Dissolved gas analysis (DGA) of oil is used to detect the incipient fault of power transformers. This paper presents a new approach for transformer fault diagnosis based on selected gas ratios concentrated in oil and support vector machine (SVM). Firstly, based on IEC TC 10 database, the optimal dissolved gas ratios (ODGR) are obtained by genetic algorithm (GA) that is designed for simultaneous DGA ratios selection and SVM parameters optimization. Three traditional methods, namely, DGA data with SVM and back propagation neural network (BPNN), IEC criteria, and IEC three-key gas ratios with SVM and BPNN are employed for effectiveness comparison. The fault diagnosis results of IEC TC 10 database show that the proposed ODGR with SVM may be used as an alternative tool for transformer fault diagnosis. In addition, the robustness and generalization ability of ODGR is confirmed by the diagnosis accuracy of 87.18% of China DGA samples. The obtained results illustrate that it is preferable to apply the proposed ODGR to transformer fault diagnosis with the assistance of SVM.

The low-carbon power transition, which is key to combatting climate change, has far-reaching effects on achieving the Sustainable Development Goals (SDGs) in terms of issues such as resource use, environmental emissions, employment, and many more. Here, we assess the potential impacts of the power transition on progress toward achieving multiple SDGs (covering 18 targets across the 17 goals) across 49 economies under nine socioeconomic and climate scenarios. We find that the low-carbon power transition under the representative concentration pathway (RCP)2.6 scenarios could lead to an approximately 11% improvement in the global SDG index score from 54.70 in 2015 to 59.89-61.33 in 2100. However, the improvement would be significantly decreased to 4.42%-7.40% and 7.55%-8.93% under the RCP6.0 and RCP4.5 scenarios, respectively. The power transition could improve the overall SDG index in most developed economies under all scenarios while undermining their resource-related SDG scores. Power transition-induced changes in international trade would improve the SDG progress of developed economies but jeopardize that of developing economies, which usually serve as resource hubs for meeting the demand for low-carbon power transition in developed economies.

The adoption of electric vehicles (EVs) on a large scale is crucial for meeting the desired climate commitments, where affordability plays a vital role. However, the expected surge in prices of lithium, cobalt, nickel, and manganese, four critical materials in EV batteries, could hinder EV uptake. To explore these impacts in the context of China, the world’s largest EV market, we expand and enrich an integrated assessment model. We find that under a high material cost surge scenario, EVs would account for 35% (2030) and 51% (2060) of the total number of vehicles in China, significantly lower than 49% (2030) and 67% (2060) share in the base-line, leading to a 28% increase in cumulative carbon emissions (2020-2060) from road transportation. While material recycling and technical battery innovation are effective long-term countermeasures, securing the supply chains of critical materials through international cooperation is highly recommended, given geopolitical and environmental fragilities.

Understanding carbon emission profile of cities from multiple perspectives is a prerequisite to design just and effective carbon reduction policies. Previous studies on CO2emissions by cities are usually confined to production or consumption-based perspective, while income-based perspective has been neglected. To fill the gap, direct emissions (a.k.a. production-based emissions), upstream emissions driven by final demand (a.k.a. consumption-based emissions) and downstream emissions enabled by primary input (a.k.a. income-based emissions) in an urban economy are comprehensively explored and compared for the first time, taking Beijing as a case. In the period of 2005_2012, Manufacture of Nonmetallic Mineral Products/Construction/Processing of Petroleum, Coking, Processing of Nuclear Fuel is identified as the key contributor to carbon emission by Beijing from the production/consumption/income-based perspective, respectively, indicating each perspective can unveil important information which the other methods fail to discover. Moreover, driving forces of CO2emissions change in Beijing are uncovered using the structural decomposition analysis (SDA) from both the demand and supply sides. Emission intensity, production input and output structure change contribute to CO2emission decrease in Beijing, which are largely offset by population, final demand/primary input level and final demand/primary input structure change, resulting in a net 3.9 Mt reduction during 2005_2012. While current policies continue to highlight end-of-pipe measures in cities, more attention should be paid to demand (e.g., encouraging low-carbon consumption) and supply side (e.g., controlling capital investment in enterprises with large income-based CO2emissions). © 2018 Elsevier Ltd

This paper presents an integrated model based upon the fuzzy approach and evidential reasoning decision-making approach to condition assessment of power transformers. An assessing index system, including the DGA data, electrical testing, and oil testing, is established to facilitate the assessing model. The model is composed of two levels, that is, the fuzzy model and evidential reasoning model. The fuzzy model is proposed for generating the original basic probability assignments for the second-level model. Afterwards, an evidential reasoning decision-making model is utilized to combine all of the evidence and give an overall evaluation conclusion. Based upon this integrated model, a decision-making procedure is put forward to serve as an effective tool for transformer condition assessments. The results show that the assessing model is capable of offering an overall evaluation of the observed transformer condition.