The Nexus among Energy Intensity, Energy Mix and Economic Performance in Europe: A Decomposition Analysis
DOI:
https://doi.org/10.52131/joe.2022.0403.0093Keywords:
Energy intensity, Energy mix, Sectoral energy consumption, Economic performance, Dynamic panel data, European countriesAbstract
Using a versatile piecewise linear regression model, this study revisits the connection between energy intensity, energy mix, macroeconomic variables, and economic performance. The analysis determines a threshold effect of income growth on changes in energy intensity based on dynamic panel data for a collection of European economies from 1990 to 2020. The study builds on the three specifications: The Skelton model (M1), the human capital model (M2), and the policy model (M3). The Skelton model is a fundamental model that explains the energy economic relationship with energy use in many economic sectors and with various energies. Additionally, in M2, achieve basic and secondary education as human capital, investment, and commerce as models for policies (M3). Numerous diagnostic procedures are carried out to examine serial correlation, heteroscacity, and cross-sectional dependence. In order to evaluate the robustness of the results, the specification of the dynamic panel is put through the Generalized Method of Movement (GMM) regression framework. Although energy intensity and income growth are negatively correlated for the entire sample and study period, the rate of decline significantly slows after the level of per capita income reaches $5,000, by more than 30%. According to the analysis's findings based on index decomposition, structural change is crucial for intensity levels across all nations. Additionally, moving from fossil fuels to renewable energy sources has a positive correlation with economic success in terms of energy intensity. While increasing energy intensity spurs global growth because of the current state of environmentally friendly technological innovation.
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Copyright (c) 2022 Fatima Gulzar, Fatima Farooq
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.