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Disaggregated renewable energy sources in mitigating CO2 emissions: new evidence from the USA using quantile regressions

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Abstract

A key objective of renewable energy development in the USA is to reduce CO2 emissions by decreasing reliance on fossil fuels in the coming decades. Using quantile-on-quantile regressions, this research examines the relationship between disaggregated sources of renewable energy (biomass, biofuel, geothermal, hydroelectric, solar, wind, wood, and waste) and CO2 emissions in the USA during the period from 1995 to 2017. Our findings support the deployment of various types of renewables in combating CO2 emissions for each quantile. In particular, a negative effect of renewable energy consumption on CO2 emissions is observed for the lower quantiles in almost all types of renewables. The effect of all the renewable energy sources taken together is significant for the lower and upper quantiles of the provisional distribution of CO2 emissions. The effect of renewable energy becomes stronger and more significant in the middle quantiles, where a pronounced causal effect of return and volatility is detected for the lower and upper middle quantiles. At the same time, heterogeneity in the findings across various types of renewable energy sources reveals differences in the relative importance of each type within the energy sector taken as a whole. Future US initiatives in renewable energy deployment at both the federal and the state levels should take into consideration the relative importance of each type, so as to maximize the efficacy of renewable energy policies in combating CO2 emissions.

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Notes

  1. https://www.eia.gov/energyexplained/renewable-sources/

  2. https://www.nrel.gov/analysis/re-futures.html.

  3. The maximum number of available time-period monthly series is captured for all variables.

  4. Koenker and Xiao (2004) developed the quantile autoregressive unit root test.

  5. The local linear regression model developed by Cleveland (1979) and Stone (1977) avoids the “curse of dimensionality” associated with non-parametric models.

  6. A different set of values for the bandwidth has also been selected in order to check the robustness of the outcome. Even after this robustness check, the outcome of the calculation remains qualitatively the same.

  7. Detailed findings from the BDS test for nonlinearity for other types of renewable energy consumption are available from the authors upon request.

  8. It should be noted, however, that these prior studies are predominantly based on calculations involving total renewable energy consumption.

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Authors and Affiliations

  1. Othman Yeop Abdullah Graduate School of Business, Universiti Utara Malaysia, Sintok, Kedah, Malaysia

    Arshian Sharif

  2. Department of Business Administration, ILMA University, Karachi, Pakistan

    Arshian Sharif

  3. Department of Economics, Monash Business School, Caulfield East, 3145, Australia

    Mita Bhattacharya

  4. Department of Management Sciences, School of Economics Finance and Banking, College of Business, Universiti Utara Malaysia, Eman Institute of Management and Sciences, Karachi, Pakistan

    Sahar Afshan

  5. Beijing Institute of Technology, Beijing, China

    Muhammad Shahbaz

Authors
  1. Arshian Sharif
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  2. Mita Bhattacharya
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  3. Sahar Afshan
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  4. Muhammad Shahbaz
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Contributions

Equal. Arshian Sharif was responsible for conceptualization, data collection, estimation, and writing up the manuscript. Mita Bhattacharya was responsible for conceptualization and writing drafts at various stages. Sahar Afshan collected data. Muhammad Shahbaz contributed in the writing up stage.

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Correspondence to Mita Bhattacharya.

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Sharif, A., Bhattacharya, M., Afshan, S. et al. Disaggregated renewable energy sources in mitigating CO2 emissions: new evidence from the USA using quantile regressions. Environ Sci Pollut Res 28, 57582–57601 (2021). https://doi.org/10.1007/s11356-021-13829-2

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