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In response to the international appeal of developing low-carbon economy and realizing carbon peaking and neutrality goals, China has been exploring the construction of the carbon emissions trading market for years. Without the guidance of advanced technology, there would not be a low-carbon economy in the first place. Based on panel data of 30 provinces and cities in China from 2005 to 2020, this paper takes Beijing—which is the first pilot city in China—as the only treated group and uses the Synthetic Control Method to study the impact of the pilot policy on low-carbon technology innovation. The results show that, firstly, the number of low-carbon technology patents in Beijing increases significantly after the implementation of the pilot policy, proving a positive influence on technology innovation. Secondly, the policy effect has a certain time lag and is sensitive to the shock from both domestic and foreign carbon market, but this effect is gradually stable over time. This paper confirms that technological innovation is the key means of promoting the development of the low-carbon economy and calls on various carbon trading markets to pay attention to the internal mechanism of promoting low-carbon technology innovation to stimulate the vitality of market entities. [ABSTRACT FROM AUTHOR]

Optimizing resource efficiency and mitigating climate change have become consensuses of human society. However, there is still a gap in assessing the carbon emission efficiency (CEE) and identifying the influence of various factors, especially in rapid urbanizing regions. In this paper, we built a stochastic frontier analysis model to assess CEE and conducted a case study in the Beijing–Tianjin–Hebei Urban Agglomeration (BTHUA), a typical area of collaborative development in China. A comprehensive influencing factor index was constructed to analyze and identify the key influencing factors of CEE. The results revealed that the average CEE among the 13 cities increased in volatility from 2000 to 2019. The average CEE in Langfang was lowest, while that in Tangshan was highest. The input-related factors had a negative effect on CEE, including carbon emissions per capita, employment per ten thousand people, total assets per capita, and energy intensity. GDP per capita, the urbanization level, and the proportion of the tertiary sector's GDP had positive impacts on CEE. Future policy formulation should focus on the transition from labor- and material-intensive industries to knowledge- and technology-intensive industries. All the results can contribute to achieving high-quality development and dual-carbon target of rapid-urbanizing areas. [ABSTRACT FROM AUTHOR]

Global warming has endangered the natural ecosystem's balance, as well as human existence and development, and it is mostly caused by carbon dioxide. Identifying carbon emission characteristics and predicting carbon emission reasonably is helpful to provide indication for the effective design of emission reduction path. The most literature use a single prediction model; this paper predicts carbon emission using a number of strategies based on previous research. Considering the prediction accuracy, advantages, and disadvantages of each method, a new method combining autoregressive integrated moving average (ARIMA) model and NAR neural network (NAR-NN) is proposed; in addition, this paper attempts to explain the carbon emission characteristics and emission reduction paths of each region from the new perspective of clustering. First, the results show that China's carbon emission features can be divided into four categories: low-carbon demonstrative type, low-carbon potential type, high-carbon developed type, and high-carbon traditional type. Moreover, low-carbon demonstrative type includes merely Beijing and Shanghai, low-carbon potential type is distributed in the southeast coastal areas of China, the high-carbon developed type is mainly distributed in Northeast China, and the western region basically belongs to high-carbon traditional type. Second, ARIMA model and NAR-NN are the two best methods in terms of prediction effect, and the combined model has better prediction effect than the single model. Third, carbon emissions in most regions of China will increase in the next few years; the time of carbon peak in the east is earlier than that in the west regions of China. Beijing will probably be the first region in China to complete the carbon peak. Besides, there is a certain correlation between the carbon peak time and the type of carbon emission in each region. [ABSTRACT FROM AUTHOR]

In this article, a comprehensive analysis is conducted on the influencing factors of carbon emissions in the transportation sector during the evolution of electrification in Beijing. We also considered the impact of indirect carbon emissions caused by carbon emissions from public and private cars and electricity consumption on overall carbon emissions. Based on the LMDI decomposition theory, nine influencing factors are separated, including development level, energy intensity, vehicle structure, number of private cars, and private transportation energy consumption. The analysis results show that, from 2010 to 2021, the carbon emissions from transportation in Beijing increased from 922.98 x 104t to 1490.6 x 104t. The private road carbon emission accounts for about 77.97%, and has become a decisive factor affecting road carbon emissions. In the public domain the contribution values of the development level and energy intensity are 25.20% and -38.71%, respectively. And they are the two most critical factors affecting carbon emissions on public road carbon emissions. In the private domain, the contribution values of the number of private cars, energy consumption of private transportation, and vehicle structure are 60.17%, 47.86%, and -12.99%, respectively. They are the three key factors affecting the private road carbon emissions. There is a significant difference in the proportion of indirect carbon emissions from electricity in the public and private domain. Indirect carbon emissions from electricity account for about 13.6% of road carbon emissions in the public sector, and about 0.9% in the private sector. The results in this paper provide useful references for decisionmaking in the adjustment of transportation energy structure and the promotion of electrified transportation in Beijing and other cities. [ABSTRACT FROM AUTHOR]

In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Using high-resolution remote sensing images to accurately obtain PV information over a large region, including location and size, has the advantages of high statistical efficiency and timely data update for the PV energy management. Due to the intra-class diversity of PV panels and the intricate variability in their deployment environments, existing semantic segmentation methods often have problems such as under-segmentation and mis-segmentation. To alleviate these problems, this paper proposes an improved DeepLabv3+ semantic segmentation network to more accurately extract PV panels from high-resolution remote sensing images. With the aim of alleviating under-segmentation, a multi-level context aggregation module is developed. This module can enhance the model's ability to learn the characteristics of PV panels and their surrounding environment by aggregating rich contextual information from multi-scale and semantic levels. To alleviate the problem of mis-segmentation, a hybrid attention module is introduced. This module sequentially and adaptively adjusts the weight distribution in both the channel and spatial dimensions, thus enabling the model to focus more on the feature information and spatial positions of PV objects. Experiments conducted on a self-constructed Beijing PV segmentation dataset show that the method in this paper has advantages of completeness and accuracy in extracting PV panels compared to the baseline model and current mainstream semantic segmentation network. In addition, the results of experiments on extracting PV panels in real region show that our model also has good stability and generalization capability. [ABSTRACT FROM AUTHOR]

Battery electric buses (BEBs) are being introduced to substitute for diesel buses worldwide owing to their zero emissions. Investigating BEB scheduling problems is crucial for minimising charging costs and satisfying passenger demands. In addition, photovoltaic and energy-storage system (PESS) adoption in public transport could offer a promising alternative towards reducing charging costs and carbon dioxide emissions. This paper presents a novel BEB scheduling problem in which a PESS is deployed at bus-charging stations. The proposed problem is formulated as mixed-integer linear programming in which BEB scheduling and photovoltaic (PV) energy scheduling are optimised. To improve solution efficiency, a two-step solution approach is proposed: a BEB scheduling problem without considering the PESS is solved in the first step, and then a PV energy scheduling problem is solved in the second step. A case study is performed using the operational data of buses 404 and 109 in Beijing, China. The results show that the sum of charging and carbon dioxide emission costs will be reduced considerably if the PESS is introduced in a transit system. This study provides new insights for engineering a sustainable future in green transportation. [ABSTRACT FROM AUTHOR]

China's policies about reducing carbon emissions mainly focus on saving energy and reducing pollutant emissions. However, the carbon emissions of the logistics industry remain high. Thus, logistics companies should not only reduce distribution costs, but also consider the impact of carbon emissions when planning distribution routes. This paper studies the supermarket delivery distribution route planning problem considering carbon emissions. Aiming at the lowest economic operating cost composed of fixed cost, driving cost, and carbon emission cost, we propose a grey wolf optimized genetic algorithm to generate delivery route plans, where the social rank and hunting behavior of the grey wolf optimization algorithm are integrated into the selection operation of the genetic algorithm. Finally, a real case of a large third-party logistics enterprise in Beijing is studied. The case study results verify the effectiveness and applicability of the model and algorithm. [ABSTRACT FROM AUTHOR]

Nowadays, sustainability-related issues have attracted growing attention due to fossil fuel depletion and environmental concerns. Considering many cities have gradually replaced taxis with electric vehicles (EVs), to reduce greenhouse gas emissions and traditional energy consumption, this paper studies the optimization strategy of battery swapping for electric taxis (ETs), and it is not only to ease congestion in the battery swapping station (BSS) but also for electric taxis to address their range anxiety and maximize their benefits. Firstly, based on the road network, the Dijkstra algorithm is adopted to provide the optimal path for ETs to BSSs with the minimum energy consumption. Then, this paper proposes the optimization objective function with minimum cost, which contains the battery service cost based on the battery's state of charge, waiting cost caused by waiting for swapping battery in BSSs and the carbon emission reduction benefit generated during ETs driving to BSSs, and uses a mixed-integer linear programming (MILP) algorithm to solve this function. Finally, taking the Leisure Park of Laoshan City in Beijing as an example, the numerical simulation is carried out and the proposed battery swapping strategy is efficient to alleviate the congestion of BSSs and maximize the total benefit of ETs, and the cost based on the proposed strategy is 14.21% less than that of disorderly swapping. [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]

The Chinese government aims to balance employment stabilization and emissions reduction when it initiates an emissions trading scheme (ETS) to control carbon emissions. However, the policymakers should not neglect the role of imperfectly competitive market structure and market power from incumbent firms. In view of this, classical framework of Berman and Bui's (2001) is extended in this paper to clarify the theoretical mechanism of employment effect of the ETS considering firm-level market power. In addition, based on the firm-level panel data from 2008 to 2019 of China, the employment effect of each pilot ETS is measured with the method of difference-in-difference-in-differences. Then a moderating effect model is established to verify the role of firm-level market power on the heterogeneous employment effect among the pilot ETSs. Moreover, the employment effect is further decomposed to evaluate the influential mechanism from the perspective of firms' strategic behavior. It is found that there is negative employment effect of China's ETS policy, due to the significant moderating role of firm-level market power. This decrease in labor demand is mainly dominated by a negative output effect rather than a positive substitution effect because strategic allowance sellers with stronger market power tend to hire fewer employees to reduce carbon emissions by controlling production scale. However, the heterogeneity of policy effects resulted by market structure is also identified in different pilot regions. Employment dividend has been found in Beijing and Tianjin where strategic allowance buyers manipulate the carbon market and hire more employees to consolidate their market power. Therefore, policymakers should improve the design of the ETS to overcome the adverse employment effect duo to the region-specific market structure. [ABSTRACT FROM AUTHOR]

The promotion and use of new energy vehicles are important means to reduce carbon emissions. However, its effect is usually affected by the rebound effect. The current research does not consider the impact of the substitution effect of new energy vehicles on traditional fuel vehicles to the carbon emission rebound effect, including its situation in different scenarios. This paper considers the purchase stage, use stage of the new energy vehicles and constructs a comprehensive two-stage almost ideal demand system model. The rebound effect of carbon emissions from 2014 to 2019 and different scenarios from 2020 to 2030 is quantified. The results show that the rebound effect of carbon emissions without car purchase subsidies is 4%–9% less than that with car purchase subsidies in the corresponding year. In the prediction results of the rebound effect under different future scenarios, the values of partial rebound effects are more than 80%, and the values of backfire effects are less than 115%, which indicates that the rebound effect of carbon emissions shows a convergence trend. In the future, the rebound effect of carbon emissions will show a trend of convergence. When the total vehicle sales remain unchanged, the replacement proportion of new energy vehicles reaches 85%, and there is no subsidy policy, the rebound effect of carbon emissions is the smallest, indicating that this scenario is the most favorable for reducing carbon emissions and reducing environmental pressure. [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]

Electric heating is helpful in resolving air pollution, and also utilizes renewable energy power. However, the environmental benefits of electric heating are restrained by regional resources endowment and regional electrical energy structure. In the context of the active promotion of electric heating in Beijing—Tianjin–Hebei, the paper studied the development potential and limits of wind power electric heating under the constraints of environmental effects to provide reference for the orderly development of electric heating. A model to predict the capacity of the installed wind power improved by the fruit fly optimized ELM (Extreme Learning Machine) algorithm and an electric heating environment effect calculation model are constructed. The optimal value and critical value of the newly added heating area in Beijing–Tianjin–Hebei are obtained through calculation under four scenarios of wind abandonment rate. As the wind abandonment rate increases from 4–10% (with interval of 2%), the optimal value of the newly added electric heating area in Beijing—Tianjin–Hebei will be 19.15, 29.47, 36.26, and 47.90 million m2 by the year 2030. Additionally, as the utilization rate of thermal power exceeds 41.16%, electric heating will produce more carbon emissions compared to coal‐fired heating. Therefore, if we want to develop electric heating on a large scale, we need to control the proportion of thermal power in the power supply structure and increase the proportion of new energy. This study can be used as theoretical reference to the development programme of electric energy substitution in the future. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The objective and accurate prediction of carbon dioxide emissions holds great significance for improving governmental energy policies and plans. Therefore, starting from an evolutionary system of carbon emissions, this paper studies the evolution of the system, establishes a grey model of the system, and expands the modeling structure of this model. The modeling mechanism of the classical feedforward neural network model is organically combined with the function of the external influencing factors of carbon emissions, and the grey model of the carbon emission dynamic system is established with a neural network. Then, the properties of the model are studied, the parameters of the model are optimized, and the modeling steps are obtained. Finally, the validity of the model is analyzed by using the carbon emissions of Beijing from 2009 to 2018. The results of the four cases show that the simulation and prediction errors of the new model are all less than 10%, and case 1 shows the best results of 1.56% and 2.07%, respectively, which are used to predict the carbon dioxide emissions in the next 5 years in Beijing. The prediction results are in accordance with the actual trend, which indicates the effectiveness and feasibility of the model. [ABSTRACT FROM AUTHOR]

As a market-based instrument for transportation demand management, a transport fee-charging policy can not only effectively reduce traffic congestion, but also improve air quality. Considering the urgent need to improve urban transport fee-charging policies and reduce transport carbon emissions, the paper focuses on the role of the performance of fee-charging policies in reducing the carbon emissions of urban transport. In this study, we propose a methodological framework for the performance evaluation of urban traffic carbon emission fee-charging policies. First, we analyze the current situation of the implementation of fee-charging policies and their relationship with urban traffic congestion. Subsequently, we analyze changing trends of carbon emissions associated with transportation travel in Beijing in recent years, to identify the main sources of carbon emissions from transport. Finally, we used the DEA method to evaluate the performance of the fee policies for urban transport, which are meant to reduce carbon emissions, analyze their implementation efficiency, and then discuss the main factors affecting their efficiency. The results show that with the implementation of fee-charging policies, urban traffic congestion has eased. The overall carbon dioxide (CO2) emissions from transportation in Beijing grew rapidly. CO2 emissions generated by car travel are the main source of carbon emissions from transportation in Beijing. The average value of the overall technical efficiency (TE) of Beijing's fee-charging policies to reduce transportation carbon emissions from 2006 to 2018 is 0.962, indicating that the overall implementation of Beijing's fee-charging policies has been effective. Adjustments to the fee structure reduce motor vehicle travel to an extent, increase the proportion of green travel, and reduce the intensity of transportation carbon emissions. The technical non-efficiency in Beijing's fee-charging policy is mainly due to non-efficiency of scale, followed by pure technical non-efficiency. Appropriately adjusting the fee structures imposed by different policies would help to improve the efficiency of policy implementation. [ABSTRACT FROM AUTHOR]

This paper explores the spatial and temporal variability and changes in characteristics of the economic efficiency of urban construction land (EEUCL) in 14 administrative regions in the Beijing–Tianjin–Hebei (BTH) urban agglomeration from 2002 to 2019, based on the super-efficiency SBM-DEA model and Malmquist index, using carbon emissions as the non-desired output. On this basis, the Tobit model is used to study the role of nine external environmental factors, such as the level of socioeconomic development and industrial structure, in influencing the EEUCL. The results indicate that (1) carbon emissions from urban construction land in Beijing, Tianjin, and Hebei have all reached their peak, and the growth rate has slowed down significantly in the past two years, although there has been a slight rebound; (2) from the 10th Five-Year Plan period to the 13th Five-Year Plan period, Beijing has maintained a high level of EEUCL and the trend is slowly increasing. However, the EEUCL values of Tianjin and Hebei are gradually declining; (3) the socio-economic development level, industrial structure, and green economy development level have a significant positive impact on the EEUCL in the BTH region, while the urban scale, scientific and technical support intensity, outward opening level and population density have significant negative effects. [ABSTRACT FROM AUTHOR]

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It is important to know how much of the increased atmospheric CO2 is derived from fossil fuel emissions. Here, we review the progress in atmospheric fossil fuel CO2 (CO2ff) tracing over recent years by measurement of Δ14C in Chinese cities. In this paper we make progress by expanding the analysis from some locations to more regional views, by combining observations with modeling, and by making a preliminary comparison of observation-derived CO2ff with inventory-derived CO2ff. We have obtained a general picture of Chinese urban CO2ff and characteristics of its spatio-temporal variations at different scale, and identified the corresponding influencing factors. Interestingly, we found that the weekend effect of CO2ff was less evident in Chinese cities. In addition, we observed simultaneous variations in CO2ff and PM2.5 in a winter haze event in Beijing and a simultaneous decrease in annual averages of CO2ff and PM2.5 in Xi'an based on multi-year (2011–2016) Δ14CO2 monitoring. We found that local coal combustion was the main source of CO2ff in Xi'an, which is located in the Guanzhong basin, by applying a WRF-Chem model and looking at δ13C signatures. Thus, reduction of coal consumption is a crucial target for carbon emissions reduction in China. [ABSTRACT FROM AUTHOR]

Since the limitation of carbon emissions, China's photovoltaic (PV) industry has developed vigorously, while some traditional heavy industries have been violently hit. Therefore, the industrial production data exhibits significant nonlinear and complexity characteristics, which may affect prediction accuracy, thus hindering the corresponding department's decision-making. Consequently, a novel structure-adaptive fractional Bernoulli grey model is presented in this paper to surmount this toughie, and the core innovations can be summarized as follows. Initially, a novel time function term is utilized to depict the accumulative time effect, which can smoothly represent the dynamic variations and significantly strengthen the robustness of the new model. Besides, the fractional-order accumulation technique, which could effectively improve the predicting accuracy, is employed in the proposed model. Furthermore, the adaptability and generalizability of the proposed model can be enhanced by the self-adaptive parameters optimized by the Particle Swarm Optimization. For illustration and verification purposes, experiments on forecasting the annual output of Photovoltaic modules in China and the annual output of steel in Beijing are compared with a range of benchmarks, including the classic GM (1, 1), conventional econometric technology, and machine learning methods. And the results confirmed that the proposed model is superior to all benchmark models, which indicates that the novel model is indeed suitable for industrial production forecasting. [ABSTRACT FROM AUTHOR]

Improving carbon emission efficiency (CEE) would promote the development of the green and low-carbon economy in the Beijing-Tianjin-Hebei (BTH) region, China. This paper uses the EBM model of unexpected output to measure the city-level CEE of the BTH region from 2007 to 2016. The spatial distribution characteristics and evolution law of CEE are analyzed with respect to overall and local aspects, and the spatial quantile regression model is used to verify the influencing factors of CEE. The main findings are as follows: (1) The carbon emission in the BTH region is considered to be of medium efficiency, and there are eight cities within the region at middle- and high-efficiency levels. The overall efficiency values show a downward trend. Beijing, Cangzhou, Baoding have high-CEE values, whereas Handan, Tangshan, and Zhangjiakou have low-CEE values. (2) The CEE values for BTH show significant spatial agglomeration characteristics at both the global and local levels. The "H–H" agglomeration areas are primarily distributed in the central region, and the "L-L" agglomeration areas are chiefly distributed in the southern and northern regions. The spatial pattern change is generally stable. (3) The selected factors, URB, PGDP, DS, ISG, FDI, and TEL, have different regression coefficients on CEE at different quantiles. [ABSTRACT FROM AUTHOR]

Using the calculation method of the United Nations World Tourism Organization (UNWTO), this paper measured the carbon emissions of tourism traffic and their evolution process in Beijing from 2005 to 2017. The Tapio model and Logarithm Mean Divisia Index (LMDI) approach were used to discuss the decoupling relationship between economic development and the change of tourism traffic carbon emissions and the influencing factors. There are six major indicators in our analysis, including (1) tourists scale, (2) per capita tourism consumption level, (3) contribution rate of tourism industry to Gross Domestic Product (GDP), (4) passenger traffic volume per unit GDP, (5) energy consumption per unit passenger traffic volume, and (6) energy structure. The results showed that: the main positive drivers are tourists scale, per capita tourism consumption level and energy consumption per unit of passenger traffic volume. In addition, per unit GDP passenger traffic volume is an effective factor to restrain the growth of carbon emissions. And the economic development of Beijing is developing in step with the change of carbon emissions in Beijing's tourism traffic. The research results have important theoretical and practical significance for Beijing to formulate emission reduction policies and develop low-carbon economy. [ABSTRACT FROM AUTHOR]

This paper constructs a system dynamics model for simulating the impact of different strategies on urban traffic's energy consumption and carbon emissions. Based on a case study in Beijing, the model includes three subsystems: (1) urban traffic, (2) population and economy, and (3) energy consumption and carbon emissions. First, the model is used to decompose the impact of different vehicles on energy consumption and carbon emissions. Decomposition results show that private cars have the most significant impact on urban traffic's energy consumption and carbon emissions; however, total vehicle kilometers traveled by private cars are the smallest among four trip modes. Then, the model is used to simulate different urban traffic policies. Policies are categorized as follows: (a) driving restrictions on vehicle registration numbers, (b) a scheme for vehicle registrations via a lottery system, and (c) development of public transportation infrastructures. Scenario simulation results show that all those measures can reduce energy consumption and carbon emissions. Though the last strategy (c) contains several delays, its effect is more stable and far-reaching. Finally, some recommendations about easing traffic pressure and reducing traffic emissions are given. [ABSTRACT FROM AUTHOR]

Beijing faces a serious problem of carbon emissions and the economic sectors are the main source of carbon emissions. Previous literatures have extensively focused on estimating the impact of carbon emissions of individual sector. Little attention has been paid to the multisectoral carbon emissions. In this paper, a multisectoral decomposition analysis was reported to explore the carbon emissions in Beijing. The emissions are decomposed into energy structure, energy intensity, economic structure (in industry), economic output, and population scale effects by the method of logarithmic mean Divisia index. Agricultural, industrial, construction, transportation, commercial, and other sectors are taken into consideration. The results show that population scale effect is the main factor for increasing carbon emissions in all sectors. The energy efficiency improvements are primarily responsible for the decrease in emissions in the industrial sector, while it increases emissions in construction, transportation, and commercial sectors. The economic output in agricultural and other sectors exerts a positive effect on emissions. In contrast, the energy structure effect only makes a minor contribution to the emission decrease in industrial, construction, commercial, and other sectors. [ABSTRACT FROM AUTHOR]

The Olympic Games are striking a balance between economic and ecological benefits. Taking the 5 ice event venues of the 2018 PyeongChang Winter Olympic Games as the research object, the CO 2 emissions under the 2018 PyeongChang Low-carbon scenario and 2022 Beijing Low-carbon scenario are compared. The empirical results show that, in terms of venue operation, the Beijing strategies can reduce CO 2 emissions by 715.7 t compared with the PyeongChang strategies. In terms of the refrigerant escape, Beijing's adoption of CO 2 refrigerant can reduce the emission of CO 2 by 1202.2 t compared with the R404 refrigerant used in PyeongChang. This paper confirms the progress of the Green Winter Olympic Games, and may provide implications for the future host countries as well as the cities which in the green transformation. [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]

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]

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]

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]

The coordination and balance between agricultural development (AD) and agricultural carbon emissions (ACE) is one of the most important ways to boost the high-quality development of agriculture in the Beijing-Tianjin-Hebei region. Taking 13 prefecture-level cities in the Beijing-Tianjin-Hebei region as the research object, this study uses the comprehensive evaluation method and Tapio decoupling model to analyze the decoupling effect between the AD level and the Agricultural Carbon emissions intensity (ACEI) from 2000 to 2020, based on the assessment of AD status and the calculation of Agricultural Carbon emissions quantities (ACEQ) and ACEI. It found that: (1) From 2000 to 2020, the AD in the Beijing-Tianjin-Hebei region generally showcased a gradual increase trend, and demonstrated a basic feature that AD in the northern areas was higher than that in the southern ones. (2) From 2000 to 2020, the ACEQ in the Beijing-Tianjin-Hebei region showed a trend of first increasing and then decreasing, with a spatial distribution feature that the ACEQ in the southern cities was higher than that in the northern ones. Regarding the source of ACE, the livestock and poultry farming took the highest proportion. ACEI was decreasing year by year, higher in the southern areas than in the northern ones. (3) The main types of decoupling in the Beijing-Tianjin-Hebei region were strong decoupling, recession decoupling, strong negative decoupling, weak negative decoupling, recession coupling, and expansion negative coupling. The decoupling relationship between AD and ACEI were in dynamic change, but the change trend of the decoupling relationship was optimistic. The results of this study deliver certain deployable practice value for improving the sustainability of regional agricultural green development and ecological environmental protection. [ABSTRACT FROM AUTHOR]

This study focuses on analyzing the provincial CO2 emission efficiency of China using data on energy consumption and carbon emissions from 2013 to 2019. Specifically, the game cross-equilibrium efficient frontier data envelopment analysis (DEA) approach, consisting of a game cross-efficiency model and a DEA fixed-sum output model, was proposed. In this method, CO2 is considered a fixed-sum undesirable output, and the decision-making units (DMUs) affect each other to achieve the efficiency result. The results show that: (1) CO2 emissions efficiency varied significantly between provinces during the study period, with Beijing performing the best and Ningxia the worst. (2) The overall national average CO2 emission efficiency showed a slow increasing trend over time. The eastern region performed best, followed by the northeastern, central, and western regions. (3) By comparing the existing DEA fixed-sum output methods, the CO2 efficiency obtained by our method, considering the DMU game relationship, is more acceptable. Based on these results, several policies and improvements were recommended to the provinces, such as reducing energy utilization in high-carbon emission industries. [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]

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]

As the primary source sector of carbon emissions, the power industry's green innovation is an indispensable component of China's low-carbon transformation. This paper took China's Carbon Emission Trading Scheme as a quasi-natural experience and adopted the synthetic control method to evaluate the impact of Carbon Emission Trading Scheme pilot policy on power industry's green innovation based on the provincial industrial level data. Furthermore, this paper examined the impact mechanisms of Carbon Emission Trading Scheme on the power industry's green innovation through policy comparison, moderating and mediating effect analyses. The research findings show that: 1) there is heterogeneity in Carbon Emission Trading Scheme's effectiveness in inducing green innovation in China, the power industry only in Beijing and Guangdong has been significantly promoted, while other pilots have not; 2) the success of Beijing and Guangdong can be attributed to distinctive features of their carbon market and power industry, including the refined quota accounting mechanism, the application of paid quota allocation, the larger enterprise scale and more research and development investment; 3) enterprise scale and research and development investment have positive moderating effect and mediating effect in the promotional effect of the Carbon Emission Trading Scheme on green innovation respectively. Finally, some policy recommendations are put forward to promote the power industry's green innovation and carbon market construction. • Evaluate the impact of China's CETS on the power industry's green innovation in each pilot. • The CETS effects among pilots are heterogeneous, only significant in Beijing and Guangdong. • Rational CETS mechanism design could improve the power industry's green innovation. • Enterprise scale and R&D investment positively moderate and mediate the CETS effect. [ABSTRACT FROM AUTHOR]

• Long-term operational monitoring of a Zero Emission Building (ZEB) in Beijing. • An existing building being retrofitted into a Zero Emission Building (ZEB). • Key Building Mounted Photovoltaic (BMPV) technologies are practically utilized and their effects are estimated. • The ZEB realized its own net-zero carbon emission and net energy production. • The ZEB could be a comprehensive experimental platform. Building mounted photovoltaic (BMPV) technology is a promised way to achieve carbon peak in building sector. Focused on the influence of heat from PV modules on building envelope's heat transfer and energy saving performance, and the power generation, the coupling mechanism of power generation and heat transfer of integrated photovoltaic technology on building façades has been investigated. The synergy of solar energy generation and building energy consumption have been studied in this paper. The installation method of building PV system was proposed, with which can achieve the maximum energy saving. Furthermore, the achievements have been applied in Zero Emission Building (ZEB) of China Academy of Building Research (CABR), demonstrating way to realize zero carbon building by integration of photovoltaic technology. Total of 236.1 kWp PV systems were installed in ZEB. Monitoring data shows that the annual power generation of ZEB could reach 212,382 kWh, and the power generation per building area is 73.79 kWh/m2, which could meet all the energy demands. In addition, it can supply extra power to surrounding buildings. The ZEB can provide experience and practice data supporting carbon peak in building sector. [ABSTRACT FROM AUTHOR]

Demand‐responsive transit has gradually attracted attention in recent years for its flexibility, efficiency, and ability to meet the diverse travel demands of passengers. To improve the operational efficiency of demand‐responsive transit (DRT) with dynamic demand, this study innovatively investigates the DRT scheduling problem from multiple perspectives, such as multi‐vehicle, non‐fixed stop, and dynamic demand, and constructs a two‐phase DRT vehicle scheduling model. In the first phase, a static scheduling model is established with the objective of minimizing vehicle setup cost, operation cost, and CO2 emission cost according to passenger travel satisfaction. In the second phase, a dynamic scheduling model is constructed with the objective of minimizing the increased vehicle operation cost in response to dynamic demand and the penalty cost of violating the time window and rejecting passengers. In addition, in the first static phase, an improved heuristic algorithm is used to obtain optimal routes based on passengers' subscriptions, while in the second phase, an insertion algorithm is designed to solve the dynamic scheduling model based on the previous schedule. Finally, cases are applied to a realistic network in Chaoyang District, Beijing, China, to verify the effectiveness of the proposed scheduling model. The results demonstrate that dynamic scheduling can enable more passengers to be served with a slight increase in total vehicle operating costs. Besides, the introduction of the non‐fixed stop service model can significantly reduce total travel time by up to 8.8% compared with the fixed stop service. The proposed models and solution algorithms in this study are practical for real‐world applications. [ABSTRACT FROM AUTHOR]

Predicting urban-scale carbon emissions (CEs) is crucial in drawing implications for various urgent environmental issues, including global warming. However, prior studies have overlooked the impact of the micro-level street environment, which might lead to biased prediction. To fill this gap, we developed an effective machine learning (ML) framework to predict neighborhood-level residential CEs based on a single data source, street view images (SVIs), which are publicly available worldwide. Specifically, more than 30 streetscape elements were classified from SVIs using semantic segmentation to describe the micro-level street environment, whose visual features can indicate major socioeconomic activities that significantly affect residential CEs. A ten-fold cross-validation was deployed to train ML models to predict the residential CEs at the 1 km grid level. We found, first, that random forest (R2 = 0.8) outperforms many traditional models, confirming that visual features are non-negligible in explaining CEs. Second, more building, wall, and fence views indicate higher CEs. Third, the presence of trees and grass is inversely related to CEs. Our findings justify the feasibility of using SVIs as a single data source to effectively predict neighborhood-level residential CEs. The framework is applicable to large regions across diverse urban forms, informing urban planners of sustainable urban form strategies to achieve carbon-neutral goals, especially for the development of new towns. [ABSTRACT FROM AUTHOR]

The main objective of this paper was seeking suitable scenarios for the Beijing-Tianjin-Hebei region, where both socio-economic development and low-carbon targets would be achieved. Potential driven factors of carbon emissions, including population, affluence, urbanization level, technology level, industrial construction, and energy consumption construction were selected to build an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, where ridge regression was applied to ensure its stability. The STIRPAT model showed the significance of each independent variable, which was the foundation of CO2 emissions' prediction. Furthermore, eight scenarios were established to explore the possible carbon footprints and the maximum of CO2 in the period from 2013 to 2050. This paper finally proposed the strategies that can be applied to reduce future carbon emissions in the Beijing-Tianjin-Hebei region. Applying reasonable policies about improvement of technological level, and adjustment of industry and energy consumption structures is a critical factor for the control of CO2 emissions. [ABSTRACT FROM AUTHOR]

To better make use of the waste heat in distributed data centers, a new design of integrated waste heat recovery system that can meet different needs including heating for users and data center cooling is introduced. The system can switch among different operation modes including waste heat recovery mode, natural cooling mode, composite cooling mode and refrigeration mode based on the ambient temperature. The integrated waste heat recovery system is experimented in the enthalpy-difference laboratory, and the experimental results show well consistency compared with the modeling results. The operation strategies to switch among different modes are determined based on the experimental and modeling results. The paper introduced PUE (Power Usage Effectiveness) and ERE (Energy Reuse effectiveness) to comprehensively evaluate the cooling and energy reutilization effectiveness of the system. According to the evaluation result, the integrated waste heat recovery system has well performed energy effectiveness, environmental and economic effect. The ERE values of using the integrated waste heat recovery system in Chinese typical climate cities including Harbin, Beijing, Xi'an, Shanghai and Guangzhou are 0.87, 1.02, 1.04, 1.17 and 1.29 respectively. Using this system at 96 kW distributed data center instead of CRAC (computer room air conditioning) system can save 152.7 MWh electricity, 56.4 tons of standard coal, and 244.7 tons of CO 2 emission in Beijing annually. The payback periods of this new system in different climate regions in China are all within 3 years. • An integrated data center cooling and waste heat recovery system is proposed. • Operation performance of the system is verified by experiment and modeling. • Energy Reuse Factor in Beijing is 1.02, and payback period is within 3 years. [ABSTRACT FROM AUTHOR]

The building sector accounts for nearly 1/3 of global energy end-use and associated carbon emissions. Fulfilling buildings' thermal demand in an efficient way is critical for decarbonizing the building sector. Therefore, this paper introduces the thermally activated building system (TABS) as an energy retrofit solution. A TABS floor-by-floor return water temperature control approach is proposed to reduce buildings' energy use and carbon emissions. The approach includes the topology optimization of TABS and flow control. The bypass pipe connections of the TABS to each floor, namely, the floor-by-floor pipe network topology, have been optimized by using genetic algorithms to achieve the lowest total cost. A case study of a high-rise building located in Beijing demonstrates that the proposed approach can improve the system's energy efficiency compared to the conventional TABS without optimization. The reduction of energy cost can be up to 11.21% with a 13.53% annual emission reduction. In general, the proposed TABS floor-by-floor return water temperature control approach can significantly improve the building efficiency comprehensively, therefore can be considered as a promising energy retrofit solution for the decarbonization of high-rise buildings. [Display omitted] • Fulfill building's thermal demand by TABS – an efficient way with less emissions. • Propose a floor-by-floor return water temperature control approach of TABS. • Proposed method can reduce energy use and emissions even without optimization. • Optimize the topology of TABS by using genetic algorithm. • Optimized TABS can save 11.21% energy cost and reduce 13.53% emissions. [ABSTRACT FROM AUTHOR]

Beijing-Tianjin-Hebei metropolitan area is a significant carbon emission center. The region's early achievement of peak carbon targets is critical to the nation's achievement of peak carbon targets. In this paper, it is proposed to use different orders of grey models to classify into three scenarios. Based on three scenarios, the grey multivariate convolutional model with new information priority accumulation is adopted to predict carbon emissions in the Beijing-Tianjin-Hebei region and select the scenario suitable for local development. The results show that: (1) The Beijing region has already achieved peak carbon, the Tianjin region may not reach its peak carbon target by 2030, and the Hebei region is expected to reach its peak carbon target by 2030. (2) The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. The low-speed growth carbon emission scenario is more in line with the future development of Tianjin city. The low-rate carbon reduction scenario is more in line with the synergistic governance of pollution reduction and carbon reduction in Hebei Province. (3) Beijing's population policy in the most recent years has been conducive to improving the local environment. Tianjin's medium-term population policy is more in line with the local area. Hebei's medium-term industrial structure reform is favorable to local development. • Grey correlation analysis is used to analyze the factors affected carbon emissions in a region. • The factors with high correlation are predicted by using grey prediction models of different orders. • The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. [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]

Integrated energy system (IES) is characterized by high self-consumption ratio of on-site generated renewable energy, high efficiency of conventional energy utilization and possesses a significant flexibility in its operation. This overall, constitute to the foundation of low-carbon communities. Considering economic and environmental benefits, this paper proposes a two-layer co-optimization model with the upper layer optimizing the IES configuration and the lower layer optimizing IES operation. A community in Beijing is introduced as a case study to analyze the benefits of IES and compared with the conventional energy system. Multiple scenarios are researched, including (a) IES for low-carbon communities with PV roof area limitation, (b) IES for each of the four building types with PV roof area limitation, (c) IES for low-carbon communities without PV roof area limitation. The results indicate that the optimized IES for community can reduce the cost by 25% and CO 2 emissions by 32% per year by considering the limitation of roof area for the PV system. Without this constraint, the costs and emissions could be reduced by 20% and 62%, respectively. In addition, IES is more appropriate for office and commercial buildings since their load characteristics allow for load shifting and community-level IES costs and emissions are 8% and 10% lower than building-level due to the complementary of community load. This study provides suggestions for the IES planning and application in low-carbon community. [Display omitted] • An IES with multiple renewable energy and energy storage subsystems is proposed. • A two-layer co-optimization model of IES design and operation is developed. • IES optimization results for building-level and community-level are compared. • Impact of roof area limitation for PV on IES optimization results is investigated. [ABSTRACT FROM AUTHOR]

• A reliable path finding model is proposed to minimize the fuel consumption budget. • Specified on-time arrival probability index is ensured by reliable path finding model. • A heuristic label setting algorithm is developed to exactly solve the formulated problem. • Real traffic data is applied for the applicability and efficiency verification. Transport activities are regarded as a major source of fuel consumption and CO 2 emission production. To reduce the negative impact of traffic-related CO 2 emission, this paper proposes a reliable path-finding algorithm for improving fuel efficiency in stochastic networks under the uncertainty of travel time and fuel consumption with the consideration of spatial correlation. A reliable constrained path-finding model is developed and formulated to minimize the fuel consumption budget while guaranteeing the specified on-time arrival probability. A heuristic label setting algorithm is developed to precisely solve the formulated problem. The proposed algorithm overcomes the time-consuming drawbacks of traditional path enumeration algorithms. The applicability and efficiency of the proposed algorithm are verified on real-world traffic data acquired from the Beijing and Xi'an networks in China. The experiments demonstrate that our proposed algorithm can significantly reduce fuel consumption compared to existing studies. The experiment in Beijing shows that using the proposed algorithm can reduce 0.9 kg of CO 2 emissions on average per trip compared to existing studies. [ABSTRACT FROM AUTHOR]

• City-centric global multi-region input-output models for Beijing and Shanghai are developed. • Factor contribution and critical path analysis are adopted. • Sector aggregation effect varies across sectoral datasets. Sector aggregation under the input-output framework may lead to deviation in results and the lack of detailed sectoral information, which hinder the targeted implementation of accurate sectoral adjustment policies. This paper explores the effects of sector aggregation on the embodied carbon emission of the residential consumptions of Beijing and Shanghai based on the city-centric global multi-region input-output (CCG-MRIO) model. Integrating with structural path analysis and structural decomposition analysis, the impacts of sector aggregation on the identifying of critical emission transmission paths and driving forces are also revealed. Three sector aggregation datasets are discussed to track the sector aggregation effect in 2012 and 2017. The findings show that the carbon emission results at 8 sector level better align with the results at the 22 sector level than those at 32 sector level. Moreover, sector aggregation will lead to the absence of some critical carbon transmission paths, some factor contributions on carbon emission also changed their directions at different sector levels. On the basis of clarifying the sector aggregation effect on embodied carbon emission, the research results provide a reference for similar studies to select the appropriate level of sector aggregation, to realize the goal of simplifying the calculation or gaining more detailed sectoral emission information. [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]

In recent years, China has been vigorously carrying out the construction and development of a sponge city. To prove that the material and energy consumption involved in the implementation of a sponge city is much less than that of the integrated urban drainage system (IUDS) in addition to saved energy and reduced carbon in the sponge city, it was important to calculate the corresponding carbon source and sink and analyze its key influence factors. The emission factor method was used to calculate carbon emissions. In view of this, based on the Intergovernmental Panel on Climate Change (IPCC) guidelines and life cycle assessment (LCA), this research established a systematic accounting method for carbon emissions from the IUDS and the sponge city, which focused on improving the calculation method of the carbon sink stage. A case study was conducted in Beijing, China, and the carbon emission reduction effect of the construction of the sponge city was discussed. The results showed that the carbon emission reduction potential (CRP) of sponge facilities in this project for 50 years was 612.45 tons of CO2 equivalent after the renovation. Compared with IUDS, sponge city construction had a positive effect on carbon emission reduction and reduced carbon emissions by 87.08% on average. For the IUDS and the sponge city, the stormwater pipe network had the largest contribution of carbon emission, and its material, transportation, pipeline laying, and maintenance of stormwater pipe networks had important influences. Morris global analysis method was used to analyze the sensitivity of LCA results and obtained that the influence degree of sensitivity factors on carbon emissions in the life cycle was in the order of annual rainfall > carbon sequestration rate of green space > high-density polyethylene (HDPE) > transport distance > fertilization and insecticide. This study can provide a positive contribution to the construction of a sponge city and planning the low-carbon development of the city in the future in China. [ABSTRACT FROM AUTHOR]

TanSat-2, the next-generation Chinese greenhouse gas monitoring satellite for measuring carbon dioxide (CO 2 ), has a new city-scale observing mode. We assess the theoretical capability of TanSat-2 to quantify integrated urban CO 2 emissions over the cities of Beijing, Jinan, Los Angeles, and Paris. A high-resolution emission inventory and a column-averaged CO 2 ( X CO 2 ) transport model are used to build an urban CO 2 inversion system. We design a series of numerical experiments describing this observing system to evaluate the impacts of sampling patterns and X CO 2 measurement errors on inferring urban CO 2 emissions. We find that the correction in systematic and random flux errors is correlated with the signal-to-noise ratio of satellite measurements. The reduction in systematic flux errors for the four cities are sizable, but are subject to unbiased satellite sampling and favorable meteorological conditions (i.e., less cloud cover and lower wind speed). The corresponding correction to the random flux error is 19–28%. Even though clear-sky satellite data from TanSat-2 have the potential to reduce flux errors for cities with high CO 2 emissions, quantifying urban emissions by satellite-based measurements is subject to additional limitations and uncertainties. [ABSTRACT FROM AUTHOR]

This study addresses the comparative carbon emissions of different transportation modes within a unified evaluation framework, focusing on their carbon footprints from inception to disposal. Specifically, the entire life cycle carbon emissions of High-Speed Rail (HSR), battery electric vehicles, conventional internal combustion engine vehicles, battery electric buses, and conventional internal combustion engine buses are analyzed. The life cycle is segmented into vehicle manufacturing, fuel or electricity production, operational, and dismantlingrecycling stages. This analysis is applied to the Beijing-Tianjin intercity transportation system to explore emission reduction strategies. Results indicate that HSR demonstrates significant carbon emission reduction, with an intensity of only 24 %-32 % compared to private vehicles and 47 %-89 % compared to buses. Notably, HSR travel for Beijing-Tianjin intercity emits only 24 % of private vehicle emissions, demonstrating the emission reduction benefits of transportation structure optimization. Additionally, predictive modeling reveals the potential for carbon emission reduction through energy structure optimization, providing a guideline for the development of effective transportation management systems. [ABSTRACT FROM AUTHOR]

The primary energy use in cities accounts for a large share of global energy consumption, leading to significant carbon emissions. Exploring the city's current characteristics and future trends in energy demand has policy implications for carbon reduction policy-making. We establish a city-level energy forecast demand model to predict Beijing's future energy demand. Results show that economic growth has strong positive correlations with energy consumption. Beijing's energy demand is predicted to stay at a pretty high level by the end of 2020, between 98.72 and 121.60 Mtce, or between 4.12 and 5.08 tce per capita under three distinctive GDP growth (High, Medium and Low) scenarios. Comparing with the start of historical time series 1980, this is a huge increase of 563.50% (High), 489.90% (Medium) and 423.91% (Low) in terms of total demand, while is increases of 147.98%, 120.47% and 95.81% per capita. Based on an energy structure model estimated by a Markov-chain method, we find that our predictions would follow the current trend of energy structure, making Beijing's coal demand decline towards zero share of energy consumption, natural gas rises approaching 50%, oil stays almost constant around 32.3%, electricity decreases below 20%, and other fuels keep in a small amount in 2035. We would expect to see almost 8000 kg per capita carbon emissions in Beijing if the GDP growth rate is high, and still over 6000 kg per capita if GDP growth is low. In terms of total carbon emissions, we show that though coal consumption will keep decreasing towards zero in the next years, total carbon emission still rises by 36.5–72.8% comparing with 2010. These findings can provide references for policymakers to implement some incentive policies to promote renewable and clean energy. [ABSTRACT FROM AUTHOR]

As a basic structural unit of the city, the community plays a crucial role in decreasing carbon emissions and creating a low carbon city. This paper selects a classic and official low carbon community named Min'an Community in Beijing as a case to illustrate the governance mechanism of the low carbon community in China from the perspective of polycentric governance. The paper summarizes the government mechanism, market mechanism, and public participation mechanism in Min'an Community and analyzes the provision, monitoring, and enforcement of each mechanism. We find that unlike the low carbon governance at the city or industry park levels, China's low carbon governance at the community level lacks a dominant mechanism. Even though it involves community-wide governance, the public participation mechanism is also inadequate. For improving low carbon governance at the community level, the government should broaden finance channels to strengthen the incentive mechanisms, make active use of new technologies, such as mobile Internet, encourage innovation of public participation mechanism, and employ various publicity and education measures to cultivate the low carbon culture. [ABSTRACT FROM AUTHOR]