Environmental Sustainability Implications and Economic Prosperity of Integrated Renewable Solutions in Urban Development

Abstract

The increasing urbanization and growth of cities worldwide have led to a significant increase in energy demand. As a transition to a low carbon environment occurs, the role of renewable and sustainable energy systems in urban areas is benefiting industry and the environment alike. From this perspective, the Sustainable Development Goals (SDGs) have a lot to offer to the energy industry, particularly the integration of renewable and sustainable energy systems for environmental protection in cities. This study presents a comprehensive view that integrates technological, economic, political, and social challenges confronted with the effective implementation of renewable and sustainable energy in urban cities and proposes a solution agenda to overcome these hurdles with the aid of the SDGs. The weights for the challenges of adopting renewable and sustainable energy systems were determined using the Fuzzy Best-Worst Method. The SDGs were then ranked using the fuzzy TOPSIS technique to overcome predetermined challenges. The originality of this study lies in finding solutions to the determined challenges by adopting SDGs, emphasizing the need for integrated solutions that address energy-related concerns, and highlighting the role and importance of SDGs in environmental protection. The study highlights the importance of SDGs in promoting renewable energy integration in urban areas, with SDG 11 being the most crucial to mitigate harmful environmental occurrences related to energy-related issues in urban areas, followed by SDG 7 and SDG 13.

1. Introduction

A significant portion of global energy consumption is met by resources for fossil fuels [1]. However, the continued use of finite fossil fuel reserves has shifted focus to the world’s future energy situation in the face of potential fossil fuel shortages [2]. The rapid depletion of fossil fuel resources may cause significant disruptions in the energy supply. Therefore, energy supply disruptions are considered a critical part of financial systems at different degrees of economic activity, which can cost nations “up to 12 percent” of their yearly growth potential and do substantial harm to their economies’ effective performance [2,3,4]. Cities are important for the global shift to a low carbon, sustainable energy future [5].

Cities were planned and built on the fundamental premise that energy demand could continue to rise during the first industrial revolution, which primarily focused on the extraction of fossil fuels (coal, gas, and oil) [6]. According to statistics, over half of the world’s population lives in cities, and urban areas constitute a greater proportion (70%) of global CO₂ emissions from energy sources, with both figures anticipated to rise even greater by 2050 [7]. However, city infrastructure is confronted with substantial issues in meeting the rising demand for energy services, which would result in increased emissions of greenhouse gases and pollutants if renewable energies are not integrated [8]. In addition to these environmental concerns, the danger of accelerated global warming has also shifted global energy emphasis toward environmentally friendly and renewable energy sources [9,10].

The imminent depletion of fossil fuel resources, rising energy use, and the imminent danger of global warming have been identified as the world’s primary concerns [11,12]. To overcome these issues, cities will require appropriate equipment and approaches to create regional sustainable energy policies as they become more active globally in climate change mitigation efforts [5]. Therefore, alternative fuels, also known as clear energy systems, are becoming more popular owing to economic and environmental concerns [1].

As part of the 2030 Agenda for Sustainable Development, the United Nations designed 17 global objectives known as the Sustainable Development Goals (SDGs) in 2015. They aspire to abolish poverty, protect the environment, and ensure prosperity by 2030. The SDGs address economic, social, and environmental issues and build on the Millennium Development Goals. They are connected, recognizing that advancement in one area frequently depends on advancement in another area. Health, gender equality, sustainable economic growth, innovation, decent work, peace and justice, education, and systems for keeping governments and organizations accountable are given top priority. They stress the importance of ending poverty, lessening inequality, and fostering inclusive economic growth. Cities are crucial for accomplishing a shift toward sustainable energy systems [8]. To satisfy the rising energy demand, urban areas are a critical issue in the context of sustainable development. The continued trend of market deregulation, the growing relevance of decentralized generation technologies utilizing energy from renewable sources, and laws emerging with the Rio Earth Summit (Agenda 21) and the Kyoto Protocol are all factors to consider [7]. Urban areas are more robust and ecologically friendly than rural areas because of their potential for high energy consumption, effective land use, strong public support for sustainability, dispersed generation potential, and so on. As a result of increased distributed generation and environmental benefits, urban energy development has become more attractive. Urban energy systems and their planning, therefore, must be regarded more broadly and account for local context.

Hence, municipal governments worldwide are developing and implementing environmentally friendly ways of generating and using energy because environmentally friendly urban development is inextricably linked to trends in energy consumption and urbanization [7]. Furthermore, the Paris Agreement emphasizes the global responsibilities of cities and local governments for global warming resulting from modern civilization [13,14]. Thus, sustainable urban development is a recurring theme in the development of urban vitality for carbon reduction [15,16].

Therefore, local governments have compelling motives for encouraging environmentally friendly energy planning techniques [17]. The procedure of selecting which local energy infrastructure to invest in, which energy efficiency measures to encourage, and which policies to enact that impact energy consumption patterns is known as urban energy planning [7]. Urban energy consumption is increasing in importance, and a model for urban energy systems can help evaluate new regulations for improved designs and associated technologies [18].

Urban energy system modelling and planning tools (UESMs) are frameworks that enable analytical system-wide studies to aid the formulation and implementation of local energy policies [5]. This process entails a wide range of concerns as well as different and opposing criteria for evaluation, as well as several stakeholders and their values [7]. Therefore, scholars, practitioners, and policymakers need a comprehensive and holistic viewpoint to bring together the academic literature with the heterogeneous body of practices and policy execution to create a guide map in the energy sector. In addition, relatively few studies have sought to highlight the important difficulties and opportunities in this subject [5]. Considering this information, the major motivation of this study is to reveal the challenges underneath the shift to renewable and sustainable energy systems in urban areas and propose a solution methodology for them by incorporating renewable energy technologies for environmental protection. Therefore, this study is twofold. Initially, an in-depth examination of the scientific literature was conducted to identify the obstacles that urban areas face in their transition to renewable and sustainable energy systems. The challenges were ranked according to their contribution to the challenges in the second stage using the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) approach, after which they were weighted using the best and worst approaches.

• RQ1: What could be the challenges that hinder the adoption of renewable and sustainable energy in urban areas?
• RQ2: Which SDGs can be incorporated to overcome the challenges confronted towards renewable and sustainable energy integration in urban areas?
• RQ3: What are the practical implications and policy recommendations that can be proposed for effective implementation for renewable and sustainable urban energy planning?

Using a comprehensive approach, this study intends to make a significant contribution to the field of urban energy planning and sustainable development. Unlike many studies that focus on individual areas, this study provides a thorough analysis of the difficulties, presents a new methodology, and provides practical consequences and policy recommendations. The direct integration of the UN’s Sustainable Development Goals (SDGs) is the study’s most distinguishing characteristic, providing a clear link between urban energy planning and global sustainability objectives. Furthermore, this study fills a substantial research gap in the field by shedding light on critical challenges and possibilities in the transition to renewable and sustainable energy systems in urban areas, as well as how to overcome them by utilizing the SDGs. Overall, this study stands out for its thoroughness, originality, and practicality.

The remainder of this paper is organized as follows: The literature reviews on renewable and sustainable energy integration in urban areas are scrutinized in Section 2. In Section 3, a list of challenges to the adoption of renewable and sustainable energy and renewable energy technologies is presented and listed. In Section 4, the methodology used in the study is explained. Next, the case study implementation and analysis of the study proposed in Section 5. In Section 6, the implications of the study are discussed. Finally, Section 7 concludes the paper with a summary, limitations, and further research ideas.

2. Literature Review

There are numerous studies in the existing literature related to energy-oriented technologies and techniques in urban areas. Recently, there has been much focus in urban systems research on the creation of computer tools for effective data analysis and process modeling [19,20,21,22,23] to handle the vast number of processes that take place in cities [24,25,26,27] and regions [28].

In addition, multi-objective analysis [29,30,31] has become common in scientific fields and engineering to address problems with many competing objectives as a result of the increased accessibility of computational resources.

For instance, Al-Shehri [32] examined techniques for anticipating energy supply requirements for diverse objects, assessing them analytically, and considering anticipation intervals to improve forecasting accuracy.

Hannan et al. [33] assessed the importance of artificial intelligence in reaching the 17 SDGs, with an emphasis on the environment, society, and economy, and investigated its potential to affect these objectives’ attainment favourably or negatively. Similarly, by including the UN’s Sustainable Development Goals (SDGs) and filling a research gap, Yasmeen et al. [34] have helped urban energy planning and sustainable development. It provides a detailed overview of the difficulties, creative approaches, and practical ramifications, emphasizing the need to connect urban energy planning to global sustainability goals.

Zhang et al. [35] investigated the theoretical and practical elements of advanced agricultural electrification loads, with an emphasis on the development of digital twin and virtual power plant technologies. Moreover, Chen et al. [36] proposed an AI-based useful assessment model (AIEM) to estimate the economic effect of renewable energy and energy efficiency. It examines AI techniques for customer selection, competitive pricing, facility management, encouraging demand response, and equitable remuneration. Yiğitcanlar et al. [37] also addressed inadequacies in mainstream AI system conception and implementation, as well as argued for a green AI approach to smart city transformation. It assessed existing AI and smart city literature, practices, trends, and applications, and taught authorities and planners the need to implement AI systems that solve municipal efficiency, sustainability, and equality issues.

With the popularity of renewable and sustainable energy concepts growing over the past 15 years, the value of incorporating local-scale energy system planning into extensive large-scale energy strategies has increased [5]. Renewable energy sources can be described as resources that can be utilized again to generate energy (such as wind energy, solar energy, geothermal energy, and biomass energy), which will be crucial to the future of the planet [38,39]. Various studies have addressed these concerns and proposed several solutions. For instance, Ikram et al., [40] aimed to develop an integrated framework for green technology in Pakistan, prioritizing eight crucial green technology indicators: environmental quality, agriculture and forestry, resource use, green buildings, green transportation, life health, ecological safety, and energy use.

Giannetti et al. [41] described the current advances in cleaner production techniques toward SDGs, highlighting the connection between these concepts and techniques and emphasizing the need for scientific knowledge and paper re-evaluation to assure responsiveness.

Yadav et al. [42] investigated the potential advantages of renewable energy technologies (RET) in three sustainability dimensions, concentrating on their influence on energy availability, security, and social and economic growth, and provided methods to alleviate the issues associated with RET projects. Raihan et al. [43] presented policy proposals for Bangladesh’s low carbon economy, with an emphasis on renewable energy, sustainable urbanization, green industry, technical innovation, and sustainable forest management. The influence of economic expansion, financial globalization, urbanization, fossil fuel use, and renewable energy usage on Mexico’s load capacity factor was investigated in this study, which uses the load capacity factor as a measure of ecological health [44]. Yang and Khan [45] investigated the effect of economic development, growing urbanization, biocapacity, industry value-added, creation of capital, and increasing population on the carbon footprint of 30 IEA member countries, with the goal of addressing the 2030 Sustainable Development Goals (SDGs) by taking into account environmental dimensions, with IEA countries chosen based on greater consumption of energy and rising interest within oil and gas extraction, etc.

Several publications in the existing literature have also focused on renewable energy potential, performance, sources, and technologies to mitigate these negative effects. For instance, Panwar et al. [38] examined the range of these renewable energy devices and how well they might be able to reduce greenhouse gas emissions, notably carbon dioxide. Similarly, Amran et al. [46] focused on renewable sources to reduce dependency on oil and natural gas and investigated the present state, potential, growth, resources, and future prospects of Renewable Energy (RnSE) technologies and sustainability performance. Furthermore, Maka and Alabid [47] emphasized the important role that solar energy plays in sustainable development, emphasizing its potential benefits to the environment and economy, as well as its function in the production of energy, the creation of jobs, and environmental protection.

Hoang et al. [48] discussed the function of renewable energy sources in smart cities, including solar, wind, geothermal, hydropower, ocean, and biofuels. To achieve cleaner processes and the sustainable development of intelligent renewable energy systems, they evaluated their integration, obstacles, and future possibilities. Jaiswal et al. [49] assessed the practicality of renewable energy sources and investigated the possibility of switching from fossil fuels to renewable energy to reduce climate change and enhance social, economic, and environmental health.

Moreover, Omer [50] examined the potential of integrated systems in the power market for cleaner energy, considering present and future energy usage patterns, environmental effects, and sustainable development. Almeida et al. [51] highlighted the value of academic research in sustainable planning, emphasizing how corporate social responsibility (CSR) programs can be integrated with sustainable development concepts and strategy definitions for assessing and evaluating potential actions, including creative research examples.

Apart from Jyotti et al. [52], recycling techniques for recovering rare earth elements (REEs) from secondary waste, their behavior during extraction, difficulties, benefits, and drawbacks, as well as their use in current and upcoming reprocessing technologies. Baños et al. [53] provided a thorough overview of the most recent advancements in this field of study through a survey of cutting-edge optimization techniques for sustainable and renewable energy.

3. Challenges of Renewable and Sustainable Energy Systems in Urban Areas

Following a thorough review of the literature, a list of challenges is offered in this area of the study. Several different combinations of the keywords relevant to the issue throughout the literature review process have been included. After the keyword search, a list of papers was compiled, and some papers were omitted after reading their abstracts. To highlight factors preventing the adoption of renewable energy technology in urban settings, words such as “challenges”, “obstacles”, “limitations”, “concerns”, “issues”, “problems”, and “barriers” were used. As a result of in-depth conversations with a group of subject-matter experts, research on publications that contained the phrases was conducted and presented in the table shown in

Table 1. Challenges Towards Renewable and Sustainable Energy Systems.

Number of Challenges	Challenges	References
C1	Uncertain and weak energy policies	[54,55,56,57,58,59,60,61,62]
C2	Absence of adequate subsidies and funds	[54,56,63,64,65,66]
C3	Unsupportive environmental laws and regulation	[54]
C4	Political instability	[55,67,68,69]
C5	Environmental degradation	[57,70]
C6	Ecosystem and biodiversity destruction	[54,70,71,72]
C7	Lack of technical expertise and personnel	[54,56]
C8	Insufficient specialized training	[55,56,57,59,63,67,69,73,74,75]
C9	Restricted access to credit and capital	[54,55,56,62,64,65,67]
C10	Lack of financial support programs	[54]
C11	Increased costs and expenses	[55,62,64]
C12	Inadequate institutional capacity and infrastructure	[55,56,57,60,64,65,74,76]
C13	Limited public interest in renewable energy	[54,55,57,58,60,61,67,74]
C14	Absence of standardized technologies	[56]

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Policies meant to avoid rising abnormal weather concerns remain at the forefront of global concern, but the launch and implementation pathways of these climate policies remain fraught with ambiguity [77]. Therefore, an uncertain policy landscape and weak energy policies in urban areas can hinder investments in sustainable energy projects. These policies lack stability and clarity, often lacking clear targets and strategies. In a volatile economic climate, policies in the public and financial sectors deteriorate [78,79,80], and environmental challenges are likely to be postponed as consumer pressures ease.

The energy market is also distorted by policies that encourage the use of conventional energy technology through various incentives (tax rebates, subsidies, etc.) and discourage the use of decentralized renewable energy systems through trade restrictions and the noninternalization of externalities [81]. Therefore, financial support from the government or other entities is also lacking, making renewable technologies hard to afford. In addition, supportive environmental policies and legislation are also lacking, affecting the adoption of renewable energy. They are hindering the development of renewable energy technology, with energy subsidies being the biggest impediment to meeting their goals [82]. Political instability can also deter investments and hinder long term planning for sustainable energy projects. The quality of institutions has a significant impact on natural resource usage and environmental sustainability [83]. Climate controls and norms become less effective as a result of political instability, and pollution emissions worsen the quality of the environment [84].

Degradation of the environment is likely to stifle growth and progress, increase vulnerability, affect people’s health, and drive them back into poverty [85]. Environmental degradation, caused by unsustainable practices in renewable energy production and consumption, can worsen issues such as climate change and air pollution. Renewable energy technologies can enhance ecosystems and biodiversity by restoring natural habitats and creating wildlife corridors. However, if not managed well, these projects can inadvertently harm local ecosystems, such as by disrupting river ecosystems or migratory patterns. To ensure sustainability and environmental preservation, effective planning, environmental impact assessments, and sustainable practices are crucial for renewable energy projects.

A lack of educated technical employees frequently limits the implementation of renewable energy solutions [86]. For instance, the rate of diffusion of biomass conversion methods for energy is rather slowing down [87] due to a lack of knowledge of the technology and a lack of public awareness. Furthermore, the lack of specialized technical staff and skilled human resources in renewable energy projects can lead to suboptimal outcomes, reduced efficiency, and higher operational costs. This kind of lack of expertise can also hinder the adoption of advanced technologies. Additionally, the absence of specialized training in the renewable energy sector can result in delays, cost overruns, and quality issues.

Therefore, investing in education and strategic workforce planning is crucial for addressing these challenges. This also shows us the need for financial support programs, which is another challenge.

The cost of accessing money impacts public budgets and governments’ capacity to engage in climate mitigation and adaptation; it also limits potential expenditures in infrastructure, education, and public health [88]. These hurdles necessitate simplified access to loan procedures to allow the participation of low-income families, as well as a complete service and maintenance framework for the long term adoption of clean energy in rural regions [89,90]. Financial mechanisms such as subsidies, grants, and accessible financing options are needed to make renewable energy projects financially feasible for a wider range of stakeholders, promoting the adoption of sustainable energy solutions in urban areas.

Institutional capacity and infrastructure are crucial for the effective planning, management, and implementation of renewable energy initiatives. Without these resources, delays, inefficiencies, and project failures can hinder the growth of the renewable energy sector. Addressing these challenges can enhance the capacity of institutions to promote sustainable energy adoption in urban areas, highlighting the importance of investing in human and physical resources. Moreover, the high capital cost of energy generation in comparison to traditional energy supplies is a key impediment to renewable energy development [55,64].

The efficacy of renewable energy policy is also dependent on an institutional structure with clear roles and responsibilities for lowering transaction costs and making projects more appealing through transparent and expedited procedures for project review, permitting, and licensing [57]. The absence of inadequate institutional capacity and infrastructure to manage can prevent the successful adoption of the SDGs and relevant technologies to attain them. Another challenge to renewable energy adoption is limited public engagement and awareness, which affects the adoption of sustainable practices. This lack of understanding can lead to lower demand for renewable energy products and services. Addressing this requires educational efforts, community engagement, and making renewable options more accessible and attractive. The lack of standardized technologies in the renewable energy sector, restricted local production of specialized equipment, and insufficient capacity in energy technologies and also hindered the successful deployment of renewable energy technologies. Standardization ensures interoperability and efficiency, while limited local production leads to longer lead times, higher costs, and challenges in obtaining the necessary components.

Sustainable Development Goals (SDGs)

The SDGs are a set of 17 global objectives included in the 2030 Agenda for Sustainable Development of the United Nations. They aim to eliminate poverty, preserve the environment, and guarantee everyone’s prosperity by 2030. The SDGs expand upon the MDGs and address environmental, economic, and social issues. With regard to topics such as eradicating poverty, promoting gender equality, reducing global warming, and responsible consumption, each SDG has specific targets and indicators. The interconnectedness of the objectives recognizes how advancement in one area frequently depends on advancement in another. In this context, a framework that integrates the SDGs with the challenges we found to ensure environmental sustainability, social unity, and economic prosperity for urban development has been prepared in Figure 1.

The SDGs are a global framework for international cooperation that aims to reduce inequality, prevent poverty, and promote inclusive economic growth. They emphasize clean energy, responsible consumption, climate action, health, gender equality, innovation, fair work, and sustainable economic growth. The SDGs also prioritize justice, peace, and education, emphasizing lifelong learning opportunities. They also provide mechanisms to hold governments and organizations accountable for their commitments, ensure prosperity for all, and promote justice and peace. The SDGs aim to empower women and girls, promote fair work, and ensure sustainable economic growth. The 17 SDGs are presented in

Table 2. Sustainable Development Goals.

SDGs	Name of the SDG	Definition	Reference
SDG 1	No Poverty	Eradicate poverty and all its variations.	[91,92]
SDG 2	Zero Hunger	End hunger, ensure food security, and promote sustainable agriculture.	[93,94]
SDG 3	Good Health and Well-being	Ensure the well-being and health of people at all stages of life.	[95,96]
SDG 4	Quality Education	Ensure inclusive, quality education and lifelong learning opportunities for everyone.	[97,98]
SDG 5	Gender Equality	Attain gender equality and empower women and girls.	[99]
SDG 6	Clean Water and Sanitation	Guarantee access to clean water and sustainable sanitation for all.	[100]
SDG 7	Affordable and Clean Energy	Provide access to affordable, sustainable, reliable, and modern energy for everyone.	[92,101,102]
SDG 8	Decent Work and Economic Growth	Promote lasting, equitable economic growth, full employment, and decent work.	[103]
SDG 9	Industry, Innovation, and Infrastructure	Develop resilient infrastructure, foster innovation, and encourage inclusive industrialization.	[104,105]
SDG 10	Reduced Inequality	Reduce inequality both within and between nations.	[106]
SDG 11	Sustainable Cities and Communities	Create inclusive, resilient, secure, and sustainable urban areas and human settlements.	[107,108,109]
SDG 12	Responsible Consumption and Production	Ensure sustainable consumption and production models.	[110,111]
SDG 13	Climate Action	Take immediate action to combat climate change and its effects.	[112,113]
SDG 14	Life Below Water	Protect and sustainably use marine resources and ecosystems for sustainable development.	[114]
SDG 15	Life on Land	Protect and restore terrestrial ecosystems, manage forests sustainably, combat desertification, and preserve biodiversity.	[115,116]
SDG 16	Peace, Justice, and Strong Institutions	Promote peaceful and inclusive societies, ensure access to justice, and build effective, accountable institutions.	[117,118]
SDG 17	Partnerships for the Goals	Strengthen the means to implement sustainable development goals and revitalize global partnerships.	[119,120,121]

.

4. Methodology

MCDM methodologies have been devised to address a wide spectrum of decision-making challenges [122]. These techniques find applicability across multiple sectors, including but not limited to engineering, logistics, supply chain management, production, healthcare, and sustainable development [123]. Numerous studies have attested to the efficacy of MCDM methods in effectively resolving complex multi-criteria problems [124,125]. Using a variety of attributes or criteria, MCDM involves the process of selecting the best option from a list of potential alternatives. The Simple Multi-Attribute Rating Technique (SMART), the Analytic Hierarchy Process (AHP), and the Analytic Network Process (ANP) are the most frequently used techniques for allocating weights to the criteria. In addition, a more recent strategy called the Best Worst Method (BWM), created by [126] in 2015, established the weights of the criteria by contrasting the most and least essential criteria in relation to the other decision criteria.

The flowchart that represents the methodology’s framework can be seen in Figure 2. First, the flowchart’s “Describing the Problem Statement” step offers a basic understanding. The next step is to collect insights through a comprehensive “Literature Review”. Next, the emphasis is on determining “Challenges Towards Renewable and Sustainable Energy Systems”, and the next step is to create a comprehensive “Questionnaire for Challenges”. Expert opinions are then sought regarding the challenges that have been identified by distributing this questionnaire to them. Next, the Fuzzy Best-Worst Method is applied to determine the challenge weights based on expert feedback. Next, a questionnaire is created to rank the SDGs in relation to the challenges. The Fuzzy TOPSIS Method is used to rank the SDGs, providing a methodical and thorough approach to addressing issues in renewable and sustainable energy systems. Experts are again consulted to provide judgments on the rankings.

4.1. Fuzzy Logic

When dealing with practical problems, the data available to people often comes with a degree of uncertainty. When describing these conditions using traditional quantitative expressions proves challenging, language variables can be employed. The idea of fuzzy set theory was first introduced by Zadeh in 1965, and it provides a useful framework for addressing the inconsistent and inaccurate nature of data related to various parameters. Generally speaking, ambiguous words such as “equally”, “moderately”, “strongly”, “very strongly”, “extremely”, and “significant degree” characterize human language [117]. In this context, there exist fuzzy numbers, including triangular and trapezoidal ones [117]. Triangular fuzzy numbers possess membership functions, and there are fundamental operations associated with fuzzy numbers:

$$\mu \left(x\right)=\left\{\begin{array}{cc}\hfill 0,& x\le a\hfill \\ \hfill \frac{x-a}{b-a},& a<x\le b\hfill \\ \hfill 1,& x=b\hfill \\ \hfill \frac{c-x}{c-b},& b<x\le c\hfill \\ \hfill 0,& x\ge c\hfill \end{array}\right.$$

$$\tilde{A}\oplus \tilde{B}=\left(a_1+b_1,a_2+b_2,a_3+b_3,a_4+b_4\right)$$

$$\tilde{A}\otimes \tilde{B}=\left(a_1{b}_1,a_2{b}_2,a_3{b}_3,a_4{b}_4\right)$$

$$\tilde{A}\ominus \tilde{B}=\left(a_1-b_4,a_2-b_3,a_3-b_2,a_4-b_1\right)$$

$$\tilde{A}\oslash \tilde{B}=\left(\frac{a_1}{b_4},\frac{a_2}{b_3},\frac{a_3}{b_2},\frac{a_4}{b_1}\right)$$

$$d_v\left(\tilde{m},\tilde{n}\right)=\sqrt{\frac{1}{4}\left[{\left(m_1-n_1\right)}^2+{\left(m_2-n_2\right)}^2+{\left(m_3-n_3\right)}^2+{\left(m_4-n_4\right)}^2\right]}$$

4.2. Fuzzy Best-Worst Method

In this research, the BWM method has been chosen for the utilization of weighting the criteria because it requires fewer comparisons compared to other methods. This preference is due to BWM being a vector-based technique [124,125,126]. Due to the reduced number of comparisons involved, using BWM results in a quicker solution and entails less complexity [127]. The Fuzzy Best-Worst Method (FBWM) is an extension of the BWM, offering a structured approach for decision-makers to evaluate and rank alternatives based on multiple criteria. FBWM excels at handling imprecise and uncertain information, making it ideal for real-world scenarios where precise data is often lacking. The FBWM is a versatile tool used in various fields such as business, finance, environmental management, healthcare, and urban planning. It uses fuzzy numbers to represent imprecise data, provides a comprehensive analysis, ensures transparency, and can be adapted to various scenarios.

It aids in portfolio selection, supplier evaluation, strategic decision-making, eco-friendly technology selection, resource allocation, and patient treatment planning. FBWM involves five sequential steps for determining the weights of decision criteria [114,118,128,129]:

Step 1. Defining a set of decision criteria, denoted as {$c_1$, $c_2$, …, $c_n$}.

Step 2. Identify the most important and least important criteria and establish separate sets for each. These pivotal criteria can be designated as $c_B$ and $c_W$ respectively.

Step 3. In the process of determining the strength of the most important criterion in relation to each of the other criteria, pairwise comparisons are performed. Given that decision-makers frequently express their preferences using linguistic statements, it is essential to convert these statements into fuzzy numbers. Subsequently, the decision criteria are compared using these fuzzy numbers, and ranks between 1 and 9 are assigned for each comparison. These rankings show how the most essential criterion compares to the other criterion in terms of influence or strength.

Implementing this procedure results in the creation of the Best-to-Others vector, which can be denoted as ${\tilde{A}}_B$ = (${\tilde{a}}_{B1}$, ${\tilde{a}}_{B2}$, …, ${\tilde{a}}_{Bn}$). As ${\tilde{A}}_B$ is a fuzzy vector, each component ${\tilde{a}}_{Bj}$ signifies the fuzzy strength of the most important criterion concerning criterion $j$. For instance, ${\tilde{a}}_{BB}$ = (1, 1, 1) would represent the case where the most important criterion has equal strength as itself, indicating a ranking of 1 in relation to all other criteria.

Step 4. Analyze each criterion in comparison to the least significant one. Similar to this, convert the qualitative states made by the decision-makers into fuzzy numbers and assess the decision-making standards. To represent the relative significance of each criterion about the least essential one, ranks ranging from 1 to 9 should be assigned to the pairwise comparisons. This process results in an Others-to-Worst vector, denoted as ${\tilde{A}}_W$ = ${\left({\tilde{a}}_{1W},{\tilde{a}}_{2W},\dots ,{\tilde{a}}_{nW}\right)}^T$. Given that ${\tilde{A}}_W$ is a fuzzy vector, ${\tilde{a}}_{iW}$ represents the fuzzy weight of criterion $j$ concerning the least important criterion. For instance, ${\tilde{a}}_{WW}$ = (1, 1, 1).

Step 5: Identify the fuzzy weights. ($\widetilde{w_1^{*}}$, $\widetilde{w_2^{*}}$, …, $\widetilde{w_n^{*}}$) as follows: For each criterion, find the optimal fuzzy weights such that ${\tilde{w}}_B$/${\tilde{w}}_j$ = ${\tilde{a}}_{Bj}$ and ${\tilde{w}}_j$/${\tilde{w}}_W$ = ${\tilde{a}}_{jW}$ for each pair. The maximum absolute differences should be minimized by these optimal weights $\left|\frac{{\tilde{w}}_B}{{\tilde{w}}_j}-{\tilde{a}}_{Bj}\right|$ and $\left|\frac{{\tilde{w}}_j}{{\tilde{w}}_W}-{\tilde{a}}_{jW}\right|$ for all $j$, and these differences should be incorporated into a minimization model. All variables must be non-negative, the sum of the weights must equal 1, and the variables ${\tilde{w}}_B$, ${\tilde{w}}_W$ and ${\tilde{w}}_j$ represent fuzzy triangular numbers. These restrictions will serve as the foundation for the mathematical model that results.

$$\mathrm{minimize}\max \left\{\left|\frac{{\tilde{w}}_B}{{\tilde{w}}_j}-{\tilde{a}}_{Bj}\right|,\left|\frac{{\tilde{w}}_j}{w_W}-{\tilde{a}}_{jW}\right|\right\}$$

$$s.t.\left\{\begin{array}{c}{\displaystyle \sum _{j=1}^n}R\left({\tilde{w}}_j\right)=1\\ l_j^w\le m_j^w\le u_j^w\\ l_j^w\ge 0\\ j=1,2,\dots ,n\end{array}\right.$$

$${\tilde{w}}_B=(l_B^w,m_B^w,u_B^w),{\tilde{w}}_W=\left(l_W^w,m_W^w,u_W^w\right),{\tilde{w}}_j=\left(l_j^w,m_j^w,u_j^w\right),$$

The model can be transformed into a subsequent mathematical model with the following constraints to minimize $\tilde{\xi }$:

$$s.t.\left\{\begin{array}{c}{\displaystyle \sum _{j=1}^n}R\left({\tilde{w}}_j\right)=1\\ l_j^w\le m_j^w\le u_j^w\\ \left|\frac{{\tilde{w}}_B}{{\tilde{w}}_j}-{\tilde{a}}_{Bj}\right|\le \tilde{\xi }\\ \left|\frac{{\tilde{w}}_j}{w_W}-{\tilde{a}}_{jW}\right|\le \tilde{\xi }\\ l_j^w\ge 0\\ j=1,2,\dots ,n\end{array}\right.$$

$$\tilde{\xi }=(l^{\xi },m^{\xi },u^{\xi }).$$

Suppose that ${\tilde{\xi }}^{\mathrm{*}}=\left(k^{\mathrm{*}},k^{\mathrm{*}},k^{\mathrm{*}}\right)$ and $k^{\mathrm{*}}\le l^{\xi }$ when $l^{\xi }\le m^{\xi }\le u^{\xi }$. The model can be transformed to minimize $\tilde{\xi }$:

$$s.t.\left\{\begin{array}{c}{\displaystyle \sum _{j=1}^n}R\left({\tilde{w}}_j\right)=1\\ l_j^w\le m_j^w\le u_j^w\\ \left|\frac{l_B^w,m_B^w,u_B^w}{\left(l_j^w,m_j^w,u_j^w\right)}-\left(l_{Bj},m_{Bj},u_{Bj}\right)\right|\le \left(k^{*},k^{*},k^{*}\right)\\ \left|\frac{l_j^w,m_j^w,u_j^w}{\left(l_W^w,m_W^w,u_W^w\right)}-\left(l_{jW},m_{jW},u_{jW}\right)\right|\le \left(k^{*},k^{*},k^{*}\right)\\ l_j^w\ge 0\\ j=1,2,\dots ,n\end{array}\right.$$

Using IBM ILOG Optimization Studio software version 12.10, the mathematical model derived from the FBWM methodology was implemented and solved. This program provides a strong environment for decision support and mathematical optimization. Because of its strong points, the mathematical model was efficiently resolved, which made it possible to determine the best fuzzy weights ($w_1^{*}$, $w_2^{*}$, …, $w_n^{*}$) for our study.

4.3. Fuzzy TOPSIS

The technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), introduced by [19] in 1981, is a widely used decision-making technique in academic literature. It is known for its efficiency in identifying the most favorable alternative and its simplicity. With m data points in an n-dimensional space, TOPSIS uses multidimensional decision-making. Its central idea, which has found widespread application in numerous academic disciplines and real-world applications, is to choose the alternative with the smallest geometric distance from the positive ideal solution and the largest distance from the negative ideal solution (Figure 3).

${\tilde{A}}^{*}$ signifies the positive ideal solution while representing the negative ideal solution. The primary objective of the TOPSIS method is to identify the optimal choice among a set of alternatives. This best option is shown as a point with the greatest geometric distance from the negative ideal solution and the smallest geometric distance to the best matrix, which is a positive ideal solution. To put this method into practice, one must complete the seven steps [124,130,131];

Step 1. Make a matrix of evaluation with m alternatives and n criteria, where the intersection of each alternative and criteria is represented by ${\tilde{x}}_{ij}=\left(l_{ij},m_{ij},u_{ij}\right)$. As a result, we obtain a matrix: ${{(\tilde{x}}_{ij})}_{mxn}$. $u_j^{*}$.

Step 2. Normalize the matrix using the normalization method. ($\tilde{R}{={(\tilde{r}}_{ij})}_{mxn}$),

$$\mathrm{Benefit}\mathrm{Criteria}:{\tilde{r}}_{ij}=\left(\frac{l_{ij}}{u_j^{*}},\frac{m_{ij}}{u_j^{*}},\frac{u_{ij}}{u_j^{*}}\right)\mathrm{and}{u}_j^{*}=\mathit{max}\left(u_{ij}\right)$$

$$\mathrm{Cos}\mathrm{t}\mathrm{Criteria}:{\tilde{r}}_{ij}=\left(\frac{l_j^{-}}{l_{ij}},\frac{l_j^{-}}{m_j^{*}},\frac{l_j^{-}}{u_{ij}}\right)\mathrm{and}{l}_j^{*}=\mathit{min}\left(l_{ij}\right)$$

Step 3. Create a decision matrix with fuzzy values that is normalized and weighted.

$${\tilde{v}}_{ij}={\tilde{r}}_{ij}*w_j^{*}$$

Step 4. Find the ideal solutions as fuzzy positive solution (${\tilde{A}}^{*}$) and the opposite, which is fuzzy negative (${\tilde{A}}^{-}$) solutions.

$${\tilde{A}}^{*}=\left({\tilde{v}}_1^{*},{\tilde{v}}_2^{*},\dots ,{\tilde{v}}_n^{*}\right),\mathrm{where}{\tilde{v}}_j^{*}=\mathit{max}\left\{{\tilde{v}}_{ij3}\right\}$$

$${\tilde{A}}^{-}=\left({\tilde{v}}_1^{-},{\tilde{v}}_2^{-},\dots ,{\tilde{v}}_n^{-}\right),\mathrm{where}{\tilde{v}}_j^{-}=\mathit{min}\left\{{\tilde{v}}_{ij1}\right\}$$

Step 5. Calculate how far each option is from the positive ideal ($A^{*}$) and the negative ideal ($A^{-}$) solutions.

$$d\left(\tilde{x},\tilde{y}\right)=\sqrt{\frac{1}{3}*\left[{\left(l_1-l_2\right)}^2+{\left(m_1-m_2\right)}^2+{\left(u_1-u_2\right)}^2\right]}$$

Distance to ($A^{*}$):

$$d_i^{*}={\sum }_{j=1}^n{d}_{ij},\mathrm{for}\mathrm{all}i=1,2 \dots , \mathrm{n}$$

Distance to ($A^{-}$):

$$d_i^{-}={\sum }_{j=1}^n{d}_{ij},\mathrm{for}\mathrm{all}i=1,2 \dots , \mathrm{n}$$

Step 6. Compare the similarity to the worst-case scenario.

$$C_i^{*}=\frac{d_i^{-}}{d_i^{-}+d_i^{*}}$$

Step 7. Sort the options according to $C_i^{*}$, ($i$ = 1, 2, …, m)

5. Case Study and Analysis

Turkey’s economy depends heavily on agriculture, which produces food, creates jobs, and spurs economic growth. However, the agriculture industry faces several difficulties, including enduring and recent problems that might impede its sustainable growth. This section of the study identifies the challenges to the successful implementation of renewable and sustainable energy systems in urban areas. In addition to that, it has been aimed at suggesting which SDGs are more pertinent to address to mitigate environmental degradation. The reason why the agriculture sector has been chosen for the implementation of this study is because the agricultural sector contributes to the economic prosperity of an emerging economy and provides food, livelihoods, and a sense of identity for millions of rural residents. To protect the environment and sustain an emerging economy, these issues must be resolved for a successful transition into renewable and sustainable energy in urban areas with the help of the SDGs.

Turkey’s agricultural industry must come up with creative solutions to overcome these challenges as the world struggles with the effects of climate change and the need to switch to renewable energy sources, encouraging adaptability and resilience while embracing sustainable practices. The BWM and TOPSIS methods have been used as the study’s methodology. Initially, the experts determined the best and worst criteria for the challenges found and listed in the extensive literature review. The TOPSIS method’s criteria were then applied using the calculated weights. Additionally, the SDGs have been chosen as an alternative to be used with the TOPSIS method to rank the SDGs according to the relevance and importance of the challenges that need to be overcome. This two-pronged methodology offers policymakers, stakeholders, and experts’ practical knowledge and provides cutting-edge solutions. Because it paves the way for a thriving agricultural sector that not only addresses today’s problems but also embraces a sustainable future while protecting the environment and advancing global sustainability goals. This methodology’s application was carried out by knowledgeable professionals, including academia and industry (

Table 3. Information about the Experts.

Expert No.	Job	Experience
1	Professor	21
2	Professor	39
3	Assoc. Prof.	17
4	Assoc. Prof.	15
5	Assoc. Prof.	18
6	Agricultural Engineer	30
7	Organic Farming Controller	16
8	Sustainability Projects Specialist	20
9	Energy Trading Specialist	22
10	Supply Chain Manager	37

).

Their opinions and contributions helped to provide a thorough understanding of the present problems. To get their opinions, a survey was created. According to the terms “Very Low”, “Low”, “Average”, “High”, and “Very High”, which correspond to “1, 1, 3”, “1, 3, 5”, “3, 5, 7”, “5, 7, 9”, and “7, 9, 9”, respectively, experts were asked to complete the questionnaire. The information gathered through this survey was used as input for our FBWM analysis and allowed us to give the identified challenges fuzzy weights.

Upon completion of the FBWM analysis, fuzzy weights for the challenges were successfully obtained (see

Table 4. The Fuzzy Weights.

Weights	L	M	U
c1	0.057495	0.11707	0.134144
c2	0.034638	0.046154	0.052885
c3	0.038959	0.057495	0.078046
c4	0.038959	0.057495	0.078046
c5	0.083554	0.167107	0.167107
c6	0.068112	0.161524	0.167107
c7	0.050132	0.083554	0.167107
c8	0.048533	0.08485	0.107683
c9	0.036424	0.051911	0.062651
c10	0.041671	0.06368	0.097225
c11	0.030797	0.03833	0.03833
c12	0.032384	0.043819	0.045408
c13	0.030797	0.035836	0.035836
c14	0.021307	0.023872	0.023872

). These weights served as basic parameters and were then used as input in the Fuzzy TOPSIS analysis.

Table 5. Alternative Rankings.

Relative Closeness	${\mathit{d}}_{\mathit{i}}^{\mathit{*}}$	${\mathit{d}}_{\mathit{i}}^{-}$	${\mathit{C}}_{\mathit{i}}^{\mathit{*}}$	Rank
SDG 1	0.393372049	0.412040791	0.511589548	14
SDG 2	0.209721356	0.57555503	0.732933067	7
SDG 3	0.356478189	0.404795319	0.531734409	13
SDG 4	0.340271901	0.514716948	0.602015978	12
SDG 5	0.669368399	0.068093568	0.092335023	17
SDG 6	0.149497979	0.610052464	0.803175707	5
SDG 7	0.111339513	0.640298772	0.851870886	2
SDG 8	0.272082469	0.506754038	0.650655219	10
SDG 9	0.236209119	0.51072325	0.683761036	9
SDG 10	0.471458365	0.31821772	0.402972467	16
SDG 11	0.088482032	0.658433251	0.881536723	1
SDG 12	0.203548413	0.552785214	0.730874834	8
SDG 13	0.119050032	0.624610277	0.839913425	3
SDG 14	0.302599615	0.463184402	0.604849921	11
SDG 15	0.137139626	0.606250742	0.815521384	4
SDG 16	0.431598942	0.317807239	0.424078753	15
SDG 17	0.169469313	0.563737825	0.768865707	6

displays the findings of our fuzzy TOPSIS analysis. When viewed in the context of current challenges, these results offer insightful information about the significance of each SDG. It is hoped to contribute to the search for resilient and sustainable agricultural practices through this research with experts, which will benefit both the industry and the larger community.

The study’s findings also provide important new information about the relative significance of the SDGs in resolving issues with the incorporation of renewable and sustainable energy in urban areas. These findings help researchers and decision-makers prioritize particular SDGs for successful sustainable urban energy planning.

The analysis places SDG 11, “Sustainable Cities and Communities”, first in terms of importance. The importance of developing inclusive, secure, resilient, and sustainable urban environments is emphasized by this SDG. Given that it discusses the difficulties urban areas have in adopting the SDGs, it is particularly pertinent to the study. The study’s overarching objectives, which include promoting the integration of renewable and sustainable energy in urban settings, are in line with the emphasis on sustainable urban development. It is clear from the evaluation of the SDGs that SDG 7, “Affordable and Clean Energy”, stands out as one of the most important objectives of the study. This SDG ranks second in importance according to the analysis, underscoring its significance in addressing the challenges of renewable energy integration in urban areas. SDG 7 focuses on ensuring access to affordable, reliable, sustainable, and modern energy for all. Given that the analysis revolves around renewable energy integration, this SDG aligns closely with the core objectives of the study, emphasizing the pivotal role of clean energy in urban sustainability and environmental protection. SDG 13 (Climate Action) ranks third in importance. While not directly related to energy integration, this goal is highly relevant to the research as it emphasizes the urgent need for climate mitigation and adaptation measures. Renewable energy integration plays a significant role in addressing climate change and aligns with this SDG’s objectives.

On the other hand, some of the SDGs rank lower in importance within the research context. SDG 5 (Gender Equality) is the least important goal in the analysis. While gender equality is a critical global issue, its direct relevance to the challenges and objectives of the research, which primarily focuses on energy policies, technology adoption, and environmental protection, may be limited.

SDG 10 (Reducing Inequality) is second to last in terms of importance. While addressing inequality is critical to overall development, it is not directly linked to the challenges of renewable energy integration in urban areas. SDG 16 (Peace, Justice, and Strong Institutions) appears to be the third least important goal in the analysis. Its focus on peace, justice, and strong institutions falls outside the primary scope of the research, which focuses on renewable energy integration in urban areas.

6. Discussion and Implications

This study’s key contribution to the existing literature is its detailed evaluation of the problems and possibilities involved with the transition to renewable and sustainable energy systems in urban settings [132]. This research takes a multifaceted approach, identifying not only the challenges towards successful renewable energy adoption but also offering a way to overcome these issues through the inclusion of SDGs for environmental preservation and sustainable urban development [133,134,135,136]. Because of its holistic approach to assessing the problems and possibilities connected with converting urban areas to renewable and sustainable energy systems, this research stands out in the current literature. Furthermore, the research takes a comprehensive approach, taking into account economic, technical, political, environmental, and social variables while acknowledging the complexity of urban energy systems and the broad stakeholder landscape. Unlike earlier research, this study aims to provide a pathway for adopting renewable energy technologies as well as practical consequences and policy suggestions, bridging the gap between academic research and real-world application. Overall, this study fills a major research need by offering a comprehensive and actionable method to increase renewable and sustainable energy integration in cities. The BWM and TOPSIS techniques used in this study are intended to weigh, identify, and analyse the relative importance of challenges to the adoption of renewable energy in urban settings by comparing the challenges with the SDGs to overcome them and provide a solution. It ranks these difficulties according to their importance to the hurdles to switching to sustainable energy systems. Furthermore, it emphasizes the significance of comparing these issues to ideal solutions, allowing for a data-driven, quantitative evaluation process that informs urban energy planning decision-making. In summary, TOPSIS identifies, prioritizes, and quantifies the challenges to urban regions shifting to renewable and sustainable energy systems via SDG adoption. This paper emphasizes the crucial relevance of cities in the worldwide transition to sustainable energy and admits that cities are large producers of energy-related CO₂ emissions and play a critical role in climate change mitigation. Furthermore, it recognizes the necessity for a holistic approach to urban energy planning and circular economy to reduce carbon emissions that takes into account economic, technological, political, environmental, and social concerns [137].

There are various studies that address related issues, such as urban energy consumption, SDGs, energy transition, etc., in the existing literature; however, there is a gap that connects the challenges and problems in the literature related to the successful renewable and sustainable energy transition in urban areas by leveraging the SDGs to facilitate this transition. For example, Si et al. [138] investigated the influence of carbon reduction strategies on urban energy consumption and emissions, proposed low carbon transition approaches, and tackled transition challenges. Pandey and Asif [139] examined South Asian nations’ progress toward the seven SDGs and potential remedies, assessing and identifying difficulties. It examines the literature on energy and environmental prospects, as well as the SDGs, noting both achievements and concerns. Furthermore, Keirstead et al. [18] defined and assessed a model of an urban energy system by integrating technology design, building design, urban climate, systems design, and policy evaluation, which are the five core areas of practice, with land use and transportation modelling being disregarded in the literature. Dowling et al. [140] explored energy reconfigurations in Australia, with a particular focus on sustainable infrastructure and demand in a fossil-fuel-dependent country, as well as the role of cities in controlling energy transitions. Carréon et al. [141] also provided an overview of energy usage in urban metabolism, with an emphasis on energy services, drivers, waste, data, dynamic modelling, and governance as a foundation for future research.

The findings of this study demonstrate the challenge of integrating sustainable and renewable energy into urban areas to achieve various SDGs. Urban planners and policymakers need to implement an integrated strategy that takes into account a number of interconnected goals, especially SDGs 11, 7, and 13. Integrated urban planning should take into account energy, as well as housing, transportation, infrastructure, and energy-related issues.

Urban development frequently involves SDG trade-offs, calling for careful balancing behaviour [141,142,143]. For instance, in order to achieve SDG 11, land use changes that affect SDG 15 may be necessary. In order to achieve a comprehensive and sustainable strategy, policymakers must make smart choices that take into account potential conflicts and interactions among various SDGs. The determination of SDG priorities can direct the allocation of resources across industries. Governments and organizations should allocate funds to initiatives and projects that fit these objectives. For instance, investments in sustainable transportation and clean energy technologies can benefit several SDGs, such as better air quality, lower greenhouse gas emissions, and improved quality of life. The active involvement of communities and individuals is necessary for the SDGs to be achieved. The public should be made aware of the value of renewable energy, sustainable urban development, and the broader SDGs through educational campaigns and awareness programs. These initiatives may inspire behavioural improvements and support long term projects.

Urban areas present a wide range of opportunities and challenges. When developing strategies for integrating sustainable energy, policymakers should take the local environment into account. Effective implementation requires adjusting solutions to particular urban environments and their different development needs. International cooperation is essential because sustainability challenges are global in nature. The integration of renewable energy, sustainable urban design, and SDG achievement are all areas where nations can benefit from others’ experiences and exchange best practices. Knowledge exchange and international collaboration may accelerate the transition to a sustainable future. Monitoring and evaluating progress toward SDG achievement on a regular basis is vital.

To assess the effectiveness of actions and change strategies as necessary, policymakers and researchers should set up reliable data collection and evaluation mechanisms. Moreover, to illustrate how our multimodal approach can be used in real-world scenarios, consider how it can be applied in urban environments. Cities may initiate large-scale energy-efficient projects by incorporating renewable energy technologies. As an illustration, installing solar panels on roofs or using wind turbines in urban environments offers concrete solutions. Important first steps toward developing robust, sustainable urban energy systems are putting smart grids into place and supporting energy storage technologies. Furthermore, the practicality of the multifaceted approach is demonstrated by providing incentives for environmentally friendly transportation modes, such as electric vehicles, combined with effective urban planning to reduce energy waste. This integration encourages social unity and economic development in urban areas in addition to environmental preservation.

The implications of this study go beyond the scope of particular SDGs and highlight the necessity of a comprehensive, flexible, and multi-stakeholder approach to the integration of sustainable energy in urban areas. A comprehensive understanding of the connected opportunities and challenges in urban development, energy, and environmental protection is necessary to achieve the SDGs.

7. Conclusions

Although fossil fuels fulfil the majority of global energy demand, their depletion may result in serious disruptions in the energy supply. Humanity is confronted with basic concerns such as the depletion of fossil fuels, increased energy use, and climate change. As cities become more popular owing to economic and environmental concerns, they require sustainable energy regulations and alternative fuels to offset these challenges. Hence, cities have become essential for achieving a low carbon and sustainable energy future. With cities housing more than half of the world’s population and accounting for more than 70% of global energy-related CO₂ emissions, they are confronted with issues in satisfying the expanding demand for energy services, resulting in increased greenhouse gas emissions. The SDGs were developed by the United Nations in 2015 to eradicate poverty, protect the environment, and ensure prosperity. These goals, which expand on the Millennium Development Goals, address economic, social, and environmental challenges. They place a premium on health, gender equality, long term economic growth, innovation, decent work, peace, justice, education, and accountability. These SDGs can help cities transition to sustainable energy systems in this context, as urban energy planning requires taking both global and local contexts into account. Local governments worldwide must adopt sustainable energy policies to address environmental issues, such as climate change, and reduce CO₂ emissions. The major results of this study suggest the role and importance of the SDGs in the successful implementation of renewable and sustainable energy to overcome the challenges confronted in building sustainable urban development with the aid of integrating the SDGs. In this context, SDG 11: Sustainable Cities and Communities, SDG 7: Affordable and Clean Energy, SDG 13: Climate Action, and SDG 15: Life on Land were identified as the most substantial and adaptable SDGs for the agriculture sector to overcome challenges. Depending on these SDGs, the proactive and reactive actions that local governments and cities need to adopt are underlined and listed. It promotes integrated urban planning that takes into account energy, housing, transit, and infrastructure. SDG trade-offs in urban development need careful balance. Priorities established by the SDGs drive resource allocation, promoting investments in sectors such as sustainable transportation and renewable energy technology. Community participation and awareness campaigns are critical for behavioral change. In addition to that, policymakers must adjust responses to local situations, and international collaboration is essential for sharing lessons learned and best practices. Regular monitoring and assessment are stressed in order to assess progress toward SDG attainment and adjust methods as needed.

The report emphasizes the global dimension of sustainability concerns as well as the need for collaboration to hasten the transition to a more sustainable future. The identified SDGs must be given top priority by stakeholders, legislators, and urban planners in order to effectively translate these findings into practical policy. Urban development agendas should prioritize initiatives that support sustainable cities and communities (SDG 11) and affordable, clean energy (SDG 7). Furthermore, in order to actively mitigate the negative environmental effects associated with energy-related challenges, policies that specifically address climate action (SDG 13) and promote life on land (SDG 15) are essential. The necessity of an all-encompassing and integrated strategy to guide urban development toward sustainability is highlighted by these policy priorities.

The primary objective of this paper is to give readers a comprehensive overview of the transition to sustainable and renewable energy sources in urban areas. It focuses on identifying problems and opportunities in this field and using renewable energy technology to protect the environment. In order to address the challenges, the TOPSIS technique is used in the study to rank and weight SDGs.

The study emphasizes Turkey’s potential for sustainable solutions by concentrating on the country’s emerging economy and urban energy environment. The proposed methodology is specifically designed to tackle the unique characteristics of Turkey’s urban energy landscape, making it an essential area of focus for cities dealing with increasing energy demands and rapid growth. The purpose of the research is to use collaborations and pilot projects to verify the framework’s effectiveness in the socioeconomic and environmental settings of Turkish urban areas. In addition to making an academic contribution, the research directly impacts Turkey’s urban energy systems’ sustainability and resilience.

One major obstacle to the implementation of renewable energy projects in urban areas is the cost and availability of suitable land. The lack of accessible land that satisfies the requirements for the effective deployment of renewable energy infrastructure is a problem for cities all over the world. The distribution of enough affordable land for wind farms, solar arrays, and other renewable facilities gets harder as cities grow and get denser. Innovative urban planning techniques, cooperation between local government and renewable energy stakeholders, and the incorporation of renewable energy concerns into broader urban development strategies are all necessary to overcome this barrier. In order to fully realize the potential of sustainable and renewable energy solutions in urban environments, land constraints must be addressed. Furthermore, subjectivity is introduced by relying on expert opinions and the intrinsic subjectivity of fuzzy weights. Further research could take into account a larger dataset and a greater variety of quantitative metrics in order to improve the overall understanding of the difficulties.

Further research ideas could concentrate on technological development, creative policy approaches, behavioural analysis, community involvement, and urban resilience. The research could examine cutting-edge technologies, creative regulatory frameworks, and tactics to promote sustainable energy practices in urban settings. Examining community-based renewable energy models, participatory decision-making procedures, and the contribution of renewable energy to disaster preparedness and recovery efforts is crucial. The availability of data, context specificity, temporal factors, the scope of the analysis, and SDG alignment are limitations, though. Planning for sustainable urban energy may be better understood through longitudinal research. The study’s conclusions might not take into account upcoming advancements in renewable energy technologies, changes in policy, or urban dynamics. Furthermore, more studies ought to examine how well policies derived from the SDGs that have been prioritized are implemented. Studies that monitor the development of urban areas and implement these strategies over time will offer significant insights into the practical implications of integrating sustainable energy. Furthermore, more sophisticated and successful approaches to sustainable urban energy planning can result from investigating cutting-edge technological solutions and evaluating their scalability.