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Renewable energy, technological innovation and the environment: A novel dynamic auto-regressive distributive lag simulation

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  • Danish,
  • Ulucak, Recep

Abstract

Promoting technology development co-operation is particularly relevant while addressing global climate change or regional pollution. Investors may contribute to increasing environmental-related patents, which may help to improve the quality of the environment as long as supported by proper regulations and policies. In the quest towards a cleaner environment via the mitigation of climate change, this study investigates the relationship between technological innovation, income level, renewable energy and carbon emissions in both the United States and China. Applying the dynamic auto-regressive distributive lag (DARDL) simulation method revealed varying impacts of regressors on CO2 emissions in the short-run and long-run. Accordingly, this study empirically confirms that (i) technological innovation reduces carbon emissions in the United States but it is not statistically significant in China. (ii) Renewable energy reduces carbon emissions in both countries. (iii) The impact of income level on emissions decreases from short-run to long-run in both countries. Decision-makers in China should stimulate more effective environmental-related innovations to decrease CO2 emissions and should permit easy access to environmental-related patents together with reducing its cost of registration.

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  • Danish, & Ulucak, Recep, 2021. "Renewable energy, technological innovation and the environment: A novel dynamic auto-regressive distributive lag simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:rensus:v:150:y:2021:i:c:s1364032121007164
    DOI: 10.1016/j.rser.2021.111433
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