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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.

We report on the real-world use over the course of one year of a nickel-metal-hydride plug-in hybrid—the Toyota Plug-In HV—by a set of 12 northern California households able to charge at home and work. From vehicle use data, energy and greenhouse-emissions implications are also explored. A total of 1557 trips—most using under 0.5 gallons of gasoline—ranged up to 2.4 hours and 133 miles and averaged 14 minutes and 7 miles. 399 charging events averaged 2.6 hours. The maximum lasted 4.6 hours. Most recharges added less than 1.4 kWh, with a mean charge of 0.92 kWh. The average power drawn was under one-half kilowatt. The greenhouse gas emissions from driving and charging were estimated to be 2.6 metric tons, about half of the emissions expected from a 22.4-mpg vehicle (the MY2009 fleet-wide real-world average). The findings contribute to better understanding of how plug-in hybrids might be used, their potential impact, and how potential benefits and requirements vary for different plug-in-vehicle designs. For example, based on daily driving distances, 20 miles of charge-depleting range would have been fully utilized on 81% of days driven, whereas 40 miles would not have been fully utilized on over half of travel days.

Modern electrical grids are transitioning from a centralized generation architecture to an architecture with significant distributed, intermittent generation. This transition means that the formerly sharp distinction between energy producers (utility companies) and consumers (residences, businesses, etc.) are blurring: end-users both produce and consume energy, making energy management and public policy more complex. The goal of the Open Power Quality (OPQ) project is to design and implement a low cost, distributed power quality sensor network that provides useful new forms of information about modern electrical grids to producers, consumers, researchers, and policy makers. In 2019, we performed a pilot study involving the deployment of an OPQ sensor network at the University of Hawaii microgrid for three months. Results of the pilot study validate the ability of OPQ to collect accurate power quality data in a way that provides useful new insights into electrical grids.

This study aims to overview the U.S. sustainable development by measuring the environmental performance of 50 states over the period of 2009–2018. To attain the objective, we employ data envelopment analysis for environmental assessment where we prioritize the minimization of CO2 emissions first and the maximization of gross state product later under the concept of managerial disposability (i.e., an environment-based performance measure). Then, we examine how the state-level environmental performance measures are associated with their political and spatial contexts. For the purpose, we conduct the Kruskal-Wallis rank sum test across groups of states characterized by their political transitions in the presidential and gubernatorial elections and defined by the regions of the U.S. Economic Development Administration and Environmental Protection Agency. Based on our empirical results, we find that (a) overall environmental performance has gradually enhanced over time, (b) there are statistically significant differences in the environmental performance measures along with the political transitions, and (c) states on both coasts have outperformed those of the middle in the measurement.

Higher carbon alcohols such as n-propanol, n-butanol, and n-pentanol that can be produced from biomass can be used as alternative fuels in diesel engines. These alcohols can mix with both diesel fuel and biodiesel without any phase separation. Currently, unregulated emissions such as toxicity and total polycyclic aromatic hydrocarbon (PAH) from the use of these alcohols are not monitored. Investigating the effects of increasing the alternative fuel concentration for use in a diesel engine on PAH emissions will contribute to the protection of the environment and extend the engine’s operating life. In this study, the effects of adding 35% (by volume) n-propanol, n-butanol and n-pentanol to diesel and biodiesel on unregulated emissions in a diesel engine were compared. In the total PAH emission of biodiesel, the mixture containing n-pentanol stood out compared to other mixtures with a decrease of 39.17%. In higher alcohol-diesel mixtures, the highest reduction was observed in the n-butanol mixture as 80.98%. With respect to toxic emissions, very close values were obtained in biodiesel blends up to 94.15%, although n-butanol showed a maximum reduction of 84.33% in diesel blends. All these reductions also prevented the formation of high-cycle PAHs. The results obtained showed that the use of high carbon alcohols in a high mixing ratio contributed to the improvement of the fuel properties of biodiesel and to an increase in the alternative fuel mixing ratio with the reduction of PAH emissions from diesel fuel.