Analysis on shadow price and abatement potential of carbon dioxide in China's provincial industrial sectors.

CO2 emission performance evaluation is crucial to make abatement policies. Knowledge about the potential and costs of CO2 reduction could provide information guides for policymakers and help them implement targeted measures. However, relevant studies are rarely subdivided into detailed industrial sectors, and results are lack of inter-industry comparisons. To fill this gap, this study estimates provincial technical inefficiency, abatement potential, and shadow price of CO2 from fuel combustion in China's 25 industries in 2001-2017. Results show that China's industry could ideally reduce CO2 emissions by a further 22.01-33.27%, averaging 1645.96 MtCO2. Technical efficiency, abatement potential, and cost vary across provinces and industries and should therefore be fully considered when designing emission reduction targets and control policies. Provinces and industries with low technical efficiency, large-scale emissions, great abatement potential and low shadow price are the key to emission reduction. We thus identify key provinces and industries that need to take on more abatement responsibility. Those findings are of great significance to the formulation of carbon reduction targets and the implementation of abatement policies, and prove the feasibility of China's trans-regional carbon trading. It is suggested to prioritize key industries into the trading system and further promote inter-provincial cooperation through carbon trading.

Convergence analysis of regional marginal abatement cost of carbon dioxide in China based on spatial panel data models.

China has announced to launch a national emission trading system (ETS). The heterogeneity of marginal abatement cost (MAC) is prerequisite for trading, and the knowledge about the evolutionary characteristics of MAC is particularly necessary. However, the ß convergence theory has been proved to be suitable yet rarely applied to the study of MAC of CO2. To fill this gap, this paper connects them creatively, and the convergence of MAC during 2001-2015 and the influential factors are explored by spatial panel data models. Results show that China's MAC converges during the study period whether the spatial effect is considered or not. When evaluating the convergence of MAC, the spatial effect should not be ignored, because it will improve the explanatory power of models and promote the convergence. The size of labor force, emission level, coal consumption, foreign direct investment, and industrial structure significantly affect the growth rate of MAC. Low-carbon policies could be formulated fully considering the factors and their spillover effects. Those findings are certainly significant in imposing carbon reduction targets and adopting policy instruments. In addition, a national ETS is more applicable to China's reality at this stage and suggested to introduce carbon tax in due course in the future.