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A good risk warning model is an effective way to guarantee the stable operation of the carbon market and exert its emission reduction function. This paper takes the China's pilot carbon market as the research object and measures the carbon market risk by ARMA-GARCH-VaR. Then, influential factors are selected as non-market characteristics from the perspective of causality and incorporated into the risk warning model, and SMOTE and SMOTEENN are introduced to deal with the unbalanced samples. Results show that (1) The risk fluctuations of Beijing and Shenzhen carbon market are large, and the risk fluctuations of Guangdong carbon market are relatively smooth in the early period and larger in the recent period. (2) Air quality, average daily temperature, CSI 300, power coal, natural gas, European carbon market, exchange rate, and Baidu index are all important factors in the risk volatility of carbon market. (3) The Classification Decision Tree (CDT) has the best performance. CDT has the best performance in Guangdong carbon market, SMOTE-CDT has the best performance in Hubei carbon market, and SMOTEENN-CDT has the best performance in Shanghai carbon market, in which SMOTEENN-CDT has a 76% Recall. The non-equilibrium treatment improves the overall Recall, and the risk warning model after non-equilibrium treatment can avoid missing important risk points. This paper can provide a certain degree of reference for the improvement of carbon trading market and risk management. [Display omitted] • A carbon market warning model is constructed. • The average VaR of the carbon trading markets in Shenzhen and Beijing is larger. • CDT performs better in market risk warning. • The model improves 34.8% in the avoidance of risk compared with other models. [ABSTRACT FROM AUTHOR]

• analyze the energy efficiency of Beijing's industrial sectors from 2002 to 2018. • model industry heterogeneity of Beijing's industrial sectors accurately. • low energy efficiency of Beijing's industrial sectors due to management inefficiency. • the energy efficiency of heavy industry is higher than that of light industry. • explore the influencing factors of energy efficiency of Beijing's industrial sectors. Improving energy efficiency is one of the effective means to achieve China's carbon peaking and carbon neutrality goals. As the capital of China, Beijing should improve energy efficiency and has a primary responsibility in achieving the goals. Considering the heterogeneity of industrial sectors, this paper proposes a data envelopment analysis (DEA) approach to analyze the total factor energy efficiency and its influencing factors of 31 industrial sectors in Beijing from 2002 to 2018. We show that the low energy efficiency of Beijing's industrial sectors, mainly due to management inefficiency, is not conducive to the realization of the goals. Besides, the energy efficiency of heavy industry is higher than that of light industry. The expansion of enterprise scale has a positive impact on energy efficiency, while the property right structure, foreign trade and energy consumption structure have a negative impact on energy efficiency. Finally, we put forward some policy recommendations. [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [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]

Pairs trading is one of the mainstream statistical arbitrage strategies in current financial market practice that can seize possible arbitrage opportunities caused by mispricing between similar assets. Based on the theory of statistical arbitrage and the similar attributes of carbon emission allowances traded in different carbon markets, this paper studies the applicability of the pairs trading strategy among different pilot markets in China. The novelty and possible contribution of this paper are as follows. Firstly, this paper extends the statistical arbitrage-based pairs trading framework to the analysis of carbon market transactions in China, which broadens the understanding of the correlation between China's carbon markets. Secondly, this study explores the arbitrage opportunities among China's regional carbon markets that have not been revealed by previous studies. Thirdly, based on the pairs trading analytical model adopted in this study, this paper further investigates the operation characteristics of China's carbon markets and the interaction between markets, as the sensitivity of strategy performance to the key parameters is also discussed. The results show that arbitrage based on pairs trading is profitable. In the baseline case, the annualized arbitrage returns between the Shanghai emission allowance and Beijing emission allowance is about 9.11%, the highest among all pairs. However, there is almost no pairing relationship between the Tianjin carbon market and other markets. By discussing the time distribution of arbitrage signals generated by the pairs trading strategy, it is found that most signals are sent out during the inactive trading period in a compliance cycle. The conclusions provide valuable insights for formulating relevant carbon market regulatory policies and investors' participation in arbitrage activities in China's carbon trading markets. • The application of the pairs trading strategy is extended to the Chinese carbon markets. • Based on relative value arbitrage theory, arbitrage opportunities between markets are examined. • Pairs trading strategy can also yield sustainable and distinctive returns in the Chinese carbon markets. • Arbitrage trading between the Shanghai and Beijing markets may yield promising annualized returns with about 9.11%. [ABSTRACT FROM AUTHOR]

In addition to its important economic value, carbon trading is also an important tool in international politics and diplomacy. Carbon price forecast accuracy has far-reaching implications for economic development and the environment. Few existing studies have been more accurate in predicting carbon prices over longer periods. In this paper, the incremental Gaussian nonlinear trend fuzzy granulation method and the Gaussian nonlinear trend fuzzy granulation method are innovatively proposed to predict carbon prices. This study first converts carbon prices into incremental granulation time series. A time-varying core line is added to produce nonlinear trend granulation on the foundation of linear trend granulation. The nonlinear trend and residual information are then predicted using the DeeAR network, respectively, and the final prediction result is obtained by adding the predicted values for each. Moreover, a new evaluation index, the comprehensive evaluation of RMSE, MAE, and MAPE as three indicators, is proposed to consider the accuracy of the evaluation index more comprehensively and reliably. The results show that the prediction method has the smallest error in long-term prediction compared with other models. The daily closing price datasets of carbon exchanges published in Shenzhen and Beijing are used to validate the efficacy of this methodology. • A modified Gaussian nonlinear trend that can better fit the actual data is proposed. • Incremental Gaussian nonlinear trend fuzzy granulation method with F test is proposed. • A new evaluation index is proposed to make the prediction result more interpretable. [ABSTRACT FROM AUTHOR]

A reasonable allocation of carbon emission quotas is essential to improving the national unified carbon trading market. A bankruptcy game model is developed for allocating carbon emission quotas to Chinese provinces based on bankruptcy theory and the Nash bargaining game. The model takes into account the disparities that exist among these provinces. The allocation scheme under the carbon peak target can be obtained by using the projection pursuit model and the subject satisfaction function. The findings reveal that certain eastern provinces bear a greater responsibility in terms of historical carbon emissions. Furthermore, the rampant transfer of carbon emissions from energy-rich to energy-demanding regions has exacerbated the imbalance in regional development. Interestingly, the carbon emission demands are fulfilled in some provinces situated in the coastal regions of the southwest and southeast, such as Beijing, Tianjin, and Fujian. However, certain provinces in the northeast express the lowest levels of satisfaction with their carbon quota allocation, necessitating a greater burden of carbon emission reduction in the future. The research offers significant implications for regional development and carbon reduction efforts. • A bankruptcy game model is developed for allocating carbon emission quotas. • The model takes into account disparities between the provinces. • The bankruptcy theory and Nash bargaining game are used in this paper. [ABSTRACT FROM AUTHOR]

• The carbon emission coefficients method and net ecosystem productivity (NEP) were used to evaluate urban carbon emissions and green space carbon sinks, respectively. • A multi-objective programming model (MOP) was constructed to derive the optimal solution of the amount of urban green space based on the carbon balance theory. • Combined with ventilation resistance and species migration resistance assessment, priority distribution areas of green space in 2035 were identified. • The PLUS model was introduced to optimize the spatial pattern of urban green space. • Optimized green space network stability and carbon sinks will be enhanced in 2035. Cities experience the most intensive level of human activity. As a result, more than 60 % of global CO 2 emissions come from urban areas. Urban green space has the dual ecological benefits of increasing carbon sinks and reducing carbon emissions. Creating green space is essential to promoting the development of a low-carbon cycle in a city. Therefore, exploring the quantitative structure and spatial pattern optimization of urban green space from the perspective of carbon balance can effectively improve the total carbon sink of a city. Based on the carbon balance theory, this paper first evaluates the carbon offsetting capability (COC) of urban green space in Beijing in 2020. Then, CO 2 emissions is predicted, COC improvement targets are established, and the quantity of standard green space is calculated under these targets in 2035. A multi-objective programming model (MOP) is constructed to derive the optimal solution to determine the amount of standard green space needed to meet the constraints of urban development planning and maximize the carbon sink. A circuit model is used to identify the priority distribution area of green space in 2035, and the Patch-generating Land Use Simulation (PLUS) model is used to simulate the spatial pattern optimization results. The results show that: (1) CO 2 emissions in Beijing caused by human activities in 2020 totaled about 240.12 million tons, the net absorption of CO 2 of green space was about 8.99 million tons, and the COC was about 3.74 %; (2) in 2035, Beijing's CO 2 emissions will be about 265.40 million tons. Under 5 %, 10 %, 15 %, 20 %, and 25 % COC improvement gradients, the demand for standard green space will be 12,204.80 km2, 12,763.80 km2, 13,353.85 km2, 13,943.90 km2, and 14,533.96 km2, respectively; (3) the results of the Multi-Objective Programming (MOP) model show that the optimal COC in 2035 is 4.19 %, and the standard green space quantity is 13,012.24 km2; (4) In 2035, Beijing's urban green space will be characterized by a network structure of circular radiation. The optimized COC is 4.37 %, and the standard green space quantity is 13,577.86 km2, which is largely consistent with the MOP model's predicted result. This study can provide theoretical and methodological support for urban green space planning, and expand nature-based solutions for the low-carbon development in cities. [ABSTRACT FROM AUTHOR]

Sand and gravel aggregate (SGA) transportation accounts for over 30% of the freight volume in China, which is greater than that of any other cargo; low-carbon development can effectively promote the decarbonization of freight transportation. This study presents a four-step evaluation method to assess CO 2 emission reductions in freight transportation; the proposed method helps determine the relationship between the policy, CO 2 emission reductions, demand, and mode market share. Using Beijing as a case study, this paper estimates the potential of reducing CO 2 emissions from SGA transportation under various scenarios. The scenarios are set based on the avoid, shift, and improve strategies; the results of the Beijing case indicate that the CO 2 emission for SGA transportation is 920.86 kt, which is approximately 1% of the total CO 2 emissions in Beijing. The maximum reductions in CO 2 emissions that can be achieved using the avoid, shift, and improve strategies are 0.92 kt (0.1% of total), 560.52 kt (60.9% of total), and 78.59 kt (8.5% of total), respectively. The shift strategy is confirmed to be the most realistic and cost-effective approach for reducing CO 2 emissions from SGA transportation. In addition, the implementation of subsidy policies is discussed. Marginal abatement costs under the subsidy scenarios are higher than 224 CNY/t, which is higher than the price in the Chinese carbon trading market. • We evaluated CO 2 emission of aggregate transportation in Beijing at the OD level. • CO 2 emission reductions of the avoid, shift, and improve strategies were tested. • Shift strategy performed the best in reducing CO 2 emission. • Avoid and improve strategies can reduce CO 2 emission; but the effects were limited. • Subsidizing rail transport reduced more emission than subsidizing electric trucks. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is one of the most effective ways to reduce energy waste and achieve carbon neutrality, but it may also bring the energy rebound effect and thus fail to achieve the desired energy conservations. In this paper, we examine the energy rebound effect of Beijing at the level of overall economy, industry and sector based on the Cobb-Douglas production function and the idea of Solow residual, so as to clarify the true impact of technological progress on energy-saving target. Results suggest that: (1) The overall economy of Beijing shows partial rebound effect in most years and appears backfire effect in some years, but the degree of the rebound effect has been gradually mitigated; (2) The energy rebound degree of the tertiary industry is higher than that of the secondary industry on the whole, but the rebound effect of the secondary industry fluctuates more obviously; (3) Energy savings at the sector level is also not ideal, and in most cases, sectors with low energy efficiency have a higher rebound effect than sectors with high energy efficiency. Based on these results, suggestions are provided to alleviate the energy rebound effect of Beijing. • The energy rebound effect of Beijing are quantified at the level of overall economy, industry and sector. • The overall economy of Beijing appears backfire effect and partial rebound effect. • The energy rebound effect of the tertiary industry is higher than that of the secondary industry in most years. • Sectors with low energy efficiency may have higher rebound effects. [ABSTRACT FROM AUTHOR]

• Waste biomass and rooftop photovoltaic is integrated into the hybrid power system. • The effects of renewable energy policy and demand response are investigated. • Sensitivity analysis is performed with the consideration of uncertainties. • Demand response program is helpful to reduce both system cost and carbon emission. Modern urban energy system with renewable energy sources has become an effective solution to enhance energy security and promote carbon neutrality. This paper presents a holistic framework for the optimal design and management of such a flexible hybrid power system at the community scale. This framework integrates the potential assessment of rooftop PV and waste biomass, a general optimization approach with multi-perspective performance evaluation, as well as the impacts analysis of energy policy, demand response, and uncertain economic parameters. The proposed framework was applied to a small neighborhood area in Beijing, China. It was found that 100% utilization of available biomass and rooftop PV would satisfy 73% of local electricity demand with the levelized cost of electricity of 0.1030 $/kWh and the embodied carbon emission of 0.5416 kg/kWh. The flexible shiftable load could lead to lower peak load and less electricity supply cost, and help to match the energy demand with the dispatchable rooftop PV output. However, the economic benefits caused by implementing demand response are more obvious than the environmental benefits. Furthermore, a higher electricity tariff and lower capital cost of PV technology would facilitate the investment in rooftop PV and achieve carbon emission reduction targets. [ABSTRACT FROM AUTHOR]

• The energy costs of heat pump are less than that of gas boiler when P e P ng < 1 COP ∙ q ng q e . • The upper limit of electricity-gas price ratio to ensure economic feasibility is 0.45 in this case. • The carbon emission of heat pump is less than that of gas boiler if f e < f ng ∙ q e ∙ C O P q ng . • The impact of two incentive modes on the economy of retrofit project was compared. Heat pump technology plays an important role in achieving the goal of carbon neutrality in the building section. This goal requires a large-scale adoption and diffusion of heat pump to achieve scale effects. Most researches concerning heat pump technology mainly focus on the technical optimization. However, the economic and environmental benefits are the key factors determining whether technology is adopted by users. In order to identify the favorable scenarios to use heat pump technology and encourage users to adopt heat pump technology, an energetic, economic and environmental evaluation approach from the life cycle perspective of heat pump retrofit project is proposed in this work. Then, by conducting a case study of a retrofit project that replaced gas boiler (GB) with Air-Flue gas Dual-Source Heat Pump (AFDSHP), the research finds that: (1) Coefficient of performance (COP) and energy price are the main factor determine the economic feasibility of heat pump retrofit project, and the energy costs of heat pump are less than gas boiler when electricity-gas price ratio meets P e P ng < 1 COP ∙ q ng q e ; (2) Because COP of heat pump can rarely reach 6 in actual operation, the retrofit project is economically feasible when the electricity-gas price ratio is below 0.45 in this case; (3) Under China's average carbon trading prices in 2020, 0.064 RMB / k g , encouraging user to adopt heat pump technology by carbon trading mechanism can achieve the same effect as government subsidies; (4) Replacing gas boiler with heat pump can reduce carbon emission only when carbon emission coefficient of power generation meets f e < f ng ∙ q e ∙ C O P q ng , which is to prevent the carbon emission leaking from the energy end-user to the power generation sector and to prevent an increase of carbon emission. This paper offers recommendations to policy makers about developing appropriate incentive policies, and provides decision criteria to users about adoption of heat pump technology. [ABSTRACT FROM AUTHOR]