Next Article in Journal
Investigating the Effect of an Elliptical Bluff Body on the Behavior of a Galloping Piezoelectric Energy Harvester
Next Article in Special Issue
Reference Power Cable Models for Floating Offshore Wind Applications
Previous Article in Journal
Macro- and Micronutrient Contents and Their Relationship with Growth in Six Eucalyptus Species
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Assessment of Barriers to Wind Energy Development Using Analytic Hierarchy Process

Priyom Das
S. M. Mezbahul Amin
Molla Shahadat Hossain Lipu
Shabana Urooj
Ratil H. Ashique
Ahmed Al Mansur
3 and
Md. Tariqul Islam
Institute of Forestry and Environmental Sciences, University of Chittagong, Chittagong 4331, Bangladesh
Department of Information Technology, University of Newcastle, Callaghan, NSW 2308, Australia
Department of Electrical and Electronic Engineering, Green University of Bangladesh, Narayanganj 1461, Bangladesh
Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(22), 15774;
Submission received: 7 September 2023 / Revised: 10 October 2023 / Accepted: 2 November 2023 / Published: 9 November 2023
(This article belongs to the Special Issue Current Advances in Offshore Wind Energy for Sustainability)


Despite the vast potential of wind energy, it has yet to be implemented widely in Bangladesh. Several barriers play major roles in obstructing the wind energy sector’s development in Bangladesh. Hence, it is necessary to identify these barriers to progress this sector’s growth. The analytic hierarchy process (AHP) is a multi-criteria decision-making method that can rank alternatives by considering multiple factors. It is a valuable tool for complex decision-making problems with multiple criteria, and their relationships must be clarified. So, in this analysis, AHP is used to rank the barriers related to wind energy development in Bangladesh. First, six main barriers and eighteen sub-barriers have been identified from a detailed and comprehensive literature review. The main barriers are categorized into technical, administrative, policy and political, economic, social, and geographic. After that, a questionnaire was sent to experts to obtain their opinions on these barriers. Based on the analysis, it was found that, with a weighted score of 0.46, technical barriers were the most significant ones. Administrative barriers ranked second with a weighted score of 0.21, and social barriers ranked last with a weighted score of 0.03. Among the sub-barriers, risks associated with technology were found to be the most significant, while land use conflict was found to be the least significant. Finally, several recommendations are provided to overcome the wind energy barriers in Bangladesh. The findings of this study can help policymakers and stakeholders develop strategies to overcome the barriers to wind energy development in Bangladesh.

1. Introduction

As cities began to develop, energy demand increased tremendously [1,2]. Fossil fuels have been used to meet this demand globally [3]. A significant 4% annual growth in the number of conventional power plants since 1997 has increased air pollution and endangered human health [4,5]. These power plants are responsible for one-third of the world’s glasshouse gas (GHG) emissions. The main GHGs that have a negative impact on the global climate order are carbon dioxide (CO2), nitrous oxide (NOx), and sulfur dioxide (SO2) [6,7]. NOx generated by power plants can form ozone, resulting in environmental photochemical pollution [8]. Even though technical developments in power plants have significantly reduced SO2 and NOx emissions, climate change brought on by CO2 emissions continues to be a severe and divisive issue [9]. According to estimates, 5% of fossil fuel-based power plants account for 73% of the world’s CO2 emissions from electricity production, which is highly concerning for the environment [4,10]. In underdeveloped nations, the situation is considerably more dire. For instance, Bangladesh is anticipated to produce 83,000 metric tonnes of CO2 and 132,520 GWh of electricity from conventional power plants in the 2024–2025 fiscal year, making it the top CO2 emitter in the world [11]. Therefore, to ensure a low-carbon future and fulfill the Sustainable Development Goals (SDGs), it is required to adopt decarbonization technologies to cut CO2 emissions and accelerate economic development [12]. Renewable energy can be beneficial in this regard [13].
In the wake of the recent Russia–Ukraine war, European nations are anticipated to take considerable action to reduce their reliance on Russian oil and gas [14]. As a result, a space has been further opened for renewable energy to influence world politics, undermining the dominance of oil and gas exporters [15]. By the end of 2019, renewable energy sources generated more than 27% of the world’s electricity, with wind turbines and solar photovoltaics (PV) contributing about 11% each [16]. By the 21st century, it is anticipated that solar and wind energy will increase globally more quickly than other energy sources [17]. In this context, wind energy can offer grid and off-grid consumers more dependable and efficient power and thus show promise to achieve SDG 7, which means that everyone can afford dependable, sustainable, and modern energy [4].
Generally, two extraction devices, horizontal-axis wind turbines (HAWTs) and vertical-axis wind turbines (VAWTs), harness wind energy. HAWTs have been widely used between these devices in offshore and onshore locations because of their high power conversion efficiencies. These devices can produce megawatts of power. However, in spite of their commercial success, the large size of HAWTs (rotor diameters over 100 m) presents some logistical problems. HAWTs require large amounts of space in water bodies or land, as the optimum spacing along the wind direction in a HAWTs wind farm could extend up to 15 times the turbine rotor’s diameter or greater. Also, HAWTs require complex yaw control, as they have to face the incoming wind for optimum power production. On the other hand, VAWTs do not require complex control, as their performance is not sensitive to wind direction. Additionally, VAWTs do not create more turbulence in the air downwind of the turbine, and this disturbance returns to the normal state, unlike in HAWTs [18].
The number of residents living in Bangladesh is 166 million, and by 2041, this number will rise to 189 million [19,20]. This year (2041) is significant for Bangladesh because the country aims to achieve high-income status within that year. Bangladesh must put industrialization first to become a high-income nation, which demands a reliable supply of electricity [21]. However, the electricity demand will be 19,034 MW in 2021 and rise to 82,292 MW in 2041 [22]. Bangladesh should be self-sufficient in energy generation to satisfy this future increase in electricity demand and become a high-income nation [23,24]. As of August 2018, Bangladesh’s current energy mix consisted of 64% natural gas, 21% furnace oil, 6% diesel, 5% power imports, 2% hydro, 2% coal, and 0.1% renewable energy. Despite making up most of Bangladesh’s energy mix, natural gas reserves will run out by 2028 if new gas fields are not found [25]. Bangladesh’s government relies on imported fuel to power its diesel power plants because it has no oil resources. Therefore, relying on imported fuel cannot be considered a suitable method of achieving self-dependency in electricity production. For its coal-fired power plants, Bangladesh also depends on Australia, India, Indonesia, and Mozambique. However, because Bangladesh’s bituminous coal reserves are comparably high-quality and will be depleted by 2050, using them rather than importing them is preferable [22]. In addition, a study from the International Energy Agency states that 30% of the CO2 emissions in the globe come from the generation of energy using coal [26]. Therefore, using coal is against SDG Goals 13 (Climate Actions) and 7 (Affordable and Clean Energy) [27]. Also, Bangladesh needs more suitable places to set up nuclear power plants since it is a densely populated country. However, with the help of Russia, a 2400 MW nuclear power plant has been established in Ruppur, Bangladesh. However, more is needed to meet energy demand by 2041 [22]. Considering these challenges, Bangladesh must focus more on renewable energy, like wind (Table 1).
Wind energy has enormous potential in Bangladesh. The country has a coastline of nearly 700 km, with strong and persistent winds. The country’s wind energy potential is predicted to be greater than 50 gigawatts (GW). Wind energy growth in Bangladesh is consistent with the country’s larger sustainable development objectives (SDGs). SDG 7 seeks widespread availability of reliable, affordable, and modern energy services. SDG 13 calls for swift action to avert climate change and its effects. Wind energy development in Bangladesh is still in its early phases. However, the nation’s commitment to the environment and sustainable energy goals could benefit significantly from unlocking the huge potential of wind energy. The government’s encouragement of wind energy development is praiseworthy. Bangladesh can fulfil its sustainable development goals while reducing its susceptibility to climate change by investing in wind energy. Several studies have researched the potential of wind energy in Bangladesh. A feasibility study in Chittagong, Bangladesh, claimed vast wind energy potential in Chittagong. For this study, the number of wind turbines required, and their maintenance and operational costs were also determined for their proposed sites [29,30]. Similar observations were also made for Bangladesh’s coastal region, as presented in Figure 1. From Figure 1, Kuakata and Kutubdia have significant wind resource potential compared to other locations in Bangladesh. Wind speed seems to be higher from June to August. Amin estimates that, except for natural gas and local coal, the unit cost of electricity for wind is far lower than any other fossil fuel and non-conventional energy sources [31]. Sandwip was found to be an exceptional place for wind energy by Azad et al. after performing numerous Weibull distribution techniques [32]. Patenga was also an attractive place for wind energy [33]. This study measured wind speed at 20 m to obtain to this outcome. Another study used the HOMER model and NASA’s SSE wind energy database to generate a wind map [34]. The study also identified the Enercon (E33) 330 kW turbine as the best wind turbine for Bangladesh for harnessing wind energy. A survey conducted jointly by USAID and NREL in 2018 reported that Bangladesh has the potential to generate 30 GW of energy from wind. This proves a promising aspect of wind energy in Bangladesh [35]. The wind resources of different parts of Bangladesh can be found in Figure 2.
Despite this vast potential, wind energy development is restricted in Bangladesh. From the literature review, it is clear that many studies have explored the feasibility of wind energy in Bangladesh. However, barriers that hinder wind energy development are yet to be identified. Several studies have used the analytic hierarchy (AHP) process to identify many countries’ main barriers to renewable energy (RE) development. Pathak et al. used AHP and modified Delphi method to rank barriers to RE technologies in India [36]. In Malawi, Africa, AHP was used to rank the barriers of RE development. In contrast, Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) was used to rank the solutions of these barriers [37]. Solangi et al. also took this same approach for Pakistan [38]. Ghimire and Kim used AHP to determine the barriers to RE development in Nepal, while Yadav et al. identified barriers to biogas technology in India using AHP [39,40]. Shahzad et al. found financial and regulatory frameworks as the main barriers to Pakistan using Pythagorean fuzzy AHP [41]. Meanwhile, obstacles to RE adoption in Chile were identified by Nasirov et al. using AHP [42]. This study found that institutional and technological barriers are the main barriers. Table 2 reports similar studies considering the multi-criteria decision analysis (MCDM) approach to rank and prioritize barriers. This study highlights the obstacles relevant to Bangladesh’s context and geographic location by reviewing the literature from several studies and reports undertaken in other countries. So, in this analysis, an MCDM method named the analytic hierarchy process (AHP) has been employed to identify the main barriers to wind energy deployment in Bangladesh. An extensive literature review was performed to determine these barriers. Government reports, websites, research, and review articles were used for this literature review.
The novelty of this study lies in the following aspects:
  • It is the first study to use the AHP to identify and rank the barriers to wind energy deployment in Bangladesh.
  • A comprehensive set of criteria to assess barriers, including technical, administrative, policy and political, economic, social, and geographic barriers, are investigated.
  • A detailed analysis of the relationships between the barriers and their impact on wind energy deployment are carried out.
  • The study identifies the most significant barriers to wind energy deployment and provides recommendations for overcoming them.
The paper is organized into five sections. Section 2 presents the energy scenario in Bangladesh. The comprehensive methods and approaches are outlined in Section 3. The analysis and evaluation of barriers to wind energy development in Bangladesh are described in Section 4. The discussion is delivered in Section 5. The study ends with a conclusion in Section 5.

2. Energy Scenario in Bangladesh

According to the latest statistics, as of January 2022, Bangladesh produced the highest energy of 13,525 MW, while the total installed capacity was reported to be (as of August 2022) 22,612 MW. The most significant energy came from the private sector (47.39%), followed by the public sector (40.02%) [50]. The rest came from joint ventures. Foreign countries’ contribution to Bangladesh’s energy consumption was 8.50%. Most electricity (65%) is generated from natural gas (Figure 3). Apart from power generation, natural gas is widely used in industrial, residential, and commercial sectors. However, this percentage came down to 42% due to the shortage of natural gas (Figure 4) [50]. If this continues, the reserve of natural gas will be depleted in the near future. Urgent response is necessary, and shifting to renewable resources is a potential solution to reduce over dependence on natural gas.
In 2021, fossil fuels supplied 99.35% of primary energy. The renewable sources supplied the rest [51]. As a result, huge amounts of CO2 are released into the atmosphere. In 2021, Bangladesh emitted 99,297.30 kt of CO2 into the atmosphere [22]. To overcome this problem, the Bangladesh government proposed a new electricity mix plan integrating a large share of renewables (Figure 5). Figure 5 shows that by 2041, Bangladesh plans to obtain 15% of its electricity from sustainable energy sources [22]. For this, Bangladesh will mainly depend on solar and wind energy resources.
Figure 3. Bangladesh’s electricity generation scenario by January 2023 (MW) [52].
Figure 3. Bangladesh’s electricity generation scenario by January 2023 (MW) [52].
Sustainability 15 15774 g003
Figure 4. Natural gas contribution in various sectors of Bangladesh [50].
Figure 4. Natural gas contribution in various sectors of Bangladesh [50].
Sustainability 15 15774 g004
Figure 5. Energy mix considered by Bangladesh government for 2041 [22].
Figure 5. Energy mix considered by Bangladesh government for 2041 [22].
Sustainability 15 15774 g005
As previously stated, several studies conducted feasibility studies to find the wind resource potential of different parts of Bangladesh. It should be noted that the highest wind speed found in Kuakata by the WEST project conducted in 1995–1997 was 4.54 ms−1. Another survey conducted by the WRAP project in 2003 found a wind speed of 6.9 ms−1 for the same location. Since more recent measuring instruments are more precise than older ones, wind readings from current studies are more largely accepted than those from earlier analyses. After analyzing the one-year wind data from the WEST project, Amin calculated the amount of energy that could be extracted from the wind (Figure 1). Table 1 shows how much wind energy can be harvested at 25 m in six potential seashore locations, including Cox’s Bazar, Patenga, Kuakata, Teknaf, Char Fashion, and Kutubdia [22,31]. From the table, it is also clear that significant wind energy can be generated between May and August. From the table, it is evident that the highest wind power available is 7.5 ms−1 at 120 m in offshore and coastal areas. A joint study by NREL and USAID measured the wind speed at 120 m height and prepared a wind map. The report stated that Bangladesh has more than 20,000 km2 of land on which to harness wind power. From the wind map (Figure 2), it can be seen that all the coastal areas of Bangladesh fall under the commercially relevant category. All coastal areas have wind speeds between 6.5 and 7 ms−1. Another good thing is that Bangladesh can generate energy from offshore wind (wind velocity 7–7.5 m/s) [35,53]. Wind energy growth in Bangladesh represents a possible path for meeting the country’s expanding energy needs while reducing dependency on fossil resources. This transformation, however, has its challenges, obstacles, and concerns.
Due to its dependence on imported fossil fuels, Bangladesh faces energy security issues, making its energy industry vulnerable to international price swings and supply interruptions [54]. Wind energy’s intermittent nature adds another layer of complexity to the energy mix, creating concerns about grid stability and energy reliability [55]. Due to conventional infrastructure, wind power integration into existing energy infrastructure is a significant concern. Despite significant advances in wind turbine technology in recent years, a technological gap has developed in Bangladesh’s wind energy sector. A lack of access to cutting-edge wind turbines and components can hamper wind farms’ efficiency and performance [56]. Furthermore, an absence of regional research and development initiatives in wind energy technology presents obstacles to adapting to local conditions. Wind energy is frequently produced in remote or rural places, demanding significant changes to the electrical system and infrastructure to deliver power to urban centers [56]. This poses logistical and financial issues for the government and energy authorities, who must ensure that wind energy is efficiently delivered and distributed. Bangladesh’s regulatory system for renewable energy, particularly wind power, has progressed but still confronts challenges. Inconsistencies in policy, insufficient incentives, and lengthy approval processes might deter investors and project developers [57]. A robust and stable policy framework is critical for attracting private investment and creating a favorable environment for wind energy growth.
It is critical to balance the benefits of renewable energy with environmental concerns. Wind farms may have environmental consequences like bird and bat collisions, noise pollution, and aesthetics changes to the area. Furthermore, procuring land for wind farm installations might be difficult due to conflicting land-use needs for agriculture and other reasons. Financing appropriate wind energy projects remains a serious concern [55]. Wind farms’ high upfront capital expenditures and concerns about the return on investment can deter potential investors. This hurdle can be overcome by developing novel finance sources and connecting with international organizations. Wind energy initiatives require public knowledge and social acceptance to succeed. A lack of understanding of the benefits of wind energy and worries about visual impact or interruptions to residents can contribute to opposition. To address these problems, effective community participation and education programs are required. With its vulnerability to sea-level rise and extreme weather events, climate change poses a considerable threat to Bangladesh. Wind energy can help to reduce greenhouse gas emissions, but its long-term impact on climate change mitigation must be carefully assessed. Bangladesh can benefit from international cooperation and partnerships to overcome these difficulties. Collaborative activities can facilitate the development of the wind energy sector by providing access to technology transfer, financial support, and expertise [54].
Analyzing wind energy barriers in Bangladesh with the Analytic Hierarchy Process (AHP) demonstrates an effective tool for dealing with complicated decision-making processes. To begin, AHP allows for the identification of critical aspects influencing wind energy projects, such as environmental effects, economic feasibility, and societal acceptance. This organized method supports stakeholders in assessing the multifaceted nature of wind energy development. Furthermore, AHP supports prioritizing various aspects, allowing decision-makers to consider them in order of priority. In the context of Bangladesh, where land-use conflicts and environmental issues abound, AHP can assist in determining which criterion should be prioritized when designing wind energy projects. This guarantees that resources are used wisely, and the most pressing concerns are addressed [58]. AHP also makes it possible to reduce the negative repercussions of wind energy growth in Bangladesh. AHP promotes transparency and consensus-building by incorporating many stakeholders in decision making, such as local communities and environmental organizations. Furthermore, it aids in evaluating alternative solutions and mitigation methods, ensuring that wind energy development is consistent with sustainable practices and the country’s energy goals [59]. Applying AHP to detect and reduce wind energy development in Bangladesh provides a methodical and comprehensive strategy. It enables decision-makers to make educated choices, prioritize essential aspects, and effectively involve stakeholders, ultimately contributing to the country’s wind energy sector’s long-term success.

3. Methods

3.1. Analysis of Different Barriers to Wind Energy Deployment in Bangladesh

An extensive literature review was performed to identify the main barriers to wind energy deployment in Bangladesh. Science Direct, PubMed, Google Scholar, and Scopus databases were used. Identified barriers are mainly classified as technical, administrative, policy and political, economic, social, and geographic.

3.1.1. Social Barriers (SB)

Social barriers are generally linked to social acceptance while implementing wind energy projects. Three social obstacles were found in Bangladesh after the literature review.

Inadequate Awareness among Consumers (SB1)

Despite being expensive, wind energy offers several benefits, including environmental friendliness and natural replenishment [60]. Unfortunately, many consumers do not have sufficient knowledge of how wind energy operates or works. They often underestimate the contribution of wind energy in fighting climate change and promoting a green future. People need to be made aware of the existence or benefits of wind energy technologies. People in Bangladesh do not have access to information about them [61]. Therefore, more widely disseminated knowledge may boost people’s desire to use wind energy sources.

Customers’ Inability to Spend (SB2)

Wind energy often needs upfront investments, and for many people, the price of purchasing and installing wind turbines and potential grid connectivity costs can be a major barrier. In countries like Bangladesh, where it is considered that wind energy technologies will be implemented in rural and distant locations, the local people have few other means of income [61]. As a result, many people need help to afford wind energy options.

Absence of Knowledge (SB3)

Due to a lack of knowledge, consumers might lose out on cost-saving options and opportunities to curtail their carbon footprint. Additionally, this lack of information might lead to misunderstandings and doubts regarding the dependability and effectiveness of wind energy installations. It is crucial that public organizations, industries, and financial institutions need to know the diverse potential of wind technologies sufficiently. Without this knowledge, proper policies will not be developed. As a result, investors might feel hesitant to invest in the project [62].

3.1.2. Geographic Barrier (GB)

Due to the highly hilly regions, Bangladesh is facing several problems in implementing wind energy projects, and these problems are given below.

Transportation (GB1)

Wind turbines are often concentrated in rural places with restricted road access for transportation. Furthermore, wind turbine components, like blades, nacelles, and towers, are heavy and big. Transporting these components sometimes requires special permits from relevant authorities. Roads and bridges might need to be strengthened or widened to acclimatize the size and weight of these components. As a result, project prices and completion times are increased [63,64].

Disorganized Households (GB2)

Households are scattered in Bangladesh due to the country’s geographic layout. A significant investment is needed to set up and maintain the infrastructure, like transformers and transmission lines. The requirement for voltage regulation and servicing in faraway regions and the continual expansion of the distribution network to meet population growth further increase this expenditure. Owing to this, it becomes expensive to distribute the generated power [64,65].

Land-Use Conflicts (GB3)

Suitable land identification for wind energy implementation is essential for developing this technology. Since Bangladesh is a densely populated country, the available land resources can be considered for agricultural or urbanization purposes. Also, due to the visual impact and potential noise generation, local communities may hesitate about wind projects. Local communities may also hesitate if there is consideration of wind energy development projects in places with significant cultural significance preservation and heritage preservation efforts. As a result, conflicts may arise between local communities and the government about the suitable use of available land [66].

3.1.3. Policy and Political Barriers (PBs)

Political and policy barriers (PBs) define a country’s solidity of political and policy cycles. The government develops guidelines based on a supply-side standpoint. PBs can affect wind energy development in a country. According to a literature review, three barriers exist in Bangladesh.

Inadequately Transparent Decision-Making Process (PB1)

Stakeholders want transparent decision-making mechanisms for wind energy development. This results from the need for open decision-making processes when disseminating information to stakeholders, consistent consultations prior to decisions, and the lack of communication to be addressed [55]. To effectively assess risks and profits, stakeholders require a stable and predictable regulatory environment. The regulatory environment and approval delays may need clarification as unclear decision-making procedures deter investment.

Less Emphasis on Wind (PB2)

Due to its unpredictable nature, wind energy is not considered a practical option at the policy-making stage. The decision to upgrade the existing grid network to incorporate intermittent wind has been hampered by an absence of prioritization, eventually reducing the penetration level [54]. Also, due to the lower priority on wind energy, responsible authorities may allot fewer resources (in terms of both workforce and funding) to assist the growth of this sector. This will lead to slower development of a project, fewer incentives, and limited research and development actions.

Corruption (PB3)

Many developing countries need help executing projects due to corruption in this sector. Bribes need to be provided to complete the project, which, as a result, disheartens stakeholders from discussing social issues regarding projects with the government [67].
Due to corruption and based on self-interest, many regulators and policymakers might choose specific projects or businesses over others, which can result in inadequate usage of resources and hinder the implementation of cost-effective wind energy projects that benefit the environment.

3.1.4. Technical Barrier (TB)

Compared to developed countries, wind energy is relatively new in developing nations. There is a lack of technical resources for research and development (R&D) activities in developing countries. So, developing countries like Bangladesh need to rely on technology transfer from rich and developed countries.

Lack of Research and Development (R&D) (TB1)

R&D is needed to reduce costs and solve technical issues. It is also necessary to make the wind energy more competitive with conventional fuels. With R&D funding, nations or regions can avoid being left behind in the worldwide market for wind energy. Without R&D, these nations may be at a competitive disadvantage compared to regions producing cutting-edge wind energy solutions. R&D is also needed to solve many problems like the intermittency of wind energy. But the practice of R&D is limited in Bangladesh due to constraints in financial and human resources [68].

Unpredictable Supply (TB2)

Renewable energy technologies are less reliable than conventional energy sources [69]. Also, supply and demand must be balanced for electricity systems to remain dependable and stable. The unpredictable nature of wind energy can make it difficult to balance supply and demand, particularly when wind conditions change quickly.
For example, the generation of power fluctuates due to changing wind conditions [54,70,71]. When the share of wind power is increased, the power system faces electrochemical oscillations and frequency deviations [72,73,74]. This problem can be amplified during storms as the wind speed constantly fluctuates [75,76].

Risks Associated with Technology (TB3)

Risk plays a significant role in the development of any project (including wind) [77]. Wind turbines can face several problems, like gearbox failure, DC offset, control blade integrity, tracking, and wear and tear due to exposure in harsh environments [75,78]. But wind projects are generally located in remote places without a skilled labor force. As a result, maintenance and repair work becomes delayed. Due to these technical uncertainties, private companies often prefer to invest in something other than wind energy projects [79].

3.1.5. Economic Barriers (EBs)

EBs play an essential role in the development of wind energy projects. The lack of funds and limited market potential for wind is the primary concern in wind energy deployment. Three EB were identified after literature review.

High Capital Cost (EB1)

The first significant economic barrier to renewable energy development is the high capital cost [80,81,82,83,84]. The cost of energy generation is relatively higher for wind energy than other conventional energy sources because wind energy technology is comparatively new in developing countries [85]. Limited funding can force project delays, resulting in failed chances to produce energy and advance renewable energy goals. Also, high capital costs make attracting and securing favorable investments challenging.

Subsidy to Fossil Fuels (EB2)

Subsidies to conventional fuels make fossil fuels cheaper than wind energy. As a result, wind energy cannot compete with conventional sources [86]. This will slow down the growth of wind energy and can discourage potential developers and investors from investing in it. Also, subsidies to fossil fuels can make people believe that these conventional forms of energy are necessary and dependable, thus undermining public support for wind energy.

Small Market Size (EB3)

The market size has a significant influence on investment. There needs to be more markets for wind energy in Bangladesh. Due to this, the private sector feels more interested in investing in something other than wind energy [55]. Also, without more market opportunities, there will be less demand for this technology. As a result, the price will not go down, eventually causing higher prices for equipment and installation. In addition, a smaller market size means there is limited revenue potential compared to investment costs. Investors and developers often look for a large market to maximize project returns.

3.1.6. Administrative Barriers (ABs)

More than policy is needed to ensure the efficient delivery of government services. An effectual administrative system is critical for reaching the development target. Three ABs were observed after the literature review for Bangladesh.

Absence of Competent Workforce (AB1)

Wind turbines are generally located in rural places with a need for more skilled human resources [87]. The project will be completed with skilled workers, and the completed tasks may be up to the standard. Moreover, with a skilled workforce, the project may be completed without any safety incidents. When there is a shortage of professional labor, the cost of acquiring and keeping qualified personnel can climb dramatically, which can raise the entire cost of wind energy projects.

Absence of Effective Coordination (AB2)

The same task may occasionally be performed by different government agencies or other development organizations; this is known as work duplication or work area overlap. The permitting procedure of wind energy projects involves different organizations, which delays project implementation. Therefore, effective coordination is the key to achieving development in this sector [88]. Engaging and addressing the public’s concerns regarding land use conflicts and other issues is also necessary, and failure to effectively coordinate can lead to public opposition.

Inadequate or Absence of Clear Regulations, Standards, and Policies (AB3)

Clear and supportive regulatory frameworks are crucial to offer direction, ensure stability, and lure investment into the wind energy sector. When there is an absence or lack of regulatory framework, developers need more precise direction on navigating the legal and regulatory prerequisites. Also, without technical, economic, or environmental standards, developers may not know if their project meets the required criteria [89]. This can cause delays in the project’s implementation. The barriers and sub-barriers identified in this study can be found in Figure 6.

3.2. Analytic Hierarchy Process (AHP)

In decision-making problems, AHP is widely used to facilitate complex problems by decomposing them into a hierarchical framework [90]. At the beginning of the AHP analysis, the research goal stands at the top of the hierarchy, followed by the criteria and sub-criteria. The decision alternatives are put at the last level, and a pairwise comparison is performed at each step or level. Our analysis aims to identify the barriers and sub-barriers of wind energy development in Bangladesh. A literature review was conducted to identify these barriers. After that, a questionnaire was sent to experts to obtain their opinions on these barriers. Experts have to meet the minimum educational requirement of a master’s degree. Respondents or experts are chosen from various fields, such as academics, engineers, etc. Their argument is based on Satty’s fundamental scale, which is used to perform pairwise comparisons [91]. The scale is given in Table 3.
Here, a score of 1 shows that both activities contribute equally to the overall objective, while a score of 9 suggests that one activity is intensely preferred. The consistency ratio (CR) is calculated to evaluate the consistency of the outcomes. The CR is the ratio between the consistency index (CI) and random index (RI). The RI values for up to ten criteria can be found in Table 4.
In AHP, a CR up to 0.1 is acceptable. The optimal CR value is 0 [38]. The necessary equations to find the CR can be found below [39].
C I = λ m a x n n 1
C R = C I R I
where λ m a x is the eigenvalue of each matrix and n is the number of criteria. Details regarding the AHP can be found in [40,91]. Similarly, the above procedures are followed and executed for the sub-barriers. The flowchart of the adopted methodology can be found in Figure 7.

4. Results and Discussion

4.1. Ranking of Barriers

First, a pairwise comparison matrix is created to determine the main barrier, which can be found in the table. Expert opinions have been used to create the matrix. Then, AHP was used to determine the weight. After completing the pairwise comparison, normalization was carried out. After that, the eigenvalue and consistency ratio were calculated. A pairwise comparison of the main barriers can be found in Table 5.
From the table, it can be seen that all the diagonal values (from top left to bottom right) are one. This means that each criterion or barrier has equal importance to itself. Also, according to experts, policy barriers are three times more influential than social barriers. Similar analyses can also be conducted for other barriers. Table 6 denotes the priority weight calculations of barriers using the AHP. Barriers are ranked according to the calculated weight, and TB stands at the top, followed by AB. The CR is found to be 0.06, which is less than 0.1. This proves that the data are consistent. Chisale and Lee determined the renewable energy adoption barriers for Malawi of Africa and found that economic barriers are the most significant [37]. The same outcome was also observed for Nepal and Pakistan [38,39]. These studies found that political and economic barriers are the top two barriers hindering renewable energy (RE) technology adoption. However, Al Asbahi et al. ranked political and technical barriers as the highest barriers for Yemen decreasing the growth of RE adoption [48]. Pathak et al. also used MCDM analysis to find that policy and technical barriers play a crucial role in renewable energy adoption in India [36].

4.2. Ranking of Sub-Barriers

Figure 8 states the local weights of sub-barriers. Considering the technical category, it is found that TB3 (risk associated with technology) stands in first compared to TB2 and TB1. TB3 is 167% higher than TB1 and 43% higher than TB2. Similarly, for economic barriers, EB1 has the highest weight (0.64), followed by EB2 (0.26) and EB3 (0.1). The consistency ratio for all sub-barriers was also found to be consistent. PB3 > PB2 > PB1 is observed among political sub-barriers, while SB2 > SB1 > SB3 is found for social barriers. For social and geographical sub-barriers, SB3 > SB2 > SB1 and GB1 > GB3 > GB2 are observed, respectively. The overall ranking of the eighteen sub-barriers can be found in Table 7. The global weight of these sub-barriers can be found after multiplying the weight with the local weights.

4.3. Discussion

According to experts, the cluster of technological obstacles is what is preventing Bangladesh from developing its wind energy industry. These barriers are formidable walls that prevent the country’s expansion and integration of this renewable energy source. Parallel to this, there are administrative barriers that, despite being considerably less overpowering, still significantly add to the difficulties this industry faces. Looking more closely at the technical obstacles, the pervasive uncertainty about the efficiency and dependability of wind energy technology emerges as a glaring problem that looms large. Since most wind turbines are installed in remote or off-grid areas, providing the local community with the necessary knowledge and expertise becomes crucial. This knowledge can be conveyed through focused initiatives that increase awareness and convey crucial understanding. Therefore, it is crucial to set up thorough training facilities that enable people in these communities to comprehend the complexities of wind energy and actively interact with and overcome the obstacles preventing its advancement. In addition to raising knowledge and understanding, integrating wind energy systems with other sustainable technologies like solar, storage, and biogas is an intelligent way to overcome these obstacles [93,94,95,96]. The symbiotic combination of resources effectively solves the unpredictable nature that frequently afflicts the energy supply. A more stable and dependable energy infrastructure can be created by connecting these various energy sources, removing many of the technological barriers currently preventing the widespread use of wind energy. Technological complexities frequently cause problems, and wind energy is no exception. The development of straightforward yet efficient algorithms and diagnostic techniques is crucial for resolving these technical difficulties. To address intermittency and improve grid stability, integrating energy storage solutions to make wind energy a more dependable power source can be viable. Some of these technologies are cheap, and their resources are abundant in nature [97]. Another helpful option is to allocate investment in improving the national grid’s infrastructure so that renewable energy sources like wind can be integrated. Voltage source grid-connected inverters are often employed to connect the renewable energy system to the utility grid. However, these also have stability issues, such as harmonic and frequency instability [98,99]. Utilizing innovative grid technologies to optimize power distribution, reduce losses, and provide a steady supply while managing the fluctuation of wind energy can be helpful too. Due to the high level of complexity and involvement of many stakeholders in wind projects, risk assessment should also be carefully carried out in the planning stage to tackle all the challenges posed by numerous risks [100]. Businesses investing in R&D for wind energy projects should be incentivized through tax credits and deductions to remove R&D barriers. Different techniques and control algorithms can be used to tackle tracking and DC offset problems [101,102]. By doing this, the challenges posed by difficulties and technical obstacles can be methodically removed, creating an atmosphere better suited to the smooth operation of wind energy systems [78].
Focusing on economic obstacles, these obstacles can seriously limit the development of wind energy projects. An all-encompassing strategy is essential to moving beyond these obstacles. Financial incentives, grants, and loans can be crucial in reducing the financial barriers that frequently turn away potential investors and stakeholders [89,90]. To spread the benefits to disadvantaged groups in society, special attention must also be paid to subsidizing the adoption of wind energy [91]. The shift to wind energy can be driven by redirecting subsidies away from fossil fuels and towards cleaner, more sustainable energy sources like wind. Economic pressures can be lessened, making wind energy a more viable and affordable option for a broader range of people. Government involvement is crucial for creating an atmosphere that will encourage the growth of wind energy. Establishing clear and open wind energy policies that draw stakeholders and offer a planned framework for development is essential to this [90]. To further ensure a streamlined and accelerated road toward wind energy integration, good collaboration between public and private entities is crucial to avoiding obstacles in project planning and execution, as well as high capital costs. The government should promote the usage of wind energy in various sectors, for instance, transportation, heating, or cooling, to create a large market. Geographic barriers are also problematic, especially in a country like Bangladesh with a diversified topography. Two concurrent tactics should be used to address this. First and foremost, training local workers in the relevant skills is crucial. This removes a further barrier by creating local employment possibilities and guaranteeing that technical skills are easily accessible. A robust transportation infrastructure is required to navigate the nation’s many terrains to exploit wind energy resources regardless of geographic limitations [89,91]. Comprehensive site assessment and selection should be made to reduce conflicts with other land users. Zoning laws and land-use guidelines should be created to identify locations for the growth of wind energy while safeguarding other land uses. Decentralized wind farms can be considered as a potential solution for disorganized households. The need for extensive transmission lines is reduced since these small farms can feed power directly into the local grid. It is critical to execute a multifaceted strategy to lessen the barriers of inadequate awareness among consumers, customers’ financial constraints, and the absence of knowledge. This includes public awareness campaigns to familiarize consumers with wind energy benefits, financial incentives to make wind technology reasonable, and developing user-friendly resources and community engagement measures to improve consumer understanding and faith in wind energy solutions. Unhindered wind energy expansion in Bangladesh requires a coherent and comprehensive strategy to overcome various obstacles. These obstacles can be removed through focused campaigns, education and training programs, regulatory changes, financial incentives, and tactical coordination. Bangladesh’s policymakers and stakeholders may play an essential role in boosting wind energy development by making financial incentives available for wind energy projects, streamlining the process for obtaining wind energy venture permits, increasing public understanding of the advantages of wind energy, investing in wind energy technology research and development, and grid expansion and modernization. Thus, a path toward a future powered by wind energy may be identified, encouraging Bangladesh to have a sustainable and prosperous energy environment.

5. Conclusions

Wind energy can help Bangladesh to achieve sustainable development. Although it has several benefits, this technology has yet to be utilized properly in Bangladesh. Several obstacles are responsible for the slow adoption of this technology in Bangladesh. This study determines these barriers and uses AHP to rank them. This research found that uncertainties associated with this technology (0.2217) and unpredictable supply (0.1550) are the main barriers to developing this sector. This research is vital since it systematically identifies challenges and outlines guiding ideas for stakeholders interested in and working on wind energy. Also, this study will give academics and decision-makers a road map for addressing the energy crisis and problems with wind energy in Bangladesh.
There are several limitations in this analysis. The opinions and expertise of the selected experts who took part in the survey significantly impacted the study’s conclusions. The number of specialists in the sample may be biased, and their perspectives may not accurately reflect all of the sector’s stakeholders. Data accessibility may have been restricted, particularly for some sub-barriers. This could have impacted the accuracy of the AHP analysis for particular factors. The AHP methodology requires subjective judgements in assigning weights and priorities to criteria and sub-criteria. While every attempt was made to assure objectivity and consistency throughout the process, some subjectivity may still occur.
For the future direction of the study, conducting a comprehensive financial analysis to overcome different barriers and expanding the expert panel to include different stakeholder groups can be considered. Moreover, a comparative analysis of various approaches to wind energy development could be conducted in Bangladesh, and other similar socio-economic and environmental contexts could be taken into account to better understand the challenges and opportunities towards sustainable development goals.

Author Contributions

Conceptualization, P.D. and S.M.M.A.; methodology, P.D.; formal analysis, P.D. and S.M.M.A.; investigation, P.D.; data curation P.D. and S.M.M.A.; writing—original draft preparation, P.D. and S.M.M.A.; writing—review and editing, R.H.A., A.A.M. and M.T.I.; visualization, S.M.M.A. and M.S.H.L.; supervision, M.S.H.L.; project administration, S.U.; funding acquisition, S.U. All authors have read and agreed to the published version of the manuscript.


This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R79), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.


Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R79), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Conflicts of Interest

The authors declare no conflict of interest.

Correction Statement

This article has been republished with a minor correction to the Funding, Data Availability Statement, and Acknowledgements. These changes do not affect the scientific content of the article.


BAECBangladesh Atomic Energy Commission
BCASBangladesh Center for Advanced Studies
BCSIRBangladesh Council for Scientific and Industrial Research
BWMBest Worst Method
CPGCBLCoal Power Generation Company of Bangladesh Limited
CRITICCriteria Importance through Intercriteria Correlation
DEMATELDecision making trial and evaluation laboratory
EGCBElectricity Generation Company of Bangladesh
ETSUEnergy Technical Support Unit
FEDIFujian Electric Power Survey and Design Institute
GIZGerman Agency for International Cooperation
IFRDInstitute of Fuel Research and Development
LGEDLocal Government Engineering Department
NWPGCLNorthwest Power Generation Company Limited
NRELNational Renewable Energy Laboratory
TERNATechnical Expertise for Renewable Application Project
TOPSISTechnique for Order of Preference by Similarity to Ideal Solution
USAIDUnited States Agency for International Development
WERMWind Energy Resource Mapping Project
WESTWind Energy Study Project
WRAPWind Resource Assessment Program


  1. Yin, Z.; Liu, Z.; Liu, X.; Zheng, W.; Yin, L. Urban heat islands and their effects on thermal comfort in the US: New York and New Jersey. Ecol. Indic. 2023, 154, 110765. [Google Scholar] [CrossRef]
  2. Jiang, J.; Zhang, L.; Wen, X.; Valipour, E.; Nojavan, S. Risk-based performance of power-to-gas storage technology integrated with energy hub system regarding downside risk constrained approach. Int. J. Hydrogen Energy 2022, 47, 39429–39442. [Google Scholar] [CrossRef]
  3. Cai, T.; Dong, M.; Chen, K.; Gong, T. Methods of participating power spot market bidding and settlement for renewable energy systems. Energy Rep. 2022, 8, 7764–7772. [Google Scholar] [CrossRef]
  4. Aghaloo, K.; Ali, T.; Chiu, Y.R.; Sharifi, A. Optimal site selection for the solar-wind hybrid renewable energy systems in Bangladesh using an integrated GIS-based BWM-fuzzy logic method. Energy Convers. Manag. 2023, 283, 116899. [Google Scholar] [CrossRef]
  5. Ban, Y.; Liu, X.; Yin, Z.; Li, X.; Yin, L.; Zheng, W. Effect of urbanization on aerosol optical depth over Beijing: Land use and surface temperature analysis. Urban Clim. 2023, 51, 101655. [Google Scholar] [CrossRef]
  6. Azzam, A.; El Zayat, M.; Marzouk, M. Integrated approach for sustainability assessment in power plant projects using Building Information Modeling. Energy Sustain. Dev. 2022, 66, 222–237. [Google Scholar] [CrossRef]
  7. Yue, H.; Worrell, E.; Crijns-Graus, W.; Zhang, S. The potential of industrial electricity savings to reduce air pollution from coal-fired power generation in China. J. Clean. Prod. 2021, 301, 126978. [Google Scholar] [CrossRef]
  8. Li, H.; Jia, M.; Zhang, X.; Wang, Z.; Liu, Y.; Yang, J.; Yang, B.; Sun, Y.; Wang, H.; Ma, H. Laboratory investigation on fumes generated by different modified asphalt binders. Transp. Res. D Transp. Environ. 2023, 121, 103828. [Google Scholar] [CrossRef]
  9. Han, Y.; Li, H.; Liu, J.; Xie, N.; Jia, M.; Sun, Y.; Wang, S. Life cycle carbon emissions from road infrastructure in China: A region-level analysis. Transp. Res. D Transp. Environ. 2023, 115, 103581. [Google Scholar] [CrossRef]
  10. Chen, J.; Liu, Z.; Yin, Z.; Liu, X.; Li, X.; Yin, L.; Zheng, W. Predict the effect of meteorological factors on haze using BP neural network. Urban Clim. 2023, 51, 101630. [Google Scholar] [CrossRef]
  11. Karmaker, A.K.; Rahman, M.M.; Hossain, M.A.; Ahmed, M.R. Exploration and corrective measures of greenhouse gas emission from fossil fuel power stations for Bangladesh. J. Clean. Prod. 2020, 244, 118645. [Google Scholar] [CrossRef]
  12. Sun, H.; Mohsin, M.; Alharthi, M.; Abbas, Q. Measuring environmental sustainability performance of South Asia. J. Clean. Prod. 2020, 251, 119519. [Google Scholar] [CrossRef]
  13. Zhang, Z.; Altalbawy, F.M.A.; Al-Bahrani, M.; Riadi, Y. Regret-based multi-objective optimization of carbon capture facility in CHP-based microgrid with carbon dioxide cycling. J. Clean. Prod. 2023, 384, 135632. [Google Scholar] [CrossRef]
  14. Oxford Analytica. Ukraine invasion threatens oil trade amid volatility. In Emerald Expert Briefings; Oxford Analytica: Oxford, UK, 2022. [Google Scholar] [CrossRef]
  15. Su, C.W.; Khan, K.; Umar, M.; Zhang, W. Does renewable energy redefine geopolitical risks? Energy Policy 2021, 158, 112566. [Google Scholar] [CrossRef]
  16. Bogdanov, D.; Ram, M.; Aghahosseini, A.; Gulagi, A.; Oyewo, A.S.; Child, M.; Caldera, U.; Sadovskaia, K.; Farfan, J.; De Souza Noel Simas Barbosa, L.; et al. Low-cost renewable electricity as the key driver of the global energy transition towards sustainability. Energy 2021, 227, 120467. [Google Scholar] [CrossRef]
  17. Al-Dousari, A.; Al-Nassar, W.; Al-Hemoud, A.; Alsaleh, A.; Ramadan, A.; Al-Dousari, N.; Ahmed, M. Solar and wind energy: Challenges and solutions in desert regions. Energy 2019, 176, 184–194. [Google Scholar] [CrossRef]
  18. Gharaati, M.; Xiao, S.; Wei, N.J.; Martínez-Tossas, L.A.; Dabiri, J.O.; Yang, D. Large-eddy simulation of helical-and straight-bladed vertical-axis wind turbines in boundary layer turbulence. J. Renew. Sustain. Energy 2022, 14, 053301. [Google Scholar] [CrossRef]
  19. Population of Bangladesh 2041— Available online: (accessed on 29 April 2023).
  20. Bangladesh Population 1950–2023|MacroTrends. Available online: (accessed on 29 April 2023).
  21. A Recipe for a Developed Country|The Financial Express. Available online: (accessed on 29 April 2023).
  22. Babu, M.T.; Nei, H.; Kowser, M.A. Prospects and Necessity of Wind Energy in Bangladesh for the Forthcoming Future. J. Inst. Eng. (India) Ser. C 2022, 103, 913–929. [Google Scholar] [CrossRef]
  23. Das, P.; Islam, K.S.; Uddin, S.M.M. Biogas production potential from animal farm waste in Bangladesh: Case studies of two selected farms. Environ. Prog. Sustain. Energy 2023, 42, e14214. [Google Scholar] [CrossRef]
  24. Das, P.; Islam, K.S.; Uddin, S.M.M. Unlocking the resource potential of livestock waste in Bangladesh. Environ. Prog. Sustain. Energy 2023, 42, e14063. [Google Scholar] [CrossRef]
  25. Das, N.K.; Chakrabartty, J.; Dey, M.; Gupta, A.K.S.; Matin, M.A. Present energy scenario and future energy mix of Bangladesh. Energy Strategy Rev. 2020, 32, 100576. [Google Scholar] [CrossRef]
  26. Emissions—Global Energy & CO2 Status Report 2019—Analysis—IEA. Available online: (accessed on 30 April 2023).
  27. THE 17 GOALS|Sustainable Development. Available online: (accessed on 30 April 2023).
  28. Sustainable and Renewable Energy Development Authority (SREDA)-Power Division, Ministry of Power, Energy & Mineral Resources. Available online: (accessed on 6 September 2023).
  29. Mazumder, P.; Gupta, V.D. Prospects of wind energy in Chittagong. In Proceedings of the 2012 International Conference on Informatics, Electronics and Vision, ICIEV 2012, Dhaka, Bangladesh, 18–19 May 2012; pp. 753–758. [Google Scholar] [CrossRef]
  30. Mukut, A.N.M.M.I. Prospect of Wind Energy in the Coastal Belt of Bangladesh. Available online: (accessed on 30 April 2023).
  31. Amin, M.T. Prospects of wind energy in Bangladesh. Int. J. Renew. Energy Res. 2015, 2, 213–218. [Google Scholar]
  32. Azad, A.K.; Rasul, M.G.; Islam, R.; Shishir, I.R. Analysis of Wind Energy Prospect for Power Generation by Three Weibull Distribution Methods. Energy Procedia 2015, 75, 722–727. [Google Scholar] [CrossRef]
  33. Rahman, M.F. Prospects of wind energy in Bangladesh. Renew. Energy 1996, 9, 806–809. [Google Scholar] [CrossRef]
  34. Mondal, M.A.H.; Denich, M. Assessment of renewable energy resources potential for electricity generation in Bangladesh. Renew. Sustain. Energy Rev. 2010, 14, 2401–2413. [Google Scholar] [CrossRef]
  35. Jacobson, M.; Draxl, C.; Jimenez, T.; O’Neill, B.; Capozzola, T.; Lee, J.A.; Vandenberghe, F.; Haupt, S.E. Assessing the Wind Energy Potential in Bangladesh: Enabling Wind Energy Development with Data Products; National Renewable Energy Lab.: Golden, CO, USA, 2018. [Google Scholar] [CrossRef]
  36. Pathak, S.K.; Sharma, V.; Chougule, S.S.; Goel, V. Prioritization of barriers to the development of renewable energy technologies in India using integrated Modified Delphi and AHP method. Sustain. Energy Technol. Assess. 2022, 50, 101818. [Google Scholar] [CrossRef]
  37. Chisale, S.W.; Lee, H.S. Evaluation of barriers and solutions to renewable energy acceleration in Malawi, Africa, using AHP and fuzzy TOPSIS approach. Energy Sustain. Dev. 2023, 76, 101272. [Google Scholar] [CrossRef]
  38. Solangi, Y.A.; Longsheng, C.; Shah, S.A.A. Assessing and overcoming the renewable energy barriers for sustainable development in Pakistan: An integrated AHP and fuzzy TOPSIS approach. Renew. Energy 2021, 173, 209–222. [Google Scholar] [CrossRef]
  39. Ghimire, L.P.; Kim, Y. An analysis on barriers to renewable energy development in the context of Nepal using AHP. Renew. Energy 2018, 129, 446–456. [Google Scholar] [CrossRef]
  40. Yadav, P.; Yadav, S.; Singh, D.; Kapoor, R.M.; Giri, B.S. An analytical hierarchy process based decision support system for the selection of biogas up-gradation technologies. Chemosphere 2022, 302, 134741. [Google Scholar] [CrossRef]
  41. Shahzad, K.; Lu, B.; Abdul, D. Entrepreneur barrier analysis on renewable energy promotion in the context of Pakistan using Pythagorean fuzzy AHP method. Environ. Sci. Pollut. Res. 2022, 29, 54756–54768. [Google Scholar] [CrossRef] [PubMed]
  42. Nasirov, S.; Silva, C.; Agostini, C.A. Assessment of barriers and opportunities for renewable energy development in Chile. Energy Sources Part B Econ. Plan. Policy 2016, 11, 150–156. [Google Scholar] [CrossRef]
  43. Dhingra, T.; Sengar, A.; Sajith, S. A fuzzy analytic hierarchy process-based analysis for prioritization of barriers to offshore wind energy. J. Clean. Prod. 2022, 345, 131111. [Google Scholar] [CrossRef]
  44. Asante, D.; Ampah, J.D.; Afrane, S.; Adjei-Darko, P.; Asante, B.; Fosu, E.; Dankwah, D.A.; Amoh, P.O. Prioritizing strategies to eliminate barriers to renewable energy adoption and development in Ghana: A CRITIC-fuzzy TOPSIS approach. Renew. Energy 2022, 195, 47–65. [Google Scholar] [CrossRef]
  45. Sadat, S.A.; Fini, M.V.; Hashemi-Dezaki, H.; Nazififard, M. Barrier analysis of solar PV energy development in the context of Iran using fuzzy AHP-TOPSIS method. Sustain. Energy Technol. Assess. 2021, 47, 101549. [Google Scholar] [CrossRef]
  46. Mostafaeipour, A.; Alvandimanesh, M.; Najafi, F.; Issakhov, A. Identifying challenges and barriers for development of solar energy by using fuzzy best-worst method: A case study. Energy 2021, 226, 120355. [Google Scholar] [CrossRef]
  47. Gündoğdu, F.K. Analyzing critical barriers of smart energy city in Turkey based on two-dimensional uncertainty by hesitant z-fuzzy linguistic terms. Eng. Appl. Artif. Intell. 2022, 113, 104935. [Google Scholar] [CrossRef]
  48. Al Asbahi, A.A.M.H.; Fang, Z.G.; Chandio, Z.A.; Tunio, M.K.; Ahmed, J.; Abbas, M. Assessing barriers and solutions for Yemen energy crisis to adopt green and sustainable practices: A fuzzy multi-criteria analysis. Environ. Sci. Pollut. Res. 2020, 27, 36765–36781. [Google Scholar] [CrossRef]
  49. Numata, M.; Sugiyama, M.; Mogi, G. Barrier Analysis for the Deployment of Renewable-Based Mini-Grids in Myanmar Using the Analytic Hierarchy Process (AHP). Energies 2020, 13, 1400. [Google Scholar] [CrossRef]
  50. Miskat, M.I.; Sarker, P.; Chowdhury, H.; Chowdhury, T.; Rahman, M.S.; Hossain, N.; Chowdhury, P.; Sait, S.M. Current Scenario of Solar Energy Applications in Bangladesh: Techno-Economic Perspective, Policy Implementation, and Possibility of the Integration of Artificial Intelligence. Energies 2023, 16, 1494. [Google Scholar] [CrossRef]
  51. Bangladesh: Energy Country Profile—Our World in Data. Available online: (accessed on 3 May 2023).
  52. The Electricity Sector in Bangladesh: What Comes Next? Available online:,of%20the%20power%20generation%20capacity (accessed on 6 September 2023).
  53. Shaikh, M.A.; Chowdhury, K.M.A.; Sen, S.; Islam, M.M. Potentiality of wind power generation along the Bangladesh coast. AIP Conf. Proc. 2017, 1919, 20035. [Google Scholar] [CrossRef]
  54. Karim, M.E.; Karim, R.; Islam, M.T.; Muhammad-Sukki, F.; Bani, N.A.; Muhtazaruddin, M.N. Renewable Energy for Sustainable Growth and Development: An Evaluation of Law and Policy of Bangladesh. Sustainability 2019, 11, 5774. [Google Scholar] [CrossRef]
  55. Mondal, M.A.H.; Kamp, L.M.; Pachova, N.I. Drivers, barriers, and strategies for implementation of renewable energy technologies in rural areas in Bangladesh—An innovation system analysis. Energy Policy 2010, 38, 4626–4634. [Google Scholar] [CrossRef]
  56. Sharma, S.; Sinha, S. Indian wind energy & its development-policies-barriers: An overview. Environ. Sustain. Indic. 2019, 2, 100003. [Google Scholar] [CrossRef]
  57. Shafiullah, G.M.; Arif, M.T.; Oo, A.M.T. Mitigation strategies to minimize potential technical challenges of renewable energy integration. Sustain. Energy Technol. Assess. 2018, 25, 24–42. [Google Scholar] [CrossRef]
  58. Aya, Z.; Samanlioglu, F.; Büyüközkan, G. A fuzzy QFD approach to determine supply chain management strategies in the dairy industry. J. Intell. Manuf. 2013, 24, 1111–1122. [Google Scholar] [CrossRef]
  59. Erol, I.; Ar, I.M.; Peker, I.; Searcy, C. Alleviating the Impact of the Barriers to Circular Economy Adoption Through Blockchain: An Investigation Using an Integrated MCDM-based QFD With Hesitant Fuzzy Linguistic Term Sets. Comput. Ind. Eng. 2022, 165, 107962. [Google Scholar] [CrossRef]
  60. Painuly, J.P. Barriers to renewable energy penetration; a framework for analysis. Renew. Energy 2001, 24, 73–89. [Google Scholar] [CrossRef]
  61. Challenges in the Development of VRE in Bangladesh|U.S. Agency for International Development. Available online: (accessed on 7 May 2023).
  62. Mahmud, H.; Roy, J. Barriers to Overcome in Accelerating Renewable Energy Penetration in Bangladesh. Sustainability 2021, 13, 7694. [Google Scholar] [CrossRef]
  63. Surendra, K.C.; Khanal, S.K.; Shrestha, P.; Lamsal, B. Current status of renewable energy in Nepal: Opportunities and challenges. Renew. Sustain. Energy Rev. 2011, 15, 4107–4117. [Google Scholar] [CrossRef]
  64. Mooney, M.; Maclaurin, G. Transportation of Large Wind Components: A Review of Existing Geospatial Data; National Renewable Energy Lab.: Golden, CO, USA, 2016. [Google Scholar] [CrossRef]
  65. Javadi, F.S.; Rismanchi, B.; Sarraf, M.; Afshar, O.; Saidur, R.; Ping, H.W.; Rahim, N.A. Global policy of rural electrification. Renew. Sustain. Energy Rev. 2013, 19, 402–416. [Google Scholar] [CrossRef]
  66. Jung, C.; Schindler, D. Distance to power grids and consideration criteria reduce global wind energy potential the most. J. Clean. Prod. 2021, 317, 128472. [Google Scholar] [CrossRef]
  67. Sovacool, B.K.; Bulan, L.C. Energy security and hydropower development in Malaysia: The drivers and challenges facing the Sarawak Corridor of Renewable Energy (SCORE). Renew. Energy 2012, 40, 113–129. [Google Scholar] [CrossRef]
  68. Tarik-Ul-Islam, M.; Ferdousi, S. Renewable Energy Development-Challenges for Bangladesh. Available online: (accessed on 7 May 2023).
  69. Ma, X.; Wan, Y.; Wang, Y.; Dong, X.; Shi, S.; Liang, J.; Zhao, Y.; Mi, H. Multi-Parameter Practical Stability Region Analysis of Wind Power System Based on Limit Cycle Amplitude Tracing. IEEE Trans. Energy Convers. 2023, 1–13. [Google Scholar] [CrossRef]
  70. Chen, H.; Wu, H.; Kan, T.; Zhang, J.; Li, H. Low-carbon economic dispatch of integrated energy system containing electric hydrogen production based on VMD-GRU short-term wind power prediction. Int. J. Electr. Power Energy Syst. 2023, 154, 109420. [Google Scholar] [CrossRef]
  71. Li, M.; Yang, M.; Yu, Y.; Lee, W.J. A Wind Speed Correction Method Based on Modified Hidden Markov Model for Enhancing Wind Power Forecast. IEEE Trans. Ind. Appl. 2022, 58, 656–666. [Google Scholar] [CrossRef]
  72. Liao, K.; Lu, D.; Wang, M.; Yang, J. A Low-Pass Virtual Filter for Output Power Smoothing of Wind Energy Conversion Systems. IEEE Trans. Ind. Electron. 2022, 69, 12874–12885. [Google Scholar] [CrossRef]
  73. Liang, J.; Kato, B.; Wang, Y. Constructing simplified models for dynamic analysis of monopile-supported offshore wind turbines. Ocean Eng. 2023, 271, 113785. [Google Scholar] [CrossRef]
  74. Ma, Y.; Zhu, D.; Zhang, Z.; Zou, X.; Hu, J.; Kang, Y. Modeling and Transient Stability Analysis for Type-3 Wind Turbines Using Singular Perturbation and Lyapunov Methods. IEEE Trans. Ind. Electron. 2023, 70, 8075–8086. [Google Scholar] [CrossRef]
  75. Huang, S.; Huang, M.; Lyu, Y. Seismic Performance Analysis of a Wind Turbine with a Monopile Foundation Affected by Sea Ice Based on a Simple Numerical Method. Eng. Appl. Comput. Fluid Mech. 2021, 15, 1113–1133. Available online: http://www.Tandfonline.Com/Action/AuthorSubmission?JournalCode=tcfm20&page=instructions (accessed on 6 September 2023). [CrossRef]
  76. Chen, W.; Liu, W.; Liang, H.; Jiang, M.; Dai, Z. Response of storm surge and M2 tide to typhoon speeds along coastal Zhejiang Province. Ocean Eng. 2023, 270, 113646. [Google Scholar] [CrossRef]
  77. Liu, Z.; Li, H.; Hou, K.; Xu, X.; Jia, H.; Zhu, L.; Mu, Y. Risk assessment and alleviation of regional integrated energy system considering cross-system failures. Appl. Energy 2023, 350, 121714. [Google Scholar] [CrossRef]
  78. Huang, N.; Chen, Q.; Cai, G.; Xu, D.; Zhang, L.; Zhao, W. Fault Diagnosis of Bearing in Wind Turbine Gearbox under Actual Operating Conditions Driven by Limited Data with Noise Labels. IEEE Trans. Instrum. Meas. 2021, 70, 1–10. [Google Scholar] [CrossRef]
  79. Kahraman, C.; Kaya, I.; Cebi, S. A comparative analysis for multiattribute selection among renewable energy alternatives using fuzzy axiomatic design and fuzzy analytic hierarchy process. Energy 2009, 34, 1603–1616. [Google Scholar] [CrossRef]
  80. Chowdhury, H.; Chowdhury, T.; Miskat, M.I.; Hossain, N.; Chowdhury, P.; Sait, S.M. Potential of biogas and bioelectricity production from Rohingya camp in Bangladesh: A case study. Energy 2021, 214, 118837. [Google Scholar] [CrossRef]
  81. Chowdhury, H.; Chowdhury, T.; Rahman, M.S.; Masrur, H.; Senjyu, T. A simulation study of techno-economics and resilience of the solar PV irrigation system against grid outages. Environ. Sci. Pollut. Res. 2022, 29, 64846–64857. [Google Scholar] [CrossRef]
  82. Chowdhury, H.; Chowdhury, T.; Sharifi, A.; Corkish, R.; Sait, S.M. Role of Biogas in Achieving Sustainable Development Goals in Rohingya Refugee Camps in Bangladesh. Sustainability 2022, 14, 11842. [Google Scholar] [CrossRef]
  83. Islam, K.S.; Hasan, S.; Chowdhury, T.; Chowdhury, H.; Sait, S.M. Outage Survivability Investigation of a PV/Battery/CHP System in a Hospital Building in Texas. Sustainability 2022, 14, 14965. [Google Scholar] [CrossRef]
  84. Chowdhury, H.; Chowdhury, T.; Hossain, N.; Chowdhury, P.; Mascarenhas, J.D.S.; Bhuiya, M.M.K. Energy, emission, profitability, and sustainability analyses of a grid-connected solar power plant proposed in airport sites of Bangladesh: A case study. Environ. Sci. Pollut. Res. 2021, 28, 61369–61379. [Google Scholar] [CrossRef]
  85. Akram, M.W.; Arefin, M.A.; Nusrat, A. Prospect of green power generation as a solution to energy crisis in Bangladesh. Energy Syst. 2021, 13, 749–787. [Google Scholar] [CrossRef]
  86. Alam, M.; Rahman, A.; Eusuf, M. Diffusion potential of renewable energy technology for sustainable development: Bangladeshi experience. Energy Sustain. Dev. 2003, 7, 88–96. [Google Scholar] [CrossRef]
  87. Kumar, D.; Katoch, S.S. Small hydropower development in western Himalayas: Strategy for faster implementation. Renew. Energy 2015, 77, 571–578. [Google Scholar] [CrossRef]
  88. Liu, J.; Zuo, J.; Sun, Z.; Zillante, G.; Chen, X. Sustainability in hydropower development—A case study. Renew. Sustain. Energy Rev. 2013, 19, 230–237. [Google Scholar] [CrossRef]
  89. Diógenes, J.R.F.; Claro, J.; Rodrigues, J.C.; Loureiro, M.V. Barriers to onshore wind energy implementation: A systematic review. Energy Res. Soc. Sci. 2020, 60, 101337. [Google Scholar] [CrossRef]
  90. Nhiavue, Y.; Lee, H.S.; Chisale, S.W.; Cabrera, J.S. Prioritization of Renewable Energy for Sustainable Electricity Generation and an Assessment of Floating Photovoltaic Potential in Lao PDR. Energies 2022, 15, 8243. [Google Scholar] [CrossRef]
  91. Chowdhury, T.; Hasan, S.; Chowdhury, H.; Hasnat, A.; Rashedi, A.; Asyraf, M.R.M.; Hassan, M.Z.; Sait, S.M. Sizing of an Island Standalone Hybrid System Considering Economic and Environmental Parameters: A Case Study. Energies 2022, 15, 5940. [Google Scholar] [CrossRef]
  92. Saaty, R.W. The analytic hierarchy process—What it is and how it is used. Math. Model. 1987, 9, 161–176. [Google Scholar] [CrossRef]
  93. Chowdhury, T.; Chowdhury, H.; Islam, K.S.; Sharifi, A.; Corkish, R.; Sait, S.M. Resilience analysis of a PV/battery system of health care centres in Rohingya refugee camp. Energy 2023, 263, 125634. [Google Scholar] [CrossRef]
  94. Chowdhury, T.; Chowdhury, H.; Miskat, M.I.; Chowdhury, P.; Sait, S.M.; Thirugnanasambandam, M.; Saidur, R. Developing and evaluating a stand-alone hybrid energy system for Rohingya refugee community in Bangladesh. Energy 2020, 191, 116568. [Google Scholar] [CrossRef]
  95. Zhang, X.; Xiong, Y.; Pan, Y.; Du, H.; Liu, B. Crushing stress and vibration fatigue-life optimization of a battery-pack system. Struct. Multidiscip. Optim. 2023, 66, 1–16. [Google Scholar] [CrossRef]
  96. Chowdhury, T.; Chowdhury, H.; Hasan, S.; Rahman, M.S.; Bhuiya, M.M.K.; Chowdhury, P. Design of a stand-alone energy hybrid system for a makeshift health care center: A case study. J. Build. Eng. 2021, 40, 102346. [Google Scholar] [CrossRef]
  97. Huang, Z.; Luo, P.; Zheng, H.; Lyu, Z.; Ma, X. Novel one-dimensional V3S4@NC nanofibers for sodium-ion batteries. J. Phys. Chem. Solids 2023, 172, 111081. [Google Scholar] [CrossRef]
  98. Lin, X.; Wen, Y.; Yu, R.; Yu, J.; Wen, H. Improved Weak Grids Synchronization Unit for Passivity Enhancement of Grid-Connected Inverter. IEEE J. Emerg. Sel. Top. Power Electron. 2022, 10, 7084–7097. [Google Scholar] [CrossRef]
  99. Lin, X.; Liu, Y.; Yu, J.; Yu, R.; Zhang, J.; Wen, H. Stability analysis of Three-phase Grid-Connected inverter under the weak grids with asymmetrical grid impedance by LTP theory in time domain. Int. J. Electr. Power Energy Syst. 2022, 142, 108244. [Google Scholar] [CrossRef]
  100. Chen, Y.; Zhu, L.; Hu, Z.; Chen, S.; Zheng, X. Risk Propagation in Multilayer Heterogeneous Network of Coupled System of Large Engineering Project. J. Manag. Eng. 2022, 38, 4022003. [Google Scholar] [CrossRef]
  101. Ma, X.; Liao, Z.; Wang, Y.; Zhao, J. Fast Dynamic Phasor Estimation Algorithm Considering DC Offset for PMU Applications. IEEE Trans. Power Deliv. 2023, 1–12. [Google Scholar] [CrossRef]
  102. Min, C.; Pan, Y.; Dai, W.; Kawsar, I.; Li, Z.; Wang, G. Trajectory optimization of an electric vehicle with minimum energy consumption using inverse dynamics model and servo constraints. Mech. Mach. Theory 2023, 181, 105185. [Google Scholar] [CrossRef]
Figure 1. Wind energy resources at 25 m height at different locations in Bangladesh [22].
Figure 1. Wind energy resources at 25 m height at different locations in Bangladesh [22].
Sustainability 15 15774 g001
Figure 2. Wind energy potential map in Bangladesh (adapted from reference [22]).
Figure 2. Wind energy potential map in Bangladesh (adapted from reference [22]).
Sustainability 15 15774 g002
Figure 6. Barriers and sub-barriers identified in this study.
Figure 6. Barriers and sub-barriers identified in this study.
Sustainability 15 15774 g006
Figure 7. Flowchart of the applied method in this study.
Figure 7. Flowchart of the applied method in this study.
Sustainability 15 15774 g007
Figure 8. Local weights of sub-barriers.
Figure 8. Local weights of sub-barriers.
Sustainability 15 15774 g008
Table 1. Different studies conducted to determine wind resource potential in Bangladesh.
Table 1. Different studies conducted to determine wind resource potential in Bangladesh.
ProjectsRef.LocationDurationWind Speed (Highest/Average)
Joint study by NREL and USAID[22]All of Bangladesh2014 (June)–2017 (December)Highest: Offshore and coastal belt, 7.5 ms−1 at 120 m
CPGCBL[28]Matarbari Island2017 (February)–2018 (February)Average speed 5.76 ms−1 at 100 m
WERM project by LGED[22]20 sites (including seven from WEST)2003–2006Wind speed for Kuakata is 4.2 ms−1 at 30 m height
FEDI, appointed by NWPGCL[28]Kolapara, Patuakhali2017–2019Average speed 5.47 ms−1 at 120 m height
EGCB[28]Sonagazi, Feni2017 (June)–2018 (September)Average speed 5.38 ms−1 at 100 m
WRAP of BPDB by Pan Asia Power Services Ltd.[28]Kuakata, Mognamaghat, Parky beach, and Muhuri DamBetween 2003 and 2005Highest wind speed in Kuakata is 6.9 ms−1
BCSIR & IFRD[22]Meghnaghat, Saint Martin, and Teknaf2001 (January)–2002 (April)Highest wind speed in Saint Martin is 4.7 ms−1 at 30 m height.
WEST by BCAS, backed by ETSU of UK and LGED[28]Patenga, Char Fasson, Noakhali, Kutubdia, Teknaf, Kuakata, and Cox’s Bazar1995–1997Highest wind speed in Kuakata is 4.54 ms−1 at 25 m height
Backed by REVB1 of GIZ
[28]Patenga, Feni, Anwara, and Teknaf1995–1997Highest wind speed in Teknaf is 4.3 ms−1 at 20 m height
Table 2. Barrier analysis to RE adoption in various countries using MCDM approach.
Table 2. Barrier analysis to RE adoption in various countries using MCDM approach.
DescriptionLocationMethodMajor BarriersReference
Identifying barriers to the promotion of REPakistanPythagorean fuzzy AHPFinancial and regulatory[41]
Barriers and solutions of RE developmentPakistanAHP, Fuzzy TOPSISFinancial and political[38]
Prioritizing barriers to RE developmentIndiaAHP, Modified DelphiPolicy and technical[36]
Prioritizing barriers to offshore wind energyIndiaFuzzy AHPFinancial and technical[43]
Ranking of barriers to RE developmentNepalAHPPolitical and policy, economic[39]
Prioritization of strategies to remove obstacles to RE development and adoptionGhanaCRITIC, Fuzzy TOPSISTechnical and financial[44]
Obstacle analysis of solar PV adoptionIranFuzzy AHP, Fuzzy TOPSISEconomic and organization[45]
Barrier determination of solar PV developmentIranFuzzy BWMSupport and economic[46]
Prioritizing barriers to smart energy citiesTurkeyFuzzy Z-numbers, DEMATEL, cognitive mappingEconomic and policy[47]
Barriers to RE adoption and developmentChileAHPInstitutional and technological[42]
Evaluating obstacles to and solutions for implementing sustainable and green practicesYemenFuzzy AHP, Fuzzy TOPSISManagerial and political[48]
Analyzing barriers to mini-grid deploymentMyanmarAHPEconomic and technical[49]
Table 3. Satty’s fundamental scale for pairwise comparison matrix [92].
Table 3. Satty’s fundamental scale for pairwise comparison matrix [92].
Importance IntensityMeaning
1Equal importance
2, 4, 6, 8Midway values
3Moderate importance
5Strong importance
7Extreme importance
9Extremely more important
Table 4. RI values for up to ten criteria [36].
Table 4. RI values for up to ten criteria [36].
Table 5. Pairwise comparison of the main barriers.
Table 5. Pairwise comparison of the main barriers.
Table 6. Ranking of main barriers.
Table 6. Ranking of main barriers.
BarriersPriority WeightRank
Table 7. Overall ranking of eighteen sub-barriers.
Table 7. Overall ranking of eighteen sub-barriers.
BarriersSub-BarriersCodeWeightLocal WeightGlobal WeightRanking
Technical (TB)Lack of R&DTB1 0.180.08286
Unpredictable supplyTB20.460.3370.15502
Risks associated with technologyTB3 0.4820.22171
Administrative (AB)Absence of competent workforceAB1 0.4040.08485
Absence of effective coordinationAB20.210.1420.029810
Inadequate or absence of clear regulations, standards, and policiesAB3 0.4540.09534
EconomicHigh capital costEB1 0.640.10243
Subsidy to fossil fuelsEB20.160.260.04168
Small market sizeEB3 0.10.01613
Policy and political barriers (PB)Inadequately transparent decision-making processPB1 0.1510.013515
Less emphasis on wind PB20.090.2780.025011
CorruptionPB3 0.5710.05137
Social barriers (SB)Inadequate awareness among consumersSB1 0.2980.014914
Customers’ inability to spendSB20.050.6320.03169
Absence of KnowledgeSB3 0.0690.0034517
Geographic barrier (GB)TransportationGB1 0.7290.021812
Disorganized householdsGB20.030.0550.0016518
Land-use conflictsGB3 0.2160.006416
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Das, P.; Amin, S.M.M.; Lipu, M.S.H.; Urooj, S.; Ashique, R.H.; Al Mansur, A.; Islam, M.T. Assessment of Barriers to Wind Energy Development Using Analytic Hierarchy Process. Sustainability 2023, 15, 15774.

AMA Style

Das P, Amin SMM, Lipu MSH, Urooj S, Ashique RH, Al Mansur A, Islam MT. Assessment of Barriers to Wind Energy Development Using Analytic Hierarchy Process. Sustainability. 2023; 15(22):15774.

Chicago/Turabian Style

Das, Priyom, S. M. Mezbahul Amin, Molla Shahadat Hossain Lipu, Shabana Urooj, Ratil H. Ashique, Ahmed Al Mansur, and Md. Tariqul Islam. 2023. "Assessment of Barriers to Wind Energy Development Using Analytic Hierarchy Process" Sustainability 15, no. 22: 15774.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop