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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]

To effectively solve the environmental pollution caused by greenhouse gases, countries around the world have successively set up carbon emission trading markets in recent years. At present, China's carbon trading market is only in its infancy, making various mechanisms and policies insufficient. As the core issue of carbon trading market, the fluctuations of carbon trading price are related to the investment decisions of market participants, the formulation of enterprise production and operation plans, and the realization of global emission reduction targets. Therefore, it is of great practical significance to study the influencing factors of carbon price fluctuations and to predict future carbon prices. In this paper, we take the carbon trading week data of the Beijing carbon trading market in China from May to September 2020 as the research object. Firstly, the gray correlation technique is employed to measure the rationality of the selected factors affecting carbon price fluctuations. Secondly, through the principal component analysis method to analyze the various influencing factors, it is found that energy prices and macroeconomic development are the main factors affecting carbon prices, and weather conditions will also cause carbon price fluctuations. Then, four different models are proposed to predict carbon trading prices, and the forecast results are evaluated through the performance evaluation index system. The results show that the GA-ELM model has the best prognosis effect. Finally, according to the analysis results, it provides a useful theoretical reference for carbon market decision makers and participants. [ABSTRACT FROM AUTHOR]

Unit pricing for domestic waste, or pay-as-you-throw (PAYT), affords a promising policy option for waste classification and reduction at source. As an emerging economy, China intends to adopt the sophisticated instrument of unit pricing. The public attitude to this policy is of high relevance for its actual implementation. For the first time in China, this paper quantitatively examined the acceptability of the policy among urban residents by randomly delivering 632 questionnaires by taking Beijing, a megacity expected to introduce the practice, as an example. Based on the ordered logit model, determinants of acceptability were empirically tested including intrinsic, external and demographic factors. The results show that the acceptability rate of PAYT in Beijing is less than half, with the proportion of firm support reaching only 42.6%. The respectively low acceptability rate indicates substantial compliance costs, due to intensive resistance to policy implementation and more resources to be invested to promote acceptability. Empirical analysis further indicates that (1) regarding demographic features, aged or less educated residents tend to accept unit pricing, (2) as for intrinsic factors, a friendly environmental attitude, deeper understanding of charging policy and better social perception can significantly improve the public acceptability of the policy, and (3) in terms of external factors, better property services and transparent funding mechanisms play significant roles in enhancing public acceptability. These findings are further confirmed by replacing the dependent variable with either the willingness to pay for household waste disposal or the acceptability of the operated sewage treatment fee. The status of residents' preference for specific pricing methods is further investigated with underlying reasons being revealed. For developing regions with an urgent demand for waste reduction, it is proposed to install unit pricing in time to avoid possible growing social acceptability costs. Specific measures are proposed including changes in intrinsic factors, paying attention to focus groups, and building social consensus for unit pricing. [Display omitted] • Public acceptability of unit pricing policy is quantitatively examined in Beijing. • A random face-to-face survey is designed and conducted by covering 632 households. • The acceptability rate is only 42.6%, indicating high policy compliance costs. • Demographic, intrinsic and external factors play roles in improving acceptability. • Specific suggestions are proposed for regions like Beijing to introduce the policy. [ABSTRACT FROM AUTHOR]