Measurement of provincial carbon emission efficiency and analysis of influencing factors in China

Abstract

The massive use of energy has caused a rapid increase in global carbon dioxide emissions, resulting in a series of environmental problems such as climate warming. Investment in the energy industry can guide funds into green and clean production, reduce carbon emissions in the energy industry, and promote the green development of the energy industry. This paper considers the energy, the environment, the economy, and other factors and focuses on energy consumption and investment structure. Taking 30 provinces in China as research samples, a dynamic spatial Durbin model is established. The results show that the first-order term of carbon emissions has a driving force of 0.5068% for current carbon emissions at a significance level of 1% and that the increase in current carbon emissions will lead to a continued increase in carbon emissions in the next period. The increase in the carbon emissions of neighbouring provinces will increase their carbon emissions through the spatial spillover effect. Whether in the short term or long term, the increase in energy investment and the optimization of the energy investment structure can reduce carbon emissions. The above conclusions can provide a reference for the formulation of government environmental policies.

The Impact of Environmental Information Disclosure on Carbon Efficiency

“…to optimize industrial structure. Different from the above conclusions, Sun and Dong [20] held industrial structure does not affect CEC.…”

Examining the Effects and Operational Mechanisms of Green Credit on Carbon Emissions in Chinese Regions

“…It is consistent with the benchmark regression's findings and demonstrates the benchmark regression's robustness. The output of building a dynamic panel model for differential GMM estimation is shown in Table 3 at (2). The first-order autocorrelation p-value is 0.01 and the second-order autocorrelation p-value is 0.20, which supports the notion that there is just a first-order autocorrelation and no second-order autocorrelation.…”

“…Lin and Liu (2010) advocated the reduction of carbon emissions by maintaining GDP growth while controlling the urbanization rate and diminishing energy consumption intensity [1] . Sun et al (2016) highlighted the optimization of industrial and energy structures to enhance carbon emissions efficiency, with government intervention playing a pivotal role in achieving energy target constraints [2] . Wang et al (2018) revealed a significant inverted U-shaped curve relationship between economic growth and carbon emissions, while population agglomeration, technological advancements, openness to global markets, and intensified highway transportation together inhibit the increase in the level of urban carbon emissions [3] .…”