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The integration or segmentation of regional carbon prices implies that barriers may hinder prices from adjusting efficiently and impede linkages between regional carbon markets. Past studies have found that crossregional carbon prices in China tend to diverge, indicating unbalanced regional markets and that carbon cap-and-trade policies may be insufficiently effective. China's carbon trading pilots opened in 2013. Is the integration efficient? This is the research question addressed in this study. This paper uses the carbon prices of four carbon-trading pilot regions in China (i.e. Beijing, Shanghai, Tianjin, and Guangdong) over the period of January 1, 2014, to March 23, 2021, to explore whether prices are correlated across three dimensions: long-term convergence, short-term correlation, and volatility transmission by adopting a vector error correction model with heterogeneous variances. And by using longer-term daily data and empirical tests that can account for the multiple-faceted correlations, this paper provides evidence showing that long-term convergence and risk transfer between the four regional carbon prices exist, which indicates significant regional integration. The results indicate that the price behavior of the Beijing pilot is efficient and that the Shanghai pilot has the lowest volatility. The paper also documents an information transfer pattern. For example, the Beijing pilot is shown to be the leading risk transfer market, whereas the Shanghai pilot is shown to be the market where most risk is transferred. Contrary to findings elsewhere, our main results demonstrate that China's carbon markets exhibit integration efficiency and no imbalance between regions. This may be because the efficiency of China's carbon markets has improved over time. The evidence also implies that if a multi-dimensional correlation has not been considered, it may underestimate the efficiency of regional carbon prices' integration. [ABSTRACT FROM AUTHOR]

Since the implementation of China's clean heating plan in 2017, the progress of natural gas replacing coal for heating is still not ideal. With the proposal of the goal of carbon neutralization and the impact of international war, the price, supply, and demand of natural gas have attracted attention. In case of gas shortage, reasonable schemes are required to be allocated to different industries and categories. Therefore, this research divides the gas field into four levels according to the social utility, and establishes the multi-objective distribution optimization model considering the social and economic benefits. The solution of the model is based on genetic algorithm and realized on MATLAB. Multi-objective optimization strategies and several constraints are used to make the algorithm's process of the search is continuously moving closer to the Pareto optimal front. Taking Beijing as an example, two scenarios are set to simulate and optimize, so as to verify the optimization effect of the model. Better quantitative results are created after reallocation of natural gas. The ideal scenario realizes 5.64% more social benefits than 2019 through additional allocation of 117.288 million cubic meters of reserve gas. The constraint scenario creates 0.67% more social benefits with only 0.07% decrease of the economic benefits. The conclusions show that allocating more gas to three main industries can improve the optimization effect and it is necessary to expand the supply of natural gas reserve volume. This paper provides a new perspective of natural gas allocation category and priority, providing theoretical and practical ideas for the planning of natural gas security emergency plan and the development of gas sales plan. [ABSTRACT FROM AUTHOR]