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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.

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.

Building energy renovations can effectively improve the environmental performance and energy sustainability of existing buildings. From 2007 onwards, the Chinese government has promoted energy-saving renovations of existing urban residential buildings. Nevertheless, various quality failures happen during the construction period in energy-saving renovation projects of residential buildings. Yet, the causes and their characters remain largely unknown. Through a literature review, this paper investigates the causes of quality failures. Validated through experts’ interviews, a total of 18 causes were identified in building energy renovation projects. These causes were analyzed from two main aspects: the importance of a cause (related to impact and frequency), and the level of effort required to address a cause (related to origin and scale), using both a questionnaire survey and a focus group. The results indicate that the critical causes of quality failures are working under high-cost and high-time pressure, adverse natural conditions, fraud of construction companies, incomplete construction site survey, poor checking procedures of supervisors, poor operational skilled workers, inadequate equipment performance, lack of experienced project managers, and incomplete building information in projects. The causes were classified as external and internal causes of building energy renovation projects. The outcome of this paper should aid policy makers and project coordinators to focus on critical causes of quality failures, and to develop effective actions and policy interventions to achieve successful renovation projects with high-quality performance.

Sustainable public transport is an important factor to boost urban economic development, and it is also an important part of building a low-carbon environmental society. The application of driverless technology in public transport injects new impetus into its sustainable development. Road traffic sign recognition is the key technology of driverless public transport. It is particularly important to adopt innovative algorithms to optimize the accuracy of traffic sign recognition and build sustainable public transport. Therefore, this paper proposes a convolutional neural network (CNN) based on k-means to optimize the accuracy of traffic sign recognition, and it proposes a sparse maximum CNN to identify difficult traffic signs through hierarchical classification. In the rough classification stage, k-means CNN is used to extract features, and improved support vector machine (SVM) is used for classification. Then, in the fine classification stage, sparse maximum CNN is used for classification. The research results show that the algorithm improves the accuracy of traffic sign recognition more comprehensively and effectively, and it can be effectively applied in unmanned driving technology, which will also bring new breakthroughs for the sustainable development of public transport.