Impact of renewable energy investment on carbon emissions in China-An empirical study using a nonparametric additive regression model

This study analyzed the comprehensive impact of renewable energy investment on carbon emissions in China. To achieve this, a nonparametric additive regression model was built. Using the STIRPAT model, we considered six influencing factors: economic growth, industrialization level, urbanization level, population aging, trade openness, and renewable energy investment. This enabled the exploration of the existence, direction, and intensity of the impact of renewable energy investment on carbon emissions. The results of the linear component of the model showed that renewable energy investment can slightly reduce carbon emissions. The results of the nonlinear component of the model showed that the impacts of renewable energy investment on carbon emissions were inconsistent at different stages of the investment. In the early stage, the renewable energy investment can increase carbon emissions. In the middle stage, the renewable energy investment begins to play a role in reducing emissions. In the later stage, renewable energy investment may be associated with increased carbon emissions again. The relationship between carbon emissions and the other five influencing factors can be represented by an inverted U-shaped curve, a U-shaped curve, or a slow rising curve. The results above provide useful references to adjust renewable energy investment and reduce carbon emissions. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study analyzed the comprehensive impact of renewable energy investment on carbon emissions in China. The results showed that renewable energy investment can slightly reduce carbon emissions in the mid-term, but may lead to an increase in emissions in the early and later stages of investment. The relationship between carbon emissions and other influencing factors can be complex and nonlinear.