• Energy Research

Energy management problems associated with rapid institutional, political, technical, ecological, social and economic development have been of critical concern to both national and local governments worldwide for many decades; thus, addressing such issues is a global priority. The main of objective of this study is to provide a review on the application and use of decision making approaches in regard to energy management problems. This paper selected and reviewed 196 published papers, from 1995 to 2015 in 72 important journals related to energy management, which chosen from the “Web of Science” database and in this regard, the systematic and meta-analysis method which called “PRISMA” has been proposed. All published papers were categorized into 13 different fields: environmental impact assessment, waste management, sustainability assessment, renewable energy, energy sustainability, land management, green management topics, water resources management, climate change, strategic environmental assessment, construction and environmental management and other energy management areas. Furthermore, papers were categorized based on the authors, publication year, nationality of authors, region, technique and application, number of criteria, research purpose, gap and contribution, solution and modeling, results and findings. Hybrid MCDM and fuzzy MCDM in the integrated methods were ranked as the first methods in use. The Journal of Renewable and Sustainable Energy Review was the important journal in this paper, with 32 published papers. Finally, environmental impact assessment was ranked as the first area that applied decision making approaches. Results of this study acknowledge that decision making approaches can help decision makers and stakeholders in solving some problems under uncertainties situations in environmental decision making and these approaches have seen increasing interest among previous researchers to use these approaches in various steps of environmental decision making process.

Trends of globalization very often cause the emergence of phenomena that asymmetrically affect the overall sustainability of the transport system. In order to predict certain situations and potentially be able to manage the transport system, it is necessary to manage risk situations and traffic safety in a timely manner. This study has conducted an investigation which implies defining the level of safety of a total of nine sections of two-lane roads. The main aim of the paper is to create a new multiphase model consisting of CRITIC (The CRiteria Importance Through Intercriteria Correlation), Fuzzy FUCOM (Full Consistency Method), DEA (Data Envelopment Analysis), and Fuzzy MARCOS (Measurement Alternatives and Ranking according to the COmpromise Solution) methods for determining the level of traffic safety on road sections under the conditions of uncertainty. In order for the created model to be adequately applied, eight parameters were created, and they were classified through four inputs and four outputs. To calculate the significance of the inputs, the CRITIC method based on the symmetric correlation matrix was used, and taking into account the nature of the outputs, the Fuzzy FUCOM method based on averaged values using the fuzzy Bonferroni Mean (BM) operator was applied to determine their weights. To determine the degree of safety, the DEA model was created. After that, the Fuzzy MARCOS method was used in order to determine the final ranking of the remaining five sections of the road network. Finally, the verification of results was performed through three phases of Sensitivity Analysis (SA).

The age of industrialization and modernization has increased energy demands globally. Solar energy has been recognized as an inexhaustible source of energy and has been applied for desalination and electricity generation. Among different non-conventional energy resources, Solar Energy (SE) is one of the main contributors to the global energy system. A photovoltaic system (PS) is applied to produce SE using photovoltaic cells. The selection of a solar panel includes many intricate factors involving both subjective and quantifiable parameters; therefore, it can be regarded as a complex Multi-Criteria Decision-Making (MCDM) problem. As the uncertainty commonly occurs in the selection of an ideal solar panel, the theory of Pythagorean fuzzy sets has been proven as one of the flexible and superior tools to deal with the uncertainty and ambiguity that arise in real-life applications. The aim of the study is to present an MCDM framework for solving the Solar Panel Selection (SPS) problem within the Pythagorean fuzzy (PF) environment. For this, first, a new integrated method is proposed based on the Stepwise Weight Assessment Ratio Analysis (SWARA) and VlseKriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) approaches in the Pythagorean fuzzy sets (PFSs) context. In the proposed approach, subjective weights of the evaluation criteria are calculated by the SWARA method, and the preference order of alternatives is decided by the VIKOR method in the PF context. The criteria weights evaluated by this approach involve the imprecision of experts’ opinions, which makes them more comprehensible. The computational procedure of the proposed methodology is established through a case study of the SPS problem under PF environment, which proves the applicability and efficiency of the proposed method. Furthermore, this study performs sensitivity analysis to reveal the stability of the developed framework. This analysis signifies that the solar panel option R4 constantly secures its highest ranking despite how the parameter values vary. In addition, a comparative study is discussed to analyze the validity of the obtained result. The results show that the proposed approach is more efficient and applicable with previously developed methods in the PFS environment.

The renewable energy industry has the important role in overall growth in the worldwide economy in the last two decades. This paper constitutes a first analysis on renewable energy companies in stock exchange by used the minimum spanning trees (MSTs) approach. In this study, the daily closure prices data of 70 stocks of renewable energy companies during the time period from 13th October 2010 to 4th March 2015 are investigated. In stock market analysis, the interrelations among stocks are represented in terms of Pearson correlation coefficient (PCC). This is the standard practice to construct the stock network. With the precondition of time series are synchronous, the similarity among the stocks is quantified by the PCC between the logarithmic exchange rate returns of i and j stocks. In order to analyze the topological properties of MST, three major centrality measures are used, namely; degree, closeness and betweenness centralities. Result of this paper indicated that; First Solar Inc., General Cable Corporation and Trina Solar are more important within the network, moreover; we found that; these stocks play a significant role in renewable energy development in terms of market capitals.

Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this technique has been rarely explored when developing sustainability assessment methods. In addition, most of the methods developed in the literature solely rely on fuzzy logic. The main shortcoming of solely using the fuzzy logic rule-based technique is that it cannot automatically learn from the data. This problem of fuzzy logic has been solved by the use of neural networks in many real-world problems. The combination of these two techniques will take the advantages of both to precisely predict the output of a system. In addition, combining the outputs of several predictors can result in an improved accuracy in complex systems. This study accordingly aims to propose an accurate method for measuring countries’ sustainability performance using a set of real-world data of the sustainability indicators. The adaptive neuro-fuzzy inference system (ANFIS) technique was used for discovering the fuzzy rules from data from 128 countries, and ensemble learning was used for measuring the countries’ sustainability performance. The proposed method aims to provide the country rankings in term of sustainability. The results of this research show that the method has potential to be effectively implemented as a decision-making tool for measuring countries’ sustainability performance.

The main purpose of this paper is to establish the plausibility and the dynamic nexus between financial developments, economic growth and tax revenue in Malaysia. The analysis of these relationships is vital considering the instability of the global economy which has affected growth. In this study, we employed annual time series data covering the period of 1970–2015. Using advanced co-integration and causality analysis, we found strong evidence on the relationship between each of the examined variables. The results from this study provide evidence on the taxes-growth nexus for Malaysia. An inverted U-shaped relationship is found between financial development and tax collection, while a U-shape reflects the economic condition. The nexus between economic growth and tax revenue enhances fiscal policies in the creation of transparent and mature financial systems which will further boost the collection of government revenues in Malaysia. The results of this study may provide an avenue for researchers and policymakers to understand the nature of the relationship between the examined variables and further assist in the formulation of new policies for economic sustainability.

To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.

Abstract The main objective of this study is to analyze and compare greenhouse gas emission profiles, dynamics thereof, and the climate change mitigation efforts of the major players in the global climate change arena, namely the G7 and BRICS countries that are accounting for more than 60% of the world greenhouse gas emissions as in 2017. Given that the energy sector is the major source of the greenhouse emissions in these countries, the framework of sustainable energy development indicators was applied to the comparative analysis. The analysis revealed the differences among the groups of countries in terms of greenhouse gas emission profiles and development trends of these profiles. The convergence of greenhouse gas emission profiles can be noticed between G7 and BRICS countries during 1990–2017 period. Greenhouse gas emissions per capita has decreased in G7 countries and increased in BRICS countries. Energy intensity, economic growth and carbon factor are the main drivers of greenhouse gas emissions per capita growth in both groups of countries. Carbon factor represents carbon intensity of a country's energy sector and is measured by greenhouse gas emissions per unit of total primary energy supply. The impact of carbon factor was least significant among the main drivers in both groups. Energy intensity was the major driver of decrease in greenhouse gas emissions per capita for both groups, however, the economic growth exceeded the contribution of energy intensity in BRICS countries and greenhouse gas emissions have increased in this group during investigated period. The comparative analysis of greenhouse gas emission profiles development trends and the main drivers of these trends in both groups of countries provides important insights in shaping future climate change mitigation policies and developing greenhouse gas emission reduction targets. Due to the high greenhouse gas profiles, low GHG reduction commitments and insufficient climate change mitigation the Perform, Achieve and Trade (PAT) scheme in India efforts in both groups there is danger of postponing implementation of Paris commitments and achieving the 1.5° target. The policies implemented in high ranked countries, like or Integrated Resources Planning in South Africa or measures under Energy efficiency directive in EU can be applied in low ranked countries by providing the substantial contribution to the country's climate targets.