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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.

The main objective of the research is to assess the ability of the Ukrainian economy and its individual industries to ensure, in the conditions of economic growth, a stable reduction of natural gas consumption and, consequently, to reduce dependence on its imports. Six types of relationships were identified between the change in sectoral added value and the change in the consumption of certain energy resources, in particular natural gas. The conditions are established under which the growth of sectoral added value is accompanied by a decrease in the consumption of certain energy resources. The index of sectoral efficiency of the use of certain energy resources was proposed and a model of the decomposition of the growth rate of this indicator was constructed. Quantitative indicators of measuring economic barriers on the way to introduction of energy-saving technologies are presented. Conditions under which economic growth is accompanied by a decrease in the level of dependence of the economy on imports of energy resources are modeled. The dynamics of natural gas consumption by sectors of the Ukrainian economy is analyzed. It is proved that reduction of natural gas consumption due to increased energy efficiency occurs mainly in industries with an average value of share of the cost of purchasing this energy in the total operating expenses. An estimation is undertaken of the possibility of achieving independence of the Ukrainian economy from the import of natural gas in different scenarios of changing main parameters that determine the probability of such an achievement.

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.

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.

This study explores how political–economic forces could affect export performance in the renewable energy technologies market. We conduct panel framework analyses to verify the characteristics of panel data for 19 countries before establishing the panel estimator meant to test the effects of political–economic forces on export specialization. We consider the results of the panel framework analyses and develop an empirical model to test casual dynamic relationships between political–economic forces and export performance. The results from the least squares dummy variable-corrected estimation indicate that the major factors promoting the export specialization of renewable energy technologies are, in order of decreasing importance, public pressure, market size, and government demand-pull policy. However, the traditional energy industry has no significant effect on export performance. Finally, this study finds that dynamic effects exist in all estimations.

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.

Reducing carbon dioxide (CO2) emissions has become a global consensus in response to global warming and climate change, especially to China, the largest CO2 emitter in the world. Most studies have focused on CO2 emissions from the production sector, however, the household sector plays an important role in the total energy-related CO2 emissions. This study formulates an integrated model based on logarithmic mean Divisia index methodology and a system dynamics model to dynamically simulate household energy consumption and CO2 emissions under different conditions. Results show the following: (1) the integrated model performs well in calculating the contribution of influencing factors on household CO2 emissions and analyzing the options for CO2 emission mitigation; (2) the increase in income is the dominant driving force of household CO2 emissions, and as a result of the improved standard of living in China a sustained increase in household CO2 emissions can be expected; (3) with decreasing energy intensity, CO2 emissions will decrease to 404.26 Mt-CO2 in 2020, which is 9.84% lower than the emissions in 2014; (4) the reduction potential by developing non-fossil energy sources is limited, and raising the rate of urbanization cannot reduce the household CO2 emission under the comprehensive influence of other factors.