Lessons learned from the COVID-19 pandemic in planning the future energy systems of developing countries using an integrated MCDM approach in the off-grid areas of Bangladesh

The COVID-19 epidemic is impeding energy development in developing countries and exacerbating the problems associated with energy planning in off-grid locations. To address such complicated decision-making issues and consider scenarios during this long-lasting pandemic, this study proposes a novel integrated MCDM (Multi-Criteria Decision Making) approach using the Delphi based FO-BWM (Fuzzy Optimistic Best-Worst Method), IDOCRIW (Integrated Determination of Objective Criteria Weights) and the Aggregated Weighting Method integrated with the CoCoSo method under different normalization methods based on a case study of the off-grid areas in Bangladesh. The results of Delphi analysis showed that a total of five criteria were agreed upon by the expert panel. After integrating 5 normalization methods with CoCoSo and using three weighting methods separately, a total of 15 MCDM models were constructed. Finally, the 8 sorted MCDM models demonstrate that Solar Home System (SHS) and Mini-Grid systems need to be prioritized, and the criterion Opportunity of Local Funding (OLF) is essential for choosing between SHS and Mini-Grid systems. Sensitivity analysis showed that the proposed method is effective for easing the dilemmas of energy planning in off-grid areas and provides useful insight to address the impacts of future pandemics on energy planning.

Introduction
The global COVID-19 outbreak has slowed the demand and development of renewable energy technologies, and thereby reduced investment in renewable energy, leading to the loss of many jobs [1,2]. In this context, developing countries are exposed to more sustainable development challenges and economic implications than developed countries [3]. Moreover, in the off-grid areas of developing countries, the dilemma of whether to extend the existing grid or adopt off-grid energy systems has been further worsened due to the impact of the pandemic. Renewable energy, both short-term and long-term, can bring economic, social, and environmental benefits for all countries, including developing countries [4]. However, the fundamental steps in legislation, and socio-political developments for renewable energy are challenges in developing countries, in which the development of UN SDG7 (Sustainable Development Goal for Affordable and Clean Energy) is under great threat [5,6]. As for remote areas in developing countries, off-grid energy systems have potential to benefit small offgrid communities and are socially acceptable and environmental friendly [7]. However, some studies have established the fact that off-grid energy systems are not economical in developing countries compared with grid connected renewable and conventional energy systems [8e10]. Such conflict has been further worsened by the COVID-19 pandemic, as some local factors could affect the future planning process. For example, the cost of off-grid energy systems or urgent installation of power plants could be critical as most developing countries lack local funds; meanwhile, the time consuming expansion of grid connected energy cannot fulfill the urgent and special needs in the hospitals of rural areas. Therefore, the impact of the pandemic should be properly addressed when making decisions among the options of grid connected renewable, off-grid and conventional energy systems in off-grid areas; and the lessons learned from this process will be crucial for the progress of SDG7 in developing countries.
In the case of Bangladesh, the total current power generation installed capacity is 25,187 MW, of which 96.96% (gas 44.98%, oil 29.17%, coal 7.02%, imported 4.61% and captive 11.12%) of electricity is generated from fossil fuel and renewables contribute only 3.1% [11]. However, Bangladesh ranks the fourth highest in the rate of off grid electricity access, showing that the country is dominating in the field of off-grid energy system [12]. For example, the Infrastructure Development Company Limited (IDCOL) has set up 4.3 million SHS (Solar Home Systems) in the rural areas of Bangladesh, which covers 12% of the population [13]. Although electricity production from Mini-Grids is relatively undeveloped in Bangladesh, Mini-Grids may be more economical, consistent, reliable, and could solve real-life economic dispatch problems [14,15]. To promote a clean environment, the GoB (Government of Bangladesh) is also promoting large scale solar park and wind energy systems which will be integrated in the national grid. It has been reported that by 2023 the gas reserve will be depleted unless new gas fields are discovered. Thus, GoB discourages gas based power generation and prioritizes coal power generation for the future, as it is cheaper than other forms of fossil fuel based power generation [16]. As for the off-grid areas, most of them are located in the southern part of the country. Yet, the north eastern part of the country also has offgrid areas that are mainly known as 'Haor' areas. Most of these areas are using off-grid energy systems, but due to grid extension a number of off-grid areas have now become on-grid areas. However, it is still necessary to choose between off-grid and on-grid energy systems in the off-grid areas of the country, particularly under the current pandemic situation. Therefore, SHS, Mini-Grids, solar parks, wind and coal based power generation systems can be considered for the off-grid areas of Bangladesh, which needs proper ranking and prioritization to achieve SDG 7. Moreover, the long-lasting impact of COVID-19 pandemic should be furthered considered; and an effective and resilient planning approach should be well developed. Acknowledging the importance of addressing such conflicting objectives and complicated criteria in prioritizing the energy alternatives, researchers often adopt Multiple Criteria Decision Making (MCDM) methods, which can consider the complexity of social, technical, economic, and environmental aspects; and, therefore, this approach is adopted in this study.
In recent years, off-grid and on-grid energy systems using MCDM methods have become popular in the field of energy planning and development research. Ramezanzade et al. [17] compared different combinations of on-grid and off-grid energy systems based on a case study in Iran using FUCOM (Full Consistency Method), BWM (Best Worst Method), AHP (Analytic Hierarchy Process), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and the EDAS (Evaluation based on Distance from Average Solution) method, where the best hybrid system found had a capacity of 550 kW PV, 500 kW WT, 300 kW DG, 800 kW battery, and a 450 kW converter. Rad et al. [18] implemented Entropy-TOPSIS to find the best technology between on-grid and off-grid energy systems in Iran. The findings of the study showed that integration of solar, wind and biogas was the most inexpensive alternative. Although the inclusion of a fuel cell to the system increased the cost of the system, it improved system efficiency. The findings also revealed that on-grid energy systems are more costeffective than off-grid energy systems. Nsafon et al. [19]. proposed a PDCA (Plan-Do-Check-Act Cycle) cycle based AHP integrated VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method to determine the best energy technology in Cameroon, and the hybrid energy system showed its superiority in terms of technical and economic benefit. Rad et al. [18] used TOPSIS to find the best hybrid system in Iran, and the result revealed that on-grid PV/wind turbines were the most cost-effective and sustainable. Ye et al. [20] utilized AHP, CRITIC (CRiteria Importance Through Inter criteria Correlation), and TOPSIS to identify the most viable hybrid energy system in China, where the results showed that an off-grid energy system was more feasible than an on-grid energy system. Babatunde et al. [21] deployed CRITIC and TOPSIS to identify the best hybrid renewable energy system in Nigeria and showed that PV/Generator/Battery is the best one. These studies have demonstrated a significant amount of findings related to the selection of various energy systems using different MCDM methods. However, none of the above work has focused on specific, category wise energy mix scenarios considering the long-term impact of the COVID-19 pandemic.
Inappropriate and unsystematic selection of criteria may cause inconsistency in the final decision, and thus the validity of the overall approach will be at risk [22]. Therefore, the Delphi technique, which was originally created by the RAND Corporation, is used in this study to select the final list of criteria [23]. The technique is used to get accurate data, with the intent to ease the deviation of experts' opinions by defining the average of the answers obtained [24]. The technique is effective when there is uncertainty and a lack of transparency among the experts regarding selecting criteria.
The BWM method proposed by Rezaei [25] is flexible and can solve real-world problems with less computational time and high  [26]. Later, Guo and Zhao [27] proposed the fuzzy BWM method, which is narrated by linguistic terms of experts and can be expressed in triangular fuzzy numbers. The outcome of the fuzzy BWM method shows that it can achieve more desirable ranking for alternatives and has higher comparison consistency than the traditional BWM [27]. Lastly, Dong et al. [28] identified several drawbacks of the fuzzy BWM method and proposed a new fuzzy BWM method, which has higher comparison consistency than fuzzy BWM and traditional BWM. In this method, a mathematical formula is converted into four linear programming formulas by four different methods, where the 'Optimistic Method' has higher consistency than the other three methods and also has improved distinctive power compared to the traditional fuzzy BWM [28]. However, the OF-BWM (Optimistic Fuzzy BWM), proposed by Dong et al. [28] has not been applied in the field for energy selection, according to our knowledge. Moreover, the aggregation of the objective weighting method IDOCRIW (Integrated Determination of Objective Criteria Weights) proposed by Zavadskas and Podvezko [29] with OF-BWM increases the accuracy of the weighting method. This is also a new approach. The IDOCRIW method is nonbiased in nature. In this method, the intense possible values gained by the entropy method can be recouped by the CILOS (Criterion Impact LOSs) method, which is a major advantage [30]. Additionally, to obtain the most precise weights of the criteria, objective and subjective weighting methods must be combined [31].
On the other hand, the CoCoSo (Combined Compromise Solution) method has not been applied to rank the energy systems in developing countries, particularly Bangladesh. Furthermore, the previous literature has not well demonstrated the effects of different normalization techniques used in the CoCoSo method. The measurement of criteria used in the decision matrix usually refers to different units, e.g. meters/centimeters, dollars, and minutes/ hours, and some are arranged in an ordinal scale, which need to be converted through normalization methods into dimensionless parameters that help to achieve the final ranking [32]. Due to the use of different normalization methods in ranking models, the final outcomes vary. This leads to need for further investigation. Nonetheless, CoCoSo is more reliable and stable compared with other related traditional MCDM methods such as TOPSIS, VIKOR, COPRAS, and EDAS [33].
Therefore, new guidelines for energy planners to make or restructure a new Power System Master Plan (PSMP) in off-grids areas considering the long-term impact of the pandemic is crucial to achieve SDG7. Only after obtaining more comprehensive information can energy planners have a good understanding of all the energy alternatives in off-grid areas and then make sound decisions to address the energy dilemmas, while considering the impact of the global pandemic. Thus, based on a case study of the off-grid areas of Bangladesh, this study proposes an integrated MCDM using Delphi based FO-BWM, Aggregated Weighting Method (OF-BWM þ IDOCRIW), and IDOCRIW integrated CoCoSo approach under different local criteria and normalization methods considering the long term impact of the COVID-19 pandemic. The proposed method is novel in the field of energy. The objectives of this research are therefore threefold: first, to identify the key local criteria that are influential for planning in response to the long term impact of the COVID-19 pandemic through the Delphi approach and check whether experts agree about the selection of these criteria; second, to calculate the weights of the criteria using the proposed method to rank the energy systems and then integrate all fifteen models to find the best models; and third, to discuss the lessons learned based on the results of two types of sensitivity analysis.

Framework and process in the integrated MCDM
To conduct the analysis, initially, a number of criteria were selected from different studies and reports and based on the COVID-19 situation. The values based on the different criteria for the data collected were both quantitative and qualitative, where quantitative data were obtained from a secondary source, and qualitative data were drawn from the experts' rating. After using the Delphi method, five criteria were chosen for the planning. Then the weights of those criteria were obtained by OF-BWM, IDOCRIW, and the Aggregated Weighting Method. Next, five normalization methods, namely Max-Min Linear (N1), Linear Max (N2), Linear Sum (N3), Vector (N4), and Logarithmic (N5), were integrated using the CoCoSo method considering each weighting method separately.

Delphi
To select the most important criteria, we applied the Delphi method. The process of this method is explained below.
Step 2: Send the list of criteria C ¼ fC 1 ; C 2 ; C 3 ; ……:; C n g to the expert panels.
Step 4: Calculate the modified Kappa (K * ) to check the level of understanding among the decision makers about the selected criteria [35].
where ICVI is the proportion of agreements on relevance and p c is the probability of chance occurring.

FO-BWM
To estimate the weights of the criteria, we applied FO-BWM. The following procedure was used to calculate the weights.
Step 1: Find the best (most desirable) C B and worst (least desirable) C W criterion [25] (see Table 1).

From
Step 4 to Step 9, we adopted the optimistic approach [28].
Step 4: Construct the linear programming model.

RðFCRÞ
When RðFCRÞ 0:10, judgments can be considered as reliable. Therefore, the defuzzification process can be carried out.
Step 9: Defuzzify the obtained weights from Step 10 and calculate the crisp weights using Graded Mean Integration Representation (GMIR) [37].

IDOCRIW
This study implemented the IDOCRIW technique according to the following steps [29].
Step 1: Calculate the normalized values of the decision matrix.
where r ij denotes the normalized value of the decision matrix for the i th alternative in thej th criterion. And m is the number of alternatives.
Step 2: Calculate the numerals of the entropy method.
Where h is the entropy constant and is denoted as h ¼ 1 lnðmÞ and lnr ij is defined as 0 if r ij ¼ 0.
Step 3: Calculate the entropy weight.
where, d j denotes the degree of diversification and is expressed as Step 4: Construct the square matrix.
Equation (10) was used to formulate the positive the negative elements of the decision matrix. After that, the decision matrix was normalized by equation (7). Lastly, a square matrix was made using equation (11).
Here, s kij states the maximum number of the jth criteria, which were taken from the decision matrix with k i rows to formulate the square matrix and s ij ¼ s Ki j and s ij ¼ s j [38].
Step 5: Estimate the relative impact loss matrix with respect to the values obtained from the previous step.
where, p ij is the relative impact loss of the jth criterion.
Step 6: Develop the weight system matrix.
Step 7: Calculate the criterion impact loss weight.
We first solved the linear system of equations as follows: The criterion Impact Loss Weight is x and the vector X containing the elements ðx; …; x 5 Þ is the solution of this system < which can be resolved mathematically as follows.
where B is a nonzero vector near vector 0 used to determine the nonzero solution as shown below: In the end, the weights of the criteria were estimated by the CILOS method as follows:

Aggregated Weighting Method
To obtain more reliable and consistent weight, we implemented the following aggregated formula, which is a combination of the OF-BWM and IDOCRIW methods [39].
where D is the contribution factor and its suggested range is from 0 to 1. Here, D ¼ 0:5 was considered.

CoCoSo under different normalization methods
Step 1: Construct the decision matrix x ij Here, m is the number of off-grid and on-grid energy system alternatives,n is the number of assessment criteria and x ij is the performance of the i th alternative with respect to the j th criterion.
Step 16: Normalize the decision matrix based on the following normalization methods [40].
N1: Max-Min linear normalization (used in original CoCoSo): n ij ðCost CriterionÞ ¼ max i x ij À x ij min i x ij À max i x ij (22) N2: Linear Max normalization: N3: Linear Sum normalization: N4: Vector normalization: N5: Logarithmic normalization: Step 17: Calculate the values of S i for each normalization method, which are derived from the grey relational generation technique.
Step 18: Calculate the values of P i for each normalization method and each weighting method, which are obtained based on the WASPAS multiplicative attitude.
Step 19: Determine the aggregated appraisal score for each normalization method.
Equation (33) demonstrates the arithmetic mean of sums of the WSM and WPM scores, equation (34) expresses a sum of relative scores of WSM and WPM and equation (35) shows the balanced compromise of the WSM and WPM model scores [28]. In this study, the value of l ¼ 0:50.
Step 20: Calculate the final appraisal scores for each energy system using each normalization method.

Selection of the criteria
After selecting the list of criteria, we implemented the Delphi process. Table 4 reveals that the value of Coefficient of Variance (CV) of each criterion is less than 0.50, which satisfies the condition of Step 3 and the Delphi process stops. However, to check the consistency among the experts, we estimated modified Kappa (K * ) using equation (1). This study brought together 6 experts from multidisciplinary backgrounds as one panel and asked each expert to rate the criteria on a Likert-scale of 1e5 (Strongly Disagree-Strongly Agree). Table 5 shows the experts' ratings and the obtained modified Kappa (K * ) values. The table shows that for all criteria k * > 0:74, which signifies 'Excellent' interpretation among experts. Finally, the findings confirmed five criteria. satisfying the condition and proving that experts were consistent regarding their decisions. Finally, we used equation (6) to defuzzify the fuzzy numbers and to get the crisp weights of the criteria. Fig. 2 represents the final weights of the criteria. The obtained criteria weights were Environmental Benefit (EB) ¼ 10.5%, Opportunity of Local Funding (OLF) ¼ 50.2%, Project Implementation Time (PIT) ¼ 21%, Power Supply Reliability (PSR) ¼ 15% and Cost of Energy (COE) ¼ 3.3%. The OF-BWM was based on expert ratings, which were subjective in nature. Therefore, to avoid biasness, we implemented an objective weighting method called IDOCRIW. After calculating the weights of the entropy and CILOS, we calculated the IDOCRIW weight of the criteria using equation (18). Fig. 2 shows that the weights of COE, PIT, OLF, PSR and EB are 33.6%, 11.9%, 15.9%, 27.3% and 11.2%, respectively. The deviation of results between these two methods raised new concern. Therefore, this study aggregated the OF-BWM and IDOCRIW methods to get more reliable and valid results. The aggregated weights of the criteria were obtained from equation (19). Fig. 2 Table 6 shows that OF-BWM and IDOCRIW have a weak relationship as both of them are negatively correlated, which points to the opposing characteristics of subjective and objective weighting methods. On the other hand, the correlation of the aggregated method shows a strong relationship between the OF-BWM and IDOCRIW methods, as both of them are positively correlated. However, the relationships among them are not the strongest as the Pearson correlation value is less than 1.

Ranking of the energy systems
Using equations 21e30, this study used each normalization method to normalize the decision matrix. Then, using equation (31), we calculated the S i for each normalization method by putting the weights in that equation obtained by the OF-BWM, IDOCRIW and the Aggregated Weighting Method. Similarly, using equation (32), we estimated the values of P i for each normalization method. Finally, we calculated appraisal scores for each energy system, using equation (36), by applying each normalization method. Considering the five (5) normalization methods and three (3) weighting methods, the simulation generated a total of fifteen (15) models which rank the energy systems. Fig. 3 shows the following ranking of the first 5 models: SHS > Mini-Grid > Solar Park > Wind > Coal. The ranking demonstrates that all energy systems get the same rank using the weight of OFBWM under the 5 normalization methods. These models also demonstrate that the CoCoSo method is adaptable with all these normalization methods, when weights were obtained from OF-BWM. Fig. 4 gives the ranking results of M6-M10, showing that M6 and M7 provide the same ranking, i.e., Mini-Grid > SHS > Solar Park > Wind > Coal, which is slightly different from M1-M5. On the other hand, M9 and M10 provide the same rankings as M1-M5. However, M8 gives a different ranking scenario, i.e., SHS > Mini-Grid > Solar Park > Coal > Wind, where fossil fuel based power generation using coal is prioritized over renewable power. Here M6 and M7 are abatable with N1 and N2, while M9 and M10 are adaptable with N4 and N5. On the other hand, M8 is adaptable with N3. Fig. 5 shows the rankings of M11-M15. As seen in the figure, except for M15, all models give different rankings. Model 15 gives the same ranking as M1-M5 and M9-M10. The model is adaptable only with N5, while other normalization methods are not well adapted with M11-M14.

Integrated ranking
The majority of the models demonstrated that SHS is the best energy system. However, some models prioritizing Mini-Grids as the ranking models work differently with different normalization and weighting methods. It is also observable that all models prioritize clean energy systems, except for one model, which also creates a dilemma regarding the final decision. Fig. 6 shows the ranking of all 15 models. To get rid of this dilemma, integrated ranking of these 15 models is necessary to choose the best models with the best solution.  Interpretation   EB  4  4  4  3  4  3  1  Excellent  OLF  4  3  3  2  3  3  0.830  Excellent  PIT  3  4  3  3  2  4  0.830  Excellent  PSR  3  4  3  3  1  4  4  Excellent  COE  2  3  4  3  3  4 0.830 Excellent Fig. 2. Weights of the criteria.    This study implemented three (3) methods to obtain the best ranking scenario, including the grade average method, Borda method [41], and Copeland method [42]. The grade average method is the mean of the rankings obtained from the 15 models. In this method, the smallest value is considered as the best alternative. The Borda method is a pair wise comparison method, where if the value of the grade average of an alternative is greater than that of the other alternative, then the value 1 is assigned, or else 0 is allocated to the associated matrix elements (Table 7). After creating the pair wise comparison matrix, we calculated the row sum for each alternative d based on the values obtained using the Borda method. We estimated the value of the Copeland method by subtracting the row sum values from the column sum values of Table 7. It should also be noted that the values obtained for the energy systems from the Borda and Copeland methods are ranked from descending to ascending order, which means the highest value is considered as the best energy system. Fig. 7 shows the final results of these three methods. The rankings obtained from the grade average, Borda and Copeland methods are SHS (1.40)>Mini-Grid (1.67)>Solar Park (3)>Wind (4.33)>Coal (4.60); SHS (4) >Mini-Grid (3) >Solar Park (2) >Wind (1) >Coal (0) and SHS (4) >Mini-Grid (2) >Solar Park (0) >Wind (À2) >Coal (À4), respectively. All of these rankings support M1-M5, M9, M10 and M15, which are considered as the best fitted models. However, sensitivity analysis is required for further verification of these eight (8) models.

Sensitivity analysis
This study carried out two types of sensitivity analysis to explore the stability of the modeling and the relative input-output model behavior [43]. Sub-section 3.5.1 shows the influence of the l value on the final ranking and sub-section 3.5.2 analyzes the effect of change in criteria weights.

Influence of the l value on the final ranking
In proposed approach considered the l value to be 0.50. To check the stability of the ranking order of the energy systems of M1-M5, M9, M10 and M15, we varied the values of l from 0 to 1. Fig. 8 demonstrates that the value of the final appraisal score (k i ) varies, when the value of l is changed. All of these figures show that the rank orders of the energy systems are stable with respect to all 8 models, which justifies SHS > Mini-Grid > Solar Park > Wind > Coal. Except for slight variation of the best energy system, SHS, in M1 and M2, the other models show excellent stability in ranking SHS. Overall, M5, M10 and M15 show better stability in ranking with respect to all alternatives compared to the other models.

Effect of change in the independent weights of the criteria
The previous SA proved the utility of M5, M10, and M15. To prove its overall effectiveness, this study created a total of 16 scenarios (including the original weight) from each weighting method and varied to 30%, 40% and 50%. Figs. 9e11 show that the rankings of the energy systems are stable and SHS and Mini-Grid are the best for off-grid areas if the impact of the pandemic lasts a long time. However, M5 and M10 demonstrate that OLF (C3) will be an important criterion for Bangladesh that will strongly influence the decision making when choosing between these two off-grid energy systems. On the other hand, M15 shows that all criteria will play almost equal roles with respect to the energy system alternatives during the decision making process.
The 1st sensitivity analysis has demonstrated the robustness of the proposed 8 models as they provide the same ranking order. However, based on changes of the final appraisal score of the CoCoSo method with respect to l, it is seen that logarithmic normalization (N5) is the most suitable with OF-BWM, IDOCRIW, and the Aggregated Weighting methods as they show the maximum stability of the alternatives compared to the other normalization methods. This is also in line with the 2nd selectivity analysis, which showed that the stability of the three models (M5, M10, and M15) did not obviously change. Additionally, if the decision matrix has smaller dimensions and fewer alternatives, the logarithmic normalization method is an excellent choice to solve the problem [44]. This finding was also confirmed by the CoCoSo method in this study.

Discussion
The final results from 8 models demonstrate that SHS and Mini-Grid systems need to be prioritized among all options. The sensitivity analysis further shows that OLF is the key criterion and will influence the off-grid energy systems if the pandemic continues for a long time. Presently, the COVID-19 pandemic has significantly affected the economies of developing countries like Bangladesh, and it is expected that the country will need to depend on local  funding in the future rather than foreign investment. Also, urgent local funding is necessary to establish power supply as soon as possible with stable system reliability and sustainability, so that emergency situations can be handled in the future. Off-grid renewable energy provides reliable, pollution free, clean and affordable power for the local people. Thus, these kinds of energy are essential in the instant response to the COVID-19 pandemic. Additionally, renewable energy systems help to recover from economic crises, reduce carbon emissions, guarantee energy security, create new jobs, strengthen social bonding and contribute to protecting local people's health and life, which will help Bangladesh to recover from the pandemic situation in the future.
The result also shows that under the long term impact of the pandemic, SHS and Mini-Grid are prioritized over grid connected Solar Park and Wind systems, as they can contribute to electrifying the rural off-grid areas of Bangladesh with low investment cost. Moreover, off-grid energy systems can quickly deal with the emergency situation (e.g. electrifying rural hospitals) as they do not need to be connected with the grid through complex technical procedures. Generally, SHSs are reliable, sustainable, quick and easy to install, and can help the local people to recover from power shortages during the pandemic under lockdown situations. The results obtained from this study also show that Mini-Grids also provide a safe option during lockdown periods, which allow consumers to get reliable electricity from stakeholders without concern for the technical difficulties.
A study has demonstrated that SHSs are a better option than Mini-Grids based on a case study in Bangladesh [45], which is also in line with this study. It is worth noting that off-grid systems can be grid connected only when the length of the grid line is less than the critical distance from the off-grid energy system [46]. Particularly, in southern areas of Bangladesh, e.g. the Char areas, which are far from the nearest sub-station, connecting to the grid is difficult. A recent review based study also suggested using SHSs and Mini-Grids in the future if another pandemic like COVID-19 occurs [47]. This study has confirmed the quantitative feasibility of implementing these two types of off-grid energy systems in the future.
At present, it is critical for GoB to plan future large scale grid connected solar parks and wind energy projects, as local fund management is difficult for the country due to the high investment cost. The pandemic has also created the risk large scale renewable energy projects being dropped in developing countries as governments initiate incentive packages to promote local investments and local businesses to stimulate the internal economy [48]. Therefore, it is necessary to find external funding from foreign donor agencies. However, even developed countries are badly affected by the pandemic, and global physical communication has been blocked, resulting into obstacles to collecting foreign funding from developed countries and getting technical help from foreign experts. Moreover, solar park projects require 145.2 square feet/KW, which is a big challenge for the off-grid areas of Bangladesh as there is severe land scarcity in the country [49]. On the other hand, the risk of wind turbine projects is high and may lead to failure as Bangladesh is frequently hit by cyclones. In this context, the results also suggest that solar parks and wind energy systems are the 3rd and 4th ranked priorities for future planning as they are more environmentally beneficial and more likely to be supported by local funds compared with fossil fuel (coal) based energy. Moreover, in 2020, the global energy demand of coal dropped by À7.73%, which is the lowest when compared with other fossil fuels. Meanwhile,   renewable energy had the highest positive growth at 0.79% [50]. This implies that in the future, if the COVID-19 pandemic extends for a long time, the energy demand from fossil fuel based energy systems will decrease and the energy demand from renewables will increase. Such a finding justifies the importance of this study, as the results show that renewable energy will be prioritized in Bangladesh. It is important to note that the reliability of the grid connected system is questionable since the power failure of the system may cause a complete blackout. On the other hand, an offgrid energy system can store electricity in batteries and can be used when necessary, particularly during a lockdown period. Therefore, SHS and mini-grid based off-grid energy systems can be promoted in the future, considering the long-term effects of the COVID-19 pandemic in Bangladesh. Because long-term pandemic funding from foreign agencies can be unpredictable and time-consuming, this study suggests increasing local funding sources through various local NGOs and IDCOL to build SHS and mini-grids. For example, IDCOL's basic subsidy package in Bangladesh provides a 76% subsidy ratio to mini-grid developers, and the package includes a capital grant of up to 50% of project costs and a concessional loan of up to 30% of project costs. If mini-grid developers are unable to take advantage of this subsidy package, they will be forced to finance their capital expenditures with commercial or foreign loans. With current commercial loan interest rates in Bangladesh and a 5-year loan term, this kind of project's net present value would be more than four times higher than it would be without IDCOL subsidies. In this context, a transparent and consistent subsidy scheme is required, which could be beneficial because most mini-grid initiatives require initial funding. In order to promote greater social empowerment during this pandemic, the GOB may also grant specific tariff subsidies for farming, livelihood enhancement, and small cottage industries.
As the global vaccination rate rises, developing countries will require more vaccines. However, it is critical to not only increase the amount of vaccines available, but also to ensure that they are distributed equally in rural and urban areas. Because of storage issues, regular vaccination is sometimes interrupted in rural and off-grid areas. This is challenging as most COVID-19 vaccines require a refrigerator temperature range of 2 Ce8 C. SHS and mini-grid systems are capable of assisting in this area by providing the electricity supply to refrigerators. SHS, in particular, can provide fast assistance in the event of an emergency. Moreover, temporary clinics can be established and powered by off-grid energy systems (SHS and mini-grid) in rural isolated areas of Bangladesh to deal with serious COVID-19 patients. For example, SHS was successfully built in rural clinics in Nigeria, Kenya, and Afghanistan, providing reliable energy for necessary surgery and necessary equipment for COVID-19 patients, including ventilators, defibrillators, centrifuges, and patient monitoring systems [51,52]. This lesson can be implemented for Bangladesh and other developing countries.

Conclusion
The COVID-19 pandemic has worsened the dilemma of whether to extend the existing grid or adopt off-grid energy systems in the off-grid areas of developing countries. This study proposed an integrated MCDM approach proposed that has been shown to be effective using two types of sensitivity analysis to address the decision dilemma of ranking and selection of energy systems in the scenario of a long-lasting pandemic. Based on the case study of Bangladesh, this study draws the following conclusions.
After the Delphi process, a total of five criteria were selected. The values of the CV of all criteria were less than 0.50. Additionally, all the experts shared strong agreement regarding the selection of the criteria. After evaluating OF-BWM, the best criterion was Opportunity of Local Funding (OLF) at 50.2%, and the worst criterion was Cost of Energy (COE), having a weight of 3.3%. The obtained weights of COE, PIT, OLF, PSR and EB from the IDOCRIW method are 11.9%, 15.9%, 27.3% and 11.2%, respectively. Similarly, results of the same criteria obtained from the OF-BWM integrated IDOCRIW method are 18.46%, 16.45%, 32.95%, 21.15% and 10.60%, respectively. After evaluating the 15 models through 3 integrated ranking approaches (Grade Average, Borda and Copeland), it was found that 8 models are feasible and provide the same ranking with the different values of the energy systems. The ranking obtained from the integrated approach is SHS > Mini-Grid > Solar Park > Wind > Coal. SHS and Mini-Grids are the best alternatives in off-grid areas, followed by large scale solar parks, wind and coal to deal with the COVID-19 pandemic in the future. Opportunity of Local Funding (OLF) will be an important criterion under a long-term pandemic, which will influence decision making when choosing between SHS and Mini-Grids. Sensitivity analysis concludes that the rankings obtained from the 8 models are stable and valid when the CoCoSo model parameter l is varied. Both sensitivity analyses also demonstrate that CoCoSo is highly stable with logarithmic normalization in the decision matrix used in this study.
The major limitation of this study is the small dimension of the decision matrix. Therefore, more criteria and alternatives can be considered in the future. Different off-grid and on-grid energy systems can also be further designed using optimization tools and the MCDM method can then be used to ease the dilemmas of energy planning in off-grid areas in developing countries.

Policy implications
This study makes the following recommendations for the development of a country's clean energy expansion during the COVID-19 pandemic, which will not only improve comprehension of the selection process but also provide useful policy and practice insights.
In a long-term pandemic scenario, funding from foreign agencies can be unpredictable and time-consuming. This study suggests increasing local funding sources through various local NGOs, credit unions, and IDCOL to build SHS and mini-grids. Analysis also shows that Power Supply Reliability (PSR) is also one of the important factors that influence the decision-making process. SHS and mini-grid systems should be implemented in off-grid locations where grid extension is unlikely in the near future. If the rate of COVID-19 rises in a particular off-grid area, lockdown or self-isolation may be required. In this regard, residential load demand would increase while industrial load demand decreases. SHS and mini-grid systems can meet this demand because they provide reliable power supply for residential loads. Massive vaccination is challenging for off-grid areas as most COVID-19 vaccines require a refrigerator temperature range of 2 Ce8 C. SHS and mini-grid systems are capable of assisting in such areas by providing electricity supply to refrigerators.
The proposed model requires the involvement of all levels of people from strategic, tactical, and operational levels, as the effectiveness of the proposed strategy is reliant on expert judgment. Therefore, the proposed Integrated MCDM has demonstrated its utility as an effective tool to achieve resilient energy planning in developing countries, considering the future impact of pandemics like COVID-19. Declaration of competing interest


Impact of Financial Technology (Fintech) on Financial Inclusion(FI) in Rural India

Transformation towards Financial technology makes possibilities throughout all areas of the economy for growth. Emerging developing economies have seen a fast expansion of financial technology and mobile money services. FinTech projects, which are regarded as some of the most significant advances in the financial sector, have also obviously been driven by the growth of digital transformation, as FinTech Mobile money and digital wallets address the economic infrastructure vacuum with an innovative technology solution and enable customers to conduct financial transactions affordably and dependably by eliminating spatial barriers, and can be used to bridge the gap of banked and unbanked. This paper investigates the critical success factors influencing the adoption of disruptive financial technology for financial inclusion in rural India. Present research empirically measures the impact of technology in promoting entrepreneurship in under-developed regions for future adoption of financial technology in rural areas. The quantitative approach uses inferential statistics for hypothesis testing. Exploratory Factor Analysis is being applied for critical factor identification and Structural Equation modeling for measuring the impact of FinTech in financial inclusion in Rural India. The result indicates that factors constructing the social influence also positively impact behavioural intention to use manager technology in the rural sector in India. An end-user habit of using financial technology systems and services has a positive relationship with behavioural intention. Factors affecting perceived ease of use towards using the financial technology are positively related to the system’s usability. Present work provides emerging good practices for policy-makers, regulators, and investors in changing financial environment. It presents empirical findings to identify the critical success factor and another growth driver for FinTech services. The results would help mobile service industry to discover an economy of scope in providing services at low cost and with maximum social benefits. Study will also provide insights to financial institutions for offering banking services via mobile to handle cross-border transactions to low-income customers of remote areas.

Abstract
Transformation towards Financial technology makes possibilities throughout all areas of the economy for growth. Emerging developing economies have seen a fast expansion of financial technology and mobile money services. FinTech projects, which are regarded as some of the most significant advances in the financial sector, have also obviously been driven by the growth of digital transformation, as FinTech Mobile money and digital wallets address the economic infrastructure vacuum with an innovative technology solution and enable customers to conduct financial transactions affordably and dependably by eliminating spatial barriers, and can be used to bridge the gap of banked and unbanked. This paper investigates the critical success factors influencing the adoption of disruptive financial technology for financial inclusion in rural India. Present research empirically measures the impact of technology in promoting entrepreneurship in under-developed regions for future adoption of financial technology in rural areas. The quantitative approach uses inferential statistics for hypothesis testing. Exploratory Factor Analysis is being applied for critical factor identification and Structural Equation modeling for measuring the impact of FinTech in financial inclusion in Rural India. The result indicates that factors constructing the social influence also positively impact behavioural intention to use manager technology in the rural sector in India. An end-user habit of using financial technology systems and services has a positive relationship with behavioural intention. Factors affecting perceived ease of use towards using the financial technology are positively related to the system's usability. Present work provides emerging good practices for policy-makers, regulators, and investors in changing financial environment. It presents empirical findings to identify the critical success factor and another growth driver for FinTech services. The results would help mobile service industry to discover an economy of scope in providing services at low cost and with maximum social benefits. Study will also provide insights to financial institutions for offering banking services via mobile to handle cross-border transactions to low-income customers of remote areas.

Introduction
India is one of the most extensive countries with rich in minerals, marble, and tourism. However, a prominent tribal and rural area suffers from slow economic growth, poverty, and exclusion from formal financial services [1,2]. With an irregular and unreliable income, managing one's finances is more important for the impoverished than anybody else [2,3,4,5,6,7]. They need a wide variety of economic and suitable financial services to meet a wide range of financial requirements, such as easy access to savings [8,9], micro-credits [10,11], insurance [9,12], payment and transfer services [13]. But due to the limited access to formal financial services, they use informal channels which are less reliable, insecure, and more expensive than standard services [14,15].
Financial technology companies (FinTechs) emerged during the global financial crisis of 2007-2008 and have since revolutionised the financial industry by bringing new technologies to bear on the market [16,17]. The twin foundations of contemporary banking development are information technology and electronic money transfer systems in technology-based banking [18]. The rapid expansion of mobile networks into previously un-served regions and communities in India during the past decade also served as a growth driver for the cause [19,20]. Payments banks have provided another alternate route to online and mobile banking, assisting in improving efficiency and lowering costs associated with serving clients in rural and semi-urban regions [11,21,22]. Traditional banking service entry barriers have been lowered due to new specialised entrants and innovative business models that have blurred the boundaries between business and technology [23,24].
Inclusive financial sector development offers two complementary contributions [17,25]. First, financial inclusion connects excluded individuals to economic development, and second, economic growth draws additional people to the economy and financial system [11,26]. Inclusive financial developments reduce poverty by financing growth factors like mobilising savings and providing entrepreneurship opportunities to poor people, reducing vulnerability and improving welfare [27,28,29]. The Financial Inclusion goal has resulted in various banking formats, including tiny banks, mobile money services, and payment banks for the unbanked people. Financial inclusion has also progressed to include new non-bank Fintech companies competing for a more significant part of the Banking value chain [30,31]. This paper explores the critical success factors (CSF) of the adoption of financial technology and its role in supporting entrepreneurship and employment creation in the rural regions of India.

Reviews of Literature
The primary objective of initiating the idea of microfinance in developing nations was to promote much needed financial sector for development [32,33,34,35]. FI has been linked to economic growth and development [36]. According to Jack and Suri [37], financial technology innovation may offer cheaper and more efficient options by reducing transaction costs. This also boosts the sales of micro and small businesses by lowering the expenses associated with alternative payment options [38]. Most of these studies emphasise the role of digital money transfers (i.e., remittances) as the channel to risk-sharing and improvements in households' financial welfare [39,40]. Aron [41] conducts an empirical assessment of mobile money and finds support for its function in enhancing risk-sharing. In addition to the findings from Jack and Suri [37], other noteworthy research like Mbiti and Weil [42] and Wieser and others [43] show that more significant usage of FinTech reduces the use of informal savings methods and boosts remittance transactions. Other related research has examined the impacts of digitalisation on social assistance for the poor [44,45].
Identify the potential opportunities and challenges for various stakeholders [27]. Indian M.F.I.s are on the brink of getting started with rapid change and inclusion of mobile money, as new players explore alternatives and collaborations, and most are keeping a close eye on the area and trying to learn from others' experiences [46] Access to financial services is severely restricted in underdeveloped areas of India due to various institutional flaws and other factors that hinder individuals from realising their particular economic potential and possibilities, resulting in limited economic development [47]. As a result, microfinance programmes have been established in developed countries such as India to assist individuals living in underdeveloped regions such as inner cities [48]. According to estimates, micro finance's global potential and possibilities, i.e., financial services, ranging from 500 to 700 million individuals, with M.F.I.s covering less than one-ninth of the market [34] Since then, the poor's need for financial services has mainly gone unsatisfied [48] which provides a boom in the financial inclusion process in the under-developed area of India [48].The following table presents the significant constructs items and factors used to study the important drivers of financial inclusion.

PR2
The use of FinTech-based financial inclusion services entails greater financial risk.

PR3
There are too many things not known related with FinTech-based financial inclusion offerings.

PR4
Transactions based on FinTech for financial inclusion are not sufficiently protected by legislation.

PR5
The use of FinTech based financial inclusion services has potential risk

AT2
The FinTech-based financial inclusion service agent stands to lose more if my transactions are not processed.

AT3
I believe it is essential to exercise caution when it comes to FinTech-based financial inclusion offerings.

Research Gap& Objectives
Exclusion from the official financial system is rapidly being highlighted as an obstacle to a future free of poverty. Stakeholders of the FinTech ecosystem suffer from procedural and cultural constraints. Researchers are also split on whether mobile money systems realise their full development potential in the rural sector [1,9]. A study on financial inclusion for Bottom of the Pyramid (B.O.P.) in Indian society also requires a shift and emphasis to design technology solutions for the unreached population [8,11]. Hence, to find new methods of financial services to the underprivileged has now become a pressing issue.
Past reviews on the adoption and socio-economic impacts of financial technology in inclusion are rare in the underdeveloped world [10]. Very small consideration has been given to the social, economic, and cultural contexts in which these systems are utilised. Moreover, lack of empirical studies in the context of India exists that identify the critical success factor for the use of technology in expanding financial services [12,13]. One of the paper's goals includes calling attention to this gap in the research literature and emphasising the need for research focusing on inclusion and empowerment of the poor. Moreover, using an empirical investigation of entrepreneurial endeavours from disadvantaged communities, the present study will attempt to uncover constraints facing a developing economy and analyse the role of digital technologies in employment generation and poverty reduction [22,24].
Few empirical studies related to developing economies to identify the critical success factor for financial inclusion using mobile technology [23,25,26,27]. The proposed study appears more contextual for poverty reduction in under-developed regions and provides emerging good practices for policy-makers, regulators, and investors in this complex and ever-changing industry [11,31]. The proposed paper aims to fill the literature gap in studies on financial inclusion and financial technology in providing financial services. Present research empirically measures the impact of technology in promoting entrepreneurship in under-developed regions and identifies the critical success factors for future adoption of financial technology in rural areas [17,18]. Our research work uses both qualitative and quantitative approaches. The qualitative approach is used to understand various adoption theories and psychological factors related to technology adoption [20,59].The quantitative approach uses inferential statistics for hypothesis testing. Exploratory Factor Analysis (E.F.A.) is being applied for critical factor identification and Structural Equation modelling (S.E.M.) for measuring the impact of FinTech in financial inclusion in Rural India.

Research Objectives
 To identify the variables that significantly influence adopting financial technologies in rural India.
 To study the impact of financial technologies (FinTech) on influencing the development goals of financial inclusion development in Rural India.

Conceptual Framework
In an endeavour to investigate the motivational influences for the adoption of technology-based financial services, the present study draws the theoretical framework based on the structure of Theory of Reasoned Action (T.R.A.) [60], Theory of Planned Behavior (TPB) [61], Technology Acceptance Model (T.A.M.) [62] and Unified Theory of Acceptance and Use of Technology UTAUT [63]. T.A.M. and UTAUT are the most suitable instruments for evaluating technology-based adoption since they have been utilised in numerous researches to predict and understand user perceptions of system usage. Considerable study has been conducted to investigate the effect of risk on conventional consumer decision making [64]. Therefore, the present research model will also consider perceived risk as influencing factors in accepting fintech services.

Sample Design
The Universe of the study as the targeted population for the survey includes users of financial technology and rural entrepreneurs using mobile money and financial technology services. As per the population of 130 Crores, it is not possible to take the views of population and thus the sampling method is institutionalised. The Sampling frame includes respondents assorted based on demographic profile with Stratified Judgmental Sampling. The Sample size for the study was 6050 respondents. Proper care is taken while selecting the respondnets, as only those were included in the study having the experience of using the mobile money services at least from last 6 months. The data for the survey is gathered as per the country's geographical regions by selecting 600 rural districts. The sample is gathered from the central states in India based upon the rural population including the state of Rajasthan, Madhyapradesh, Maharastra, Uttar Pradesh, Punjab and Hariyana. For each of 6 states, the rural population data are collected as per the population survey of India-2011. From each state the sample of 1000 rural respondents is taken.

Data Collection Method
The data collection technique includes primary and secondary data sources-a survey method used for primary data collection. Primary data was collected from major rural regions in India using stratified sampling techniques. A structured Questionnaire was tested using a pilot survey from consumers of mobile money and other financial technologies. The questionnaire was prepared using a Likert-Rating scale along with a nominal and rank-order scale for data scaling. The questionnaire was pretested for reliability Cronbach alpha and validity using a pilot survey.

1) Stage I -Exploratory Research
In the preliminary stage of the study, an Exploratory Factor Analysis (E.F.A.) was an essential tool for identifying factors of adoption of financial technologies.

2) Stage II -Conclusive Research
At the conclusive research stage, the existence and strength of the relationship are tested using statistical tests. The primary statistical tool used to test the hypotheses is the Structure Equation Model (S.E.M.) to analyse the impact of FinTech for financial inclusion using mobile money services.

Results
Each FinTech-related question was asked in the assessment to explain the respondents' responses, and the mean and standard deviation for both measures were calculated appropriately. Tables 2 and 3    Because the Kaiser-Meyer -Olkin test resulted in a score of 0.839, all scale items were utilised to gather the latent variables investigated in the data analysis. The initial C.F.A. model provided a good match without eliminating any dimensions to create a better fitting measurement model. The quality of fit indicators of the primary examination pattern assessment recommended that the original model's findings be regarded as the final model. C.F.A. revealed that χ 2 =4181.7, p.001 suggest that the data match the measurement model. Furthermore, χ 2 / df (4181.742/1655) = 2.527; this is an absolute match index for a severe point of view, with a threshold of 3.0 or 5.0 for suitable parameters. For acceptable constraints, the incremental fit index of 0.89, the Tucker-Lewis index of 0.86, the comparative fit index of 0.872, and the projected average root square error of 0.0523 all surpass the 1.0 cutoff [65]. Based on these fit indices, the calculating model indicated a good match of the sample data.
The reliability and validity of the study model, factor loadings, Cronbach's alpha, composite reliability, and AVE were computed. The factor loadings were more than 0.500 [66,67], indicating convergent validity. All aggregate dependability values surpassed 0.600, indicating excellent internal reliability for the latent variables. LIKEWISE, every AVE value was more than 0.500 [65], proving convergent validity [68].
The structural equation modelling investigation yielded hypothesis testing findings, as shown in Figure 2 and Table  4.  In addition, path analysis was used to examine the structural model. The model is also determined to be fit since the fit indices G.F.I., N.I.F., TLI and CFI are all more than 0.9, and the RMSEA is less than 0.08. We evaluated the study hypotheses using the statistical significance of the uniform regression weights for endogenous test variables (i.e. t value) and the coefficient of measurement R 2 [69]. The result of the path diagram shows that there is a positive relationship between behavioural intention and effort expectancy. The factors constructing the social influence also positively impact behavioural intention to use manager technology in the rural sector in India. An end-user habit of using financial technology systems and services has a positive relationship with behavioural intention. The relationship between service charge, agent trust and service trust positively influence the perceived risk in using financial Technology Services. Factors affecting perceived ease of use towards using the financial technology are positively related to the system's usability. Moreover, results of path analysis also reveal that the positive behavioral intention to use manager Technology and the usability of the system is positively affecting the use of Financial Technology for financial inclusion in rural India; however, the perceived risk is found to be negatively associated with the use of Financial Technology systems in the rural Indian context.

Conclusions
Financial inclusion is widely recognised as essential for poverty reduction, balanced economic development, and economic stability. A large part of the population does not have a basic bank account in developing countries. The majority of them belong to Asia and the Middle East. Essential financial services like banking remain an unrealised dream for millions of people, especially for citizens in rural and remote regions in India. The usage of financial technology and mobile money services is required for transaction reasons and to alter one's livelihood by investing in the development of new businesses or self-employment initiatives. These efforts are gaining traction quickly thanks to the sharing economy, laws, and information technology. However, research in the area of FinTech is still in its early stages. Based upon the extensive literature review and discussions with representatives of the key players, the proposed study appears more contextual in the current situation by investigating the critical success factors at the different levels of adoption of financial technologies [68]. Financial inclusion is a development priority for policy-makers in most developing countries.
This paper adds to the literature dealing with the determinants of Fintech with financial inclusion, especially for the financially challenged people for the rural India. The main conclusion emerged from this empirical research is the model that includes the rural people with the Finance. Further, the results for S.E.M. and path analysis concluded that consumers are intended to be part of financial inclusion with the use of FinTech services, always try to use FinTech based mobile services for transactions. They plan to continue using FinTech for Financial Inclusion and find services easy to use. Most respondents are using FinTech based financial inclusion services frequently and perceive that this has improved profitability and savings and allowed them to send money inexpensively to anyone in the world [69]. People in Rural India believe that Fintech can be used for Financial Inclusion and can be used for income improvement in rural India.
Respondents from Rural India believe in FinTech based financial inclusion Techniques and found that their interaction with FinTech based financial inclusion services is easy and understandable. People around that expected that they should use FinTech based financial inclusion services, also influence behaviour. Some users also presented a view that the use of FinTech based financial inclusion services has become a habit. However, the user also shows concern, and they think using FinTech based financial inclusion services puts privacy at risk. People perceive that service charges under FinTech based financial inclusion are reasonable and value for money. They also showed trust in agents facilitating conditions in providing FinTech services. But they also find it necessary to be cautious with FinTech based financial inclusion services.

Implications
Present work provides emerging good practices for policy-makers, regulators, and investors in changing financial environment. It presents empirical findings to identify the critical success factor and another growth driver for FinTech services. The results of this study provide necessary inputs to the decision-makers, including service providers like payment banks, mobile money partners, government, law enforcement agencies for developing a strategy to navigate the existing barriers for inclusive financial growth. Drawing from insight from the study, Policy-makers and industry stakeholders may use mobile phone technology to create policies and new service offers to improve employment, income, and citizen well-being. The paper helps in the creation of a citizen dataset of financial technology beneficiaries. The results would help mobile service industry to discover an economy of scope in providing services at low cost and with maximum social benefits. Study will also provide insights to financial institutions offers banking services via mobile to handle cross-border transactions to low-income customers of remote areas. Finding of the study also add to the existing literature of financial inclusion interventions in developing countries [69].
Large populations in emerging nations like India see financial access as a significant barrier to development. The current study's findings help society in two ways. First, they will know about accessible savings, microcredits, insurance, and payment and transfer services. Secondly, financial inclusion allows previously excluded individuals to access growth. Inclusive financial sector development makes complementary contributions by attracting more people to the economy and ultimately helps develop rural regions. Entrepreneurship possesses immense societal implications beyond its commercial significance [71]. Inclusive financial growth and entrepreneurship can reduce poverty by financing growth factors like mobilising savings and providing access to services to poor people, reducing vulnerability and improving social welfare.