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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]

China has made remarkable achievements in reducing carbon emissions in recent years. However, there is still much reduction room before achieving carbon neutrality. In Beijing, the capital of China, it is a strategic choice to respond to global climate change by promoting green and low-carbon development. This paper calculates the carbon dioxide emissions of key industries in Beijing and analyzes the temporal evolution trend of carbon emissions. Carbon dioxide emissions in Beijing before 2030 are predicted based on the grey prediction GM (1,1) and BP neural network model. The effects of factors of carbon dioxide emissions are discussed using the threshold regression model under different economic conditions. The results show that energy consumption intensity, GDP per capita, and the ownership of civil cars have a positive impact on carbon dioxide emissions, while the number of permanent residents and urban green space areas have a negative impact on carbon dioxide emissions. These findings of carbon emission prediction and influencing factors contribute to carbon reduction path design. Related policy implications on carbon emission reduction are put forward from the aspects of promoting industrial upgrading, accelerating the construction of advanced economic structures, optimizing transportation structures, and strengthening green building development. [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]

China has set a goal to achieve peak CO2 emissions before 2030 and carbon neutrality by 2060. To achieve the goals of carbon peak and carbon neutrality, China needs to address the challenge of the large and still growing CO2 emission base. This paper investigated the energy consumption and CO2 emission in Beijing from 2020–2035 based on the energy elasticity coefficient and contribution value of the sub-energy increment (CVSI) method. Beijing is one of the first cities in China to propose the "carbon peak" target as of 2020. From 2020 Beijing will strive to achieve the goal of carbon neutrality. The results show that in 2035 the CO2 emission in Beijing may drop to 50% of 2020. This decline would be affected by economic growth, energy efficiency and the proportion of renewable energy use. Beijing's energy supply mainly comes from outside the region. Therefore, for Beijing, in addition to increasing the proportion of renewable energy sources outside the region, its own energy acceptance also needs to be strengthened, including strengthening energy storage construction, actively researching and promoting carbon capture and utilization of gas-fired units, which are effective ways to achieve carbon neutrality target. [ABSTRACT FROM AUTHOR]

• The Monte Carlo method is used to construct the charging load of EV in the planning area divided into four different vehicles type. • The Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines. • Proposed collaborative planning modeling promotes the time-of-use electricity price scheme to change the temporal and spatial characteristics of EV charging load. • The growth of EV charging load in the planning area has not increased the investment and operation-maintenance costs of DES equipment, and reduced carbon transaction costs as well as improved economic of system operation. • Simulations are conducted to verify the effeteness of proposed method. Under the dual-carbon goal, the number of electric vehicle (EV) has surged, and the uncertainty of the temporal and spatial characteristics of the load has become prominent, which brings the challenge for higher requirements to the equipment composition of distributed energy station (DES) and performance of station-network. This paper proposes a collaborative planning method for low-carbon DES with networks, which is focusing on the characteristics of EV charging behavior. First of all, based on the quantitative analysis of the temporal and spatial distribution of charging load of four typical types of EV, including private vehicle, public vehicle, taxi and bus, the Monte Carlo method is used to construct the charging load modeling of EV in the region area. In addition, the low-carbon DES is modeled equipped with photovoltaic (PV) and power to gas (P2G) devices, and the Prim algorithm is adopted to search for the optimal network layout of energy transmission pipelines in the region area. Under carbon trading mechanism, aiming at the lowest annualized total cost of the regional station-network, a station-network collaborative planning modeling strategy for the capacity allocation of low-carbon DES with networks is established. Finally, the case study for a project in Beijing China is implemented on MATLAB, to prove the effectiveness of the method proposed in the paper. The time-of-use electricity price scheme changes the temporal and spatial characteristics of EV load. For example, more than 2 MW the EV load can be brought into the valley price range, which reduces the carbon transaction cost and energy purchase cost of DES by 50 thousand yuan almost when peak-valley electricity price difference is close to 1.2 yuan. At the same time, the growth of EV charging load(the penetration rate of EV can reach up to 20%) in the planning area has not increased the investment and operation-maintenance costs of DES equipment. [ABSTRACT FROM AUTHOR]

Abstract Chinese energy consumption structure has been dominated by coal for a long time, which makes China facing serious environmental problems. To achieve the target of CO2 emission reduction, a carbon emission trading (CET) market and promoting the development of new energy should be worthy choices. The issues of dynamic linkages and spillover effects between the CET market, the coal market and the stock market of new energy companies (NEC) are important topics for studies. The paper applies a VAR(1)-DCC-GARCH(1,1) model and a VAR(1)-BEKK-AGARCH(1,1) model to obtain the following conclusions: (1) There exists significant time-varying correlations and long-run persistence of shocks to the DCCs between Beijing CET market, the coal market and the stock market of NEC, and the coal market and the new energy stock market have higher volatility persistence; (2) There is a bi-directional spillover effects between the coal market and the stock market of NEC, which is consistent with the results of the Granger causality; (3) The optimal weight is 82.65% for new energy stock and 17.35% for coal. The optimal hedge ratio is 49.98% for coal, while the optimal hedge ratio is 17.34% for new energy stock. Highlights • There are significant time-varying correlations and long-run persistence of shocks to the DCCs between three markets. • There is a bi-directional volatility spillover between the coal market and the new energy stock market. • The optimal weight is 82.65% for new energy stock and 17.35% for coal. • The optimal hedge ratio is 17.34% for new energy stock and 49.98% for coal. [ABSTRACT FROM AUTHOR]