Renewable Energy Transition: Comparison
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Please note this is a comparison between Version 2 by Wendy Huang and Version 1 by Jeyhun Mammadov.

The renewable energy transition of oil- and gas-producing countries has specific peculiarities due to the ambivalent position of these countries in the global energy market, both as producers and consumers of energy resources. This task becomes even more challenging when the share of oil and gas in the country’s GDP is very high. These circumstances pose serious challenges for long-term energy policy development and require compromising decisions to better align the existing and newly created energy policies of the country. The scale, scope, and pace of changes in the transition process must be well balanced, considering the increasing pressure of economic and environmental factors.

  • renewable energy transition
  • oil
  • gas
  • energy policy
  • fuzzy MOORA
  • fuzzy AHP
  • fuzzy TOPSIS

1. Introduction

Increased energy production and environmental deterioration, related to economic growth, are creating a set of interrelated issues for society and development policymakers. The most challenging issues are caused by increased energy consumption and production and their negative influence on the natural environment. Despite globally accepted sustainable development and green energy policies, the implementation of these policies requires long-term, continued efforts and contributions from each country. This is a challenging task, and no unified, generally accepted solution exists for all economic parties. Countries involved in this process have different backgrounds and development histories. Some of them are only consumers of energy resources, while others are producing, consuming, and selling energy resources.
Countries producing and exporting energy resources significantly differ in economic power and the share of energy resources in the GDP. The range of variations is quite large—from oil rent being 0.4% of the GDP for the UK (natural gas rents at 0.17% of the GDP, and total natural resource rents at 0.59% of the GDP) up to 53% for Iraq (natural gas rents at 0.65% of the GDP, and total natural resource rents at 43.4% of the GDP), and 56% for Libya (natural gas rents at 4.58% of the GDP, and total natural resource rents at 61% of the GDP). A small share allows for the relatively swift replacement of traditional energy resources with renewables. However, in the case of a large share, a country needs significant and long-lasting efforts for energy resource replacement. The issue is that in oil-rich countries, energy resources are not only used for generating electricity but are also the main export item and the primary source of hard currency for the country [1].
During the transition period, policy developers must align the current energy policy, the desired policy, and the steps to transition from the current state to the desired one. The transition to a renewable-based energy system is not a one-step process, especially for countries with a high share of oil and gas in their GDP. Addressing the complexities inherent in the transition requires the development of special models and pre-scenarios before creating detailed long-term scenarios and policies. To find a justified solution to the task, it is necessary to analyze approaches for selecting renewables and designing scenarios.
The solution to the renewable energy transition task requires an analysis of multiple alternatives considering a set of contradictory and conflicting beneficial and cost criteria, often in conditions of partial uncertainty. To address this task, various Multiple-Criteria Decision-Making (MCDM) methods have been used.
In some cases, researchers face situations where statistics describing the implementation of renewable energy technologies are limited and non-representative. In such instances, fuzzy models that rely on experts’ knowledge can help compensate for the lack and deficiency of statistical data. Traditionally, models used in the energy sector have been based on precise and exact data, with a primary focus on the efficiency of solutions. However, in areas without well-established decision-making approaches or with limited experience, decision-makers often have to deal with vague information expressed in linguistic form.

2. Fuzzy Method in Decision-Making within the Energy Sector

Energy transition, the most important issue of sustainable development, is a complex, country-oriented task that is difficult to formalize with traditional approaches [7][2]. The selection of a relevant approach for renewable energy transition is inherently a Multiple-Criteria Decision-Making task for the energy sector, and several tools have been developed and utilized for such problems. In [8[3][4],9], detailed reviews of MCDM methods’ applications (crisp and fuzzy) for energy policy-making are presented. These papers offer comprehensive explanations of the methods and examples related to the selection of traditional and renewable energy resources. For energy policy development, planning, and the selection of renewables in various countries, different combinations of MCDM are employed. Fuzzy AHP (Analytic Hierarchy Process) and fuzzy TOPSIS are utilized for selecting energy alternatives [10][5]. This combination is used for the selection of renewable energy sources (RES) in Turkey [11,12,13][6][7][8]. The selection of RES based on the application of AHP is carried out in Saudi Arabia and Jordan [14,15][9][10]. Various approaches, such as the use and combination of AHP and QFD (Quality Function Deployment) [16][11]; SWOT analysis, AHP, and FTOPSIS [17][12]; interpretive structural modeling (ISM), benefits, opportunities, costs, and risks (BOCR), and fuzzy analytic network process (FANP) [18][13]; and Delphi analysis, AHP, and FTOPSIS [19][14], are also employed to address RES-related problems. A comparative analysis was conducted in [20][15] to rank the renewable energy sources (RES) in Taiwan. The analysis involved the application of the Weighted Sum Method (WSM), VIKOR (Serbian: VIekriterijumsko Kompromisno Rangiranje, meaning Multicriteria Optimization and Compromise Solution), TOPSIS, and ELECTRE (French: Élimination et Choix Traduisant la Réalité, meaning Elimination and Choice Translating Reality). Fuzzy models provide a suitable framework for representing the main ideas of decision-makers in a way that is convenient for them. These models allow decision-makers to efficiently utilize their accumulated experience and knowledge in solving strategic and emerging operational tasks in the field of renewable energy. In [21][16], a multiple-criteria approach, extending the fuzzy TOPSIS method, was used to achieve the 2030 renewable energy targets in European member states. In Serbia, the fuzzy AHP method was applied to assess the potential of renewable energy sources for electricity generation [22][17]. The approaches presented in [21,22,23,24][16][17][18][19] differ in the models used, the categories and number of criteria applied, the number of alternatives analyzed, and their specific applications. Fuzzy TOPSIS has been widely used for decision-making in the energy sector [8[3][20][21],25,26], including solutions related to renewables [25,27,28][20][22][23]. The method has been used as stand-alone or in combination with other methods [19,29][14][24]. Fuzzy VIKOR is also one of the actively used decision-making methods in the energy sector [30,31,32][25][26][27]. Renewable-related tasks in China, India, Iran, and Turkey are also solved by using the fuzzy VIKOR technique [33,34,35,36][28][29][30][31]. In recent years, the use of the MOORA method has increased for the solution of various tasks [37][32]. In the energy sector, fuzzy MOORA is utilized for the ranking of G7 countries according to energy center selection performance [38,39][33][34]. For sustainability-oriented tasks, combinations of methods have been used, such as fuzzy MOORA and fuzzy AHP [40][35]; fuzzy MOORA and fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) [41][36]; and fuzzy Shannon Entropy, MOORA, VIKOR, EDAS (Evaluation Based on Distance from Average Solution), and ARAS (Additive Ratio Assessment) [42][37]. Furthermore, for evaluating wastewater treatment technologies, fuzzy SWARA (Stepwise Weight Assessment Ratio Analysis) was used to define criteria weights, and then ranking was implemented using fuzzy MOORA. Finally, the results were validated with F-TOPSIS [43][38]. The fourth fuzzy method, Simple Additive Weighting, was chosen because of its simplicity, effectiveness, and relative prevalence of use. According to [44][39], SAW belongs to the 20 most cited methods in the “ScienceDirect” database. Fuzzy extensions of the Simple Additive Weighting method have been successfully used to solve various selection problems [45,46][40][41]. Indeed, the studies mentioned earlier highlight the significance of Multiple-Criteria Decision-Making (MCDM) methods in tackling the complex and multifaceted challenges of transitioning to renewable energy sources. These methods play a crucial role in making informed decisions for sustainable energy planning and policy development. Determining the weights of criteria for decision making is one of the important stages of Multiple-Criteria Decision-Making. Various approaches have been described in the literature, such as using AHP (with crisp and fuzzy approaches) [10[5][6][7][8],11,12,13], and the entropy-based approach [20,25][15][20]. A renewable selection model for Indonesia was developed in [23][18], based on fuzzy AHP (Analytic Hierarchy Process) and a new procedure for aggregating experts’ judgments, including a procedure of pairwise comparison and aggregation of experts’ comparison matrices in a single matrix via the similarity aggregation method (SAM) [47][42]. Modified SAM was successfully applied to address the investment problem of offshore wind farms [48][43]. The abovementioned papers demonstrate the effectiveness of the fuzzy approach in formalizing uncertainty in decision making within the energy sector. Additionally, there are alternative approaches to formalizing uncertainty, such as intuitionistic, grey [49][44], hypersoft set, and Z-numbers. For instance, in Malaysia, the intuitionistic fuzzy AHP method was proposed for sustainable energy planning [24][19]. Paper [50][45] presents the results of the application of the Z-numbers and Z-extension of the TOPSIS method for the selection of renewables in economic regions with diverse conditions and high uncertainty in the case of Azerbaijan. The selection of hydrogen generation technologies employed the intuitionistic hypersoft set methodology with the VIKOR method [51][46]. Trapezoidal intuitionistic fuzzy linguistic number-based VIKOR was used for the renewable energy technology (RET) selection problem [52][47]. In summary, fuzzy models serve as valuable tools in situations where traditional statistical data are lacking or uncertain, enabling effective decision making in the realm of renewable energy. They provide a means to harness expert knowledge and subjective input to make meaningful strides in sustainable energy planning and policy implementation.

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