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The analysis of global renewable energy diffusion using policy diffusion theory
Least Squares Dummy Variable Model
Geographically Weighted Regression
Geographic Information System
|Issue Date:||2021-07-01 22:13:57 (UTC+8)|
This study used a theoretical framework of policy diffusion to explore the influential factors to global renewable energy diffusion at the country level. The policy diffusion framework in this research consisted of four diffusion approaches: learning, competition, coercion, and citizen pressure. Meanwhile, some internal factors were considered, including economic status, carbon intensity, and political stability. The data covered 168 countries within a period of eight years (2009 to 2016). The analysis could be summarized into two segments. Firstly, the eight-year panel data was analyzed using Least Squares Dummy Variable Model (LSDV) for examining the correlations in the long term. The second analysis used Geographically Weighted Regression (GWR) to reveal the spatial distribution of local coefficients of each variable. The findings indicated that the learning approach, the competition approach, and the coercion approach positively correlated with global renewable energy diffusion, but not for the citizen pressure approach. Besides, economic status was positively correlated, and carbon intensity was negatively associated with national renewable energy adoption. In GWR analysis, the local coefficients of each variable were spatially clustered among regions. Therefore, it could be inferred that the spatial disparity of the strength of policy diffusion approaches and internal factors existed.
|Reference:||1. United Nations Framework Convention on Climate Change. The Paris Agreement. 2021 [cited 2021 January 27th ]; Available from: https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement.|
2. BP p.l.c., Statistical review of world energy 2020 | 69th edition. 2020. p. 8.
3. Brown, M.A., et al., Forty years of energy security trends: A comparative assessment of 22 industrialized countries. Energy Research & Social Science, 2014. 4: p. 64-77.
4. Shahzad, U., The need for renewable energy sources. Energy, 2012. 2: p. 16-18.
5. Natural Resources Defense Council. Renewable energy: The clean facts. 2018 [cited 2021 February 24th]; Available from: https://www.nrdc.org/stories/renewable-energy-clean-facts.
6. International Renewable Energy Agency, Global renewables outlook: Energy transformation 2050. 2020. p. 58.
7. U.S. Energy Information Administration. EIA World Electricity Generation Dataset. 2020 [cited 2020 Oct 7th]; Available from: https://www.eia.gov/international/data/world.
8. Carbon Tax Center. Where carbon is taxed. 2020 [cited 2021 January 27th ]; Available from: https://www.carbontax.org/.
9. United Nations Framework Convention on Climate Change. The Kyoto Protocol. 2021 [cited 2021 January 27th ]; Available from: https://unfccc.int/kyoto_protocol.
10. Carbon Disclosure Project. CDP disclosure insight action. 2021 [cited 2021 January 27th ]; Available from: https://www.cdp.net/en.
11. International Renewable Energy Agency. About IRENA. 2020 [cited 2021 January 27th ]; Available from: https://www.irena.org/aboutirena.
12. RE100. About us. 2021 [cited 2021 January 27th ]; Available from: https://www.there100.org/about-us.
13. Taiwan Semiconductor Manufacturing. TSMC becomes the world’s first semiconductor company to join RE100, committed to 100% renewable energy usage. 2020 [cited 2021 March 7th]; Available from: https://csr.tsmc.com/csr/en/update/greenManufacturing/caseStudy/37/index.html.
14. Ørsted. Ørsted and TSMC sign the world’s largest renewables corporate power purchase agreement. 2020 [cited 2021 March 7th]; Available from: https://orsted.tw/en/news/2020/07/orsted-tsmc-cppa.
15. Executive Yuan Taiwan R.O.C. Executive Yuan press releases. 2016 [cited 2021 February 24th]; Available from: https://www.ey.gov.tw/Page/6485009ABEC1CB9C.
16. Bureau of Energy Ministry of Economic Affairs Taiwan R.O.C., Energy White Paper. 2020, Ministry of Economic Affairs Taiwan R.O.C.,.
17. Executive Yuan Taiwan R.O.C. Major policy. 2019 [cited 2021 February 24th]; Available from: https://www.ey.gov.tw/Page/2124AB8A95F79A75.
18. World Meteorological Organization (WMO), United in Science 2020. 2021.
19. The Intergovernmental Panel on Climate Change (IPCC), IPCC Report: Global warming of 1.5°C. 2018.
20. The New Climate Economy, The global commission on the economy and climate. 2018.
21. Newmark, A.J., An integrated approach to policy transfer and diffusion. Review of Policy Research, 2002. 19(2): p. 151-178.
22. Stone, J.A. and E.M. Madigan, Policy diffusion and municipal wireless initiatives. Perspectives in Public Affairs, 2009. 6: p. 25.
23. Lee, C.P., Factors affecting global E-governance development: An application of policy diffusion theory. Journal of Public Administration, 2010(36): p. 39-89.
24. Lee, C.P., K. Chang, and F.S. Berry, Testing the development and diffusion of e‐government and e‐democracy: A global perspective. Public Administration Review, 2011. 71(3): p. 444-454.
25. Weyland, K., Theories of policy diffusion: Lessons from Latin American pension reform. World Politics, 2005: p. 262-295.
26. Hoberg, G., Sleeping with an elephant: The American influence on Canadian environmental regulation. Journal of Public Policy, 1991: p. 107-131.
27. Liao, H.C., The spatial analysis on the policy diffusion factors of the Bookstart Program in Taiwan. Journal of Democracy and Governance, 2020. 7(1): p. 89-116.
28. Allard, S.W., Competitive pressures and the emergence of mothers’ aid programs in the United States. Policy Studies Journal, 2004. 32(4): p. 521-544.
29. Berry, F.S. and W.D. Berry, State lottery adoptions as policy innovations: An event history analysis. The American Political Science Review, 1990: p. 395-415.
30. Dobbin, F., B. Simmons, and G. Garrett, The global diffusion of public policies: Social construction, coercion, competition, or learning? Annual Review Sociology, 2007. 33: p. 449-472.
31. Berry, F.S. and W.D. Berry, Theories of the policy process: Innovation and diffusion models in policy research. 2nd ed. 2007. 223-260.
32. Alves, E.E.C., et al., From a breeze to the four winds: A panel analysis of the international diffusion of renewable energy incentive policies (2005–2015). Energy Policy, 2019. 125: p. 317-329.
33. Zhou, S., et al., Understanding renewable energy policy adoption and evolution in Europe: The impact of coercion, normative emulation, competition, and learning. Energy Research & Social Science, 2019. 51: p. 1-11.
34. Rogers, E.M., Diffusion of innovations. 5th ed. 2003: Simon and Schuster New York.
35. Pfeiffer, B. and P. Mulder, Explaining the diffusion of renewable energy technology in developing countries. Energy Economics, 2013. 40: p. 285-296.
36. Fadly, D. and F. Fontes, Geographical proximity and renewable energy diffusion: An empirical approach. Energy Policy, 2019. 129: p. 422-435.
37. Baldwin, E., S. Carley, and S. Nicholson-Crotty, Why do countries emulate each others’ policies? A global study of renewable energy policy diffusion. World Development, 2019. 120: p. 29-45.
38. Lin, B. and O.E. Omoju, Focusing on the right targets: Economic factors driving non-hydro renewable energy transition. Renewable Energy, 2017. 113: p. 52-63.
39. United Nations Statistics Division. Methodology-Standard country or area codes for statistical use (M49). 2021 [cited 2021 January 3rd]; Available from: https://unstats.un.org/unsd/methodology/m49/.
40. Zhou, Y., et al., The impacts of carbon tariff on green supply chain design. IEEE Transactions on Automation Science and Engineering, 2015. 14(3): p. 1542-1555.
41. Drake, D.F., Carbon tariffs: Effects in settings with technology choice and foreign production cost advantage. Manufacturing & Service Operations Management, 2018. 20(4): p. 667-686.
42. Larch, M. and J. Wanner, Carbon tariffs: An analysis of the trade, welfare, and emission effects. Journal of International Economics, 2017. 109: p. 195-213.
43. Böhringer, C., J.C. Carbone, and T.F. Rutherford, Embodied carbon tariffs. The Scandinavian Journal of Economics, 2018. 120(1): p. 183-210.
44. European Commission, The European Green Deal. 2019.
45. World Bank. Worldwide Governance Indicators. 2019 [cited 2021 January 27th]; Available from: http://info.worldbank.org/governance/wgi/Home/Documents.
46. del Río González, P., The empirical analysis of the determinants for environmental technological change: A research agenda. Ecological Economics, 2009. 68(3): p. 861-878.
47. Aguirre, M. and G. Ibikunle, Determinants of renewable energy growth: A global sample analysis. Energy Policy, 2014. 69: p. 374-384.
48. Marques, A.C., J.A. Fuinhas, and J.P. Manso, Motivations driving renewable energy in European countries: A panel data approach. Energy Policy, 2010. 38(11): p. 6877-6885.
49. Yaseen, E.B., Renewable energy applications in Palestine. Palestinian Energy and Environment Research Center (PEC), 2009.
50. Radu, M., Political stability-a condition for sustainable growth in Romania? Procedia Economics and Finance, 2015. 30: p. 751-757.
51. Komendantova, N., et al., Perception of risks in renewable energy projects: The case of concentrated solar power in North Africa. Energy policy, 2012. 40: p. 103-109.
52. Milio, S., How political stability shapes administrative performance: The Italian case. West European Politics, 2008. 31(5): p. 915-936.
53. Shah, S.Z., S. Chughtai, and B. Simonetti, Renewable energy, institutional stability, environment and economic growth nexus of D-8 countries. Energy Strategy Reviews, 2020. 29: p. 100484.
54. Nir, A.E. and B.S. Kafle, The effect of political stability on public education quality. International Journal of Educational Management, 2013.
55. Wang, D.M. and F.Y. Chan, Political business cycles in Taiwan local fiscal budget: an estimation and comparison between fixed effect and random effect models. Taiwan Political Science Review, 2006. 10(2): p. 63-100.
56. Park, H.M., Practical guides to panel data modeling: A step-by-step analysis using Stata. Public Management and Policy Analysis Program, Graduate School of International Relations, International University of Japan, 2011. 12: p. 1-52.
57. Fotheringham, A.S., C. Brunsdon, and M. Charlton, Geographically weighted regression: The analysis of spatially varying relationships. 2003: John Wiley & Sons.
58. Liao, H.C., The association between corruption and E-governance in the world: A pilot study on spatial heterogeneity. The Taiwanese Political Science Review, 2018. 22(1): p. 89-141.
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