The influence of renewable energy and financial development on testing the environmental Kuznets curve in Lebanon: ARDL approach

  • 64 Views
  • 11 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

This study considers the impacts of financial development and the consumption of renewable energy in Lebanon for the period 1990–2021, employing the Environmental Kuznets Curve. The financial sector in Lebanon is considered a major engine in the economic development. Green energy sources and environmental protection are taking higher importance nowadays with the increase of implications for climate change and global warming worldwide. This paper examines the Environmental Kuznets Curve’s presence and implications for Lebanon’s financial development and renewable energy consumption. The econometric model used annual data from the World Development Indicators. Utilizing the autoregressive distributed lag (ARDL) technique, both near- and long-term relationships were estimated. The findings support the Environmental Kuznets Curve hypothesis and show that energy consumption and real income have a statistically significant beneficial effect on carbon emissions and that their square has a statistically significant negative impact on carbon emissions over the long and short term. The results show variations in signs for financial development between the short and long term and stable results for renewable energy with negative signs in both terms. These results show the importance of further research on the influence of financial development and green energy consumption on EKC. Therefore, policymakers need to pay more attention to these variables for a sustainable economy that is facing the effects of climate change.

view full abstract hide full abstract
    • Figure 1. CO2 emissions variation
    • Figure 2. Variation of GDP, FD Index, renewal energy consumption, and trade openness in Lebanon over time
    • Figure 3. CUSUM test
    • Table 1. Unit root tests
    • Table 2. Optimal lags
    • Table 3. Bounds test
    • Table 4. ARDL long- and short-run results
    • Table 5. Breusch-Godfrey LM test for autocorrelation
    • Table 6. Heteroscedasticity white test
    • Table 7. Shapiro-Wilk W test for normal data
    • Table 8. “Cumulative sum test for parameter stability
    • Conceptualization
      Hanadi Taher
    • Formal Analysis
      Hanadi Taher
    • Funding acquisition
      Hanadi Taher
    • Methodology
      Hanadi Taher
    • Resources
      Hanadi Taher
    • Software
      Hanadi Taher
    • Validation
      Hanadi Taher
    • Writing – original draft
      Hanadi Taher
    • Writing – review & editing
      Hanadi Taher