Reduction effect of carbon markets: A case study of the Beijing-Tianjin-Hebei region of China

Abstract

The carbon market is a crucial market-oriented tool in achieving carbon neutrality and has been adopted by many countries and regions. China has established a policy system covering eight carbon trading pilots since 2013 and has implemented effective practices. Despite the evaluation of the carbon markets at the national level, few studies identified the carbon emission reduction effect for a specific region or assessed the differentiated characteristics that may significantly impact the development of the carbon market. This study took the Beijing-Tianjin-Hebei (BTH) region as the research sample and aimed to estimate the carbon emission reduction effect of the carbon market by using a difference-in-differences approach. Our findings suggested that the carbon market in the BTH region effectively reduced its carbon intensity by 14.04%. Our estimations were robust after using a winsorization panel or establishing a new control group. Furthermore, the results also indicated that the carbon market’s effect on reducing carbon intensity differs across cities and shows an increasing trend yearly. Relevant recommendations for promoting carbon trading policies were proposed.

Dynamic Simulation of Industrial Synergy Optimisation Pathways in Beijing-Tianjin-Hebei Region Based on Energy-Water Nexus

“…The methodology used in these studies are in some cases based on summarizing historical patterns or current situations focused on data statistics with predictivity limitations, while in other cases it involves further development forecasting by setting exogenous parameters, such as the regression on population, affluence, and technology (STIRPAT) model (Yu et al, 2023), the logarithmic mean Divisia index (LMDI) decomposition method (Takayabu, 2020), the long-range Energy Alternatives Planning System (LEAP) model (Huang et al, 2023), and other forecasting models (Weng et al, 2019). Nevertheless, setting the exogenous parameters ignores the interactions among energyeconomy-environment (3Es) systems, which have a significant effect on the synergistic reduction of CO2 and water pollutants (Xiang et al, 2023).…”