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Eco-innovation and energy productivity: New determinants of renewable energy consumption

pmid: 32778308
This study provides new empirical evidence on the determinants of renewable energy consumption in the case of OECD economies over the period from 1990 to 2017. To examine the long run relationship among variables of renewable energy consumption and its determinants, this study uses the Durbin Hausman group mean cointegration test. The long-run and short-run coefficients are estimated via the cross-sectional Autoregressive Distributive Lag (CS-ARDL) method. The significant cointegration vector confirms the long-run equilibrium among the variables presented in the model. The results show that income, human capital, energy productivity, energy prices, and eco-innovation are important factors in explaining renewable energy consumption. This study adopts the Augmented Mean Group (AMG) method to check the robustness of the model. The results are found to be consistent with the estimates of the cross-sectional Autoregressive Distributive Lag Model method. To offer viable solutions to environmental problems and to achieve the targets set in the Paris Climate Agreement, policies and strategies should be devised to increase the share of renewable energy in the overall energy mix.
- Sichuan University China (People's Republic of)
- Sichuan University China (People's Republic of)
- Tsinghua University China (People's Republic of)
- University of Swat Pakistan
- University of Swat Pakistan
Carbon Dioxide, Cross-Sectional Studies, Income, Humans, Economic Development, Renewable Energy
Carbon Dioxide, Cross-Sectional Studies, Income, Humans, Economic Development, Renewable Energy
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).216 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 0.1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 0.1%
