Exploring Future Renewable Energy Technologies Using a Developed Model and a Variety of MCDM Approaches

Abstract

Jordan, which depends largely on imported fuel, is confronted with an increasing demand for energy due to regional socio-political dynamics and population growth. This dependence highlights the necessity of locally supplied, sustainable energy sources to reduce environmental damage and economic vulnerability. Thus, the goal of this study is to categorize these sources based on their priorities for use, as well as to develop and evaluate sustainable RE technologies. In order to diversify the energy portfolio, lessen reliance on fossil fuels, and offer suggestions for sustainable growth, this study investigates renewable energy sources (RES) in Jordan. A model is developed to study and evaluate RES technologies and introduce actions for their exploitation. In order to prioritize renewable energy sources and improve energy independence and sustainability, this paper looks at renewable energy technologies that are feasible for Jordan, using a variety of MCDM methodologies. This study emphasizes Jordan’s urgent need for green, sustainable energy, highlighting solar, hydropower, and wind projects as the most advantageous options, promoting energy independence. The outcomes designate the applicability of the proposed model and integrated tools for analyzing renewable energy technology. The findings assist a shift toward energy independence and sustainability by providing decision-makers with a systematic, data-driven method for prioritizing green energy investments.

1. Introduction

Global energy demand is increasing daily, and the traditional way of producing energy has many implications for both the cost of operation and the environment. The Jordanian government is adhering to global agreements on environmental protection, with legal facilities and tax discounts paving the road for investors [1]. The world’s technological maturity and the availability of energy alternatives are making it easier to produce clean, sustainable energy and address significant challenges such as population growth and climate change. Due to the recent instable political situation and increased prices of oil, governments perceive renewable energy (RE) sources as more attractive alternatives [2].

The significance of RE and its effects on the environment and climate change are well understood by the Jordanian government and populace. Jordan’s population has grown as a result of regional political unrest, which has frequently resulted in higher electricity usage. In this crucial industry, having green and sustainable solutions is of utmost importance. Furthermore, 94% of Jordan’s gas and oil are imported to meet the country’s expanding energy needs; thus, changes in fuel costs pose a threat to this industry [3]. In this regard, the Jordanian government is working hard and providing incentives for investments. Using decision-making methodologies, this article will examine RE sources and their significance for the nation.

The world is transitioning toward sustainable green energy sources to address environmental concerns and promote economic stability. Consequently, in Jordan, available RE technologies like solar, wind, hydroelectric, biomass, geothermal, and hydrogen are being introduced to replace fossil fuels, but intermittent and high initial costs still pose challenges. Researchers worldwide are working to improve RE efficiency to overcome these challenges in the energy sector. The objectives are to explore RE technologies and their applications in Jordan’s power generation, offering recommendations for sustainable development. This study compares green energy sources, assesses environmental impact, and evaluates economic feasibility, contributing to Jordan’s RE strategy.

Previous studies, to the best of our knowledge, did not address the analysis, classification, and arrangement of RE sources according to their implementation and exploitation priorities in Jordan. Therefore, this research seeks to identify and analyze sustainable RE technologies, as well as to arrange these sources according to their utilization priorities. The following questions are the focus of the investigation: What are the renewable energy sources available in Jordan? What projects have been or are being implemented in Jordan? What is the appropriate framework for studying RE? What are the priorities for employing renewable energy sources? What are the best sources that are significant to Jordan energy sustainability?

Given the diversity of RE sources and the large abundance of some sources, such as solar energy, this research aims to explore and analyze these sources and indicate the sources that can be exploited efficiently. To achieve this goal, MCDM, represented by COPRAS, and Fuzzy COPRAS are used, in addition to merging CRITIC-COPRAS and FCRITIC-FCOPRAS to arrange these sources and indicate their importance. The primary contributions are the investigation and analysis of renewable energy sources in Jordan, as well as the prioritization of their use based on both local and global decision variables.

The CRITIC method, a decision matrix-based approach, uses correlation analysis, fuzzy set theory, and the COPRAS method, utilized for systematic decision-making in contrast intensity and conflicting relationships. CRITIC is an objective weighting method that assigns greater importance to criteria with higher informational content and independence. It avoids subjective bias and is widely used in manufacturing, logistics, and environmental assessments. Fuzzy CRITIC combines CRITIC principles with fuzzy logic, making it useful in uncertain or imprecise decision-making contexts like future renewable energy technologies and environmental planning. The CRITIC-coupled COPRAS method ensures systematic decision-making. The CRITIC method has a number of benefits over other objective weighting techniques. It takes into consideration the correlation between criteria, highlighting both the possible relationships between various criteria as well as the change of each indicator.

COPRAS is a proportional model that considers interactions among criteria using Complex Proportional Assessment. It allows assigning different weights to criterion weights based on their significance in decision-making. Fuzzy COPRAS, which incorporates fuzzy logic, is particularly effective in MCDM where criteria values are imprecise or vague. It can be applied in fields like green renewable energy sources and engineering risk assessment. One of the capabilities of the COPRAS technique is the ability to compute both maximizing and minimizing criteria. This method improves the calculation of both qualitative and quantitative criteria. The ability to demonstrate utility degree is the focal benefit of the COPRAS methodology when compared to other multi-criteria decision-making techniques.

2. Literature Review

As the globe attempts to move toward sustainable energy sources and confronts the problems posed by climate change, countries like Jordan have been actively exploring the potential for future sustainable green energy technologies. Previous studies have addressed the subject, some of which describe and analyze the economic effect and cost for the best alternative to be implemented in Jordan without considering specific location [4], focus on studying the feasibility of implementing wind and solar plants in Aqaba city [5], propose a full forecasting study including what if 100% of the energy consumed in Jordan were from green resources with a plan to achieve that by 2050 [6], investigate the current generation of renewable energy sources in Jordan and indicate how their growth requires a deep plan for better results [3], analyze the financial issues and clarify the image of the current situation in generating RE in Jordan [7], propose mathematical models highlighting wind turbines [8], direct attention to hydropower generation [9], study wastes and biomass with optimal utilization according to the situation in Jordan [10], and identify new categories for comparing sustainable green energy in Jordan, considering social, economic, architectural, and social economics, and assessing the impact on individual lives using a weighted method [11].

MCDM has been used in many fields, such as in the selection of materials for engineering equipment design [12], oil and gas well drilling projects [13], the selection of a center’s locations for logistics [14], and for civilian engineering foundations [15]. Also, there are many applications of MCDM methods in the medical field [16,17,18,19]. COPRAS, which was first introduced in 1996 [20], is a proportional model that considers the interactions and dependencies among criteria by using Complex Proportional Assessment based on the significance of each attribute [21,22]. COPRAS allows assigning different weights to each criterion based on its significance in the decision problem and provides a more nuanced approach, enabling decision-makers to enhance precision by creating customized models for informed decision-making. Fuzzy COPRAS extends the traditional COPRAS method by incorporating fuzzy logic, allowing for the evaluation of alternatives under uncertainty. This approach is particularly effective in MCDM, where the criteria values or expert judgments are imprecise or vague. By utilizing fuzzy sets, Fuzzy COPRAS captures the inherent ambiguity of real-world problems, offering a nuanced ranking of alternatives. The method calculates criteria weights and utility scores based on linguistic terms converted into fuzzy numbers. It has been applied in diverse fields such as green supply-chain management and risk assessment in engineering, demonstrating its flexibility and robustness in handling complex decision-making scenarios [23,24].

On the other hand, CRITIC is a widely used objective weighting method in MCDM. It determines the weights of criteria based on their contrast intensity (variance) and conflict (correlation) among criteria. This method ensures that criteria with higher informational content and independence from others are assigned greater importance. CRITIC avoids subjective bias, as it relies purely on the mathematical relationships among criteria. It has been extensively applied in areas such as machine selection in manufacturing, logistics, and environmental assessments. By objectively deriving weight, CRITIC provides a reliable foundation for subsequent ranking methods. Additionally, Fuzzy CRITIC combines the principles of CRITIC with fuzzy logic, enhancing its applicability in environments where decision data are uncertain or imprecise. By employing fuzzy numbers to represent criteria values and their interrelationships, this approach refines the weight calculation process, ensuring that weight criteria reflect not only their statistical properties but also the uncertainty inherent in real-world data. It is particularly useful in dynamic and complex decision-making contexts, such as supply chain risk management and environmental planning [25,26,27].

Jordan is working to improve energy security and lessen its carbon footprint. Researchers have examined Jordan’s adoption and integration of solar, wind, and hydroelectric energy sources in recent case studies. They highlighted the many advantages of green energy, such as lower greenhouse gas emissions, energy mix diversification, and less reliance on finite fossil resources. Numerous studies have shed light on the significance of sustainable green energy technologies in reducing climate change and promoting sustainable development at the national level by looking at regulatory contexts, technological developments, and socioeconomic effects. This study proposes to cover the gaps in previous studies by focusing on medium-sized projects in Jordan to improve utilization, reduce costs, and combat global warming impacts. It highlights the lack of existing research on RE technologies analysis and ranking for Jordan projects. This article uses a unique scaling methodology, expert opinions, and innovative tools to evaluate alternative RE technologies based on their rewards.

3. Research Methodology

This study explores renewable energy sources (RES) in Jordan, aiming to diversify the energy portfolio, reduce fossil fuel dependency, and provide sustainable development recommendations. A framework model is developed to study and evaluate RES, including their government support, regional integration, private sector investments, and strategies and plans. The model includes five main elements: available technologies, drivers of RE exploitations, decision variables, benefits, and drawbacks, and RE project analysis for developing RES. It also outlines criteria for evaluating different energy technologies and introduces actions for exploitation.

The main contributions are the exploration and analysis of RESs in Jordan, along with the prioritization of their use based on both local and global decision variables. This study aims to address gaps in research on renewable energy technologies in Jordan, focusing on medium-sized projects. It uses a unique scaling methodology, expert opinions, and innovative tools to analyze renewable energy sources, prioritize their use, and assess their environmental impact and economic feasibility. Previous studies in Jordan have not reasonably analyzed, classified, and arranged renewable energy sources based on their implementation and exploitation priorities. This research aims to analyze sustainable renewable energy technologies in Jordan, identify projects, study frameworks, priorities, and the best sources for energy sustainability. The study uses different MCDM methods to identify efficient sources.

As shown in Figure 1, the research method consists of seven parts that are designed to reach the desired result. The first part represents the objective, which is to analyze and rank renewable energy technologies in Jordan according to their priorities. The second part aims to collect information and data, whether through literature reviews, questionnaires, official websites and reports issued by government agencies or international bodies, and the opinions and inputs of decision-makers and evaluators. The third part is concerned with proposing a framework that shows the main elements of renewable energy in Jordan through studying renewable energy sources, the related projects that have been implemented, and the benefits and challenges of each source. The fourth part deals with the methods used to evaluate the different energy technologies, the steps of each method, and the accompanying mathematical equations. These methods include COPRAS, FCOPRAS, combined CRITIC-COPRAS, and combined FCRITIC-FCOPRAS. The fifth part represents the numerical applications of the methods used in the case study by determining the criteria and sources of renewable energy available in Jordan and the calculations accompanying the determination of their priorities in exploitation. Part six compares the different methods used and the final ranking of the available alternatives, in addition to evaluating the model, stating practical implications, and analyzing the results. The seventh part concludes with the results, research limitations, recommendations, and directions for future studies.

The inputs are based on the questionnaire outputs and the opinions of decision-makers. The assessments and data from a team of 11 experts related to renewable energy in Jordan from various relevant public and private sectors, academics, industrialists, and managers in related companies are utilized. The team of specialists used verbal expression to evaluate the criteria to show their importance, as well as to evaluate the different technologies compared to the criteria used to show the importance of these alternatives. This study presents a unique scaling methodology for projects of a moderate size, evaluating renewable energy sources’ viability and long-term impact in Jordan, using expert-driven criteria and fuzzy logic for adaptable decision-making.

This paper comprises seven sections. Section 1 introduces the research problem, questions, contributions, and objectives. Section 2 reviews earlier studies and authorized local and worldwide data. Section 3 outlines the components of the research method. Section 4 presents the evaluation’s framework, elements, methodology, and stages. Section 5 explains FMCDM techniques, their steps, and integration strategies. Section 6 includes the use of the tools, their numerical analysis, and a case study, and presents the comparisons and analysis of the findings. Section 7 outlines the findings, suggestions, and recommendations for upcoming research.

4. Renewable Energy Analysis

To study and analyze renewable energy technologies in Jordan, as shown in Figure 2 a conceptual framework is presented that contains five main parts: sources, drivers, criteria, benefits and drawbacks, and projects. In this section, the components of each part will be discussed. The Jordan renewable energy (JRE) framework is circumscribed by government support, strategies and general plans, and integration with surrounding countries to ensure sustainability and bridge the gap in renewable resources, and encourages and motivates the private sector to exploit available energy sources within the general agenda of state policy.

4.1. RE Technology

The need for energy is growing throughout the world, and the conventional methods of energy production have numerous negative effects on the environment and costs. The Jordanian government is adhering to global agreements on the environment and has taken legislative and financial measures related to taxes to encourage investors to participate in projects of renewable green energy [1]. Technological advancements enable clean, sustainable energy production, addressing population growth and climate change. Governments view renewable energy sources as attractive due to political instability and the increase in oil prices [2]. Below is a brief explanation of the feasible RE sources available in Jordan.

• Wind energy: Jordan is well positioned to harness wind energy for electricity generation, given its significant wind energy resources. The average wind speeds in the country range from 6 to 8 m per second in favorable locations [28]. According to a study carried out by Jordan’s Ministry of Energy and Mineral Resources (MEMR) in collaboration with the Royal Scientific Society, approximately 16% of the nation’s territory is considered viable for wind energy generation, offering a total technical capacity of 3.6 GW [29]. Jordan’s Wind Atlas reports a promising future for wind energy generation, with two projects, the first near Hofa and the second in Ibrahimyya, contributing 0.32 MW to the country’s energy output [8]. Another 117 MW project has been in operation since the second quarter of 2015 and is situated in Tafila, in the south of the nation. An additional project, located in Ma’an in the south of the country and producing 66 MW, has been connected to the national grid since 2016 [30]. There are several suitable locations for wind energy, which is considered a suitable source for generating electricity in Jordan.
• Solar energy: Jordan’s climate is highly suitable for utilizing solar energy, with the nation receiving an average solar irradiation ranging from 4 to 7 kWh/m² daily and enjoying approximately 310 sunny days each year. This results in an annual average solar energy potential from 1400 to 2300 kWh/m². These conditions have positioned Jordan as one of the leading locations in the region for solar projects, facilitating the production of electricity at a remarkably low cost from new installations [30]. There are currently over 300 PV installation companies in Jordan, 200 MW signed projects in Ma’an, and an additional 74–100 MW plants in Aqaba [30]. Solar cells are so common in Jordan that they can be used to pump water, lightning, and provide electrical services in the desert regions. They have a 1000 kW peak capacity. Approximately 20% of Jordanians have their own solar water heaters, the majority of which are made locally by 25 small companies that produce 4500 units a year, and 30% of residential buildings in the country are equipped with solar water heating systems [31]. Photo voltage technology installed on roofs has generated nearly 150 kWh for the grid. Jordan’s grid exhibits high harmonics, causing instability without proper filtering, as evidenced by frequent enological electric bills and frequent flutes in some areas [32,33].
• Hydropower: Jordan has limited hydropower resources because of the lack of water sources. Currently, the King Talal Dam is the country’s only hydropower plant, with a capacity of 7 MW. Additionally, the Aqaba Power Station includes hydropower turbines with a total capacity of 6 MW, which operate using the flow of returning cooling seawater. Despite these limitations, there is significant potential to generate more hydropower from the difference in altitude between the Dead Sea and the Red Sea, which is estimated to support a capacity of 400–800 MW. This could be achieved through the planned Red Sea–Dead Sea Canal project. To enhance the flexibility of the power system, MEMR is exploring the possibility of creating pumped storage hydropower projects by assessing the country’s dams and reservoirs [9].
• Bioenergy: Bioenergy, which includes biomass, biofuel, and biogas, can be created from organic materials like agricultural waste, companies waste, municipalities, and industries through the processes of gasification and anaerobic digestion. The Jordanian government’s initiative to use methane gas is backed by the United Nations Development Program (UNDP). Because of this help, a plant that generates 1 MW of grid flows was established in a waste dump. This alternative is a promising sector to be used in Jordan because it can be produced from a number of sources, such as animal manure, organic industrial wastes, urban wastes, and agricultural waste. Unfortunately, only 5% of the land is used for agriculture due to climate change and global warming, which limits the potential sources’ large-scale and profitable use. Based on daily waste generation which is estimated to be from 0.45 to 0.95 kg/capita, Jordan can generate up to 60 MW annually. Researchers advise that this source be used efficiently, particularly for livestock and poultry farms in Jordan [10,34,35]. Municipal solid waste (MSW) is a crucial energy source due to the growing population in Jordan. The Greater Amman Municipality has implemented a biogas project using methane gas from the Ghabawi landfill, with capacity expected to reach 7 MW. Animal waste, primarily from cattle, poultry, and sheep, has potential for heating and cooking, but has lower resource potential compared to municipal waste. Additionally, Irbid and Al-Mafraq offer olive ash and veneer, a byproduct of harvesting olive oil. Its total supply in these areas is estimated to be about 19,000 MT per year, and it can be used as feed or fuel [36].
• Geothermal energy: Geothermal energy is used to heat water and produce electricity, but the opinions of experts and researchers differed on the feasibility of using geothermal energy in Jordan, between supporters and pessimists. Due to the limited quantity of these pools and its favorable reputation for physical therapy applications, some studies claimed that this kind of energy could not be utilized in Jordan [3]. Jordan, however, has active geothermal activity, according to the Natural Resources Authority (NRA), with two energy levels: medium energy (110–114 °C) and low energy (30–65 °C) [37]. Therefore, using these sources is possible in certain locations in Jordan. In addition to thermal wells in the central and eastern plateau, the Rift Valley is home to a number of thermal water sources, such as springs and wells. Jordan’s geothermal gradient map shows two different areas with high gradients of up to 50 °C/km. One of these areas is in the northeast part of the country, and the other is close to the east Dead Sea escarpment. This indicates that the eastern Dead Sea Rift, where temperatures range from 45 to 63 °C, has a locally accessible energy source for heating [38].
• Sea energy: Ocean energy includes wave, tidal, and sea geothermal energy. Tidal energy (TE) is produced by intermittent currents and tides between water bodies but is limited in Jordan due to lack of oceans or seas. There are not many of these resources in Jordan and Aqaba, the country’s sole modest Red Sea seaport, is the only one. Exploiting such resources is currently not effective, considering infrastructure requirements, environmental impact assessment, and economic feasibility.
• Hydrogen energy: Hydrogen, a clean and adaptable energy source, is produced when excess electrical current from renewable sources like solar or wind power flows through water and uses electrolysis to separate hydrogen from oxygen. Despite its versatility, high manufacturing costs, storage and transportation issues, and energy-intensive manufacturing, hydrogen offers advantages such as resource availability, social acceptability, and reliability. It can be used as a fuel for transportation and electricity generation, making it a versatile energy carrier [39,40]. Jordan is exploring the use of RES like wind and solar power to produce green hydrogen, a clean fuel suitable for various applications. The country conducts feasibility studies and investigates the economic and technological feasibility of hydrogen production. Government regulations and partnerships with the private sector are crucial for a supportive environment. The hydrogen economy could integrate into energy storage, transportation, and industry sectors, promoting energy sustainability and diversification. Infrastructure development is essential for successful hydrogen projects. Jordan aims to generate 8 GW of renewable energy by 2030, increasing to 22 GW by 2040 and 47 GW by 2050. It also plans to produce 0.6 million metric tons per annum of hydrogen by 2030, increasing to 1.5MMTPA by 2040 and 3.4MMTPA by 2050. Jordan RE strategies and initiatives comprise many agreements and Energy Partnerships with several countries which aim to develop green hydrogen projects, promote innovation, and contribute to a sustainable energy transition. MEMR is developing a green hydrogen strategy to ensure Jordan’s competitiveness and independence in the energy sector, focusing on hydrogen energy as a promising renewable energy source [41,42].
• Other RE sources: There are additional SGE sources, such as piezoelectric and electromagnetic. Although piezoelectric technology might not be the best option to replace the other options, it might be applied on a medium scale in certain establishments, such as universities and airports, to lessen the strain on the grid. In certain locations, this technology is regarded as a good solution because it requires little maintenance and has low operating expenses [43], particularly after some studies confirmed its viability [44]. Additionally, the cost of the storage unit is the biggest problem. Therefore, putting these cells in a large area, such as the airport’s passenger hall, and connecting them to the grid with converters and high-quality filters will provide electricity required to light such facilities. While there are currently no projects or policies in Jordan regarding this type of energy source, it could be suitable for energy production in some areas with small capacity. However, since the energy produced is small and the technology is still in its infancy, the evaluation of other sources, except piezoelectric, will be disregarded.

4.2. RE Drivers

The main drivers for RE include securing sustainable energy supply, diversifying energy mix, reducing energy costs, ensuring future energy independence, and implementing environmentally friendly factors to reduce greenhouse gas emissions and comply with UN sustainable objectives and international agreements. The Jordanian government has outlined specific goals for the energy sector in its 2020–2030 Master Strategy. These goals focus on securing a reliable energy supply for the future, using a variety of energy sources, making the energy system sustainable, and lowering electricity costs. Meeting these goals is essential for boosting economic growth and supporting Jordan’s development plans, as intended in the Jordan Economic Growth Plan [45].

Jordan’s plan to diversify its energy sources emphasizes the use of renewable technologies, aiming to make the country more competitive and reach 31% of its total energy capacity from renewables by 2030. Expanding the use of renewables in electricity, heating, cooling, and transportation is vital for improving energy security, making it more accessible, affordable, and sustainable in the long run. Efforts in energy efficiency and renewable energy have also helped reduce the country’s reliance on imports from 99% to 94% [45]. A strong argument for governments to seek a larger role for renewables in the future energy mix is provided by the significant cost reductions of RE technologies over the last ten years. Renewable energy is currently the least expensive new power generation source in the majority of the world including Jordan. Opportunities for the creation of new industries and jobs are presented by the diversification of the energy mix. For emerging economies hoping to get the most out of the shift in terms of job creation and local value creation, the socioeconomic aspect of renewable energy is vital. Since 2013, nearly 300 licensed businesses that specialize in solar PV design, procurement, installation, and operation and maintenance have been established, employing thousands of people full time. Different skills and material inputs are needed for various renewable energy technologies across the value chain segments. With the exception of 2012, when the influx of refugees from nearby countries caused the consumption rate to rise, Jordan’s overall energy consumption has declined in recent years [46,47,48].

4.3. RE Technology Evaluation Criteria

In this section, the main criteria used to assess renewable energy technologies are presented. Although there are many criteria that can be used and suggested, experts have identified the five main criteria to use in evaluating renewable energy sources.

Table 1. RE decision variables.

Attribute	Indicators	Explanation
Source Availability	Technology readiness, reliability, energy generated, scalability, energy potential, quantity, availability, trends, developed projects, initiatives, growth prospects, and cost-effectiveness.	Resource availability is crucial for renewable energy projects’ success, influencing operational performance and financial viability. It ensures a stable, consistent, and reliable energy supply.
Technology Development	Technical viability, grid combination, storage abilities, scalability, safety, flexibility, commercialization, efficiency, source security, technological soundness, lifespan, integration capability, capacity, waste plans, transfer, variety, simplicity of employment, reduced risks, execution requirements, infrastructure requirements, and number of implemented projects.	The evaluation of RE source involves assessing technical requirements, practicality, feasibility, and expansion capabilities. It also evaluates the technology’s ability to meet rising demand, assesses the reliability and maturity of renewable energy sources, and manages implementation risks.
Local Conditions	Community improvement, social benefits, health impacts, area used, employment generation, political stability, cost-effectiveness, continuity, confidence, supply security, social acceptance, regulations and policies, initiatives, legislation, incentives, stability, and strategies.	Local conditions significantly influence renewable energy sector appeal, attracting investments and deployment. Energy security systems, infrastructure, and projects impact efficiency, effectiveness, economic feasibility, and experience. Laws, regulations, and policies support expansion, with societal reactions affecting local communities.
Environmental Effect	Air pollution, water contamination, emissions, biodiversity, resource conservation, area usage, water usage, ecosystem effect, noise, protection, natural resource reduction, and decreasing global warming.	Consider the environmental impacts of RE projects and sources, focusing on greenhouse gas emissions, pollution, land use, legislations conformity, and natural effects in Jordan.
Economic Feasibility	Research and development, startup costs, potential return on investment, investor’s interests, operating expenses, maintenance costs, sustainment costs, subsidies, incentives, financing choices, investment cost, energy cost, service life, global marketplace trends, generation capacity, payback duration, potential revenue streams, and disposal costs.	Evaluates the economic viability and cost-effectiveness of renewable energy sources, considering project-related costs, expenditures, and investment concerns. Return on investment is a crucial criterion in Jordanian RE investments.

shows the decision variables, their main indicators, and a description.

The framework outlines the aspects of RE technologies for Jordan’s market, determining opportunities for modernization and helping create a sustainable energy scheme. Important areas taken into account to evaluate RE sources include source availability, technology development, local conditions, environmental effect, and economic feasibility.

4.4. Benefits and Drawbacks

Table 2. Benefits, drawbacks, and limitations of RE technologies.

Technology	Benefits	Drawbacks	Limitations
Solar Energy	Availability, environmental friendliness, independence, reliability, competitiveness, sustainability, and employment prospects.	The quantity of energy that solar panels can capture changes with the time of day and the season, and solar energy is dependent on the amount of sunshine that falls during the day. The quality of solar panels in converting solar energy into electricity is not high. There is a decrease in the effectiveness of solar energy depending on the region. The ability to capture solar energy decreases with distance from the equator to the poles. Decreased capacity to absorb solar radiation in the presence of rain, fog, and clouds. Installing solar panels outside and constantly exposing them to sunlight may increase their damage due to exposure to external conditions.	Release of toxic chemicals used in heat transfer system. Use of toxic chemicals in PV system. Water scarcity in arid regions. Disposal and recycling of toxic materials can bring negative impacts to the environment.
Wind energy	Availability, environmental friendliness, independence, reliability, competitiveness, sustainability, job opportunities, low operating cost, and cost-effective.	Variable source of energy: although wind is renewable, it is not permanent, which sometimes causes a shortage in meeting energy needs. Biological impact: wind energy may cause disruption to wildlife, especially birds. Noise pollution: turbines produce too much noise to be ignored. Transportation: wind energy is unable to supply the transportation sector with the necessary energy, which makes its dependence on petroleum products inevitable. Location: establishing turbines and wind farms requires choosing suitable sites with large areas.	Birds collide with the supporting towers and rotating blades. Noise pollution. Adverse ecosystem. Social health issues.
Hydropower	Sustainability, stable source of energy, enhancement of tourism, independence, reliability, and can be integrated with other energy sources such as wind and sun when energy demand is high or reduced intermittently.	Affects marine life by converting running water into stagnant water, preventing fish and marine animals from migrating. Characterized by its limitations, as it can only be established near water areas. Establishing hydroelectric stations requires a high initial cost, although it is an economical solution in the long term. Carbon and methane emissions are produced as turbines break down green cover. Dams pose a threat of potential flooding; this results in great destruction to the areas surrounding the dams.	Changes in hydrologic characteristics. Affects water body’s ecology by disturbing the ecological continuity of sediment transport and fish migration. Artificially created structure leads to flooding of the former natural environment.
Bioenergy	A sustainable and renewable energy source, independence, a very low energy cost per unit, and worldwide availability.	Biofuel production can be quite inefficient. Requires large quantities of raw materials. High gas emissions in the production chain. The use of chemical fertilizers and pesticides. High initial investment is required. Water consumption.	Release of chemical pollutants. Overexploitation of forest. Diversion of crops or land. Air pollution. Soil erosion, vegetation degradation. Increase of food commodity prices and risks of food security.
Geothermal energy	Reliability, availability, low operational costs, requires limited areas of land, and allows accuracy of energy calculation.	Can only be built in limited locations. Harm to the environment. High initial costs. It may cause surface destabilization. Land subsidence occurs.	Hydrogen sulfide production. Release of toxic metal (arsenic, boron, lead, mercury, radon, and vanadium). Disposal and recycling of highly toxic materials can bring negative impacts to the environment.
Piezoelectric	No emissions during operation, compact size, suitable for micro-scale applications, and reliable in specific uses.	Limited efficiency in large-scale energy conversion, fragility of materials, and dependency on consistent mechanical pressure or vibrations.	Limited scalability, requires precise conditions for energy generation, and dependent on specialized materials that may not be widely available.

shows the advantages, disadvantages, and limitations of each energy source that can be exploited in Jordan. Some renewable energy sources are excluded because they are not practical or adequate in Jordan. These include hydrogen energy, which is not a source in itself but rather acts as a conduit for other renewable energy sources to produce hydrogen fuel, ocean energy in its various forms, and other sources, for the reasons mentioned above. The following are the renewable energy sources that are adopted based on the questionnaire, the opinions of specialists, and that are available in Jordan.

Renewable energy (RE) funding and support are becoming a priority for industries, governments, and organizations worldwide to boost policies, economies, and production in both advanced and developing nations. In many developing regions, hydropower remains a key energy source, while areas rich in RE resources are focusing on sustainable practices. However, obstacles like dependence on fossil fuels, limited funding, and a lack of expertise in addressing environmental challenges still exist. Wealthier countries with higher GDP and concerns about energy sustainability often have the resources to invest in RE research and implementation, which can promote economic growth and secure energy supplies. While the early stages of RE development are expensive, developed nations tend to create new policies, and developing countries adjust by adopting supportive measures. The main goal of RE is to enhance living conditions in remote and rural areas while reducing the environmental damage caused by fossil fuels.

4.5. RE Projects

Case studies of successful green energy projects provide valuable lessons and best practices in sustainable energy development. They reveal socio-economic, technological, and regulatory aspects, facilitating knowledge transfer and adoption of best practices. Collaborative examination of successful RE projects can shape policies, drive innovation, and accelerate global transition.

Table 3. List of RE projects in Jordan [49,50].

RE Type	Project	RE Type	Project	RE Type	Project
Hydropower	King Talal Dam with a capacity of 7 MW. Aqaba Power Station with a capacity of 6 MW.	Soler	60.3 MW King Hussein Bin Talal, Mafraq region. 13 company’s projects in Mafraq 10MW. 20 MW Solar Cells Jordan Solar comp. 133.4 MW FRV Al Mafraq. 50 MW Risha. 200 MW Baynouna, Ma’an. 103 MW Al Quweira. 100 MW ACWA + AES.	Soler	51 MW Al Safawi. 5 MW Mafraq. 200 MW Mafraq. 200 MW Masdar. 50 MW Jordanian Government. 200 MW Decentralized. 179 MW Decentralized. 123 MW Decentralized. 200 MW Prequalified.
Biomass	A pilot plant using MSW with a capacity of 3.5 MW
Wind	50 MW New Tafileh Wind “Korea Southern Company” & “Daelim Company”. Fujeij 89.1 MW. Seoul, Republic of Korea 82 MW Al-Rajef. 45 MW Shobak. 80 MW Ma’an. 100 MW Prequalified.

illustrate the main RE projects in Jordan [49,50].

Green energy sources, also known as renewable energy, are environmentally friendly alternatives to fossil fuels, producing fewer greenhouse gas emissions and contributing to a cleaner future. Common green energy sources include solar, wind, hydropower, geothermal, biomass, and sea energy. RES helps mitigate climate change and reduce finite resource dependence.

5. RE Evaluation Approaches

5.1. COPRAS Method

The COPRAS method is an MCDM technique that systematically evaluates and ranks alternatives based on multiple criteria. COPRAS is widely used for prioritizing alternatives in sustainable energy projects and other fields due to its structured and comprehensive framework. The steps of COPRAS method are adopted from [51,52,53,54,55]. The method involves the following steps:

Step 1: The construction of the decision matrix. The process begins by forming a decision matrix that represents $m$ alternatives evaluated across $n$ criteria. Each value in the matrix is normalized using a linear normalization technique to ensure the comparability of data across different units. This step ensures that the decision problem is translated into a structured matrix format for analysis. The matrix is represented by Equation (1):

$$X=\left[\begin{array}{c}{x}_{01} \dots x_{0j} \dots x_{0n}\\ ⋮ \dots ⋮ \dots ⋮ \dots \\ x_{i1} \dots x_{ij} \dots x_{in}\\ ⋮ \dots ⋮ \dots ⋮ \dots \\ x_{m1} \dots x_{mj} \dots x_{mn}\end{array}\right];i=\mathrm{0,1},2,\dots , m;j=\mathrm{1,2},\dots ,n$$

(1)

where $x_{ij}$ is the value of the $i$th alternative for the $j$th criterion. The weights for the criteria are represented as follows, where $w_j$ is the weight of the $j$th criterion.

$$w_j=\left[w_1\dots w_n\right]$$

(2)

Step 2: The normalization of the decision matrix. In this step, the values in the decision matrix are normalized to create a standardized decision matrix. This creates the normalized decision matrix, ensuring that all criteria are measured on a comparable scale. The normalized values ${\tilde{r}}_{ij}$ of the decision matrix are calculated using Equation (3):

$${\tilde{r}}_{ij}={\displaystyle \frac{x_{ij}}{\sum _{i=1}^m{x}_{ij}}}$$

(3)

Step 3: Determining the weighted normalized decision matrix.

The normalized decision matrix $\acute{D}$ is then weighted according to the importance of each criterion, represented by its weight ($w_j$). The criteria weights are calculated by taking the average experts’ evaluations. This step accounts for the varying significance of each criterion in the decision problem, ensuring that more important criteria have a greater impact on the rankings. The weighted normalized decision matrix is calculated using Equation (4):

$$\acute{D}=d_{ij}=r_{ij}^{*} . w_j$$

(4)

where $d_{ij}$is the weighted normalized value and $r_{ij}^{*}$ represent the values after the normalization step for the $i$th alternative under the $j$th criterion.

Step 4: Calculating of maximizing and minimizing index for each alternative. The weighted values are summed separately for beneficial criteria (to be maximized) and non-beneficial criteria (to be minimized). The resulting indices (S+ and S−) represent the performance of each alternative in terms of both maximizing desirable outcomes and minimizing undesirable ones

$$S_{+i}={\sum }_{j=1}^k{d}_{ij }, j=\mathrm{1,2},\dots ,k maximizing index$$

(5)

$$S_{-i}={\sum }_{j=k+1}^n{d}_{ij }, j=k+1,k+2,\dots ,n minimizing index$$

(6)

where:

• $S_{+i}$: sum of weighted normalized values for beneficial criteria.
• $S_{-i}$: sum of weighted normalized values for non-beneficial criteria.
• $d_{ij }$: weighted normalized value for the $ith$ alternative under the $jth$ criterion.
• $k$: the number of beneficial criteria.
• $n$: total number of criteria.

Step 5: Calculating the relative weight of each alternative.

The relative importance $Q_i$ of each alternative is calculated using the values of S+ and S−. Alternatives with higher $Q_i$ values are considered more favorable. This step highlights the overall performance of each alternative by integrating both beneficial and non-beneficial criteria.

$$Q_i=S_{+i}+{\displaystyle \frac{\min S_{i-}\sum _{i=1}^m{S}_{i-}}{S_{i-}\sum _{i=1}^m\frac{\min S_{i-}}{S_{i-}}}} ;i=\mathrm{0,1},2,\dots , m$$

(7)

$${\mathrm{Q}}_{\mathrm{i}}={\mathrm{S}}_{+\mathrm{i}}+{\displaystyle \frac{\sum _{\mathrm{i}=1}^{\mathrm{m}} {\mathrm{S}}_{-\mathrm{i}}}{{\mathrm{S}}_{-\mathrm{i}}\sum _{\mathrm{i}=1}^{\mathrm{m}} \left(1/{\mathrm{S}}_{-\mathrm{i}}\right)}};i=\mathrm{0,1},2,\dots , m$$

(8)

where:

• ${\mathrm{Q}}_{\mathrm{i}}$: the relative weight of the ith alternative.
• ${\mathrm{S}}_{+\mathrm{i}}$: beneficial index for the ith alternative.
• ${\mathrm{S}}_{-\mathrm{i}}$: non-beneficial index for the ith alternative.
• $\min ({\mathrm{S}}_{-i})$: minimum value of the non-beneficial index across all alternatives.
• $m$: number of alternatives.

Step 6: Calculation of performance index $\left(P_i\right)$ value for each alternative. The performance index for each alternative is calculated as a percentage of the highest ${\mathrm{Q}}_{\mathrm{i}}$value ($Q_{max}$). This provides a clear indication of how each alternative performs relative to the best option, enabling easy comparison.

$$P_i={\displaystyle \frac{Q_i}{Q_{max}}}\times 100\%$$

(9)

Step 7: Ranking the alternatives. Rank one represents the best alternative.

5.2. FCOPRAS Method

Fuzzy COPRAS, a method using fuzzy sets, effectively handles real-world problems by calculating criteria weights and utility scores, proving flexible and robust in various fields like green supply-chain management. The Fuzzy COPRAS method is adopted from [55,56,57,58].

Step 1: Define the decision problem by identifying the objective of the decision-making process with input from a committee of experts. Assume there are $K$ experts ${(E}_k , t=\mathrm{1,2},\dots , K)$ tasked with assessing $m$ options$(A_i , i=\mathrm{1,2},\dots , m)$ while considering the importance or weight of $n$ criteria ($C_j , j=\mathrm{1,2},\dots , n)$.

Step 2: Identify and define the relevant linguistic variables. Each linguistic variable must be expressed as a positive Triangular Fuzzy Number (TFN). These variables are important for identifying the main weights of the criteria and comparing options using multiple factors. Before converting these linguistic variables into positive TFNs, it is necessary to define the linguistic terms that represent the criteria weights and the fuzzy scores given to each option for every criterion.

Step 3: The weights of the evaluation criteria are established based on expert judgments. The fuzzy significance weight ${\tilde{\mathrm{w}}}_{\mathrm{i}}$ for a criterion $C_j$ is calculated as follows:

$${\tilde{w}}_j={\displaystyle \frac{1}{k}}\left( {\tilde{w}}_j^1\oplus {\tilde{w}}_j^2\oplus \dots \oplus {\tilde{w}}_j^k\right)$$

(10)

where $w_j^k$ represents the fuzzy weight assigned to the criterion $C_j$, by the kth expert, and is expressed as a Triangular Fuzzy Number: ${\tilde{w}}_j^k=(l_j^k\oplus m_j^k\oplus u_j^k)$. Where $l_j^k,m_j^k$, and $u_j^k$ represent the lower, middle, and upper bounds of the fuzzy weight, respectively.

Step 4: Once the decision-makers have assessed the alternatives against the criteria, the following equation is applied to aggregate their evaluations into a combined fuzzy decision matrix:

$${\tilde{x}}_{ij}={\displaystyle \frac{1}{k}}\left( {\tilde{x}}_{ij}^1\oplus {\tilde{x}}_{ij}^2\oplus \dots \oplus {\tilde{x}}_{ij}^k\right)$$

(11)

where $x_{ij}^k$represents the evaluation score assigned to the alternative $A_i$under criterion $C_j$ by the expert kth. Each evaluation score is expressed as a fuzzy number, ${\tilde{x}}_{ij}^k=(l_{ij}^k\oplus m_{ij}^k\oplus u_{ij}^k)$, where $l_{ij}^k,m_{ij}^k,u_{ij}^k$ correspond to the lower, middle, and upper bounds of the TFN, respectively. This aggregation ensures that all decision-makers’ inputs are combined into a single representative evaluation for each alternative under each criterion.

Step 5: Construct the fuzzy decision matrix ($\tilde{D}$) and the fuzzy criteria weight vector ($\tilde{W}$). The expert input is used to evaluate each criterion, where the evaluations are represented using TFNs. These TFNs translate qualitative linguistic terms into quantitative fuzzy values, allowing decision criteria to be rated effectively. Additionally, the aggregated weights for all criteria are expressed in matrix form, particularly useful for supplier selection or similar multi-criteria decision problems. The ratings of m alternatives (options) for n criteria are represented in the fuzzy decision matrix $\tilde{D}$, as follows:

$$\tilde{D}=\left[\begin{array}{cccc}{\tilde{x}}_{11}& {\tilde{x}}_{12}& \dots & {\tilde{x}}_{1n}\\ {\tilde{x}}_{21}& {\tilde{x}}_{22}& \dots & {\tilde{x}}_{2n}\\ ⋮& ⋮& \ddots & ⋮\\ {\tilde{x}}_{m1}& {\tilde{x}}_{m2}& \dots & {\tilde{x}}_{\mathrm{m}\mathrm{n}}\end{array}\right], i=\mathrm{1,2},\dots ,m; \mathrm{j}=\mathrm{1,2},\dots ,n$$

(12)

where ${\tilde{x}}_{ij}$ denotes the TFN score for alternative $A_i (i=\mathrm{1,2}\dots ,m)$ corresponding to criterion $C_j$ ($\mathrm{j}=\mathrm{1,2},\dots ,n$). The fuzzy criteria weight vector $\tilde{W}$ is expressed as $\tilde{W}=\left( {\tilde{w}}_1,{\tilde{w}}_2,\dots ,{\tilde{w}}_n\right)$.

Step 6: Calculate the normalized fuzzy decision matrix $\tilde{Y}$. Where each value ${\tilde{y}}_{ij}$ consists of three parts: lower ($l$), middle ($m$), and upper ($u$) fuzzy bounds. The normalization is performed using the given equations:

$$y_{ij}^l={\displaystyle \frac{x_{ij}^l}{\sum _{i=1}^m{x}_{ij}^l}}$$

(13)

$$y_{ij}^m={\displaystyle \frac{x_{ij}^m}{\sum _{i=1}^m{x}_{ij}^m}}$$

(14)

$$y_{ij}^u={\displaystyle \frac{x_{ij}^u}{\sum _{i=1}^m{x}_{ij}^u}}$$

(15)

where $x_{ij}^l$, $x_{ij}^m$, $x_{ij}^u$ are the lower, middle, and upper bounds of the fuzzy decision matrix $\tilde{Y}={\left[{\tilde{y}}_{ij}\right]}_{m\times n}$, $m$ is the number of alternatives (rows), and $n$ is the number of criteria (columns).

Step 7: Compute the weighted normalized decision matrix $\tilde{F}$. using the following expression

$$\tilde{F}={\left[{\tilde{f}}_{ij}\right]}_{m\times n}$$

(16)

where ${\tilde{f}}_{ij}={\tilde{y}}_{ij}.{\tilde{w}}_j$ and ${\tilde{f}}_{ij}=(f_{ij}^l,f_{ij}^m,f_{ij}^u)$. ${\tilde{y}}_{ij}$ represents the normalized value and ${\tilde{w}}_{ij}$ denotes the corresponding weight.

Step 8: Compute the normalized assessments for the alternatives and determine the overall benefit ratings. The following equations are used to calculate the overall ratings for a given alternative concerning beneficial criteria across lower, medium, and upper values, respectively:

$$z_i^{(+l)}={\sum }_{j=1}^n{f}_{ij}^l \left| j\in J^{max}\right.$$

(17)

$$z_i^{(+m)}={\sum }_{j=1}^n{f}_{ij}^m \left| j\in J^{max}\right.$$

(18)

$$z_i^{(+u)}={\sum }_{j=1}^n{f}_{ij}^u \left| j\in J^{max}\right.$$

(19)

Similarly, the overall ratings for the non-beneficial criteria across lower, medium, and upper values are calculated using the following equations:

$$z_i^{(-l)}={\sum }_{j=1}^n{f}_{ij}^l \left| j\in J^{min}\right.$$

(20)

$$z_i^{(-m)}={\sum }_{j=1}^n{f}_{ij}^m \left| j\in J^{min}\right.$$

(21)

$$z_i^{(-u)}={\sum }_{j=1}^n{f}_{ij}^u \left| j\in J^{min}\right.$$

(22)

• $z_i^{(+)}$: represents the benefit ratings for beneficial criteria.
• $z_i^{(-)}$: corresponds to the ratings for non-beneficial criteria.
• The subscripts $l,m,u$ stand for lower, medium, and upper values, respectively.
• $J^{max}$ refers to the set of beneficial criteria, and $J^{min}$ refers to the set of non-beneficial criteria.

Step 9: Perform defuzzification of each factor’s fuzzy weight and the fuzzy decision matrix using the following equation:

$$Z_i\left(z_i^{-}, z_i^{+}\right)={\displaystyle \frac{\left(z_i^{+l}-z_i^{-l}\right)+\left(z_i^{+m}-z_i^{-m}\right)+(z_i^{+u}-z_i^{-u})}{3}}$$

(23)

This equation calculates the defuzzified value by averaging the differences between the upper and lower bounds across the lower, middle, and upper fuzzy intervals.

Step 10: Determine the relative importance of each option using Equation (23). In this context, $Q_i$ represents the proportional weight for the $i^{th}$ alternative

$$Q_i=Z_i^{+}+{\displaystyle \frac{\underset{i}{\min }{z}_i^{-}\sum _{i=1}^m{z}_i^{-}}{z_i^{-}\sum _{i=1}^m\frac{\underset{i}{\min }{z}_i^{-}}{z_i^{-}}}}$$

(24)

where $Z_i^{+}$ represents the total positive performance value for the $i^{th}$ alternative and $Z_i^{-}$ represents the total negative performance value for the $i^{th}$ alternative. Moreover, the minimum values are $Z_{min}=\min Z_i, for i=\mathrm{1,2},\dots ,m$.

Step 11: Determine the priority or significance of the alternatives. Rank the alternatives based on their respective weights. The alternative with the greatest relative weight is considered the most preferable and is given the highest priority (rank). The optimality criterion is established using the following equation

$$A^{*}=\left\{A_i\left|\underset{i}{\max }{Q}_i\right.\right\}$$

(25)

where a higher $Q_i$for an alternative indicates a higher rank and $A^{*}$ is the optimal or best alternative for the $A_i$ alternative within the set of alternatives.

Step 12: Calculate the utility value for each alternative $P_i$, using Equation (9), where ${\mathrm{Q}}_{\mathrm{i}}$ represents the weight of the specific alternative and ${\mathrm{Q}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$ denotes the highest value among all alternatives.

5.3. CRITIC Method

The CRITIC method is a widely used objective method for weighting criteria, considering both contrast intensity and conflicting relationships. It uses correlation analysis to determine contrasts between criteria and computes the weights of these criteria, influencing the ultimate rank of alternatives. The CRITIC steps used in this study are adopted from [59,60,61].

Step 1: Construct the decision matrix. Formulate a decision matrix $X$, where each row represents an alternative ($A_i ,i=\mathrm{1,2},\dots ,m$) and each column represents a criterion ($C_j, j=\mathrm{1,2},\dots ,n$). Each element $r_{ij}$ in the matrix refers to the value of the ith alternative under the jth criterion. The decision matrix is represented using the following equation

$$X=\left[\begin{array}{ccccc}{r}_{11}& \dots & r_{1j}& \dots & r_{1n}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ r_{i1}& \dots & r_{ij}& \dots & r_{in}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ r_{m1}& \dots & r_{mj}& \dots & r_{mn}\end{array}\right]$$

(26)

where $r_{ij}$ is the value of the ith alternative under the jth criterion.

Step 2: Normalize the decision matrix.

Normalize the decision matrix to eliminate the influence of different measurement units for criteria. Depending on whether the criterion is beneficial (higher values are better) or cost-related (lower values are better), the normalization formulas are.

For benefit criteria:

$$x_{ij}={\displaystyle \frac{\left( r_{ij}-r_{min}\right)}{\left( r_{max}-r_{min}\right)}}$$

(27)

For cost criteria:

$$x_{ij}={\displaystyle \frac{( r_{ij}-r_{max})}{( r_{min}-r_{max})}}$$

(28)

Step 3: Compute the correlation coefficient. Calculate the correlation coefficient ($q_{jk}$) between each pair of criteria j and k. This measures the linear relationship between the two criteria

$$q_{ik}={\displaystyle \frac{{\sum }_{i=1}^m\left(x_{ij}-{\overline{x}}_j\right)\left(x_{ik}-{\overline{x}}_k\right)}{\sqrt{{\sum }_{i=1}^m{\left(x_{ij}-{\overline{x}}_j\right)}^2 {\sum }_{i=1}^m{\left(x_{ik}-{\overline{x}}_k\right)}^2}}}$$

(29)

where ${\overline{x}}_j={\displaystyle \frac{1}{n}}\sum _{i=1}^m{x}_{ij}$ is the mean of normalized values for criterion j.

Step 4: Calculate the standard deviation. Compute the standard deviation (${\sigma }_j$) for each criterion. The standard deviation indicates the degree of dispersion or variability in the normalized values.

$${\sigma }_j=\sqrt{{\displaystyle \frac{1}{n-1}}{\sum }_{i=1}^m{\left(x_{ij}-{\overline{x}}_j\right)}^2}$$

(30)

Step 5: Calculate the index (C). Combine the standard deviation ${\sigma }_j$ and correlation coefficients to calculate the $C_j$ index for each criterion. The $C_j$ index reflects both variability and conflict among criteria:

$$C_j={\sigma }_j{\sum }_{k=1}^n\left(1-q_{ik}\right)$$

(31)

Step 6: Determine the weights of attributes. Normalize the $C_j$ indices to calculate the weights ($w_j$) for each criterion. This ensures that the sum of all weights equals one:

$$w_j={\displaystyle \frac{C_j}{\sum _{j=1}^n{C}_j}}$$

(32)

Step 7: Carry out a final ranking of attributes. Rank the criteria based on their weights, $w_j$, in descending order. Higher weights indicate more critical criteria for the decision problem.

5.4. Fuzzy CRITIC Method

The CRITIC method is a decision matrix-based approach that determines criteria weights, including contrast and conflict intensity. It normalizes the decision matrix by handling cost and benefit criteria simultaneously. Fuzzy set theory can capture vagueness in linguistic evaluations. The method uses fuzzy numbers and incorporates uncertainty associated with attributes for dealing with uncertain and inaccurate information. The steps used in this study are adopted from [62,63].

Step 1: Assign membership functions to linguistic variables. Define membership functions for linguistic terms used to evaluate criteria and alternatives. Membership functions (TFNs) describe the degree of membership of a given element within the fuzzy set. This allows imprecision and uncertainty in judgments to be mathematically represented as $\tilde{x}=\left(l,m,u\right)$, where ($l,m,u$) are the lower, middle, and upper bounds of the fuzzy number.

Step 2: Construct the fuzzy decision matrix. Create the fuzzy decision matrix, where each element represents the fuzzy evaluation of an alternative under a criterion. The fuzzy values are derived from expert evaluations or collected data, translated into fuzzy numbers. This matrix organizes all alternatives ($A_i$) and criteria ($C_j$) for analysis.

$$X=\left[\begin{array}{ccccc}{\tilde{x}}_{11}& \dots & {\tilde{x}}_{1j}& \dots & {\tilde{x}}_{1n}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ {\tilde{x}}_{i1}& \dots & {\tilde{x}}_{ij}& \dots & {\tilde{x}}_{in}\\ ⋮& \ddots & ⋮& \ddots & ⋮\\ {\tilde{x}}_{m1}& \dots & {\tilde{x}}_{mj}& \dots & {\tilde{x}}_{mn}\end{array}\right]$$

(33)

For the decision matrix with fuzzy values for each alternative under each criterion ${\tilde{x}}_{ij}=(x_{ij}^L,x_{ij}^M,x_{ij}^U)$, ${\tilde{x}}_{ij}$ represents the fuzzy score of alternatives $i$ for criterion $j$.

Step 3: Normalize the fuzzy decision matrix.

Standardize the fuzzy values to a comparable scale using normalization. The type of normalization depends on whether the criterion is beneficial or cost related. This ensures all criteria are measured consistently. Normalize the decision matrix based on the type of criterion (benefit or non-beneficiary).

For benefit criteria:

$${\tilde{n}}_{ij}=({\displaystyle \frac{x_{ij}^L}{x_j^{Max}}},{\displaystyle \frac{x_{ij}^M}{x_j^{Max}}},{\displaystyle \frac{x_{ij}^U}{x_j^{Max}}})$$

(34)

For cost criteria:

$${\tilde{n}}_{ij}=({\displaystyle \frac{x_j^{Min}}{x_{ij}^U}},{\displaystyle \frac{x_j^{Min}}{x_{ij}^M}},{\displaystyle \frac{x_j^{Min}}{x_{ij}^L}})$$

(35)

Here, $x_j^{Max}$. and $x_j^{Min}$ are the maximum and minimum fuzzy values for criterion j.

Step 4: Calculate the fuzzy standard deviation. Measure the variability or spread of the normalized values for each criterion. The fuzzy standard deviation reflects the extent of disagreement or diversity in evaluations

$$\widetilde{{\delta }_j}=\sqrt{{\displaystyle \frac{1}{m}} {{\displaystyle \sum }}_{i=1}^m{({\tilde{n}}_{ij}-{\tilde{n}}_j)}^2}$$

(36)

where ${\tilde{n}}_j$ is the fuzzy mean for criterion $j$.

Step 5: Construct the correlation matrix. Compute the correlation coefficients between pairs of criteria. This captures the interdependence and redundancy among criteria, helping identify how strongly each criterion is related to others using the following equation:

$${\tilde{r}}_{jk}^{L,M,U}={\displaystyle \frac{{{\displaystyle \sum }}_{i=1}^m\left({\tilde{n}}_{ij}-{\tilde{\overline{n}}}_j\right)\left({\tilde{n}}_{ik}-{\tilde{\overline{n}}}_k\right)}{\sqrt{{{\displaystyle \sum }}_{i=1}^m{({\tilde{n}}_{ij}-{\tilde{\overline{n}}}_j)}^2.{({\tilde{n}}_{ik}-{\tilde{\overline{n}}}_k)}^2}}}$$

(37)

Step 6: Calculate the H index. Combine the fuzzy standard deviation and the correlation coefficients to compute the H index. This index quantifies the importance of a criterion by considering its variability and its independence from other criteria. The H index for each criterion is calculated as follows:

$${\tilde{H}}_j^{L,M,U}=\widetilde{{\delta }_j}{{\displaystyle \sum }}_{k=1}^n\left(1-{\tilde{r}}_{jk}\right)$$

(38)

${\tilde{r}}_{jk}$ is the correlation coefficient between criteria j and k.

Step 7: Determine fuzzy weights. Normalize the H indices to derive the fuzzy weights of the criteria. This ensures that the sum of all criteria weights equals 1, allowing for consistent comparison of their relative importance.

$${\tilde{w}}_j={\displaystyle \frac{{\tilde{H}}_j}{{{\displaystyle \sum }}_{k=1}^n{\tilde{H}}_k}}$$

(39)

Step 8: Defuzzify the weights.

Convert the fuzzy weights into crisp values for decision-making using a defuzzification method, such as the centroid method. This step simplifies the fuzzy data into single numerical values for ranking.

$$w_j={\displaystyle \frac{H_j^L+H_j^M+H_j^U}{3}}$$

(40)

Step 9: Rank the criteria. Arrange the criteria in descending order based on their defuzzified weights ($w_i$). Criteria with higher weights are considered more important in the decision-making process.

5.5. Combined CRITIC-COPRAS and FCRITIC-FCOPERAS Techniques

The Criteria Importance Through Intercriteria Correlation (CRITIC)-weighted Complex Proportional Assessment (COPRAS) technique is used for experimental planning and optimization. It assigns individual weights to output responses, avoiding subjective biases and allowing for more informed choices. The CRITIC-coupled COPRAS method is adopted for optimal solutions, including RE selection criteria, ensuring a systematic and structured approach to decision-making. Based on the opinions of experts, an integrated MCDM strategy is suggested using FCRITIC-FCOPRAS methodologies. The Fuzzy CRITIC-COPRAS technique is utilized to rank the RE technologies and assess the sources with respect to the weighted problems. The weights of the criterion were established using the Fuzzy CRITIC approach. Then, the Fuzzy COPRAS approach and weighted indices were used to rank RE technologies based on their sustainable activities. In the combined CRITIC-COPRAS method, the weights resulting from CRITIC, given in Section 5.3, are integrated into the COPRAS method and the calculations are performed as in Section 5.1. The weights of the criteria generated in FCRITIC, shown in Section 5.4, are integrated into the FCOPRAS method, shown in Section 5.2, then the calculations are performed to determine the best alternatives [64,65,66,67].

There are many studies that have presented a set of integrated methods for calculating and arranging alternatives. For example, three MCDM methods (Gra, CoCoSo, VIKOR) were used to rank alternatives for manufacturing PHPs, with Spearman’s rank correlation coefficients calculating consistency and efficiency. The methods showed promising properties [68]. Eight MCDM methods were also used to assess RES [55]. Another study presented a hybrid multi-criteria decision-making approach for material selection in dentistry, evaluating five methods: AHP-VIKOR, AHP-TOPSIS, AHP-MOORA, AHP-ELECTRE, and AHP-PROMTHEE. These methods provide equivalent ranking and comparison, using AHP weights to derive rankings for given alternatives [69]. However, the hybrid MCDM methods used in this study are chosen based on the contexts of the current problem for their effectiveness and simplicity in finding decision-making solutions.

6. Numerical Analysis

Jordan is considered a non-fossil-fuel-producing country, so most of its energy crudes are imported from abroad, including oil. Since the beginning, the Jordanian state has sought to search for alternatives and exploit them, especially RE sources. Jordan is rich in some renewable energy sources, such as solar and wind energy. There are a number of projects that have been implemented, as previously stated, and there are many projects currently under implementation. The state has also issued a number of legislations to regulate, facilitate, accelerate, and encourage the use of renewable energy. It provides support for solar energy projects in homes and regulates their operations. It has also made progress in cooperation with foreign countries to exploit bioenergy and wind energy. Similarly, it is currently researching the development of regulations, laws, and legislation for other types of renewable energy, such as hydrogen energy and other energy sources. The criteria and alternatives, i.e., the main renewable energy sources in Jordan, which will be used in this analysis, were identified and selected based on the results of the questionnaire and the opinion of the team of specialists. The main energy sources adopted in this study include solar (SE), wind (WE), hydropower (HP), biomass (BM), and piezoelectric (PE). Other sources, like geothermal, ocean energy, and hydrogen fuel, will be excluded for now, due to their small and not fully mature energy production. Five key criteria are used in this analysis to assess and contrast the most effective alternative energy sources in Jordan. The criteria used in this analysis include source availability (SA), technology development (TD), local conditions (LC), economic feasibility (EF), and environmental effect (EE).

6.1. Criteria Importance

In order to quantify the significance of assessment criteria and assess the effectiveness of alternatives, evaluators use linguistic factors in their decision-making processes. Experts evaluate the significance of sources by ranking them according to criteria and their opinions. Linguistic criteria and expert assessments for each source are shown in

Table 4. Linguistic parameters and their corresponding values.

Linguistic Value	Criteria		Alternatives
	Crisp Value	TFN	Crisp Value	TFN
Very Low (VL)	0.1	(0.0,0.1,0.2)	1	(0,1,2)
Low (L)	0.2	(0.1,0.2,0.3)	2	(1,2,3)
Medium Low (ML)	0.3	(0.2,0.3,0.4)	3	(2,3,4)
Medium (M)	0.5	(0.4,0.5,0.6)	5	(4,5,6)
Medium High (MH)	0.6	(0.5,0.6,0.7)	6	(5,6,7)
High (H)	0.7	(0.6,0.7,0.8)	7	(6,7,8)
Very High (VH)	0.9	(0.8,0.9,1.0)	9	(8,9,10)

. As shown in the table, the seven-point scale is used to determine linguistic values associated with crisp and TFN for evaluating alternatives and criteria weights.

Based on the experts’ linguistic evaluations of RE technologies, the weight of each criterion was determined by calculating the average rating of the expert team as shown in

Table 5. The average criteria weights based on experts’ evaluations.

Expert/Criteria	SA	TD	LC	EE	EF
E1	M	MH	VH	H	ML
	(0.4,0.5,0.6)	(0.5,0.6,0.7)	(0.8,0.9,1)	(0.6,0.7,0.8)	(0.2,0.3,0.4)
E2	H	H	VH	M	H
	(0.6,0.7,0.8)	(0.6,0.7,0.8)	(0.8,0.9,1)	(0.4,0.5,0.6)	(0.8,0.9,1)
E3	VH	H	H	VH	H
	(0.8,0.9,1)	(0.6,0.7,0.8)	(0.6,0.7,0.8)	(0.8,0.9,1)	(0.6,0.7,0.8)
E4	H	M	M	VH	ML
	(0.6,0.7,0.8)	(0.4,0.5,0.6)	(0.4,0.5,0.6)	(0.8,0.9,1)	(0.2,0.3,0.4)
E5	M	H	H	M	H
	(0.4,0.5,0.6)	(0.6,0.7,0.8)	(0.6,0.7,0.8)	(0.4,0.5,0.6)	(0.6,0.7,0.8)
E6	M	MH	VH	VH	VH
	(0.4,0.5,0.6)	(0.5,0.6,0.7)	(0.8,0.9,1)	(0.8,0.9,1)	(0.8,0.9,1)
E7	M	H	H	M	H
	(0.4,0.5,0.6)	(0.6,0.7,0.8)	(0.6,0.7,0.8)	(0.4,0.5,0.6)	(0.6,0.7,0.8)
E8	VH	H	H	VH	H
	(0.8,0.9,1)	(0.6,0.7,0.8)	(0.6,0.7,0.8)	(0.8,0.9,1)	(0.6,0.7,0.8)
E9	H	M	M	VH	ML
	(0.6,0.7,0.8)	(0.4,0.5,0.6)	(0.4,0.5,0.6)	(0.8,0.9,1)	(0.2,0.3,0.4)
E10	M	H	H	M	H
	(0.4,0.5,0.6)	(0.6,0.7,0.8)	(0.6,0.7,0.8)	(0.4,0.5,0.6)	(0.6,0.7,0.8)
E11	M	MH	VH	VH	VH
	(0.4,0.5,0.6)	(0.5,0.6,0.7)	(0.8,0.9,1)	(0.8,0.9,1)	(0.8,0.9,1)
Crisp weight	0.627	0.636	0.736	0.736	0.645
Fuzzy weight	(0.527,0.627,0.727)	(0.536,0.636,0.736)	(0.636,0.736,0.836)	(0.636,0.736,0.836)	(0.545,0.645,0.745)

. The experts determined the weight of each criterion through the verbal values, which were converted to crisp and TFN values to be used in COPRAS and FCOPRAS calculations. For the integration methods, the weights of the criteria are determined using CRTIC and FCRITIC approaches, as will be presented later in this section.

6.2. Outcomes Using COPRAS and FCOPRAS Approaches

Table 6. The decision-making matrix.

RE Technology	Weighted Normalized Matrix
Criteria	SA	TD	LC	EE	EF
B/NB	B	B	B	NB	B
Wind Energy	7.75	6.167	5	7.917	7.167
Solar Energy	7.833	8.75	4.083	7.25	4.917
Hydropower	7.5	4.917	6.25	5.75	6.667
Biomass	4.833	4.583	5.75	5.75	5.583
Piezoelectric	2.667	3.083	6.583	6.75	6.5

shows the decision matrix that was calculated based on the inputs from the decision-makers. The linguistic values were converted into numerical values and then the average was calculated for the eleven evaluators, with B standing for the beneficiary values and NB for the non-beneficiary values.

Table 7. Calculation results of COPRAS technique.

RE Technology	Weighted Normalized Matrix					S+i	S−i	Qi	Rank
Criteria	SA	TD	LC	EE	EF
Wind Energy	0.043	0.010	0.007	0.010	0.057	0.060	0.120	0.760	2
Solar Energy	0.044	0.014	0.006	0.01	0.039	0.063	0.081	0.763	1
Hydropower	0.042	0.008	0.009	0.008	0.053	0.058	0.123	0.758	3
Biomass	0.027	0.007	0.008	0.008	0.045	0.042	0.129	0.742	5
Piezoelectric	0.030	0.007	0.010	0.006	0.053	0.047	0.132	0.747	4

displays the results of the calculations using the COPRAS approach. The final ranks using the COPRAS method indicate that solar energy has the highest rank, followed by wind energy. The final order of the RE technologies is: SE-WE-HP-BM-PE.

The capabilities of classical COPRAS are enhanced by the Fuzzy COPRAS approach, which incorporates uncertainty into decision-making processes. This approach captures ambiguity and imprecision in expert opinions, representing criteria in a more subtle manner.

Table 8. The outcomes of the Fuzzy COPRAS technique.

	Fuzzy Weighted Normalized Matrix					S+i	S−i	Qi	Ui	Rank
RE Technology	C1	C2	C3	C4	C5
Criteria	SA	TD	LC	EE	EF
Wind Energy	(0.011,0.013,0.014)	(0.008,0.01,0.012)	(0.008,0.01,0.006)	(0.011,0.013,0.014)	(0.01,0.012,0.013)	(0.026,0.031,0.035)	(0.027,0.031,0.036)	0.055	84.2	3
Solar Energy	(0.011,0.013,0.014)	(0.012,0.014,0.016)	(0.007,0.008,0.005)	(0.01,0.012,0.013)	(0.006,0.008,0.01)	(0.028,0.033,0.038)	(0.019,0.024,0.029)	0.066	100	1
Hydropower	(0.01,0.012,0.014)	(0.006,0.008,0.01)	(0.01,0.012,0.008)	(0.008,0.009,0.011)	(0.009,0.011,0.012)	(0.025,0.03,0.035)	(0.024,0.028,0.033)	0.058	87.45	2
Biomass	(0.006,0.008,0.009)	(0.006,0.007,0.009)	(0.009,0.011,0.008)	(0.008,0.009,0.011)	(0.007,0.009,0.011)	(0.02,0.024,0.029)	(0.024,0.028,0.033)	0.052	78.64	5
Piezoelectric	(0.007,0.009,0.01)	(0.006,0.007,0.009)	(0.011,0.013,0.01)	(0.006,0.007,0.009)	(0.009,0.011,0.012)	(0.022,0.027,0.032)	(0.023,0.028,0.032)	0.055	83.78	4

shows the results of the FCOPRAS calculations. The final ranking shows that solar energy ranked first, and hydraulic energy ranked second. The final ranking using this method appeared as follows: SE-HP-WE-PE-BM.

Comparing the results of COPRAS and FCOPRAS, it is clear that there is a similarity in the rankings of three of the five technologies, as solar energy maintained the first place, piezoelectric ranked fourth, and bioenergy ranked fifth. However, there was a difference in the ranking of wind energy, which ranked second in COPRAS and third in FCOPRAS, and hydraulic energy, which ranked third in COPRAS and second in FCOPRAS.

6.3. Outcomes Using the Combined CRITIC-COPRAS

Table 9. The results of the criteria weight evaluations using CRITIC.

Criteria	Sigma	Sum(1-Rik)	Cj	Wj	Rank
SA	0.444	4.254	1.89	0.205	3
TD	0.375	4.208	1.577	0.171	5
LC	0.441	4.659	2.055	0.223	1
EE	0.437	4.326	1.891	0.205	2
EF	0.402	4.483	1.8	0.195	4

shows the results of the calculations of the CRITIC-COPRAS method for the weights of the criteria. It is clear that the ranking of the criteria, according to their importance, is as follows: local conditions–environmental effect–source availability–economic feasibility–technology development.

After combining the weights resulting from the CRITIC method with the COPRAS method, the calculation results are as shown in

Table 10. Results of the combined CRITIC-COPRAS technique.

RE Technology	Weighted Normalized Matrix					S+i	S−i	Qi	Rank
Criteria	SA	TD	LC	EE	EF
SA	0.052	0.037	0.038	0.052	0.044	0.171	0.052	0.173	3
TD	0.053	0.053	0.046	0.034	0.036	0.187	0.034	0.189	1
LC	0.05	0.036	0.047	0.038	0.041	0.174	0.038	0.176	2
EE	0.032	0.028	0.043	0.038	0.034	0.137	0.038	0.139	4
EF	0.018	0.019	0.049	0.044	0.04	0.126	0.044	0.128	5

. Using the combined CRITIC-COPRAS, the ranking of renewable energy technologies is as follows: SE-HP-WE-BM-PE.

6.4. Outcomes Using the Combined FCRITIC-FCOPRAS

Fuzzy logic is used in the Fuzzy CRITIC–Fuzzy COPRAS procedure, a decision-making process that addresses expert judgment uncertainty. It enables a more thorough evaluation of RE sources by combining the benefits of the FCRITIC and FCOPRAS methodologies.

Table 11. The results of the FCRITIC-FCOPRAS approach.

RE Technology/Criteria	Fuzzy Weighted Normalized Matrix					S+	S−	Fuzzy Qi	Qi	Rank
	SA	TD	LC	EE	EF
SA	(0.075,0.059,0.181)	(0.076,0.046,0.184)	(0.008,0.046,0.152)	(0.152,0.008,0.036)	(0.116,0.016,0.035)	(0.176,0.185,0.613)	(0.008,0.036,0.116)	(0.176,0.186,0.625)	(0.075,0.059,0.181)	2
TD	(0.075,0.059,0.181)	(0.114,0.065,0.25)	(0.011,0.065,0.183)	(0.183,0.005,0.023)	(0.079,0.01,0.024)	(0.21,0.205,0.684)	(0.005,0.023,0.079)	(0.21,0.206,0.693)	(0.075,0.059,0.181)	1
LC	(0.071,0.056,0.174)	(0.058,0.037,0.152)	(0.011,0.037,0.183)	(0.183,0.005,0.026)	(0.088,0.015,0.032)	(0.155,0.182,0.6)	(0.005,0.026,0.088)	(0.155,0.183,0.609)	(0.071,0.056,0.174)	3
EE	(0.042,0.036,0.119)	(0.053,0.034,0.143)	(0.01,0.034,0.171)	(0.171,0.005,0.026)	(0.088,0.012,0.027)	(0.117,0.15,0.511)	(0.005,0.026,0.088)	(0.117,0.151,0.521)	(0.042,0.036,0.119)	4
EF	(0.018,0.02,0.075)	(0.031,0.023,0.105)	(0.012,0.023,0.192)	(0.192,0.007,0.031)	(0.101,0.014,0.031)	(0.075,0.135,0.46)	(0.007,0.031,0.101)	(0.075,0.136,0.471)	(0.018,0.02,0.075)	5

shows the order of the RE technologies using the combined FCRITIC-FCOPRAS technique. The final ranking of the RE sources is as follows: SE-WE-HP-BM-PE.

When comparing the outcomes of the two combined approaches, CRITIC-COPRAS and FCRITIC-COPRAS, we see that both approaches score three sources out of five similarly. The top, fourth, and fifth ranks (solar—biomass energy—piezoelectric) were among the similarities. While wind energy ranked third in CRITIC-COPRAS and second in FCRITIC-FCOPRAS, it exchanged ranks with hydraulic energy.

6.5. Analysis of Comparison Results

The results indicate that there is one similarity between the COPRAS and CRITIC-COPRAS methods. Solar energy remained in first place, while the ranking of the other four sources differed. The results also indicate that there is one similarity in the ranking of RES when comparing FCOPRAS and FCRITIC-FCOPRAS. Solar energy maintained its first rank while the ranking of the rest of the sources differed.

The results of the comparison between the different methods used are shown in

Table 12. Comparison between different methods of arranging RE sources.

RE Technology	COPRAS	FCOPRAS	CRITIC-COPRAS	FCRITIC-FCOPRAS
Wind Energy	2	3	3	2
Solar Energy	1	1	1	1
Hydropower	3	2	2	3
Biomass Energy	5	5	4	4
Piezoelectric	4	4	5	5

. The results indicate that there is no perfect similarity in the results of arranging RES in the four MCDM methods used. This is due to the difference in the calculations of each method and the difference in the weights of the criteria used. While the COPRAS and FCOPRAS methods use the average weights estimated by the expert panel, the CRITIC-COPRAS and FCRITIC-FCOPRAS integration methods use the weights resulting from CRITIC technique, though there is a similarity in the arrangement in some sources and a difference in others. However, the most important observations are that solar energy ranked first in all MCDM used. This is due to its abundance in Jordan, as well as the advancement of its technologies, the presence of a number of successful projects, the number of projects under construction, government support for the use of this type of renewable energy source in homes, and the presence of legislation, encouragement, and international partnerships in this field, in addition to the presence of a number of experts and local factories concerned with manufacturing this type of energy at prices acceptable to the public.

Comparing the different results of MCDM methods and the experts’ opinions, the CRITIC-COPRAS method matched the ranking of the evaluators. This gives evidence that this method is suitable for evaluating renewable energy technologies. Thus, the final ranking is as follows: solar energy–hydropower–wind energy–biomass energy–piezoelectric. These results are logical, as the second place for hydraulic energy came as a result of the obtainability of such resources in Jordan, as well as the availability of some projects and the trend toward more projects despite the scarcity of water in Jordan. Wind energy came in third place due to the availability of the technology, the number of successful projects, and the expertise in this field, as well as the availability of suitable places for this type of energy in various regions of the Kingdom. Bioenergy came in fourth place due to the availability of expertise and technology in this field, and the number of successful projects, partnerships, and international support in this field. Piezoelectric power ranked last due to a lack of specific legislation, a lack of expertise in this field, low energy output, a lack of projects, and a lack of encouragement and promotion for this type of renewable energy source. Figure 3 shows the ranking of renewable energy sources according to the type of technology and the evaluation method used.

7. Conclusions

Jordan’s growing energy demand and dependence on imported fuel highlight the need for sustainable, locally sourced energy alternatives. Renewable energy offers security, accessibility, affordability, and long-term permanence, with cost reductions making it a promising source. Jordan’s energy diversification strategy aims to develop renewable energy technologies for competitiveness and sustainable growth. The Jordanian government is promoting RES like solar, wind, hydropower, biomass, geothermal, piezoelectric, and hydrogen energy to meet its growing energy needs. However, challenges like intermittent and high initial costs remain.

This research aims to analyze and categorize renewable energy sources in Jordan based on their implementation and exploitation priorities. This study focuses on medium-sized projects in Jordan to combat global warming impacts and highlights the lack of existing research on renewable energy technologies analysis and ranking for Jordan projects. This research uses a unique scaling methodology, expert opinions, and innovative tools to evaluate alternative renewable energy sources based on their rewards. This study addresses the gaps in previous studies and promotes sustainable development at the national level.

This study explores Jordan’s renewable energy technologies to diversify the country’s energy portfolio, reduce fossil fuel dependency, and provide sustainable development recommendations. A framework model is introduced to evaluate the potential of RE technologies in Jordan and help decision-makers assess their viability. This conceptual framework explores renewable energy sources in Jordan and comprises sources, drivers, criteria, benefits, drawbacks, and projects, encompassing government support, strategies, integration with other countries, and private sector motivation. Jordan is currently incorporating solar, wind, and hydroelectric energy sources to improve energy security and reduce its carbon footprint. The model highlights the benefits of green energy, including lower emissions, energy mix diversification, and less reliance on finite resources. It focuses on medium-sized projects in Jordan and uses a unique scaling methodology to evaluate renewable energy technologies.

This study evaluates renewable energy technologies in Jordan using MCDM techniques, analyzing five sources based on source availability, technology development, local conditions, economic feasibility, and environmental effect, providing policymakers with valuable insights. It uses different MCDM techniques like COPRAS, Fuzzy COPRAS, CRITIC-COPRAS, and FCRITIC-FCOPRAS to analyze and prioritize these sources based on local and global decision variables. This study utilizes fuzzy logic adaptations to prioritize renewable energy industries. This approach helps mitigate uncertainties in expert input and provides a strategic roadmap for energy independence and sustainability.

This case study explores renewable energy in Jordan, involving eleven decision-makers from businesses, academia, government, and the private sector. This study highlights the urgent need for green sustainable energy due to economic fragility and fluctuating global energy prices. The results show that the most advantageous for Jordan are solar, hydropower, and wind power. Ultimately, our results support a transition to sustainability and energy independence by giving Jordanian decision-makers a methodical, data-driven approach to setting priorities for green energy expenditures. The CRITIC-COPRAS approach matched the evaluators’ ranking by comparing the various outcomes of MCDM methods with the opinions of experts. This demonstrates that this approach is appropriate for assessing technology related to renewable energy. It provides a comprehensive ranking of solar, hydropower, and wind energy as the most viable technologies for large-scale deployment. These findings make sense, given Jordan’s abundance of solar energy sources, and that advancements in technologies, successful projects, government support, legislation, international partnerships, and local factories producing affordable energy are contributing to its widespread adoption. Hydropower, while limited by water scarcity, can contribute to the energy mix as a result of the availability of the technology, successful projects, and dams; wind energy is considered a promising source due to the advanced technology, successful projects, and suitable locations. Biomass energy has niche applications with moderate potential. Piezoelectric energy, although intriguing, is best suited for specific localized uses due to its high costs, lack of legislation, limited scalability, and expertise and promotion.

This study is restricted by a number of limitations, such as the limited number of experts and evaluators, the availability of data and information, the tools used in the evaluation, and the evaluation of projects in terms of efficiency and productivity compared to costs. This research holds significance for decision-makers and has real-world applications related to the government and private sector, businesses, and organizations working in the field of RE, in addition to companies concerned with the manufacturing of RE systems. It could help them make the right decision regarding the exploitation and selection of renewable energy projects, their implementation locations, and assist in calculating the economic feasibility of future improvements.

Future studies could include using other MCDM methods and comparing them with the methods used in this study, perhaps increasing the number of experts and evaluators, and combing different types of energy, such as mixing more than one type of RE, to obtain uninterrupted renewable energy sources. Future work could consider regional cooperation, integration with national electrical grids, and analyze existing projects to come up with best practices for making appropriate future decisions in the field of renewable energy plans and their technological developments.