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The Carbon-Neutral Energy Consumption and Emission Volatility: The Causality Analysis of ASEAN Region

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  • Shu Wu

    (Sino-German College, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Majed Alharthi

    (Finance Department, College of Business, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia)

  • Weihua Yin

    (Business School, Shanghai Jian Qiao University, Shanghai 200444, China)

  • Qaiser Abbas

    (Department of Economics, Ghazi University, Dera Ghazi Khan 32200, Pakistan)

  • Adnan Noor Shah

    (Department of Agricultural Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan)

  • Saeed ur Rahman

    (Department of Economics, Ghazi University, Dera Ghazi Khan 32200, Pakistan)

  • Jamal Khan

    (Institute for Region and Urban–Rural Development, Wuhan University, Wuhan 430072, China)

Abstract

The use of renewable energy sources and carbon emissions has been debated from various perspectives throughout recent decades. However, the causal relationship between green energy sources and carbon emissions volatility has received limited attention. This study aims to close a knowledge gap in this area. The current study analyzes the renewable energy sources (wind, hydro, and geothermal) and carbon emissions of four ASEAN countries (Indonesia, Thailand, Vietnam, and the Philippines) between 2000 and 2019. The present study combined Chudik and Pesaran’s (2015) newly developed Dynamic Common Correlated Effects (DCCE) with cutting-edge investigation tools such as first- and second-generation unit root tests; CS-dependence; Variance inflation factor test for multicollinearity; and Pedroni, Kao, and Wester Lund tests of co-integration. The Granger causality test is also used to check the short-term and long-term causal effects within the renewable energy sources and green energy sources, and carbon volatility. According to the empirical results, green energy sources make a positive and vital contribution to reducing carbon emissions growth in the above-noted ASEAN economies. Furthermore, short- and long-run causality runs from green energy sources to carbon emission volatility in the region. A significant causality relationship has also been observed within the green energy sources of ASEAN.

Suggested Citation

  • Shu Wu & Majed Alharthi & Weihua Yin & Qaiser Abbas & Adnan Noor Shah & Saeed ur Rahman & Jamal Khan, 2021. "The Carbon-Neutral Energy Consumption and Emission Volatility: The Causality Analysis of ASEAN Region," Energies, MDPI, vol. 14(10), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2943-:d:557799
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    as
    1. Chen, Yulong & Wang, Zheng & Zhong, Zhangqi, 2019. "CO2 emissions, economic growth, renewable and non-renewable energy production and foreign trade in China," Renewable Energy, Elsevier, vol. 131(C), pages 208-216.
    2. Shi, Xunpeng, 2016. "The future of ASEAN energy mix: A SWOT analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 672-680.
    3. Joakim Westerlund & David L. Edgerton, 2008. "A Simple Test for Cointegration in Dependent Panels with Structural Breaks," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(5), pages 665-704, October.
    4. Pedroni, Peter, 2004. "Panel Cointegration: Asymptotic And Finite Sample Properties Of Pooled Time Series Tests With An Application To The Ppp Hypothesis," Econometric Theory, Cambridge University Press, vol. 20(3), pages 597-625, June.
    5. Shuai, Chenyang & Shen, Liyin & Jiao, Liudan & Wu, Ya & Tan, Yongtao, 2017. "Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011," Applied Energy, Elsevier, vol. 187(C), pages 310-325.
    6. Westerlund, Joakim & Edgerton, David L., 2007. "A panel bootstrap cointegration test," Economics Letters, Elsevier, vol. 97(3), pages 185-190, December.
    7. Mohd Haizam Mohd Saudi & Obsatar Sinaga & Djoko Roespinoedji & Mohd Shahril Ahmad Razimi, 2019. "The role of renewable, non-renewable electricity consumption and carbon emission in development in Indonesia: Evidence from Distributed Lag Tests," International Journal of Energy Economics and Policy, Econjournals, vol. 9(3), pages 46-52.
    8. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    9. Kapetanios, G. & Pesaran, M. Hashem & Yamagata, T., 2011. "Panels with non-stationary multifactor error structures," Journal of Econometrics, Elsevier, vol. 160(2), pages 326-348, February.
    10. Bhattacharya, Mita & Awaworyi Churchill, Sefa & Paramati, Sudharshan Reddy, 2017. "The dynamic impact of renewable energy and institutions on economic output and CO2 emissions across regions," Renewable Energy, Elsevier, vol. 111(C), pages 157-167.
    11. Joakim Westerlund, 2007. "Testing for Error Correction in Panel Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 69(6), pages 709-748, December.
    12. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
    13. Bekhet, Hussain Ali & Othman, Nor Salwati, 2018. "The role of renewable energy to validate dynamic interaction between CO2 emissions and GDP toward sustainable development in Malaysia," Energy Economics, Elsevier, vol. 72(C), pages 47-61.
    14. Jushan Bai & Serena Ng, 2004. "A PANIC Attack on Unit Roots and Cointegration," Econometrica, Econometric Society, vol. 72(4), pages 1127-1177, July.
    15. Chudik, Alexander & Pesaran, M. Hashem, 2015. "Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors," Journal of Econometrics, Elsevier, vol. 188(2), pages 393-420.
    16. Sugiawan, Yogi & Managi, Shunsuke, 2016. "The environmental Kuznets curve in Indonesia: Exploring the potential of renewable energy," Energy Policy, Elsevier, vol. 98(C), pages 187-198.
    17. Alper, Aslan & Oguz, Ocal, 2016. "The role of renewable energy consumption in economic growth: Evidence from asymmetric causality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 953-959.
    18. Nguyen, Kim Hanh & Kakinaka, Makoto, 2019. "Renewable energy consumption, carbon emissions, and development stages: Some evidence from panel cointegration analysis," Renewable Energy, Elsevier, vol. 132(C), pages 1049-1057.
    19. M. Hashem Pesaran, 2006. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," Econometrica, Econometric Society, vol. 74(4), pages 967-1012, July.
    20. Edward F. Blackburne III & Mark W. Frank, 2007. "Estimation of nonstationary heterogeneous panels," Stata Journal, StataCorp LP, vol. 7(2), pages 197-208, June.
    21. Chen, Yulong & Zhao, Jincai & Lai, Zhizhu & Wang, Zheng & Xia, Haibin, 2019. "Exploring the effects of economic growth, and renewable and non-renewable energy consumption on China’s CO2 emissions: Evidence from a regional panel analysis," Renewable Energy, Elsevier, vol. 140(C), pages 341-353.
    22. Giacomo Grassi & Jo House & Frank Dentener & Sandro Federici & Michel den Elzen & Jim Penman, 2017. "The key role of forests in meeting climate targets requires science for credible mitigation," Nature Climate Change, Nature, vol. 7(3), pages 220-226, March.
    23. Zoundi, Zakaria, 2017. "CO2 emissions, renewable energy and the Environmental Kuznets Curve, a panel cointegration approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1067-1075.
    24. Inglesi-Lotz, Roula & Dogan, Eyup, 2018. "The role of renewable versus non-renewable energy to the level of CO2 emissions a panel analysis of sub- Saharan Africa’s Βig 10 electricity generators," Renewable Energy, Elsevier, vol. 123(C), pages 36-43.
    25. Joakim Westerlund, 2005. "New Simple Tests for Panel Cointegration," Econometric Reviews, Taylor & Francis Journals, vol. 24(3), pages 297-316.
    26. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    27. Badi H. Baltagi & Peter Egger & Michael Pfaffermayr, 2013. "A Generalized Spatial Panel Data Model with Random Effects," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 650-685, August.
    28. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    29. Perron, Pierre, 1997. "Further evidence on breaking trend functions in macroeconomic variables," Journal of Econometrics, Elsevier, vol. 80(2), pages 355-385, October.
    30. Hu, Hui & Xie, Nan & Fang, Debin & Zhang, Xiaoling, 2018. "The role of renewable energy consumption and commercial services trade in carbon dioxide reduction: Evidence from 25 developing countries," Applied Energy, Elsevier, vol. 211(C), pages 1229-1244.
    31. Xi Xie & Wenjia Cai & Yongkai Jiang & Weihua Zeng, 2015. "Carbon Footprints and Embodied Carbon Flows Analysis for China’s Eight Regions: A New Perspective for Mitigation Solutions," Sustainability, MDPI, vol. 7(8), pages 1-17, July.
    32. Jan Ditzen, 2018. "Estimating dynamic common-correlated effects in Stata," Stata Journal, StataCorp LP, vol. 18(3), pages 585-617, September.
    33. Sinha, Avik & Shahbaz, Muhammad, 2018. "Estimation of Environmental Kuznets Curve for CO2 emission: Role of renewable energy generation in India," Renewable Energy, Elsevier, vol. 119(C), pages 703-711.
    34. Damiaan Persyn & Joakim Westerlund, 2008. "Error-correction–based cointegration tests for panel data," Stata Journal, StataCorp LP, vol. 8(2), pages 232-241, June.
    35. Markus Eberhardt, 2012. "Estimating panel time-series models with heterogeneous slopes," Stata Journal, StataCorp LP, vol. 12(1), pages 61-71, March.
    36. Myung K. Shin & John M. Levorse & Robert S. Ingram & Shirley M. Tilghman, 1999. "The temporal requirement for endothelin receptor-B signalling during neural crest development," Nature, Nature, vol. 402(6761), pages 496-501, December.
    37. Sharif, Arshian & Raza, Syed Ali & Ozturk, Ilhan & Afshan, Sahar, 2019. "The dynamic relationship of renewable and nonrenewable energy consumption with carbon emission: A global study with the application of heterogeneous panel estimations," Renewable Energy, Elsevier, vol. 133(C), pages 685-691.
    38. Kim, Jeayoon & Park, Kwangwoo, 2016. "Financial development and deployment of renewable energy technologies," Energy Economics, Elsevier, vol. 59(C), pages 238-250.
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    2. Chau, Ka Yin & Sadiq, Muhammad & Chien, FengSheng, 2023. "The role of natural resources and eco-financing in producing renewable energy and carbon neutrality: Evidence from ten Asian countries," Resources Policy, Elsevier, vol. 85(PA).

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