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Energy is essential for socio-economic development and due to the increase in energy consumption it is necessary to look for renewable and clean energy over the coming decades. Wind energy has received more attention than other sources due to better economic conditions as well as lack of environmental pollution and unlimited wind resources. This paper describes a practical method for measuring the potential of wind resources with the ability to generate electricity, using the geographic information system and considering the selection criteria of wind farms. The present study analyzed a multi-criteria decision support system to define wind energy resources in western Iran. In the method of calculating wind potential in this paper, the desired items are considered with equal importance and these criteria include technical, environmental, economic and geographical factors. The aims of this research are measuring the potential of areas suitable for generating electricity from wind energy for large turbines according to international standards, stating the standards and criteria for measuring the potential of wind resources and finally implementing the items mentioned in regional electricity. The results show that assuming the use of Gamesa G58 turbine, a maximum of 1897 MW of wind power can be generated in the study area, which is 26% of the region's electricity supply in 2025 horizon. The method used in this study can be applied for future wind potential measurements and it is recommended to be integrated with fuzzy AHP method to enhance the accuracy of the results. • Describing a practical method for measuring the potential of wind resources with the ability to generate electricity, using the geographic information system and considering the selection criteria of wind farms. • Measuring the potential of areas suitable for generating electricity from wind energy for large turbines according to international standards. • Obtaining the necessary factors to select suitable locations for wind farms in Iran's Western Regional Electricity in order to provide criteria based on GIS and evaluating their impact. [ABSTRACT FROM AUTHOR]

Abstract: The vegetable oil industry is considered as one of the major industries contributing to energy consumption in Iran. Despite the increasing need to improve energy efficiency in this industry, previous studies show that energy-efficient technologies have not been applied in most plants and other energy efficiency measures have not yet been adopted. Accordingly, this paper addresses the implementation of energy efficiency measures in the vegetable oil industry. Using a data envelopment analysis (DEA), the relative efficiency of plants in the vegetable oil industry is compared at different stages of the implementation of energy efficiency measures. The most effective indices for improving energy efficiency were identified and a model was designed to implement energy efficiency measures in industrial plants. In the model, the implementation process consisted of 5 stages, each of which had its own inputs and outputs. A multi-stage DEA model was employed to evaluate the performance of vegetable oil plants at each individual stage as well as the aggregate efficiency of each plant. After estimating the total efficiency of all plants and evaluating performance at the individual stages for each plant, the plants with ideal conditions for implementing energy efficiency measures were identified. These plants can serve as a benchmark for other plants. Estimation of the average efficiency across plants at each stage indicated that the plants suffered from poor performance in the first and fourth stages. Therefore, the results of this study indicate a need to develop new strategies for improving energy efficiency at these two stages. [Copyright &y& Elsevier]

The methods to collect and transport various types of medical waste are vital to the management of healthcare waste. The lack of a safe plan for the timely collection and transportation of such wastes may have an undesirable impact on the environment and public health. The present research addresses a real-life healthcare waste collection vehicle routing problem (HWCVRP) for small medical centers in Iran that produce minor amounts of waste and lack on-site facilities to treat wastes of various kinds. This problem involves social, environmental, and economic objective functions, aiming to achieve sustainable development. The social objective function is optimized based on a novel definition of risk in medical waste collection and designed to reduce public health risk by minimizing the time of waste collection from centers that produce a larger volume of more hazardous waste. Furthermore, the present paper provides a comprehensive estimate of fuel consumption in vehicles to be accurately minimized to diminish the environmental hazards involved in sustainable transportation. Also, the economic objective function is set to minimize the variable and fixed costs of transportation. A self-adaptive multi-objective evolutionary algorithm is generated with numerous effective operators to solve the model. Specific metrics are employed to compare the performance of the proposed meta-heuristic algorithm with those of multiple other evolutionary algorithms. The results reveal the effectiveness of the proposed method in reaching high-quality non-dominated solutions. Ultimately, a real-life case study is used to implement the proposed approach and to evaluate its performance. Image 1 • A real perspective to the triple bottom line of health-care waste collection. • A new social aspect of sustainability based on the risk of different medical waste. • A new multi-objective self-adaptive evolutionary algorithm (MOSAEA). • The results show that MOSAEA dominates recently competitive approaches. [ABSTRACT FROM AUTHOR]

Livestock production systems, such as dairy farming, are one of the most important contributors to resource use and if not managed well, it can be environmentally detrimental. Iranian livestock sector faces a variety of the challenges such as high costs of energy and environmental legislations as well as an increasing demand for dairy products to respond the growing population. This paper aims to contribute to the discussion on technical efficiency as a key indicator of energy use within dairy farming systems. A Window Data Envelopment Analysis (W-DEA) with energy use as inputs and milk production as output was modelled with data from 25 provinces during the last 22 years (1994–2016) in Iran. In addition, the Slack-Based Model (SBM) was used to compare the radial DEA model with non-radial SBM, both in a dynamic environment (window analysis). The average efficiency score of Iranian dairy farming production system was estimated at approximately 0.85. Through the years, three provinces including Zanjan, Ardabil and Hormozgan had the highest technical efficiencies. Window analysis represented that provinces are distinctive in terms of their technical efficiencies and energy consumption over the years. Applying the SBM model improved the accuracy of the estimated efficiency scores compared to the radial (DEA) model. Further analysis represented a significant difference between the technical efficiency of different milk production levels. Provinces that produced higher volumes of milk had lower technical efficiencies. Based on the results it can be concluded that there is a substantial space for upgrading the technical efficiency of dairy farming in Iran by improving resource use efficiency which leads to an optimized energy consumption. It is recommended to reform Iranian livestock farming policies by applying mechanized systems, optimal strategies for water, electricity and fossil fuel consumption, use of renewable energy and better feed management while enhancing milk productivity and technical efficiency. In this respect, it is suggested that policy makers consider different indicators such as energy use efficiency and environmental impacts when allocating subsidies and resources to different provinces and farms. • Iranian dairy farming was assessed for the technical efficiency over 22 years. • Efficiency of 25 provinces were identified using window data envelopment analysis. • The average efficiency score of dairy farming was approximately 0.85. • We propose improvement of resource management in order to minimize energy use. • Milk production was found as an effective factor on efficiency of provinces. [ABSTRACT FROM AUTHOR]

The general policy of the European Union in reducing greenhouse gas emissions is such that the increased average global temperature is kept below 2 °C. Many countries are looking for more effective solutions and methods to reduce carbon emissions. There are effective factors in the scheduling passenger and freight trains in the inter-city rail transport industry such as number of trains requested at the station, the number of stops, delays and waiting times for trains at the station which lead to increased fossil fuel consumption and carbon emission. In this paper, we want to present a new mixed integer non-linear model that can choose the best schedule for movement of passenger and freight shuttle trains on a single-line route of the railway network with the least environmental pollution. In this model, the carbon emission factor is based on the weight and prioritization of passenger and freight trains. Also, the average number of demands for using trains at a station and the number of planned or non-planned stops and delays at the middle stations are considered. Also, the proposed model selects the best plan for moving trains with the lowest carbon emissions caused by fuel consumption. To validate the proposed model, a case study of a crowded passenger and freight route of an inter-city railway network in Iran will be solved. We solve the real studied problem using the GAMS v24.9.1 optimization software with BARON solver. Finally, after sensitivity analysis of the results, we demonstrate the convenient and efficient performance of the same model in solving the raised problems in an applied manner. Image 10162 • A new model for reducing Carbon emissions through optimal scheduling of trains is developed. • We apply the carbon emission factor based on the weight and prioritization of passenger and freight trains. • We consider the number of planned and non-planned stops, delays, and waiting times of trains. • A real case study is applied for validating the proposed model. [ABSTRACT FROM AUTHOR]

Particleboard is one of the non-structural wood-based composites developed in the late 1950s in Iran to utilize agricultural and industrial wood residues. Since there was no published document about its Life Cycle Inventory in Iran, the aim of this study was to document the life cycle inventory data from the particleboard manufacturing in the second hand lines in Iran. The study covers the energy consumption and environmental impacts of raw materials and processes during particleboard production. In order to identify the main hotspots and characterize the production process, impact categories were considered using CML methodology and cumulative exergy demand (CExD) indicator. The results revealed that the board shaping subsystem was the main contributor to the most of impact categories. The main hotspot for resin production was known as Urea-formaldehyde (UF). Electricity was the main hotspot in acidification (AC) and ozone layer depletion (OLD) impact categories, while transportation and production process were the main hotspots in marine aquatic ecotoxicity (ME) and photochemical oxidation (PO) impact categories, respectively. Moreover, rotary dryer, hot press, flaker and hammer mill, vacuum pump and diesel fuel were the most effective sectors in the emissions of the production process. Results for substitution of natural gas showed that, except some impact categories, the use of alternative source of thermal energy such as wood residues could improve emissions from the manufacturing process. [ABSTRACT FROM AUTHOR]

This study deals with the interface between energy consumption and its related emissions in the manufacturing of one cubic meter medium density fiberboard (MDF) in Iran and provides a comprehensive life cycle inventory (LCI) data for production process. The study was developed following the methodological guidelines of ISO 14040. Inventories data for a gate-to-gate assessment of MDF manufacturing were collected by on-site surveys. Ten impact categories including of: Abiotic depletion (AD), Acidification (AC), Eutrophication (EP), Global warming (GW), Ozone layer depletion (OLD), Fresh water aquatic ecotoxicity (FE), Marine aquatic ecotoxicity (ME), Human toxicity (HT), Terrestrial ecotoxicity (TE) and Photochemical oxidation (PO) in three subsystem of manufacturing process were assessed. The subsystem of fiber preparation was the major contributor to the environmental impacts. Urea-formaldehyde (UF) resin production was identified as the main hotspot, except in AD, AC, OLD and PO impacts that natural gas, electricity, transport and production process were the main hotspots, respectively. Moreover, Tube dryer, MDF saw and hogger were the most effective sectors in the emissions of production process. Results for substitution of natural gas showed that except in some impacts categories, use of alternative source of thermal energy such as wood residue could improve emissions from manufacturing. [ABSTRACT FROM AUTHOR]