Modeling the Dynamic Linkage between Renewable Energy Consumption, Globalization, and Environmental Degradation in South Korea: Does Technological Innovation Matter?
Abstract
:1. Introduction
2. Materials and Methods
3. Findings and Discussion
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- BP. 2019. Available online: https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy/co2-emissions.html (accessed on 29 May 2021).
- Adebayo, T.S.; Kirikkaleli, D. Impact of renewable energy consumption, globalization, and technological innovation on environmental degradation in Japan: Application of wavelet tools. Environ. Dev. Sustain. 2021, 1–26. [Google Scholar] [CrossRef]
- Adebayo, T.S.; Awosusi, A.A.; Kirikkaleli, D.; Akinsola, G.D.; Mwamba, M.N. Can CO2 emissions and energy consumption determine the economic performance of South Korea? A time series analysis. Environ. Sci. Pollut. Res. 2021, 1–16. [Google Scholar] [CrossRef]
- Adebayo, T.S.; Adedoyin, F.F.; Kirikkaleli, D. Toward a sustainable environment: Nexus between consumption-based carbon emissions, economic growth, renewable energy and technological innovation in Brazil. Environ. Sci. Pollut. Res. 2021, 1–11. [Google Scholar] [CrossRef]
- Ahmed, Z.; Wang, Z.; Mahmood, F.; Hafeez, M.; Ali, N. Does globalization increase the ecological footprint? Empirical evidence from Malaysia. Environ. Sci. Pollut. Res. 2019, 26, 18565–18582. [Google Scholar] [CrossRef] [PubMed]
- Jin, L.; Duan, K.; Shi, C.; Ju, X. The Impact of Technological Progress in the Energy Sector on Carbon Emissions: An Empirical Analysis from China. Int. J. Environ. Res. Public Health 2017, 14, 1505. [Google Scholar] [CrossRef] [Green Version]
- Ahmad, M.; Khan, Z.; Rahman, Z.U.; Khattak, S.I.; Khan, Z.U. Can innovation shocks determine CO2 emissions (CO2e) in the OECD economies? A new perspective. Econ. Innov. New Technol. 2021, 30, 89–109. [Google Scholar] [CrossRef]
- Su, H.-N.; Moaniba, I.M. Does innovation respond to climate change? Empirical evidence from patents and greenhouse gas emissions. Technol. Forecast. Soc. Chang. 2017, 122, 49–62. [Google Scholar] [CrossRef]
- Dauda, L.; Long, X.; Mensah, C.N.; Salman, M.; Boamah, K.B.; Ampon-Wireko, S.; Dogbe, C.S.K. Innovation, trade openness and CO2 emissions in selected countries in Africa. J. Clean. Prod. 2021, 281, 125143. [Google Scholar] [CrossRef]
- Shahbaz, M.; Raghutla, C.; Song, M.; Zameer, H.; Jiao, Z. Public-private partnerships investment in energy as new determinant of CO2 emissions: The role of technological innovations in China. Energy Econ. 2020, 86, 104664. [Google Scholar] [CrossRef] [Green Version]
- Ali, W.; Abdullah, A.; Azam, M. The dynamic linkage between technological innovation and carbon dioxide emissions in Malaysia: An autoregressive distributed lagged bound approach. Int. J. Energy Econ. Policy 2016, 6, 389–400. [Google Scholar]
- Shaari, M.S.; Abdullah DN, C.; Alias, N.S.; Adnan NS, M. Positive and negative effects of research and development. Int. J. Energy Econ. Policy 2016, 6, 767–770. [Google Scholar]
- Garrone, P.; Grilli, L. Is there a relationship between public expenditures in energy R&D and carbon emissions per GDP? An empirical investigation. Energy Policy 2010, 38, 5600–5613. [Google Scholar] [CrossRef]
- Kalayci, C. The impact of economic globalization on CO2 emissions: The case of NAFTA countries. Int. J. Energy Econ. Policy 2019, 9, 356. [Google Scholar]
- Rahman, M.M. Environmental degradation: The role of electricity consumption, economic growth and globalisation. J. Environ. Manag. 2020, 253, 109742. [Google Scholar] [CrossRef]
- Shahbaz, M.; Shahzad, S.J.H.; Mahalik, M.K.; Sadorsky, P. How strong is the causal relationship between globalization and energy consumption in developed economies? A country-specific time-series and panel analysis. Appl. Econ. 2018, 50, 1479–1494. [Google Scholar] [CrossRef] [Green Version]
- Haseeb, A.; Xia, E.; Danish; Baloch, M.A.; Abbas, K. Financial development, globalization, and CO2 emission in the presence of EKC: Evidence from BRICS countries. Environ. Sci. Pollut. Res. 2018, 25, 31283–31296. [Google Scholar] [CrossRef] [PubMed]
- Zaidi, S.A.H.; Zafar, M.W.; Shahbaz, M.; Hou, F. Dynamic linkages between globalization, financial development and carbon emissions: Evidence from Asia Pacific Economic Cooperation countries. J. Clean. Prod. 2019, 228, 533–543. [Google Scholar] [CrossRef]
- World Bank. World Development Indicators. Washington. 2020. Available online: http://data.worldbank.org/data-catalog/worlddevelopment- (accessed on 15 May 2021).
- BP. 2021. Available online: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2020-full-report.pdf (accessed on 15 May 2021).
- Adebayo, T.S. Revisiting the EKC hypothesis in an emerging market: An application of ARDL-based bounds and wavelet coherence approaches. SN Appl. Sci. 2020, 2, 1–15. [Google Scholar] [CrossRef]
- Olanrewaju, V.O.; Adebayo, T.S.; Akinsola, G.D.; Odugbesan, J.A. Determinants of Environmental Degradation in Thailand: Empirical Evidence from ARDL and Wavelet Coherence Approaches. Pollution 2021, 7, 181–196. [Google Scholar]
- Adebayo, T.S.; Akinsola, G.D.; Kirikkaleli, D.; Bekun, F.V.; Umarbeyli, S.; Osemeahon, O.S. Economic performance of Indonesia amidst CO2 emissions and agriculture: A time series analysis. Environ. Sci. Pollut. Res. 2021, 1–15. [Google Scholar] [CrossRef]
- He, X.; Adebayo, T.S.; Kirikkaleli, D.; Umar, M. Consumption-based carbon emissions in Mexico: An analysis using the dual adjustment approach. Sustain. Prod. Consum. 2021, 27, 947–957. [Google Scholar] [CrossRef]
- Orhan, A.; Adebayo, T.; Genç, S.; Kirikkaleli, D. Investigating the Linkage between Economic Growth and Environmental Sustainability in India: Do Agriculture and Trade Openness Matter? Sustainability 2021, 13, 4753. [Google Scholar] [CrossRef]
- Soylu, Ö.; Adebayo, T.; Kirikkaleli, D. The Imperativeness of Environmental Quality in China Amidst Renewable Energy Consumption and Trade Openness. Sustainability 2021, 13, 5054. [Google Scholar] [CrossRef]
- Oluwajana, D.; Adebayo, T.S.; Kirikkaleli, D.; Adeshola, I.; Akinsola, G.D.; Osemeahon, O.S. Coal Consumption and Environmental Sustainability in South Africa: The role of Financial Development and Globalization. Int. J. Renew. Energy Dev. 2021, 10. [Google Scholar] [CrossRef]
- Muhammad, B.; Khan, S. Understanding the relationship between natural resources, renewable energy consumption, economic factors, globalization and CO2 emissions in developed and developing countries. In Natural Resources Forum; Blackwell Publishing Ltd.: Oxford, UK, 2021; Volume 45, pp. 138–156. [Google Scholar]
- Pata, U.K. Linking renewable energy, globalization, agriculture, CO2 emissions and ecological footprint in BRIC countries: A sustainability perspective. Renew. Energy 2021, 173, 197–208. [Google Scholar] [CrossRef]
- Kirikkaleli, D.; Adebayo, T.S. Do renewable energy consumption and financial development matter for environmental sustainability? New global evidence. Sustain. Dev. 2020. [Google Scholar] [CrossRef]
- Altinoz, B.; Dogan, E. How renewable energy consumption and natural resource abundance impact environmental degradation? New findings and policy implications from quantile approach. Energy Sources Part B Econ. Plan. Policy 2021, 16, 345–356. [Google Scholar] [CrossRef]
- Pata, U.K. Renewable and non-renewable energy consumption, economic complexity, CO2 emissions, and ecological footprint in the USA: Testing the EKC hypothesis with a structural break. Environ. Sci. Pollut. Res. 2021, 28, 846–861. [Google Scholar] [CrossRef] [PubMed]
- Mohsin, M.; Kamran, H.W.; Nawaz, M.A.; Hussain, M.S.; Dahri, A.S. Assessing the impact of transition from nonrenewable to renewable energy consumption on economic growth-environmental nexus from developing Asian economies. J. Environ. Manag. 2021, 284, 111999. [Google Scholar] [CrossRef]
- Cheng, Y.; Awan, U.; Ahmad, S.; Tan, Z. How do technological innovation and fiscal decentralization affect the environment? A story of the fourth industrial revolution and sustainable growth. Technol. Forecast. Soc. Chang. 2021, 162, 120398. [Google Scholar] [CrossRef]
- Adebayo, T.S.; Udemba, E.N.; Ahmed, Z.; Kirikkaleli, D. Determinants of consumption-based carbon emissions in Chile: An application of non-linear ARDL. Environ. Sci. Pollut. Res. 2021, 2021, 1–15. [Google Scholar]
- Chien, F.; Ajaz, T.; Andlib, Z.; Chau, K.Y.; Ahmad, P.; Sharif, A. The role of technology innovation, renewable energy and globalization in reducing environmental degradation in Pakistan: A step towards sustainable environment. Renew. Energy 2021, 177, 308–317. [Google Scholar] [CrossRef]
- Alola, A.A.; Bekun, F.V.; Sarkodie, S.A. Dynamic impact of trade policy, economic growth, fertility rate, renewable and non-renewable energy consumption on ecological footprint in Europe. Sci. Total Environ. 2019, 685, 702–709. [Google Scholar] [CrossRef]
- Sarkodie, S.A.; Adams, S.; Owusu, P.A.; Leirvik, T.; Ozturk, I. Mitigating degradation and emissions in China: The role of environmental sustainability, human capital and renewable energy. Sci. Total Environ. 2020, 719, 137530. [Google Scholar] [CrossRef]
- Rjoub, H.; Odugbesan, J.A.; Adebayo, T.S.; Wong, W.-K. Sustainability of the Moderating Role of Financial Development in the Determinants of Environmental Degradation: Evidence from Turkey. Sustainability 2021, 13, 1844. [Google Scholar] [CrossRef]
- Kirikkaleli, D.; Adebayo, T.S. Do public-private partnerships in energy and renewable energy consumption matter for consumption-based carbon dioxide emissions in India? Environ. Sci. Pollut. Res. 2021, 28, 30139–30152. [Google Scholar] [CrossRef] [PubMed]
- Kirikkaleli, D.; Adebayo, T.S.; Khan, Z.; Ali, S. Does globalization matter for ecological footprint in Turkey? Evidence from dual adjustment approach. Environ. Sci. Pollut. Res. 2021, 28, 14009–14017. [Google Scholar] [CrossRef]
- Khan, A.; Chenggang, Y.; Hussain, J.; Kui, Z. Impact of technological innovation, financial development and foreign direct investment on renewable energy, non-renewable energy and the environment in belt & Road Initiative countries. Renew. Energy 2021, 171, 479–491. [Google Scholar] [CrossRef]
- Pesaran, M.H.; Shin, Y.; Smithc, R.J. Bounds testing approaches to the analysis of level relationships. J. Appl. Econ. 2001, 16, 289–326. [Google Scholar] [CrossRef]
- Breitung, J.; Candelon, B. Testing for short- and long-run causality: A frequency-domain approach. J. Econ. 2006, 132, 363–378. [Google Scholar] [CrossRef]
- Tufail, M.; Song, L.; Adebayo, T.S.; Kirikkaleli, D.; Khan, S. Do fiscal decentralization and natural resources rent curb carbon emissions? Evidence from developed countries. Environ. Sci. Pollut. Res. 2021, 1–12. [Google Scholar] [CrossRef]
- Zhang, L.; Li, Z.; Kirikkaleli, D.; Adebayo, T.S.; Adeshola, I.; Akinsola, G.D. Modeling CO2 emissions in Malaysia: An application of Maki cointegration and wavelet coherence tests. Environ. Sci. Pollut. Res. 2021, 28, 26030–26044. [Google Scholar] [CrossRef] [PubMed]
- Shan, S.; Ahmad, M.; Tan, Z.; Adebayo, T.S.; Li, R.Y.M.; Kirikkaleli, D. The role of energy prices and non-linear fiscal decentralization in limiting carbon emissions: Tracking environmental sustainability. Energy 2021, 234, 121243. [Google Scholar] [CrossRef]
Authors | Period | Country(s) | Technique(s) Used | Findings |
---|---|---|---|---|
Impact of Energy Use and Economic Growth on CO2 Emissions | ||||
Adebayo [21] | 1970–2016 | Mexico | ARDL, Granger Causality | GDP→CO2 (+) GDP→CO2 EC→CO2 (+) EC→CO2 |
Awosusi et al. [3] | 1965–2019 | South Korea | ARDL, Wavelet Tools, Granger Causality | GDP→CO2 (+) GDP→CO2 |
Olanrewaju et al. [22] | 1970–2016 | Thailand | ARDL, Granger Causality | GDP→CO2 (+) GDP→CO2 EC→CO2 (+) EC→CO2 |
Bekun et al. [23] | 1965–2019 | Indonesia | ARDL, Wavelet Tools | GDP→CO2 (+) GDP→CO2 |
He et al. [24] | 1990–2018 | Mexico | ARDL, Frequency Domain Causality | GDP→CO2 (+) GDP→CO2 EC→CO2 (+) EC→CO2 |
Orhan et al. [25] | 1970–2019 | India | Wavelet Coherence | GDP→CO2 |
Soylu et al. [26] | 1965–2019 | China | PWC, MWC, WC | GDP→CO2 (+) EC→CO2 (+) |
Impact of Globalization on CO2 Emissions | ||||
Zaidi et al. [18] | 1960–2016 | APEC Nations | CUP, Panel Causality | GLO→CO2 (−) GLO→CO2 |
Oluwajana et al. [27] | 1980–2018 | South Africa | ARDL, Frequency Domain Causality | GLO→CO2 (+) GLO→CO2 |
Haseeb et al. [17] | 1994–2014 | BRICS nations | Panel DOLS, FMOLS | GLO→CO2 (−) GLO↔CO2 |
Muhammad and Khan [28] | 1991–2018 | 155 emerging and advanced Nations | GMM | For Advanced nations GLO→CO2 (−) For Developing Nations GLO→CO2 (+) |
Pata [29] | 1971–2016 | Brazil and China | Fourier ADL cointegration, Granger Causality | GLO→CO2 |
Impact of Renewable Energy Consumption on CO2 Emissions | ||||
Kirikkaleli and Adebayo [30] | 1970–2018 | Global Economy | FMOLS, DOLS, BC Causality | REC→CO2 (−) REC→CO2 |
Altinoz and Dogan [31] | 1990–2014 | 82 countries | ARDL | REC→CO2 (−) REC→CO2 |
Pata [32] | 1991–2018 | BRICS and developing countries | GMM | REC→CO2 (−) |
Mohsin et al. [33] | 2000–2016 | 25 developing Asian countries | Panel FMOLS | REC→CO2 (−) |
Impact of Technological Innovation on CO2 Emissions | ||||
Adebayo and Kirikkaleli [2] | 1990Q1–2015Q4 | Japan | Wavelet Tools | TI→CO2 (−) TI→CO2 |
Cheng et al. [34] | 2005Q1–2018Q4 | China | ARDL | TI→CO2 (−) |
Ahmad et al. [35] | 1990–2014 | OECD economies | Panel FMOLS | TI→CO2 (−) |
Dauda et al. [9] | 1990 to 2016 | Panel Techniques | TI≠CO2 |
ADF | PP | |||
---|---|---|---|---|
Variables | Level | First Difference | Level | First Difference |
CO2 | −1.0957 | −6.2803 * | −0.9183 | −6.7159 * |
GDP | −0.6664 | −6.4591 * | −0.3546 | −10.160 * |
REC | −2.0133 | −6.0307 * | −2.0133 | −20.106 * |
GLO | −1.0407 | −4.3327 * | −1.4199 | −4.2704 * |
EC | 0.0489 | −5.0848 * | −0.1406 | −5.0391 * |
TI | −1.1665 | −5.4308 * | 0.2867 | −8.0468 * |
Level | First Difference | |||
---|---|---|---|---|
Variables | T-Statistics | BD | T-Statistics | BD |
CO2 | −5.1778 ** | 1998 | −7.388 * | 1998 |
GDP | −4.879 | 2006 | −7.046 * | 1998 |
REC | −5.6645 | 2006 | −6.054 * | 1994 |
GLO | −3.0211 | 1993 | −6.125 * | 1988 |
EC | −4.9090 | 1996 | −7.165 * | 1998 |
TI | −4.3905 | 1995 | −5.901 * | 1997 |
F-Statistics | 15.42411 * | |||||
---|---|---|---|---|---|---|
Cointegration | Yes | |||||
10% | 5% | 1% | ||||
F-statistics CV | 2.26 | 3.35 | 2.62 | 3.79 | 3.41 | 4.68 |
Long-Run Outcomes | Short-Run Outcomes | |||||||
---|---|---|---|---|---|---|---|---|
Variable | Coefficient | Std. Error | t-Statistic | Prob | Coefficient | Std. Error | t-Statistic | Prob |
EC | 1.098 * | 0.156 | 6.998 | 0.000 | 1.098 * | 0.120 | 9.132 | 0.000 |
GDP | 0.385 *** | 0.222 | 1.732 | 0.094 | 0.385 ** | 0.148 | 2.588 | 0.015 |
GLO | 1.606 * | 0.206 | 7.788 | 0.000 | 1.606 * | 0.147 | 10.90 | 0.000 |
REC | −0.007 | 0.010 | −0.724 | 0.475 | −1.008 | 0.173 | −1.000 | 0.325 |
TI | −0.081 ** | 0.033 | −2.414 | 0.022 | −0.036 | 0.027 | −1.323 | 0.196 |
ECT(−1) | −0.110 * | 0.010 | −10.47 | 0.000 | ||||
R2 | 0.98 | |||||||
Adj-R2 | 0.97 | |||||||
F-stat | 1161.5 | |||||||
Prob(F-stat) | 0.000 |
Tests | Value | Probability |
---|---|---|
χ2 ARCH | 0.7060 | 0.4065 |
χ2 RESET | 0.3518 | 0.7278 |
χ2 Normality | 1.1290 | 0.5686 |
χ2 LM | 1.2328 | 0.3086 |
Long-Term | Medium-Term | Short-Term | ||||
---|---|---|---|---|---|---|
Causality Path | wi = 0.01 | wi = 0.05 | wi = 1.00 | wi = 1.50 | wi = 2.00 | wi = 2.50 |
EC→CO2 | 5.369 *** | 5.374 *** | 1.4954 | 0.552 | 1.144 | 2.110 |
GDP→CO2 | 0.857 | 0.951 | 8.162 * | 5.442 *** | 6.981 ** | 9.895 * |
GLO→CO2 | 0.349 | 0.390 | 3.882 | 1.8755 | 4.576 *** | 6.964 ** |
TI→CO2 | 1.377 | 1.386 | 4.629 *** | 4.683 *** | 5.643 *** | 3.385 |
REC→CO2 | 0.757 | 0.557 | 0.386 | 0.177 | 0.133 | 0.135 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Adebayo, T.S.; Coelho, M.F.; Onbaşıoğlu, D.Ç.; Rjoub, H.; Mata, M.N.; Carvalho, P.V.; Rita, J.X.; Adeshola, I. Modeling the Dynamic Linkage between Renewable Energy Consumption, Globalization, and Environmental Degradation in South Korea: Does Technological Innovation Matter? Energies 2021, 14, 4265. https://doi.org/10.3390/en14144265
Adebayo TS, Coelho MF, Onbaşıoğlu DÇ, Rjoub H, Mata MN, Carvalho PV, Rita JX, Adeshola I. Modeling the Dynamic Linkage between Renewable Energy Consumption, Globalization, and Environmental Degradation in South Korea: Does Technological Innovation Matter? Energies. 2021; 14(14):4265. https://doi.org/10.3390/en14144265
Chicago/Turabian StyleAdebayo, Tomiwa Sunday, Manuel Francisco Coelho, Dilber Çağlar Onbaşıoğlu, Husam Rjoub, Mário Nuno Mata, Paulo Viegas Carvalho, João Xavier Rita, and Ibrahim Adeshola. 2021. "Modeling the Dynamic Linkage between Renewable Energy Consumption, Globalization, and Environmental Degradation in South Korea: Does Technological Innovation Matter?" Energies 14, no. 14: 4265. https://doi.org/10.3390/en14144265
APA StyleAdebayo, T. S., Coelho, M. F., Onbaşıoğlu, D. Ç., Rjoub, H., Mata, M. N., Carvalho, P. V., Rita, J. X., & Adeshola, I. (2021). Modeling the Dynamic Linkage between Renewable Energy Consumption, Globalization, and Environmental Degradation in South Korea: Does Technological Innovation Matter? Energies, 14(14), 4265. https://doi.org/10.3390/en14144265