Carbon Emissions and Socioeconomic Drivers of Climate Change: Empirical Evidence from the Logarithmic Mean Divisia Index (LMDI) Base Model for China

Abstract

The main objective of the present study was to examine the impact of socioeconomic factors on environmental degradation or preservation using the logarithmic mean disivia index (LMDI). The study used the latest data from thirty Chinese provinces from 2012 to 2020. Pooled mean group (PMG) results were estimated to determine the long-term and short-term impact of the aforementioned compound variables on carbon emissions. The study results revealed that population growth, per capita GDP growth, and fossil fuel-led energy consumption, positively impacted environmental degradation in China at the provincial level. However, clean energy intensity and a transition towards renewable energy in China are helping to reduce carbon emissions. Similarly, clean energy intensity is also helping to lower carbon emissions. The study proposed that at the provincial level, joint efforts were required to control environmental degradation in China. The positive impact of renewable energy intensity on carbon emissions encourages the transition from fossil fuels to clean energy sources for environmentally friendly growth.