-2316
    DOI: 10.1177/0958305X221094573
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    References listed on IDEAS

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    1. David I. Stern & Jeremy Dijk, 2017. "Economic growth and global particulate pollution concentrations," Climatic Change, Springer, vol. 142(3), pages 391-406, June.
    2. Galvao Jr., Antonio F., 2009. "Unit root quantile autoregression testing using covariates," Journal of Econometrics, Elsevier, vol. 152(2), pages 165-178, October.
    3. Mehmet Balcilar & Stelios Bekiros & Rangan Gupta, 2017. "The role of news-based uncertainty indices in predicting oil markets: a hybrid nonparametric quantile causality method," Empirical Economics, Springer, vol. 53(3), pages 879-889, November.
    4. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    5. Jeong, Kiho & Härdle, Wolfgang K. & Song, Song, 2012. "A Consistent Nonparametric Test For Causality In Quantile," Econometric Theory, Cambridge University Press, vol. 28(4), pages 861-887, August.
    6. Nishiyama, Yoshihiko & Hitomi, Kohtaro & Kawasaki, Yoshinori & Jeong, Kiho, 2011. "A consistent nonparametric test for nonlinear causality—Specification in time series regression," Journal of Econometrics, Elsevier, vol. 165(1), pages 112-127.
    7. Pata, Ugur Korkut & Isik, Cem, 2021. "Determinants of the load capacity factor in China: A novel dynamic ARDL approach for ecological footprint accounting," Resources Policy, Elsevier, vol. 74(C).
    8. Pata, Ugur Korkut & Caglar, Abdullah Emre, 2021. "Investigating the EKC hypothesis with renewable energy consumption, human capital, globalization and trade openness for China: Evidence from augmented ARDL approach with a structural break," Energy, Elsevier, vol. 216(C).
    9. Richard Wood & Karsten Neuhoff & Dan Moran & Moana Simas & Michael Grubb & Konstantin Stadler, 2020. "The structure, drivers and policy implications of the European carbon footprint," Climate Policy, Taylor & Francis Journals, vol. 20(S1), pages 39-57, April.
    10. Olimpia Neagu & Mircea Constantin Teodoru, 2019. "The Relationship between Economic Complexity, Energy Consumption Structure and Greenhouse Gas Emission: Heterogeneous Panel Evidence from the EU Countries," Sustainability, MDPI, vol. 11(2), pages 1-29, January.
    11. Dervis Kirikkaleli & Tomiwa Sunday Adebayo, 2021. "Do renewable energy consumption and financial development matter for environmental sustainability? New global evidence," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(4), pages 583-594, July.
    12. Shahbaz, Muhammad & Balsalobre-Lorente, Daniel & Sinha, Avik, 2019. "Foreign Direct Investment–CO2 Emissions Nexus in Middle East and North African countries: Importance of Biomass Energy Consumption," MPRA Paper 91729, University Library of Munich, Germany, revised 19 Jan 2019.
    13. Roger Koenker & Zhijie Xiao, 2004. "Unit Root Quantile Autoregression Inference," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 775-787, January.
    14. Sarwar, Suleman & Shahzad, Umer & Chang, Dongfeng & Tang, Biyan, 2019. "Economic and non-economic sector reforms in carbon mitigation: Empirical evidence from Chinese provinces," Structural Change and Economic Dynamics, Elsevier, vol. 49(C), pages 146-154.
    15. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    16. Olimpia Neagu, 2020. "Economic Complexity and Ecological Footprint: Evidence from the Most Complex Economies in the World," Sustainability, MDPI, vol. 12(21), pages 1-18, October.
    17. Lan Khanh Chu, 2021. "Economic structure and environmental Kuznets curve hypothesis: new evidence from economic complexity," Applied Economics Letters, Taylor & Francis Journals, vol. 28(7), pages 612-616, April.
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