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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]

This paper constructs a novel stress measurement system of carbon market from the perspective of trading, emission reduction, and external shocks and simulates the stress indices of national and pilot carbon markets of China with the methods of functional data analysis and criteria importance through intercriteria correlation. It concludes that the overall carbon market stress is in the shape of "W" and still at a high level, with frequent fluctuations and an upward trend. In addition, the stress of Hubei, Beijing, and Shanghai carbon market fluctuates and rises, while the stress of Guangdong carbon market decreases. Moreover, carbon market stress mainly comes from trading and emission reduction. Furthermore, stress fluctuation of Guangdong and Beijing carbon market is more prone to "big waves," indicating that the two markets are sensitive to big events. Finally, the pilot carbon markets are divided into stress-driven and stress-release market and the type of which keeps change in different period. [ABSTRACT FROM AUTHOR]

In order to deal with severe problems such as environmental pollution and climate change, the Chinese government has proposed the goal of carbon neutrality in 2030 and carbon peak in 2060. Strategic emerging industries have become key areas of high-quality growth of green economy. In order to solve the practical problems of insufficient funds and financing constraints, this paper empirically measures the financing efficiency of strategic emerging industries. Based on the Super Slack-Based Measure model, this paper selects the data analysis of listed companies in Beijing, Tianjin and Hebei from 2011 to 2020. At the same time, this paper systematically combs the index system that affects financing efficiency based on grounded theory. Based on the binary relation and structural level of adjacent matrix and reachable matrix, the explanatory analysis is carried out. On this basis, a systematic GMM model is established to explore the significance of different factors influencing financing efficiency. The research shows that the strategic emerging industry is still in the initial stage, the financing efficiency is not high and the financing output is insufficient. The factors affecting financing efficiency can be divided into 6 dimensions, 20 indicators in total and 5 multipole hierarchical levels. Credit financing, equity financing, financing constraints, technological innovation and government support are the important factors affecting financing efficiency. [ABSTRACT FROM AUTHOR]

In recent years, blockchain technology has generally provided a wide range of application values in various fields. However, interdisciplinary research on blockchain technology and carbon trading companies is currently rare. Based on this, this paper takes the integration of blockchain technology and carbon trading companies as the background and draws on relevant theories such as blockchain technology, carbon trading mechanisms, and major technological changes to study the influencing factors of carbon trading companies applying blockchain technology by combining theoretical deduction with an empirical test. The results show that (1) the number of companies in China's eight carbon trading pilots shows an obvious regional imbalance. Among them, more companies participated in the carbon trading pilot in Shenzhen and Beijing while less in Tianjin and Hubei. Meanwhile, the participating companies in the eight carbon trading pilots are mainly concentrated in eight industries, including electric power, petrochemical, chemical, building materials, iron and steel, nonferrous metals, papermaking, and domestic civil aviation. (2) The willingness of carbon pilot companies to use blockchain technology is stronger; the time for carbon pilot companies to learn and master blockchain technology is shorter, otherwise, the longer. Therefore, priority can be given to applying blockchain technology in carbon pilot industries with strong use willingness. (3) If more companies participate in carbon trading, the easier it is for companies to learn to use blockchain technology in the early stage. Therefore, if the pilot for the application of blockchain technology is carried out in carbon trading companies, the two carbon pilots in Shenzhen and Beijing can be given priority. This paper further enriches the relatively scarce existing interdisciplinary literature on blockchain technology and carbon trading in theory and also provides guidance for the effective application of blockchain technology by carbon trading companies in reality. [ABSTRACT FROM AUTHOR]

Since the 18th National Congress of Chinese Communist Party, ecological civilization construction has been incorporated into Five-sphere Integrated Plan, that is, to promote coordinated progress in the economic, political, cultural, social, and eco-environmental fields. The Chinese government has proposed the goal of "carbon peak" and "carbon neutrality". Low-carbon transportation becomes the mainstream. To achieve carbon peak and carbon neutrality in public transport, this paper takes Beijing's public transport sector as a case study, using system dynamics model with relevant policies and the current situation. It calculates and predicts carbon emissions under electric vehicle substitution scenario, clean energy generation, and carbon capture, utilization and storage scenario for the period of 2020–2060. The following main conclusions are obtained: Direct carbon emissions have peaked and started to decline based on the current measures, while indirect carbon emissions have peaked and started to decline under the suggested intervention. The direct carbon emissions peaked at 2.58005 × 106 tons in 2014. The indirect carbon emissions peaked at 1.59027 × 106 in 2025. They all achieved carbon neutrality under the suggested intervention. Electric vehicle substitution and carbon capture, clean energy generation, utilization and storage are the main factors of carbon emission reduction. The outcomes of this study can provide essential information for policy-makers to advance Beijing's future low-carbon development in public transport. The electric vehicle substitution, clean energy generation and carbon capture, utilization and storage provide a new perspective to research carbon emissions in the transport sector. [ABSTRACT FROM AUTHOR]

With the decarbonization of the transportation sector and the diversification of travel demand, the development of shared electric vehicles has become crucial. Based on survey data of travel mode and destination of shared electric vehicles in Beijing, this paper aims to explore the formation and distribution mechanisms of the demand for shared electric vehicles. First of all, a multi-index and multi-cause (MIMIC) model was established to quantify the psychological latent variables that cannot be directly observed and to analyze the mechanisms between individual socio-demographic attributes and latent variables. Secondly, these psychological latent variables were added to mixed logit (ML) models as explanatory variables to form hybrid choice models to explore the travel mode choice behavior and travel destination choice behavior, respectively, when using shared electric vehicles for leisure travel. The results show that potential users of shared electric vehicles are characterized by higher education, employees of enterprises, no car availability and high driving years, and most of them travel for the purpose of connecting to transport hubs. Latent variables such as individual carbon trading, subjective norms, risks and behavioral intentions all affect the demand for shared electric vehicles; in-car time, out-of-car time, travel cost and the number of subway stations have negative impacts on the demand, while mall properties and the number of parking lots have positive impacts on the demand. Furthermore, the use of shared electric vehicles is highly correlated with the use of cars and subways, and part of the travel demand could be transferred to shared electric vehicles by taking certain measures. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic power generation will not only help to achieve the strategic targets of peaking carbon emissions and carbon neutrality but also cause a series of impacts on the power grid at the same time. Forecasting the long-term development of regionally distributed photovoltaics can provide a reference for power grid planning and stable operation. In this paper, considering the effect of factors such as subsidies and countywide promotion policy of photovoltaics, a forecasting model for the development tendency of regionally distributed photovoltaics based on system dynamics is established. Then, taking Beijing as an example, an empirical analysis is carried out, and the effect of the proportion of self-consumption and the time when the subsidy is adjusted on distributed photovoltaic penetration is explored through sensitivity analysis. The simulation results show that the installed capacity achieved by the countywide promotion policy will become the main source of the installed capacity growth of distributed photovoltaics in Beijing after 2024. To continuously boost distributed photovoltaic penetration, relevant policymakers should consider the appropriate time when the subsidy is adjusted according to the installation cost of photovoltaic systems. [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]

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• 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]

The Olympic Games is a comprehensive social and cultural activity with the most complicated system and the largest scale. With the development of the Olympic Games, the ecological problems brought by the Olympic Games have attracted increasing attention, and the sustainable development of the Olympic Games has been put on the agenda. Based on the comprehensive index of Ecological Carrying Capacity (ECC), Environmental Kuznets Curve, and carbon footprint analysis of the Olympic Games host city competition cycle, this study analyzes three modes of ECC of the host city: light urban ecological burden mode, heavy urban ecological burden mode, and overload urban ecological burden mode. Based on the temperature sensor and GPS positioning, the land surface temperature change map of Tokyo, Japan, from 1990 to 2015 is obtained, and the heat island effect of Tokyo is obtained. This paper analyzes the case of using sensors for intelligent event management such as venue detection in the sustainable development plan of the 2020 Tokyo Olympics; the idea and practice of thrifty hosting of 2022 Beijing Winter Olympics and 2022 Hangzhou Asian Games holds that in most cases, the ECC of the host city of the Olympic Games is under great pressure, so it is necessary to pay attention to the ECC of the host city. The sustainable development of the Olympic Games is an important issue in the development of the Olympics, but the sustainable development plan of the Olympics is still being explored. It is suggested that the sustainable development of the Olympic Games needs to evaluate the ECC of the host city in the whole cycle, establish the principle of ecological priority to avoid the overload mode of ecological burden, strive to achieve carbon neutrality in the competition, and practice the idea of scientific frugality in running the competition. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Bridges are special infrastructures that emit large amounts of carbon dioxide from construction. Attention should be given to the carbon cost generated by the bridge, which includes its direct economic cost; the carbon cost is the largest driving force encouraging the enterprise to implement carbon emission reduction measures. In this study, the life cycle assessment (LCA) method is applied to carbon emissions in the bridge construction stage, which include emissions from material production, transportation and on-site construction; then, a carbon emission calculation model for the construction stage is established. Next, the carbon cost calculation model for the bridge in the construction stage is determined by combining the carbon pricing mechanisms of carbon emission taxing and trading to monetize carbon emissions. Finally, by taking a bridge in Beijing as an example, the carbon emissions in the bridge construction stage are calculated, and the carbon cost is calculated. The results show that carbon emission monetization is beneficial for clarifying the environmental impact of bridge construction; these calculations should be included in cost accounting. [ABSTRACT FROM AUTHOR]

This study investigates the carbon compensation mechanism as a tool to promote coordinated economic and environmental development in China's Jing-Jin-Ji region (Beijing, Tianjin, and Hebei). A novel approach for calculating carbon compensation cost that integrate emission responsibility as well as carbon abatement cost through a bi-level programming model is proposed. Shared producer and consumer responsibility is established for carbon emissions under the multi-regional input-output model. Marginal abatement costs of Beijing, Tianjin, and Hebei are further calculated through the input-output and linear programming method. Results highlight that the emission responsibility allocation method significantly affects the inter-regional carbon compensation amount. The production-based approach leads to a 77% reduction of carbon compensation amount from Beijing to Hebei, and a 170% overestimation from Tianjin to Hebei compared with the shared responsibility approach in 2060. The optimal carbon compensation unit cost ranges from 1539 to 6438 yuan/ton when the carbon reduction ratio ranges from 10% to 20%. Beijing would compensate Hebei 3.56 and 29.74 billion yuan for achieving 10% and 20% emission reduction, respectively. Similarly, Tianjin's compensation for Hebei would be 0.23 and 1.87 billion yuan in the two scenarios. Results further show that implementing the proposed carbon compensation mechanism can reduce total abatement cost by 43% and 84% in Jing-Jin-Ji under the two scenarios. The methods and results of this paper could help inform future carbon compensation implementation in China and other countries. • Develop a bi-level programming model to determine optimal carbon compensation cost. • Consider emission responsibility and carbon abatement cost in Jing-Jin-Ji. • Resulting mechanism reduces abatement cost by 43% for 10% emission reduction. • Production-based allocation underestimates carbon compensation amount by 62%. • Optimal carbon compensation unit cost changes with marginal abatement cost. [ABSTRACT FROM AUTHOR]

In the context of China's dual carbon target, Beijing, as the capital of China, should play an exemplary role in carbon emission reduction. On the premise of optimizing high-emission sectors such as coal and industry, Beijing is still a certain distance from the goal of carbon neutrality. Therefore, on the basis of Beijing's energy resource endowment, considering Beijing's economic development and carbon neutrality goals and scientifically and reasonably planning Beijing's carbon emission reduction path are important tasks. We construct an energy structure optimization model to achieve the goal of carbon neutrality by 2050. The model analysis concludes that the residents and transportation sectors will account for a large proportion of Beijing's total carbon emissions in the future. To achieve the goal of carbon neutrality, the electricity substitution of fossil energy and the high proportion of external power are two necessary measures, and the optimal path of carbon emission reduction is proposed. [ABSTRACT FROM AUTHOR]

Environmental degradation and climate change are two important issues that threaten human survival and sustainable social development. Based on the characteristics of the same source of sulfur dioxide and greenhouse gases, we analyze the collaborative governance mechanism of carbon emission trading on sulfur dioxide. The results show that carbon emission reduction trading may not necessarily deliver sulfur dioxide reduction co-benefits. Then, we conduct an empirical using data of 30 regions in China from 1998 to 2017. The results show that the co-benefits of carbon emission trading in terms of the reduction of sulfur dioxide are regionally heterogeneous. The carbon emission trading policies implemented in Beijing, Chongqing, Hubei, and Guangdong have realized collaborative governance on sulfur dioxide. However, the carbon emission trading policies implemented in Tianjin and Shanghai have not realized collaborative governance on sulfur dioxide. Moreover, Chongqing has the highest average annual sulfur dioxide reduction co-benefits, followed by Guangdong. The gap of sulfur dioxide reduction co-benefits between Beijing and Hubei's synergistic emission reduction effect is small. Moreover, the sulfur dioxide reduction co-benefits of carbon emission trading are mainly achieved by reducing fossil energy consumption. Meanwhile, the carbon emission trading implemented in Beijing also delivers sulfur dioxide reduction co-benefits by improving pollution reduction technology. [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]

As a politically and culturally important city cluster, the Beijing–Tianjin–Hebei region (BTH) has huge electricity and water consumption, while the local power generation capacity and water resources may not be sustainable long term according to the current power generation ratio. We introduced up-to-date strategic policies in China, carbon peaking and carbon neutrality (dual carbon goals), to assess their impact on environmental protection and economic development. Thus, we constructed a dynamic multi-regional computable general equilibrium model to simulate these changes in BTH. We designed four carbon reduction scenarios to explore the impact of policies including carbon taxes, investment, and renewable energy subsidies individually or combined. The findings reveal that implementing these policies together can increase the proportion of renewable energy in power generation to 17.2% (Beijing), 9.4% (Tianjin), and 33.2% (Hebei) by 2060. Under various policy scenarios, the Tax Scenario shows the largest water withdrawal savings, while the Investment Scenario shows the largest GDP growth by 2060. The combined implementation of these policies may minimize the negative impact on the economy while developing an environmentally friendly region. The results of the Combination Scenario are closest to carbon neutrality, narrowing the gap to 10.71%, 9.98%, and 9.85%, respectively. Conservation (water and electricity) and carbon reduction can provide significant support for carbon neutrality and sustainable development strategies. This study contributes to the evaluation of mitigation policies in BTH and provides a concrete policy option to achieve carbon neutrality. [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]

This study elaborated an evolutionary game model to optimize the decision-making process on the interaction between civil aviation and high-speed rail under alternative passenger ticket prices (PTPs) and carbon trading prices (CTPs). First, a logit model was used to calculate the passenger flow distribution rate in civil aviation and HSR, and their revenue loss function was determined according to varying PTPs and CTPs. Second, an evolutionary game model with incomplete information was developed to assess the respective revenues. Third, the stable strategy solution of the game model was derived from replicator dynamics, and an investigation of stable conditions under variable cases was performed. Finally, the simulation of a case study on the Beijing–Shanghai corridor was conducted to validate the proposed model's feasibility. The additional revenue is shown to be the key influencing factor, mainly controlling the strategic decisions of airport and high-speed rail companies. Besides, the final strategy was strongly influenced by the alteration of PTPs and CTPs: higher PTPs promoted civil aviation and high-speed rail collaboration, while increased CTPs forced their competing behavior. The results obtained are instrumental in outlining the optimal strategy range for passenger ticket and carbon trading prices, encouraging high-speed transportation system growth. [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]

With the increase in energy demand, environmental issues such as carbon emissions are becoming more and more prominent. China will scale its intended nationally determined contributions by adopting more vigorous policies and measures. China aims to have CO2 emissions peak before 2030 and achieve carbon neutrality before 2060. The current challenge and priority of China's high-quality development is to ensure a harmonious balance between the ecological environment and the economy. The South-to-North Water Diversion Project passes through Beijing, Tianjin, Henan, and Hebei, which were chosen as the study sites. The carbon emission data was from the China Carbon Emission Database 2000–2019. Decoupling modeling using statistical yearbook data from four provinces and municipalities. KMO and Bartlett's test used SPSS 27 software. The selection of indicators was based on relevance. Analyses were performed using the extended STIRPAT model and ridge regression. Moreover, projections of carbon peaks in the study area for 2020–2035 under different rates of change were simulated by the scenario analysis method. The results show that: (1) The decoupling analysis of the four provinces and cities from 2000-2019 gradually shifts to strong decoupling; (2) Resident population, energy structure, and secondary industry as a proportion of GDP significantly impact carbon emissions; (3) From 2000–2035, Beijing and Henan experienced carbon peaks. The peak time in Beijing was 96.836 million tons in 2010. The peak time in Henan was 654.1004 million tons in 2011; (4) There was no peak in Hebei from 2000–2035. [ABSTRACT FROM AUTHOR]

For curbing the global climate crisis, China has set an ambitious target of peak carbon emissions by 2030. Beijing, the capital of China, has implemented a carbon reduction policy since 2012. Using the reduced and generalized forms of the Environmental Kuznets Curve (EKC), we deduce that both the cubic EKC and the genetic algorithm-based EKC have an N-shape. The first turning point of the three-order EKC occurs around 2011, demonstrating the effectiveness of the carbon reduction policy. However, the time series model predicts that Beijing will reach the second turning point around 2026, when the gross domestic product (GDP) is about CNY 5000 billion and carbon emissions will begin to increase again. Interpretable machine learning is proposed to explore the socio-economic drivers in carbon emissions, indicating that total energy consumption and GDP contribute the most. Therefore, we should accelerate the upgrading of energy consumption and adjust the industrial structure, thus facilitating Beijing to its peak carbon emissions and achieving carbon neutrality. [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]

Under the "Digital China" strategy and "Carbon Peaking and Carbon Neutrality" goal, it is significant to explore the carbon reduction effect from the digital economy development in a multi-dimensional way. Based on the panel data of 13 cities in the Beijing-Tianjin-Hebei (BTH) region from 2011 to 2019, this study uses mechanism test model, threshold effect model, and spatial Durbin model which empirically test the influence mechanism and spatial spillover effect of digital economy development on regional CEI. The research found that (1) the digital economy development in the BTH region can reduce regional CEI, and it passes the endogenous test; (2) the digital economy indexes of 13 cities in the BTH region have significantly increased with time evolution, but there is obvious spatial unevenness; the CEI of each city except Tianjin decreases significantly with time evolution, and Tianjin shows a trend of decreasing and then increasing; (3) digital economy has a positive spatial correlation, showing the characteristics of "H–H" and "L-L" clustering. Furthermore, the digital economy has a spatial spillover effect on the CEI of neighboring cities; (4) the digital economy development can promote the industrial structure rationalization and upgrade, improves the urban green innovation quantity and quality, then reduces the regional CEI through them; and (5) the impact strength of digital economy on CEI varies at different threshold intervals of the mechanism variable. [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]

Charging behavior is essential to understanding the real performance and evaluating the sustainability of battery electric vehicle (BEV) development and providing the basis for optimal infrastructure deployment. However, it is very hard to obtain the rules, due to lack of the data support, etc. In this research, analyzing the charging behavior of users with private charging piles (PCPs) is carried out based on the real vehicle data of 168 BEV users in Beijing, covering 8825 charging events for a one-year duration. In this study, the charging behaviors are defined by five indexes: the starting state of charge (SOC) of batteries, charging location selection, charging start time, driving distance, and duration between two charging events. To further find the influencing rules of the PCPs owning state, we setup a method to divide the data into two categories to process further analysis and comparison. Meanwhile, in order to better observe the impact of electric vehicle charging on the power grid, we use a Monte Carlo (MC) simulation to predict the charging load of different users based on the analysis. In addition, an agent-based trip chain model (ABTCM), a multinomial logistic regression (MLR), and a machine learning algorithm (MLA) approach are proposed to analyze the charging behavior. The results show that with 40% or lower charging start SOC, the proportion of users without PCPs (weekday: 55.9%; weekend: 59.9%) is larger than users with PCPs (weekday: 45.5%; weekend: 42.6%). Meanwhile, users without PCPs have a certain decrease in the range of 60–80% charging start SOC. The median charging time duration is 51.44 h for users with PCPs and is 17.25 h for users without PCPs. The charging peak effect is evident, and the two types of users have different power consumption distributions. Due to the existence of PCPs, users have lower mileage anxiety and more diverse charging time choices. The analysis results and method can provide a basis for optimal deployment and allocation of charging infrastructure, and to make suitable incentive policies for changing the charging behavior, targeting the carbon neutral objectives. [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]

Scientific prediction of net carbon emissions in the Beijing–Tianjin–Hebei (BTH) region is of significance to examine carbon emission reduction in the context of the "double carbon" target. In this study, the carbon peak and carbon neutrality states in the BTH region are determined through a regional double carbon target analysis framework, and a logistic chaotic sparrow search algorithm backpropagation neural network hybrid model (LCSSA-BP) optimized by a logistic chaotic sparrow search algorithm (LCSSA) is used to forecast the net carbon emissions. The findings reveal that the net carbon emissions in the BTH region generally increased during the study period; Beijing, Tianjin, and Hebei are in different stages toward the realization of the double carbon target; population size, affluence, and urbanization rate are positively correlated, whereas the proportion of foreign direct investment and energy intensity is negatively associated with net carbon emissions; the prediction accuracy of the LCSSA-BP is superior to that of the SSA-BP and BP neural network, and it can be used to forecast the net carbon emissions in the BTH region. [ABSTRACT FROM AUTHOR]

Promoting the development and utilization of solar energy is a practical way to alleviate the energy crisis and achieve the goal of carbon neutrality. Recently, interest has arisen in the dual-functional active solar thermal façade (ASTF) system that produces hot water throughout the whole year and reduces cooling and heating load as a function of the building façade. Here, a mathematical model of the ASTF system is built and validated by the experimental data, and the annual performance of the ASTF system in representative cities in three climate regions is evaluated. The results are that compared with the common solar water system, the ASTF system adds passive energy savings, which accounts for 5.8%, 7.2% and 11.4% of the total primary energy savings of the system for Shanghai, Beijing and Lanzhou. Compared with the traditional wall, the ASTF saves 16.4% and 23.0% of cooling energy consumption and 102.3% and 92.4% of heating energy consumption for Shanghai and Beijing, respectively. Additionally, it saves 74.7% of heating energy consumption for Lanzhou. Lastly, the impact of the design parameters and operation parameters of the system are investigated, respectively. This study demonstrates a viable path to promoting cost-effective active solar thermal façades in different climates, and the results can be beneficial to further research. [ABSTRACT FROM AUTHOR]

Due to rapid urban expansion, urban agglomerations face enormous challenges on their way to carbon neutrality. Regarding China's urban agglomerations, 25% of the land contains 75% of the population, and all types of land are used efficiently and intensively. However, few studies have explored the spatiotemporal link between changes in land use and land cover (LULC) and carbon storage. In this work, the carbon storage changes from 1990 to 2020 were estimated using the InVEST model in China's Beijing–Tianjin–Hebei (BTH) region. By coupling the Future Land Use Simulation (FLUS) model and InVEST model, the LULC and carbon storage changes in the BTH region in 2035 and 2050 under the natural evolution scenario (NES), economic priority scenario (EPS), ecological conservation scenario (ECS), and coordinated development scenario (CDS). Finally, the spatial autocorrelation analysis of regional carbon storage was developed for future zoning management. The results revealed the following: (1) the carbon storage in the BTH region exhibited a cumulative loss of 3.5 × 107 Mg from 1990 to 2020, and the carbon loss was serious between 2000 and 2010 due to rapid urbanization. (2) Excluding the ECS, the other three scenarios showed continued expansion of construction land. Under the EPS, the carbon storage was found to have the lowest value, which decreased to 16.05 × 108 Mg in 2035 and only 15.38 × 108 Mg in 2050; under the ECS, the carbon storage was predicted to reach the highest value, 18.22 × 108 Mg and 19.00 × 108 Mg, respectively; the CDS exhibited a similar trend as the NES, but the carbon storage was found to increase. (3) The carbon storage under the four scenarios was found to have a certain degree of similarity in terms of its spatial distribution; the high-value areas were found to be clustered in the northwestern part of Beijing and the northern and western parts of Hebei. As for the number of areas with high carbon storage, the ECS was found to be the most abundant, followed by the CDS, and the EPS was found to be the least. The findings of this study can help the BTH region implement the "dual carbon" target and provide a leading example for other urban agglomerations. [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]