• Energy Research
• 13. Climate action close
• 12. Responsible consumption close
• 2. Zero hunger close
• Energies close

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 develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outreach. It aims at evaluating and optimizing energy deployment strategies with higher shares of VRE, assessing the impact of new technologies and of climate variability and conducting sensitivity studies. Specifically, to limit the algorithm’s complexity, we avoid solving a full-mix cost-minimization problem by taking the mean and variance of the renewable production–demand ratio as proxies to balance services. Second, observations of VRE technologies being typically too short or nonexistent, the hourly demand and production are estimated from climate time series and fitted to available observations. We illustrate e4clim’s potential with an optimal recommissioning-study of the 2015 Italian PV-wind mix testing different climate data sources and strategies and assessing the impact of climate variability and the robustness of the results.

This research investigates the relationships among renewable energy consumption, economic growth, and financial development in five sub-Saharan African nations utilizing panel data from 2000 to 2020. Econometric methods are used to ascertain the existence or absence of cross-sectional dependence and the short-run and long-run connections between the following factors: Pesaran cross-sectional dependence (CD) and cross-sectionally augmented IPS (CIPS) unit root tests, pooled mean group (PMG), and dynamic ordinary least squares (DOLS) estimations. The presence of cross-sectional dependence is found and represented with the CIPS unit root test. No significant short-run relationship is found between the variables of the study, yet a significant long-run relationship is present among them. A positive relationship exists between CO2 emissions and financial development, while financial development and renewable energy consumption are found to have negative relationships with CO2 emissions. The study also supports the scale effect of the environmental Kuznets curve hypothesis. Additionally, no causality is found among the variables, and impulse response and variance decomposition estimation are carried out to recommend future effects. Policy implications of findings are discussed, with accompanying suggestions.

We analyzed the role of predictable and unpredictable variability in the identification of optimal renewable energy mixes in an electricity system. Renewable energy sources are the fastest growing energy generation technology, but the variable nature of production linked to climate variability raises structural, technological and economical issues. This work proposes the differentiation of the treatment applied to predictable and unpredictable variability in the context of Markowitz portfolio theory for optimal renewable deployment. The e4clim model was used as a tool to analyze the impact of predictable sources of generation variability on the optimal renewable energy mixes. Significant differences appeared, depending on the consideration of risk, all of them showing room for improvement with respect to the current situation. The application of the methods developed in this study is encouraged in mean-variance analyses, since its contribution favors scenarios where unpredictable variability in the climate-powered renewable energy sources are considered for their risk introduction.

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.

Fire hazard during ship transportation of certain modern solid fuels is identified. A particular mechanism which may lead to catastrophic ship fire is discussed. A mathematical model predicting time to such fully developed fire (flashover time) I s proposed. An analytical solution is obtained for the developed model. An important part of this solution is a closed form analytical description of the smoke filling dynamics in a closed compartment. It is demonstrated that flashover time depends on a number of parameters. The relative importance of these parameters is discussed. Particular significance is attributed to fuel soot propensity (fuel soot yield) which is an intrinsic fuel property. This parameter controls the intensity of radiative heat exchange in the compartment, and its influence on flashover time is of paramount importance. It is suggested that fuels are ranked with respect to this parameter in order to describe their relative fire hazards. This ranking should be implemented into the regulatory framework governing fuel transportation by ships.