The Overview for the Greenhouse-gas Emission Characteristics and Intensity in the Electric Power Industry

China has ranked the first in carbon emission in the world, and the electric power industry is listed as the predominant field in all industries, which causes the biggest environmental pollution. It is useful to identify the carbon emission sources in electric power industry in China and concludes the possible ways to calculate the greenhouse gases emissions. It will be the base to set up the possible estimation model for emission, and help to analyze the methods to reduce the emission

Treatment of electric vehicle battery waste in China: A review of existing policies

This paper reviews existing policies for supporting the treatment of electric vehicle (EV) battery waste in China, and identifies some of their major shortcomings that policy makers may like to consider while making policy decisions. The shortcomings of existing policies identified in this paper include: 1) no clear provisions for historical and orphan batteries; 2) no target for battery collection; 3) unclear definition of the scope of authority among various central and local agencies involved in the regulation of waste battery treatment; 4) unclear requirements for data auditing and verification for tracking the entire life cycle of EV batteries; 5) limited consideration of the challenges to ensure stakeholder cooperation; and 6) no explicit specification of the mechanisms for financing waste battery treatment. This paper also makes some practical policy suggestions for overcoming these shortcomings

The Influence Paths of Agricultural Mechanization on Green Agricultural Development

For sustainable agricultural development, increasing efforts are put on promoting agricultural mechanization and green agricultural development all over the world. Based on the panel data of Chinese provincial agriculture from 2002 to 2018, the System Generalized Method of Moments model and mediation model are constructed to explore the paths of agricultural mechanization affecting green agricultural development. The results show that agricultural mechanization can not only promote the green agricultural development directly but also indirectly by transferring the agricultural labor force and increasing fertilizer input. However, because of the surge of pesticide demand, agricultural mechanization also leads to serious pollution indirectly. With the development of large-scale agricultural machinery, the direct promotion of agricultural machinery on green agricultural development will be more significant. However, it will be less efficient to substitute more agricultural labor force with machinery power. The problem of pesticide abuse will also become more serious. Therefore, it is important for green agricultural development to encourage human capital investment in agricultural mechanization. In addition, more attention should be paid to improving the input efficiency of fertilizers and pesticides so that agriculture will be sustainable in production and the ecological environment

Driving Factors of CO2 Emissions in China’s Power Industry: Relative Importance Analysis Based on Spatial Durbin Model

The low-carbon transformation of the power industry is of great significance to realize the carbon peak in advance. However, almost a third of China’s CO2 emissions came from the power sector in 2019. This paper aimed to identify the key drivers of CO2 emissions in China’s power industry with the consideration of spatial autocorrelation. The spatial Durbin model and relative importance analysis were combined based on Chinese provincial data from 2003 to 2019. This combination demonstrated that GDP, the power supply structure and energy intensity are the key drivers of CO2 emissions in China’s power industry. The self-supply ratio of electricity and the spatial spillover effect have a slight effect on increasing CO2 emissions. The energy demand structure and CO2 emission intensity of thermal power have a positive effect, although it is the lowest. Second, the positive impact of GDP on CO2 emissions is decreasing, but that of the power supply structure and energy intensity is increasing. Third, the energy demand of the industrial and residential sectors has a greater impact on CO2 emissions than that of construction and transportation. For achieving the CO2 emission peak in advance, governments should give priority to developing renewable power and regional electricity trade rather than upgrading thermal power generation. They should also focus on promoting energy-saving technology, especially tapping the energy-saving potential of the industry and resident sectors

Provincial CO2 Emission Measurement and Analysis of the Construction Industry under China’s Carbon Neutrality Target

The construction industry plays a crucial role in China’s fulfillment of the goal of achieving “carbon neutrality” in 2060. Based on the data of energy and building materials consumption of the construction industry in China and 30 provinces from 2008 to 2018, this paper constructs a model for measuring provincial CO2 emissions of China’s construction industry and establishes a Kuznets curve and elastic decoupling model of the industry’s CO2 emissions. The analysis based on the models shows that: (1) the CO2 emissions of China’s construction industry show a trend of increasing first and then decreasing; (2) in terms of the decoupling effects, most provinces are in a weak decoupling status of CO2 emissions; and (3) the Kuznets curve of the provincial construction industry shows an inverted “U” shape in recent years, and it is predicted that the CO2 emissions of the construction industry will reach the peak in 2034. It is possible for the construction industry to achieve “carbon neutrality”, but long-term efforts must be made for strategic planning, policies and regulations, industry standards, etc

A New Multicriteria Decision-Making Method for the Selection of Sponge City Schemes with Shapley–Choquet Aggregation Operators

The construction of sponge cities is of great strategic significance to solving the urban water resource problem in the future. According to the policy guidance of sponge city construction, the evaluation index system of sponge city construction projects is constructed. In order to overcome the interference caused by the interaction between indexes, a nonadditive measure and Shapley function are combined to determine the weights of attribute indexes, and the generalized Shapley interval-valued intuitionistic uncertain linguistic Choquet averaging (GS-IVIULCA) operator is used to calculate the comprehensive evaluation value of the schemes. On this basis, a new evaluation method of sponge city construction project selection under an uncertain information environment is presented and empirically evaluated. The results show that the index weight of rainwater collection and utilization is the largest, indicating that decision makers pay more attention to the ecological and environmental benefits of this item in the sponge city construction process

Financial Risk Assessment of Photovoltaic Industry Listed Companies Based on Text Mining

At present, the research on photovoltaic companies’ financial risk early warning model mainly focuses on financial indicators and non-financial indicators from corporate governance structure and external audit opinions. There are few literature studies on the companies’ internal information from their annual report. To solve the above problem, firstly, this paper aims to establish a comprehensive assessment indicators system including financial and non-financial indicators considering the companies’ internal information. Secondly, this paper uses text mining and a binary logistic regression model to evaluate the financial risk for 37 listed companies in the photovoltaic industry. The results showed that profitability was the most significant factor. Probability, as well as negative sentiment ratios, are both negatively correlated with the occurrence of financial risk, while development capability is positively associated with financial risk. These findings can be used as an effective supplement for financial risk evaluation in the photovoltaic industry and provide reference strategies for developing listed companies in the photovoltaic industry

Enabling Smart Transportation Systems:A Parallel Spatio-Temporal Database Approach

We are witnessing increasing interests in developing "smart cities" which helps improve the efficiency, reliability, and security of a traditional city. An important aspect of developing smart cities is to enable "smart transportation," which improves the efficiency, safety, and environmental sustainability of city transportation means. Meanwhile, the increasing use of GPS devices has led to the emergence of big trajectory data that consists of large amounts of historical trajectories and real-time GPS data streams that reflect how the transportation networks are used or being used by moving objects, e.g., vehicles, cyclists, and pedestrians. Such big trajectory data provides a solid data foundation for developing various smart transportation applications, such as congestion avoidance, reducing greenhouse gas emissions, and effective traffic accident response, etc. Instead of proposing yet another specific smart transportation application, we propose the parallel-distributed network-constrained moving objects database (PD-NMOD), a general framework that manages big trajectory data in a scalable manner, which provides an infrastructure that is able to support a wide variety of smart transportation applications and thus benefiting the smart city vision as a whole. The PD-NMOD manages both transportation networks and trajectories in a distributed manner. In addition, the PD-NMOD is designed to support general SQL queries over moving objects and to efficiently process the SQL queries on big trajectory data in parallel. Such design facilitates smart transportation applications to retrieve relevant trajectory data and to conduct statistical analyses. Empirical studies on a large trajectory data set collected from 3,500 taxis in Beijing offer insight into the design properties of the PD-NMOD and offer evidence that the PD-NMOD is efficient and scalable.We are witnessing increasing interests in developing "smart cities" which helps improve the efficiency, reliability, and security of a traditional city. An important aspect of developing smart cities is to enable "smart transportation," which improves the efficiency, safety, and environmental sustainability of city transportation means. Meanwhile, the increasing use of GPS devices has led to the emergence of big trajectory data that consists of large amounts of historical trajectories and real-time GPS data streams that reflect how the transportation networks are used or being used by moving objects, e.g., vehicles, cyclists, and pedestrians. Such big trajectory data provides a solid data foundation for developing various smart transportation applications, such as congestion avoidance, reducing greenhouse gas emissions, and effective traffic accident response, etc. Instead of proposing yet another specific smart transportation application, we propose the parallel-distributed network-constrained moving objects database (PD-NMOD), a general framework that manages big trajectory data in a scalable manner, which provides an infrastructure that is able to support a wide variety of smart transportation applications and thus benefiting the smart city vision as a whole. The PD-NMOD manages both transportation networks and trajectories in a distributed manner. In addition, the PD-NMOD is designed to support general SQL queries over moving objects and to efficiently process the SQL queries on big trajectory data in parallel. Such design facilitates smart transportation applications to retrieve relevant trajectory data and to conduct statistical analyses. Empirical studies on a large trajectory data set collected from 3,500 taxis in Beijing offer insight into the design properties of the PD-NMOD and offer evidence that the PD-NMOD is efficient and scalable

Prediction Method of Beijing Electric-Energy Substitution Potential Based on a Grid-Search Support Vector Machine

Recently, “power cuts” and “coal price surges” have been significant concerns of individuals and societies. The main reasons for a power cut are a recent rapid increase in power consumption, shortage of thermal coal or the large shutdown capacity of thermal power units, resulting in a tight power supply in the power grid. In recent years, the shortage of fossil resources has led to frequent energy crises. In the context of carbon peaks and carbon neutralization, how to better develop electric-energy substitution and eliminate the dependence on fossil energy has become a problem that needs to be solved at present. In this paper, the influencing factors of electric-energy substitution in Beijing are analyzed, and the indexes affecting the electric-energy substitution are outlined. By constructing various machine-learning models, the prediction is performed. The results show that the Gaussian kernel support vector machine model based on a grid search has a good prediction effect on the electric-energy substitution potential in Beijing, which has certain guiding significance for electric-energy substitution potential analysis

Scenarios Analysis of the Energies’ Consumption and Carbon Emissions in China Based on a Dynamic CGE Model

This paper investigates the development trends and variation characteristics of China’s economy, energy consumption and carbon emissions from 2007 to 2030, and the impacts on China’s economic growth, energy consumption, and carbon emissions under the carbon tax policy scenarios, based on the dynamic computable general equilibrium (CGE) model. The results show that during the simulation period, China’s economy will keep a relatively high growth rate, but the growth rate will slow down under the benchmark scenario. The energy consumption intensity and the carbon emissions intensity per unit of Gross Domestic Product (GDP) will continually decrease. The energy consumption structure and industrial structure will gradually optimize. With the economic growth, the total energy consumption will constantly increase, and the carbon dioxide emissions are still large, and the situation of energy-saving and emission-reduction is still serious. The carbon tax is very important for energy-saving and emission-reduction and energy consumption structure optimization, and the effect of the carbon tax on GDP is small. If the carbon tax could be levied and the enterprise income tax could be reduced at the same time, the dual goals of reducing energy consumption and carbon emissions and increasing the GDP growth can be achieved. Improving the technical progress level of clean power while implementing a carbon tax policy is very meaningful to optimize energy consumption structure and reduce the carbon emissions, but it has some offsetting effect to reduce energy consumption