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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Large amounts of carbon dioxide (CO2) are discharged into air by combustion of gasoline and energy is input in a passenger internal combustion engine vehicle (ICEV) system through lifecycle phases. Thus, this study aims to evaluate the lifecycle carbon emissions and primary energy input of passenger ICEVs. With the life cycle assessment method, the calculations of carbon emission intensity and energy input intensity of materials used in lifecycle phases were made. The results show that the total lifecycle gasoline consumption of a vehicle was estimated to be 33,000 kg and the lifecycle CO2 emissions reached 81,311 kg per vehicle. Lifecycle CO2 emissions in use period occupied 89.43%, mainly attributed to the combustion of petrol. The average lifecycle CO2 emission intensity of the materials used was 2.33 kg/kg. The lifecycle carbon footprint of the passenger ICEVs was 163 g/km. The total CO2 emissions of ICEVs in Beijing were largest of all cities due to the maximum owning amount. The ratio of total lifecycle CO2 emissions of ICEVs in 13 large cities studied in this work to those of nationwide ICEVs was 0.179. Thus, more new energy vehicles will be given the prioritized choice in the future. [ABSTRACT FROM AUTHOR]