Fueling the Future with Green Economy: An Integration of Its 1 Determinants from Renewable Sources

16 Green hydrogen energy is a clean alternative fuel that can help developing economies to 17 increase energy security. This study assesses possible solutions for Pakistan's energy scarcity 18 based on a renewable source of green hydrogen generated through wind, solar, biomass, and 19 geothermal energy. For this purpose, four main criteria: economic, commercialization, 20 environmental, and social acceptance, have been assessed. The study used two-step models, the 21 Fuzzy-analytical hierarchal process, and the Data Envelopment Analysis techniques to evaluate 22 hydrogen energy production through available renewable energy sources. According to the fuzzy- 23 led analysis's empirical results, wind energy source optimization is best suited to produce hydrogen 24 energy in Pakistan for all four criteria (economic benefit, environmental impacts, commercial 25 potential, and social acceptance). At the same time, solar is the second-best option in all the given 26 criteria. The DEA-led analysis also considers wind energy as the most efficient source to produce 27 hydrogen energy in Pakistan. This study can help policymakers develop fact-based hydrogen 28 energy projects in their respective areas, especially in developing economies, as most share the 29 same characteristics. 30


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The energy demand is expected to increase by around 30% globally during the next 34 twenty-five year between 2015 to 2040, while developing Asia may demand 50% more energy 35 sources than currently. Nearly 13% of the world population (940 million) are suffering from 36 finding necessary energy consumption (electricity), and Sub-Saharan Africa and South Asia 37 account for 89% (840million) of the total electricity shortage (Iqbal et al., 2019). It has been 38 estimated that 40% (more than three billion) of the world population must bear the health cost of 39 indoor air pollution due to dirty energy sources for cooking and heating. The consumption of solid 40 fuel for this purpose has been reported very high in sub-Saharan Africa (77%) and South Asia 41 (61%). Therefore, increasing energy demand has become the primary reason to invests in safe, methodological purpose. Without the analytical framework, the analysis of renewable energy may 122 be inconsistent and limited due to the scenario of deep decarburization, which leads to suboptimal 123 policy decisions. Governments and decision-makers of developing economies face difficulties 124 selecting the optimum electricity sources among the renewable energies (wind, solar, biomass, 125 solid municipal wastages, small hydropower, and geothermal energy). In this context, this study 126 aims to provide an MCDA-based approach for measuring the optimum renewable energy through 127 green hydrogen production. 128 The weights criteria of F-AHP are considered the output of the DEA method with the 129 development cost as its input. The relative efficiency of the given renewable energy source for 130 hydrogen energy is calculated based on their respective ranks. This study's outcomes can be The possible renewable energy sources for green hydrogen generation in Pakistan are wind, 144 solar, biomass, small hydro, geothermal, and municipal solid waste energy. These renewable   Pakistan has a variety of renewable energy sources including wind, solar, biomass, and 169 geothermal energy. Such RES sources can be very suitable inputs for green hydrogen production 170 using the new conversion processes. Therefore, this paper complete reviews and estimates 171 potential of the renewable energy sources for hydrogen production from available RES resources. 172 Our main objective of estimation to the select optimal renewable energy sources (RES) for increase 173 the green hydrogen production for country wide. In Table 2. Statistics show the accessibility of solar radiation of 150,000 square kilometer 178 area, and these are best for maximum green hydrogen production. Based on the above estimate, 179 less than 2 percent site available for the one hundred solar plant installation, from these small areas 180 generated 20GW energy in the system (Duffie and Beckman, 2013

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Pakistan wind source sites 1 to 7 classes. We measure country wind power potential based 201 on universal units. The Class 4,5,6,7 are best and high potential wind farm installation system for 202 green hydrogen production, those area class between 3 or above, it means have high wind power 203 installation capabilities. Table 3, Shows Classes from 1 to 7 of wind energy potential in country, 204 these classes are well-defined at10 to 50 meters up length and parameters from above ground level.

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According to results that the approximately 9 percent area in Pakistan, highly potential for wind energy installation. Derives class 4 to 7 levels that can provide economic and practical wind power 207 production. 208

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See Table 4 Provides the potential of hydrogen energy from wind recourse in Pakistan. Based 211 on the estimation, we produce (45,000 tons) of hydrogen energy, which is equals to 53,Kwh per 212 kilogram. (Ivy, 2004). Pakistan was producing a total 900 MW wind energy that can be the ability 213 to produce 360 tons of hydrogen energy.
214 Table 4. Estimated hydrogen generation using wind energy (Ivy, 2004 with a capacity of 20 megawatts (see table 7). After completed, these projects then the country will 242 be getting more hydrogen production in the system.

Geothermal energy 267
Pakistan is also suitable for geothermal energy due to the high number of tectonic plates available. 268 According to the map, Pakistan can generate 100,000 MW of economical and clean electricity with its 269 available geothermal resource. Table 8 presents the temperature of the potential hydrogen production areas binding sites for hydrogen energy production. Based on the above-mentioned renewable energy source in 272 Pakistan, the following figure 4 presents hydrogen energy through given resources. Pakistan will generate 100,000 megawatts economical and clean electricity, with the 276 following available geothermal resource. Table 8 present the temperature of the area with potential 277 hydrogen production from geothermal energy in these mentioned areas. Mostly , urban areas 278 industrial zones of Sindh province produce significant amount of hot, dry rock (Zaigham and 279 Nayyar, 2010) which should provide high hydrogen energy production from these areas.

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In this section, we describe the research framework, which contains research methodologies. 283 We are using the two-step proficient MCDA methods.

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Analytical hierarchal process (AHP) is a multiple-choice decision analysis (MCDA) method, which 313 compares specific choices or alternatives and assigns weights to the criteria. The analytical hierarchal 314 process levels are useful for converting complex problems into sub problems. Each sub-problem level 315 contains the criteria and attribute. However, these criteria offer according to their relative importance and 316 additive weighting process. Also, the AHP has been used in different areas for ranking purposes, which 317 uses a pairwise comparison for calculating the importance of criteria in a hierarchical method. However, 318 the AHP method has the following shortcomings: 319  Unstable decisions 320  Decision-making ambiguity judgments 321  Inaccurate ranking subjectivity in judgment 322  Decision-makers based on the AHP model results. 323 According to AHP limitations, its qualitative analysis produces an absolute magnitude of decided 324 evaluation while these linguistic outcomes cannot be converted into mathematical form. Due to these 325 limitations, it is proposed to convert the linguistic outcomes of AHP into fuzzy numbers. 2. The value of ith Fuzzy synthetic set is as follows: The equation number 4,5,6 and 7 measure the SE i = (x i , y i , z i ).

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The formulation equivalent is as follows: Where shows the intersection between and ; A comparison between the values is required  We then define this latter vector as where 1 ( = 1,2,3, … , ) represents elements: This last step norms the weight of the vectors 361 as follows: Here is a non-fuzzy number indicating W.

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Since the objective of this study is to maximize the outputs, the following output oriented CCR 389 model is proposed. Figure 6 provide the working of CCR model. (kg/period) in the given period is measured as follows: we argued, annual wind-generated renewable hydrogen production is a function of marginal hydrogen prices and the electrolyzer system's energy efficiency. As the marginal price of the wind-448 generated renewable hydrogen increases, there will be an increase in hydrogen production until it 449 levels off. where ( , ) is the electrolyzer unit rate, f is the power factor, and , ℎ is the electrolyser's energy 488 requirement. The comparison case assumes that the electrolyzer unit cost is $368/kWh, which is the goal amount. We believe that annual maintenance costs and repair costs are equal to 2% and 490 25% of the initial cell expenditure, respectively, and that the electrolyzer has a seven-year 491 operating period.

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After calculated the values of , we compared them and computed possibility degree of = 515 ( , , ) ≥ = ( , , ) by solving Eq. 8. Table 11 presents the values of ( ≥ ). We   respectively. The preference of these four criteria is economic benefits > environmental impacts > 533 commercial potential > social acceptance.

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The steps followed in 4.1 have been applied for different renewable energy sources 536 considered alternatives for hydrogen production to estimate the priority weights criteria. The fuzzy 537 techniques' assessment matrixes of renewable energy sources have been used as substitutes under specific criteria  score. Here, the overall wind energy source of renewable energy is relatively more suitable for Pakistan's 568 hydrogen energy production, followed by solar and biomass considering the four main aspects. 569

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In second stage, we assess the relative efficiency scores with help of DEA of renewable energy sources 571 for hydrogen generation, after efficiency scores to rank available renewable energy sources. The final 572 weights obtained by multiplying each renewable energy source (RES) with criteria priority weights are presented in table 18. According to the DEA results presented in table 18, the wind is ranked as one to be 574 converted into hydrogen energy as it has a score of one efficiency level. Biomass is the second efficient 575 energy source for hydrogen energy with 0.975 DEA efficiency, while solar is the third efficient (0.756) 576 source for hydrogen energy conversion. The geothermal energy source is the least efficient for hydrogen 577 production as it has a 0.662 efficiency score and is ranked four. According to DEA results, the wind is a 578 highly efficient renewable energy source to be converted into hydrogen energy due to its efficient cost and 579 benefit outcomes. Here, biomass is the second-best available option for this purpose. 580 Table 18. Presents ranking and relative efficiency scores of various renewable energy sources.

Techno-economic analysis of wind-generated renewable hydrogen production 582
There is no zero-emission vehicle in Pakistan when it has increased hydrogen demand for a vehicle of 583 zero-emission. In contrast, all the provinces in Pakistan aggregate the rural and urban intensities of gasoline 584 consumption. Table 19 shows a total demand of 14.6 billion kg of gasoline for all the provinces. 585 Alternatively, the H2-demand (in billion kg) 4.88 billion kg renewable hydrogen is needed to the fleet the 586 equivalent amount of vehicles fleet by 14.6 billion kg of gasoline in the country. Similarly, 6.63 billion kg 587 is needed for LDV H2 Demand (kg/Annually). The price of renewable hydrogen fluctuates between 588 USD0/kg to USD5/kg, having a growth of USD0.1/kg. There is an increase in hydrogen prices when there 589 is an increase in renewable hydrogen production when the minimum price of hydrogen goes above the USD2.99/kg kg-H2. Table 19 shows that for renewable hydrogen, the province of Punjab's potential 591 demand is 4.54 billion kg. Moreover, provinces with high potential for producing wind energy have little 592 possibility for hydrogen mandate, mainly coastal areas of Baluchistan and Sindh and interior parts of Sindh. 593 In urban areas, gasoline consumption is nearly 3.2% lesser than the average consumption of gasoline, and 594 in a rural region, the consumption of gasoline is nearly 6.66% higher than the average consumption. The  both paths' overall cost will be around 29% higher than the national energy cost system's situation, 630 and the gap between these paths is less than 1%. However, in these cases, the cost structure is substantially different. The pathway's dependence on crude oil is heavily dependent on the import 632 of crude oil. Pakistan's import dependency remained around 70%-72% until 2016. It fell to 70% 633 in 2016 and 69% in 2017 due to the new gas field and wind energy increases. The system efficiency 634 analysis at rated stack current showed that the electrolyzer system had 57% efficiency while the 635 maximum alkaline system efficiency reached 41%. It noted that the hydrogen production was 636 about 20% lower than the manufacturer's rated flow rate, and if the rated flow were achieved, 50%

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This study is based on four criteria (commercial potential, economic impacts, economic 650 benefits, and social acceptance) to identify optimal renewable energy sources (RES) for hydrogen 651 production in developing economies such as Pakistan. This work may also help conduct specific technological-based techno-economic assessment or 693 find out the best available alternative ways to develop RES base hydrogen energy.

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An efficiency score of 1.000 indicates that the origins of renewable energy source at the 695 efficient frontier. In other terms, the concept of cost-benefit analysis is optimal efficiency score of 696 1.000 or above. For example, optimal efficiency score of wind renewable energy is 1.000, therefore 697 no need to increase the input data and output data in system. While, biomass efficiency score in 698 our paper is 0.975 that need to increases and decreases the inputs and outputs until achieved an 699 efficiency score of 100.The efficiency scores acquired provided the basis for the overall ranking of RES for hydrogen production. Based on current findings that the wind energy is feasible 701 renewable energy source for future hydrogen production in Pakistan. Study second phase, we used the data envelopment analysis to measurement the relative 727 efficiency of available each renewable energy analysis after that ranked them based on their 728 calculated scores. In DEA model results showed that the Wind energy obtained highest efficiency 729 score of 1.000, thus, that is achieved best optimal Rank 1 from another RES. It means wind is best 730 renewable energy for Green hydrogen energy production in Pakistan. In next Biomass get the 731 highest score of 0.975 and then Rank 2 in the ranking, as well as solar energy 3 in ranking, which 732 is score 0. 756. Geothermal energy attained a 0.662 efficiency score, which implies that geothermal 733 is the minimum efficient renewable energy source for hydrogen production in Pakistan.  The outcome of this study may be helpful in selecting the best choices for politicians to choose 745 for a future hydrogen economy. Nevertheless, this study's results apply only to Pakistan. It is 746 because the experts provided their advice in Pakistani language. Again, the economic situation,