Due to the rapid development of recycling technologies in recent years, more data have appeared in the literature on the environmental impact of the final stages of the life cycle of wind and solar energy. The use of these data in the eco-design of modern power generation systems can help eliminate the mistakes and shortcomings when planning wind and solar power plants and make them more eco-efficient. The aim of this study is to extend current knowledge of the environmental impacts of most common renewables throughout the entire life cycle. It examines recent literature data on life cycle assessments of various technologies for recycling of wind turbines and photovoltaic (PV) panels and develops the recommendations for the eco-design of energy systems based on solar and wind power. The study draws several general conclusions. (i) The contribution of further improvements in PV’s recycling technologies to environmental impacts throughout the entire life cycle is insignificant. Therefore, it is more beneficial to focus further efforts on economic parameters, in particular, on achieving the economic feasibility of recycling small volumes of PV-waste. (ii) For wind power, the issue of transporting bulky components of wind turbines to and from the installation location is critical for improving the eco-design of the entire life cycle.

Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them.

Hydrogen energy is expected to become one of the most efficient ways to decarbonize global energy and transportation systems. Green hydrogen production costs are currently high but are likely to decline due to the economy of scale and learning-by-doing effects. The purpose of this paper is to forecast future green hydrogen costs based on the multicomponent learning curves approach. The study investigates the learning curves for the main components in hydrogen value chains: electrolyzers and renewable energy. Our findings estimate the learning rates in the production of PEM and AE electrolyzers as 4%, which is quite conservative compared to other studies. The estimations of learning rates in renewable energy electricity generation range from 14.28 to 14.44% for solar-based and 7.35 to 9.63% for wind-based production. The estimation of the learning rate in green hydrogen production ranges from 4% to 10.2% due to uncertainty in data about the cost structure. The study finds that government support is needed to accelerate electrolysis technology development and achieve decarbonization goals by 2050.

This paper contributes to the literature on sustainable consumption by in-depth analysis of the factors affecting the probability of 57 different practices of proenvironmental behavior (PEBs) in Russia. The set of studied PEBs includes not only popular energy-saving and waste-management practices but also more circular patterns of plastic consumption, shopping, and city mobility. To study real and potential barriers to greening consumer behavior models, we conducted a survey of 623 respondents using a questionnaire developed based on a comparative analysis of similar studies conducted in other countries. The processing of the survey results was carried out using nonparametric statistics due to the absence of a normal distribution of the sample for most of the studied characteristics. The results of the study revealed that the main barriers to sustainable consumption in Russia are the lack of appropriate infrastructure as well as a lack of knowledge. Infrastructural barriers in some situations makes sustainable consumer behavior impossible or inconvenient (in this case, preference is given to other types of consumption), or in some cases necessitates spending additional time and money (then sustainable consumer behavior is not completely denied but practiced less often).

In order to effectively regulate the circular economy (CE) at the national and international levels, it is essential to have a unified and informative system of indicators for monitoring the progress in the CE. The lack of standard indicators for measuring the progress of cyclicality leads to contradictions and misunderstandings, which is a problem for the implementation of CE strategies. This paper aims to adapt dynamic clustering approaches to solving strategic management problems of circular production and consumption processes. To achieve this goal, the authors performed the following tasks: (1) tested clustering algorithms by ranking EU countries by the level of development of the circular economy; (2) identified the approach that allows the best classification of EU countries, considering changes in the indicators of the level of CE development in 2000–2019 (dynamic classification); (3) developed a software module using python libraries to classify and visualize the results. The results illustrate that the k-means algorithm has a good discriminatory ability in division of all countries of the training sample (EU countries) into several clusters with different dynamics in the development of the CE. The best quality of classification is obtained by the indicator “Generation of municipal waste per capita”; satisfactory quality of the classification is obtained by the indicator “Generation of waste excluding major mineral wastes per GDP unit”. The study results demonstrate the fundamental applicability of the object-oriented and classical statistical approach to solving strategic management problems of the CE and their potential effectiveness in terms of the clarity and information content of reflecting cyclical processes.

Nowadays, information technology and the Internet are becoming a driver for a shared consumption economy (sharing economy). The paper aims to analyse the current level of use and prospects for the development of models of the sharing economy in Russia. We identify the effects of sharing consumption for the circular economy and study factors that stimulate the use of peer-to-peer models. One of the study’s tasks was to investigate the involvement of the young generation in the sharing economy, as the most promising part of the population. The study is based on the analysis of open Internet sources on the operation of sharing platforms in Russia and a survey among students at two Russian universities (RUDN University in Moscow and Kuban State University in Krasnodar) to identify the awareness of young people regarding sharing platforms. Analysis of the current state of sharing in Russia showed that the use of most types of sharing is at an initial stage. At the same time, the prospects for economic growth and population development are quite high. The article identifies several effects of digital platforms for sustainable development, the main of which are informed consumption and production. For the further growth of users of the sharing economy, the authors highlight the level of penetration of the Internet and the need for high-quality services on digital platforms. Educational organisations can also become a platform for the development of models of the sharing economy by involving students in sharing processes, as demonstrated by the survey.