Yaxue Yan

Carbon pricing is one of the key policy tools in the green recovery of the post-COVID-19 era. As linkages among ETSs worldwide are future trend, the carbon price spillover effects among markets are needed to be explored. This study examines the spillover effects and dynamic linkages of carbon prices using the example of China’s pilot carbon markets during 2015–2019, which are seemingly independent carbon markets. A structural vector error correction model and an improved directed acyclic graph approach are applied. The main results are as follows. First, the linkages among the five pilots demonstrate features of “two small-world networks.” Specifically, these are the Guangdong and Hubei network and the Beijing, Shenzhen and Shanghai network. Second, Shenzhen, Beijing and Hubei ranked as the top three pilots in terms of external spillover effect, accounting for 36.25%, 29.76%, and 25.59%, respectively. Second, Guangdong pilot has increasing influence on the Hubei, Shenzhen and Beijing pilots. Third, trading activities are positive contributors to the spillover, while the allowance illiquidity ratio and volatility are negative factors. The findings imply that to retain an expectable abatement costs in achieving the climate goals in green recovery, carbon prices in other potentially related markets should be considered by the policy maker in addition to its own policy design.

Directing energy transition toward decarbonization involves transitions to new low-carbon energy carriers and improvements in energy efficiency. Based on the panel data of 30 provinces in China from 2001 to 2016, this paper empirically assesses the contributions of different driving factors to low-carbon energy transitions from both energy mix and energy efficiency. We find the uneven effects of each factor on the direction of energy mix transition. Especially three kinds of carbon-reduction policies can significantly promote the use of natural gas. State capital is particularly important for making the energy mix more low-carbon, while low-carbon technology stock is not obvious. After estimating the Baron and Kenny’s three-step procedure of mediating effects and performing the Sobel and bootstrap tests, the most obvious change observed is the total effect of the energy-saving goals (ESGs) on industrial energy intensity of 0.395, which is close to the contribution of technical knowledge stock. Furthermore, we conclude that ESGs and new energy subsidies (NESs) both have a lagged impact, however, the command-control mechanisms produce a 'hurry-up energy-saving effect'. Finally, we derive some policy implications from the findings and discuss further research.