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Energy regulation, energy innovation, and carbon intensity nexus in China: A nonlinear perspective
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Considering the role of energy regulations and innovations in reducing energy-driven emissions, our main objective is to scrutinize the influence of energy regulations and innovations on carbon intensity in China over the period 1991–2020. In contrast to the previous studies, the analysis also focuses on the asymmetry assumption. The scientific value added to this study lies in its innovative methods and empirical evidence. The autoregressive distributed lag model, both linear and nonlinear, is utilized in the research to examine the short- and long-run estimates. The linear model highlights that energy regulations, energy innovations, and human capital reduce carbon intensity in the long run, while per capita income and financial development escalate it. However, only energy regulations and human capital help reduce carbon intensity in the short term. In the nonlinear models, the positive shock in environmental regulations reduces carbon intensity in short and long run. Conversely, the positive shock in energy innovations reduces carbon intensity in the long run. Negative shocks in energy regulations and innovations have a statistically insignificant impact on carbon intensity in both the short and long term. In addition, per capita income and financial development only increase carbon intensity in the long run, while human capital reduces carbon intensity in the short run. These findings advocate for implementing heavy energy taxes on firms and industries that rely heavily on fossil fuels. Increasing investment in green energy innovations is the need of the hour in lowering carbon intensity in China.
Title: Energy regulation, energy innovation, and carbon intensity nexus in China: A nonlinear perspective
Description:
Considering the role of energy regulations and innovations in reducing energy-driven emissions, our main objective is to scrutinize the influence of energy regulations and innovations on carbon intensity in China over the period 1991–2020.
In contrast to the previous studies, the analysis also focuses on the asymmetry assumption.
The scientific value added to this study lies in its innovative methods and empirical evidence.
The autoregressive distributed lag model, both linear and nonlinear, is utilized in the research to examine the short- and long-run estimates.
The linear model highlights that energy regulations, energy innovations, and human capital reduce carbon intensity in the long run, while per capita income and financial development escalate it.
However, only energy regulations and human capital help reduce carbon intensity in the short term.
In the nonlinear models, the positive shock in environmental regulations reduces carbon intensity in short and long run.
Conversely, the positive shock in energy innovations reduces carbon intensity in the long run.
Negative shocks in energy regulations and innovations have a statistically insignificant impact on carbon intensity in both the short and long term.
In addition, per capita income and financial development only increase carbon intensity in the long run, while human capital reduces carbon intensity in the short run.
These findings advocate for implementing heavy energy taxes on firms and industries that rely heavily on fossil fuels.
Increasing investment in green energy innovations is the need of the hour in lowering carbon intensity in China.
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