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Analyzing the co-movement between CO 2 emissions and disaggregated nonrenewable and renewable energy consumption in BRICS: evidence through the lens of wavelet coherence.

Tomiwa Sunday AdebayoMehmet AğaMustafa Tevfik Kartal
Published in: Environmental science and pollution research international (2023)
This study investigates the time-frequency nexus of carbon dioxide (CO 2 ) emissions with economic growth, nonrenewable (i.e., coal, natural gas, and oil), and renewable (i.e., hydro and geothermal) energy consumption. In this context, BRICS countries (namely, Brazil, Russian Federation, India, China, and South Africa), which are leading emerging countries, are included, and quarterly data from 1990/Q1 to 2019/Q4 is used. The study employs the wavelet coherence (WC) approach to explore the co-movement between the variables at different frequencies. The empirical results show that (i) there is a strong and positive co-movement between CO 2 emission and economic growth; however, it is weak for Russia and South Africa in the medium and long-term; (ii) coal energy consumption is strongly and positively co-moved with CO 2 emission for all BRICS countries; (iii) natural gas energy consumption is strongly and positively co-moved with CO 2 emissions in Brazil, India, and China; however, it is weakly and positively co-moved in Russia and South Africa; (iv) oil energy consumption is strongly and positively co-moved with CO 2 emissions in Brazil, India, and China; however, it changes a bit for Russia and South Africa; (v) hydro energy consumption is weakly and positively co-moved with CO 2 emissions in general, whereas country-based results vary; (vi) geothermal energy consumption is also similar to hydro energy consumption. Thus, the WC results highlight the strong co-movement of economic growth and nonrenewable energy consumption with CO 2 emissions, whereas renewable energy consumption has a relatively lower co-movement. Based on the results, policy implications are also discussed for BRICS countries.
Keyphrases
  • south africa
  • carbon dioxide
  • healthcare
  • life cycle
  • hiv positive
  • public health
  • heavy metals
  • particulate matter
  • machine learning
  • fatty acid