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The U.S. Environmental Protection Agency’s (EPA) Superfund—the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) database—has collected and built an open-source database based on nearly 2000 US soil remediation cases since 1980, providing detailed information and references for researchers worldwide to carry out remediation work. However, the cases were relatively independent to each other, so the whole database lacks systematicness and instructiveness to some extent. In this study, the basic features of all 144 soil remediation projects in four major oil-producing states (California, Texas, Oklahoma and Alaska) were extracted from the CERCLA database and the correlations among the pollutant species, pollutant site characteristics and selection of remediation methods were analyzed using traditional and machine learning techniques. The Decision Tree Classifier was selected as the machine learning model. The results showed that the growth of new contaminated sites has slowed down in recent years; physical remediation was the most commonly used method, and the probability of its application is more than 80%. The presence of benzene, toluene, ethylbenzene and xylene (BTEX) substances and the geographical location of the site were the two most influential factors in the choice of remediation method for a specific site; the maximum weights of these two features reaches 0.304 and 0.288.

The importance and urgency of energy efficiency in sustainable development are increasing. Accurate assessment of energy efficiency is of considerable significance and necessity. The data envelopment analysis (DEA) method has been widely used to study energy efficiency as a total factor efficiency assessment method. In order to summarize the latest research on DEA in the field of energy efficiency, this article first analyzes the overall situation of related literature published in 2011–2019. Subsequently, the definition, measurement and evaluation variables of energy efficiency are introduced. After that, this article reviews the current DEA model and its extension models and applications based on different scenarios. Finally, considering the shortcomings of the existing DEA model, possible future research topics are proposed.

We propose an emission-intensity-based carbon-tax policy for the electric-power industry and investigate the impact of the policy on thermal generation self-scheduling in a deregulated electricity market. The carbon-tax policy is designed to take a variable tax rate that increases stepwise with the increase of generation emission intensity. By introducing a step function to express the variable tax rate, we formulate the generation self-scheduling problem under the proposed carbon-tax policy as a mixed integer nonlinear programming model. The objective function is to maximize total generation profits, which are determined by generation revenue and the levied carbon tax over the scheduling horizon. To solve the problem, a decomposition algorithm is developed where the variable tax rate is transformed into a pure integer linear formulation and the resulting problem is decomposed into multiple generation self-scheduling problems with a constant tax rate and emission-intensity constraints. Numerical results demonstrate that the proposed decomposition algorithm can solve the considered problem in a reasonable time and indicate that the proposed carbon-tax policy can enhance the incentive for generation companies to invest in low-carbon generation capacity.

Under the “new normal”, China is facing more severe carbon emissions reduction targets. This paper estimates the carbon emission data of various provinces in China from 2008 to 2014, constructs a revised gravity model, and analyzes the network structure and effects of carbon emissions in various provinces by using social network analysis (SNA) and quadratic assignment procedure (QAP) analysis methods. The conclusions show that there are obvious spatial correlations between China’s provinces and regions in terms of carbon emissions: Tianjin, Shanghai, Zhejiang, Jiangsu and Guangdong are in the center of the carbon emission network, and play the role of “bridges”. Carbon emissions can be divided into four blocks: “bidirectional spillover block”, “net beneficial block”, “net spillover block” and “broker block”. The differences in the energy consumption, economic level and geographical location of the provinces have a significant impact on the spatial correlation relationship of carbon emissions. Finally, the improvement of the robustness of the overall network structure and the promotion of individual network centrality can significantly reduce the intensity of carbon emissions.

Analysis of the change of overall energy efficiency performance of an energy system is a fundamental work for the energy-saving policymaking. However, previous studies seldom focus on energy stages from useful energy to final service, while most attention are paid on stages from energy source to useful energy. In this paper, we develop a high-resolution the Societal Exergy Analysis and Logarithmic Mean Divisia Index (SEA-LMDI) method to analysis changes and driving factors of the aggregate exergy efficiency, in which the boundary of the SEA is extended to passive systems and final services, and a LMDI decomposition method is developed to quantify contributions of efficiency factors and structure factors of all six stages on the aggregate exergy efficiency. A case study of China during 2005–2015 reveals that: (a) the aggregate exergy efficiency from energy source to final service is only from 3.7% to 4.8% during 2005–2015, showing a huge theoretical potential of efficiency improvement. (b) Large passive losses are identified in passive systems and nearly 2/3 of useful energy can be theoretically saved by improving passive systems. (c) Deep analysis of industrial coal-fired boilers indicates that the internal structural adjustments are also important for the aggregate improvement.

As one of the crucial factors contributing to coal spontaneous combustion, the oxidation of pyrite is a complex process involving multiple reactions, particularly in the presence of oxidants (Fe3+ and O2) and bacteria. However, experimental results based on mineral-pyrite are not entirely applicable to coal-pyrite due to their differences in formation environments and compositions. This study selected two types of coal-pyrite and one type of mineral-pyrite as research to conduct oxidation experiments with the participation of oxidant (Fe3+) and bacteria (Acidithiobacillus ferrooxidans), respectively, to obtain the following conclusions. Under natural conditions, the chemical oxidation rate of pyrite is slow, but the addition of oxidant Fe3+ and bacteria can significantly accelerate the oxidation rate. The promotion effect of oxidant Fe3+ on the oxidation reaction is stronger than that of bacteria. Under the same conditions, the oxidation rate of coal-pyrite samples is slightly higher than that of mineral-pyrite, due to the relatively higher impurities content, poorer crystal structure, and humic acid in the coal seams. Additionally, different compositions of coal-pyrite samples can lead to various oxidation degrees under different conditions. Therefore, the oxidation process and mechanism of pyrite in coal seams are complex and affected by many factors, which need further study to prevent coal spontaneous combustion accurately and effectively.

Perovskite CsPbBr3 quantum dot (CsPbBr3-QD) recovery was performed using lead scrap from lead storage batteries. The perovskite CsPbBr3-QD characteristics were analyzed using different PbO/recycled PbO2 ratios. Scanning electron microscopy (SEM) was used to observe the film surface morphology and cross-section. High-resolution transmission electron microscopy (HRTEM) and X-ray diffraction (XRD) were used to observe the perovskite CsPbBr3-QDs’ structural characteristics. A photoluminescence (PL) measurement system was used to analyze the optical properties. The results show that lead scrap from lead–acid batteries as a material for perovskite CsPbBr3-QD production can be successfully synthesized. This saves material and also proves that recycling is valuable. The proposed approach is helpful for future material shortages and materials not easily accessible. Although the efficiency is not very high, this process will be purified using recycled lead in the future to achieve higher quantum yield.