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Closed-loop supply chain networks are gaining research popularity due to environmental, economic and social concerns. Such networks are primarily designed to overcome carbon footprints and to retrieve end of life products from customers. This study considers a multi echelon closed-loop supply chain in the presence of machine disruption. A multi-objective model is presented to optimize the total cost, the total time and emissions in a closed-loop supply chain network. The aim is to analyze the trade-off between the objectives of cost, time, and emissions and how these decisions are impacted by the selection of different available machines. A number of solution approaches are tested on a case study from the tire industry. The results suggest the improved performance of the hybrid heuristic and the importance of controlling disruption in a closed-loop supply chain network. Furthermore, there is a trade-off between the different objective functions which can help the decision maker to choose a particular solution according to the preference of an organization. Finally, conclusion and future research avenues are provided.

Is it true that a nuclear technology approach to generate electric energy offers a clean, safe, reliable and affordable, i.e., sustainable option? In principle yes, however a technology impact on the environment strongly depends on the actual implementation bearing residual risks due to technical failures, human factors, or natural catastrophes. A full response is thus difficult and can be given first when the wicked multi-disciplinary issues get well formulated and “resolved”. These problems are lying at the interface between: the necessary R&D effort, the industrial deployment and the technology impact in view of the environmental sustainability including the management of produced hazardous waste. As such, this problem is clearly of multi-dimensional nature. This enormous complexity indicates that just a description of the problem might cause a dilemma. The paper proposes a novel holistic approach applying Multi-Criteria Decision Analysis to assess the potential of nuclear energy systems with respect to a sustainable performance. It shows how to establish a multi-level criteria structure tree and examines the trading-off techniques for scoring and ranking of options. The presented framework allows multi-criteria and multi-group treatment. The methodology can be applied to support any pre-decisional process launched in a country to find the best nuclear and/or non-nuclear option according to national preferences and priorities. The approach addresses major aspects of the environmental footprint of nuclear energy systems. As a case study, advanced nuclear fuel cycles are analyzed, which were previously investigated by the Nuclear Energy Agency (NEA/OECD) expert group WASTEMAN. Sustainability facets of waste management, resource utilization and economics are in focus.

Nowadays, most countries of the world are implementing the transition to the low-carbon economy which implies the need to carry out a full-scale eco-modernization of the energy sector. Green energy may be identified as one of the core concerns of energy sector modernization as it allows a considerable decrease in emissions of harmful substances into the atmosphere. Therefore, nuclear and renewable energy may become key areas of global energy development in the near future, which is also in agreement with circular economy concepts. However, public opinion (and other controversial visions/aspects) is one of the barriers to their development. The purpose of this study is to analyze the relationship between attitudes towards nuclear and renewable energy in two countries: a EU country (Italy) and a non-EU country (Russia), considering the level of their development. The authors conducted a survey among residents regarding their attitude towards nuclear and renewable energy, as well as their attitude to the present energy policy. The cluster analysis technique was used to analyze the results. The obtained results confirmed the dependence between the level of development of nuclear and renewable energy and the public attitude towards it. The national energy policy also might influence public opinion on the development of nuclear or renewable energy. The authors identified public attitude as one of the key factors in the development of energy and the achievement of environmental and social sustainability.

Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

Drought is a noteworthy cause of low agricultural profitability and of crop production vulnerability, yet in numerous countries of Africa little to no consideration has been paid to readiness for drought calamity, particularly to spatial evaluation and indicators of drought occurrence. In this study, biophysical and socio-economic data, farmers’ community surveys and secondary data from remote sensing on soil characteristics and water demand were used to evaluate the predictors of drought in inland valley rice-based production systems and the factors affecting farmers’ mitigation measures. The study intervened in three West African countries located in the Sudan-Sahel zone, viz. Burkina Faso, Mali and Nigeria. Significant drying trends occurred at latitudes below 11°30′ whilst significant wetting trends were discerned at latitude above 11°30′. Droughts were more frequent and had their longest duration in the states of Niger and Kaduna located in Nigeria and in western Burkina Faso during the period 1995–2014. Among 21 candidate predictors, average annual standardized precipitation evapotranspiration index and duration of groundwater availability were the most important predictors of drought occurrence in inland valleys rice based-production systems. Land ownership and gender affected the commitment of rice farmers to use any mitigation measure against drought. Drought studies in inland valleys should include climatic water balance and groundwater data. Securing property rights and focusing on women’s association would improve farmers’ resilience and advance drought mitigation measures.

Predicting the variability of wind energy resources at different time scales is extremely important for effective energy management. The need to obtain the most accurate forecast of wind speed due to its high degree of volatility is particularly acute since this can significantly improve the planning of wind energy production, reduce costs and improve the use of resources. In this study, a method for predicting the speed of wind flow in an isolated power system of the Gorno-Badakhshan Autonomous Oblast (GBAO), based on the use of a neural network with a learning process control algorithm, is proposed. Predicting is performed for four seasons of the year, based on hourly retrospective meteorological data of wind speed observations. The obtained wind speed average error forecasting ranged from 20–28% for a day ahead. The prediction results serve as a basis for optimizing the energy consumption of individual generating consumers to minimize their financial and technical costs. In addition, this study takes into account the possibility of exporting electricity to a neighboring country as an additional income line for the isolated GBAO power system during periods of excess energy from hydropower plants (March–September), which is a systematic vision of solving the problem of improving energy efficiency in the conditions of autonomous power supply.

As an effective regulatory tool, environmental information disclosure is significant in promoting the green upgrading of industrial structures and achieving green transformation of enterprises. In order to explore the impact mechanism of environmental information disclosure on corporate sustainability performance, this paper constructs a two-way fixed-effect model using balanced panel data of Chinese A-share listed manufacturing companies from 2015 to 2020. We find that environmental information disclosure significantly impacts green innovation, thereby improving corporate sustainability performance. Furthermore, financing constraints inhibit the impact of environmental disclosure on sustainability performance, while female directors have only symbolic effect. The reliability of the paper’s findings is verified by replacing the dependent variable and introducing instrumental variables. Heterogeneity analysis shows that the effect of environmental information disclosure on corporate sustainability performance is more substantial among non-state and eastern and heavily polluting enterprises. Comprehensive analysis from the financing perspective shows the differences in the moderating effects of debt and equity financing regarding the impact mechanism. This study enriches the theory of green innovation and provides financing strategies for enterprises to achieve green transformation, as well as suggestions for improving the government environmental information disclosure system.