How the new energy industry contributes to carbon reduction? -Evidence from China.

New energy is an inevitable choice to cope with global climate change. China has invested heavily in new energy, but it still faces enormous pressure to reduce emissions. The effectiveness and path of new energy industry development still need to be solved. This paper studies the relationship between the development of new energy industry and carbon emissions. A theoretical model of new energy firms' production behaviour was constructed, reflecting that the internal carbon emissions of the new energy industry mainly depend on its cost structure and R&D intensity. Specifically, part of the carbon emission caused by scale effect comes from direct capacity construction, and the other part comes from the production-cost effect of R&D. Based on the provincial panel data in China from 2005 to 2019, empirical tests are carried out from two aspects of scale effect and technology effect. Results show that the scale expansion has an inverted U-shaped relationship with carbon emissions, which is supported by the regression with GDP as the threshold variable. The effect of new energy technologies in reducing emissions is continuous. The threshold for technology to play a role in reducing emissions is smaller than the threshold for scale. The findings explain the expansion of the new energy industry in the early stages may lead to an increase in carbon emissions. Our study provides important insights that the scale and technology are two dimensions that cannot be ignored in the process of energy transformation. It is necessary to act in the reasonable range and pay attention to the accumulation of technology innovation and the orderly expansion of production capacity.

How to advance China's carbon emission peak?- A comparative analysis of energy transition in China and the USA.

This paper investigates carbon emission peak in China based on a comparative analysis of energy transition in China and the United States (US). The LMDI model is adopted to decompose carbon emissions into several driving factors in 2000-2018 for China and the US. Gray forecasting and NAR neural network are combined to predict peak time and identify optimal transition paths. The factor decomposition indicates that energy intensity is the main inhibitory factor for increased carbon emissions, while economic growth and population size are contributors for increased carbon emissions. There are significant differences in the impact of structure effect on carbon emissions in the two countries. The industry decomposition indicates that industry development is a critical inhibitor for increased carbon emissions after 2014 in China. The growth of transport and agriculture are basically contributing to increase carbon emissions in China and the US. The forecast results illustrate that China could complete carbon emission peak by 2030 under the baseline scenario, with a peak volume of 11354.72Mt CO2. Under the industrial structure adjustment scenario, the carbon peak year may be advanced to 2028. While adjusting industrial structure and energy consumption structure at the same time, China could achieve carbon emission peak at 9918.21Mt CO2 in 2025.