Feasibility study of renewable energy sources for developing the hydrogen economy in Pakistan

international journal of hydrogen energy xxx (xxxx) xxx

Available online at www.sciencedirect.com

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Feasibility study of renewable energy sources for developing the hydrogen economy in Pakistan Syed Ahsan Ali Shah College of Economics and Management, Nanjing University of Aeronautics and Astronautics, 29 Jiangsu Avenue, Nanjing 211106, China

highlights
Evaluate renewable energy sources for hydrogen production in Pakistan.
Develop fuzzy MCDA framework by integrating FAHP and DEA.
Wind energy is found to be the best renewable energy source for hydrogen production.
Geothermal received least relative efficient score.

article info

abstract

Article history:

Hydrogen energy can play a pivotal part in enhancing energy security and decreasing

Received 6 July 2019

hazardous emissions in Pakistan. However, hydrogen energy can be sustainable and clean

Received in revised form

only if it is produced from renewable energy sources (RES). Therefore, this study conducts

17 September 2019

feasibility of six RES for the generation of hydrogen in Pakistan. RES evaluated in this study

Accepted 19 September 2019

include wind, solar, biomass, municipal solid waste (MSW), geothermal, and micro-hydro.

Available online xxx

RES have been evaluated using Fuzzy Delphi, fuzzy analytical hierarchy process (FAHP), and environmental data envelopment analysis (DEA). Fuzzy Delphi finalizes criteria and

Keywords:

sub-criteria. FAHP obtains relative weights of criteria considered for choosing the optimal

Hydrogen economy

RES. Environmental DEA measures relative efficiency of each RES using criteria weights as

Renewable energy sources

outputs, and RES-based electricity generation cost as input. The results revealed wind as

MCDA

the most efficient source of hydrogen production in Pakistan. Micro-hydro and Solar energy

FAHP

can also be used for hydrogen production. Biomass, MSW, and geothermal achieved less

DEA

efficiency scores and therefore are not suggested at present.

Pakistan

© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

Introduction Hydrogen has gained interest as the desired energy carrier, and the energy scientists endorse the hydrogen economy as a sustainable future [1]. In a typical hydrogen economy, hydrogen is used as a primary fuel for running vehicles and generating electricity. Hydrogen is preferred because it has a

greater energy exchange efficiency, it can be obtained from several resources, it encompasses higher heating value compared to most of the conventional fuels, and it only emits water when used [2]. However, studies argue that hydrogen can only be sustainable when it is produced from clean sources such as renewable energy sources (RES) [3]. Whereas, most of current technologies are based on conventional fuels, which have negative implications for the environment as they

E-mail address: [email protected]. https://doi.org/10.1016/j.ijhydene.2019.09.153 0360-3199/© 2019 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved. Please cite this article as: Shah SAA, Feasibility study of renewable energy sources for developing the hydrogen economy in Pakistan, International Journal of Hydrogen Energy, https://doi.org/10.1016/j.ijhydene.2019.09.153

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emit hazardous gases. Therefore, it is important that the hydrogen should be produced using RES. Literature suggests that RES are best and sustainable choice for hydrogen production [4,5]. There is a variety of RES including solar, wind, biomass, municipal solid waste (MSW), micro-hydro, and geothermal. The hydrogen production viability of these RES varies due to various factors such as technical potential, electricity generation cost, economic benefits, environmental implications and social acceptance. Such factors make the decision to choose best renewable energy source difficult; therefore, in this study we develop a framework which enables the selection of optimum energy resource for hydrogen production in Pakistan. Pakistan is a third world country which has consistently been facing an energy crisis for the last two decades. More than 144 million population do not have electricity access whereas the rest of the country suffers blackouts more than 12 h a day [6]. Due to energy shortage, scores of industries have been compelled to shut their operations. Nearly 500,000 employees lost their jobs following the slowdown in industrial operations [7]. It is perplexing that even being in such a bleak condition, Pakistan has not opted to include hydrogen in the country's energy portfolio [8]. Whereas, around the world, several countries have been transitioning towards a hydrogen economy. The momentous growth of hydrogen is due to recent advancement in hydrogen fuel cell technologies, which have potential to replace the need to use fossil fuels for electricity generation. Also, hydrogen-fueled vehicles have increased market confidence in hydrogen, and it is anticipated that hydrogen vehicles shall replace petroleum vehicles in future [9]. Hydrogen energy can play an essential part in mitigating Pakistan's energy crisis. The use of hydrogen energy in the country can also tackle the challenge of increasing greenhouse gases, which are detrimental not only for Pakistan but the whole world. Pakistan holds the potential to produce abundant green hydrogen, which will suffice energy needs of Pakistan's prime energy-consuming sectors including electricity sector and transport. The hydrogen production process uses renewable electricity to split water and fuel cells. This process converts chemical energy of hydrogen into electricity directly without combustion. Therefore, Pakistan's abundant RES favour the production of green hydrogen. For the purpose of selecting hydrogen production methods, the relevant studies used multi-criteria decision analysis (MCDA) mainly because the process of selecting hydrogen methods include multi and complex criteria. In a recent study, Xu et al. [8] integrated environmental data envelopment analysis (DEA) with fuzzy analytical hierarchy process (FAHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) to evaluate eleven hydrogen production technologies in Pakistan. They found that wind electrolysis, photovoltaic (PV) electrolysis, and biomass electrolysis are fully efficient and can be chosen for green hydrogen production. Acar et al. [10] proposed an MCDA technique, novel hesitant FAHP, to evaluate the sustainability of hydrogen processes including grid electrolysis, PV electrolysis, wind electrolysis, nuclear thermochemical, solar thermochemical, and photoelectrochemical. They

evaluated hydrogen processes based on five performance criteria of economic, environmental, social, technical, and resource availability. Their results revealed that the sustainability of grid electrolysis is higher than other processes. Yu [11] conducted the selection of optimum hydrogen production process among three options (water electrolysis, nuclear electrolysis, and coal gasification) using intuitionistic fuzzy set theory. The selection was based on three criteria including government support, economic, and social performance. The study found nuclear electrolysis as best option for hydrogen production and coal gasification as the least choice. The given literature provides adequate evidence that the MCDA methods are proficient in making a decision regarding the selection of hydrogen production techniques. Therefore, we applied three most common MCDA techniques, Fuzzy Delhi, FAHP, and DEA to choose the most optimum renewable energy source for the hydrogen production in Pakistan. Firstly, Fuzzy Delphi was applied to select the relevant and important criteria and sub-criteria identified through literature survey. Then, I used FAHP to define and obtain weights of criteria and sub-criteria. Finally, I applied environmental DEA to compute the relative efficiency of selected RES-based hydrogen production processes. The rest of the paper proceeds as follows: Section Renewable energy sources for hydrogen production in Pakistan discusses the potential of various RES for hydrogen production in Pakistan. Section Research framework develops methodology. Section Results contains results and analysis. Section Discussion discusses the results, and section Conclusions presents the conclusion of the study.

Renewable energy sources for hydrogen production in Pakistan Pakistan has abundant RES, including solar energy wind energy, biomass, municipal solid waste (MSW), micro-hydro, and geothermal. These RES can be vital feedstock for green hydrogen production using advanced conversion technologies. In this section, we review potential of various RES available in Pakistan and estimate their hydrogen production capacity.

Wind power potential Pakistan’s wind energy sources have excellent potential to generate electricity for wind electrolysis. The National Renewable Energy Laboratory (NREL) in collaboration with Alternative Energy Development Board (AEDB), USAID, and Pakistan Meteorological Department conducted the wind resource assessment of Pakistan and developed the first wind map of the country. It is apparent from the map (see Fig. 1) that Sindh, Balochistan, and some parts of Punjab have propitious wind power potential. According to the assessment, the exploitable wind resources are estimated as 346 GW. Different sites were also identified for the installation of wind power generation facilities. Among others, GharoeKeti Bandar was reported to be the best site where the average wind

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Table 2 e Hydrogen production estimation from wind [14]. Potential in MW 86,875 87752.5 60812 235,439.5

Fig. 1 e Wind resource assessment of Pakistan [13].

speed was recorded as 7 m/s at 50 m above the ground level [12]. The wind sources of Pakistan are categorized into seven classes (see Table 1). These classes are a universal unit to assess wind power potential. Areas which come under class 3 or higher are suitable for a variety of utility-scale wind farm installations. Table 1 lists all the seven wind power classes in Pakistan which are defined at 10 m and 50 m above the ground level along with their parameters. It is evident that 9% of land areas in the country are suitable for utility-scale wind turbine applications. Also, around 3.5% of land comes under wind power class 4 or great and can provide cost-effective wind power generation. Table 2 provides estimates with the potential of hydrogen generation from Pakistan’s wind resources. According to the estimation, 45,000 tonnes of hydrogen can be produced with conversion rate of 53 KWh per kg [14]. Correspondingly, the existing wind power projects of 1900 MW in Pakistan can produce 360 tonnes of hydrogen.

Solar energy potential The geographic location of Pakistan makes her an ideal country to harness solar energy, as long shiny hours remain almost 300 days a year [15]. The World Bank and the US

10 h - aval

The conversion rate of 53 KWh/kg)

868,750 877525 608,120

16,610898 16,778680 11,627533 45,017112

government helped Pakistan with solar resource mapping. The National Renewable Energy Laboratory (NREL) of the United States and German Aerospace Center Institute reported that Pakistan receives 8e10 mean hours of sunshine a day, and annual solar radiation of 15  1014 kW h, which implies that the country has solar energy potential of almost 1600 GW [16]. The World Bank within its Energy Sector Management Assistance Program (ESMAP) program conducted a solar resource assessment and launched high-resolution maps of solar energy in Pakistan. Ninety-per cent areas of Pakistan found to receive 2000 kW h/m2 of global horizontal irradiance (GHI) annually (see Fig. 2). Whereas, northern areas of Balochistan were reported to best compared to the Sinai Peninsula in the Middle East, which is considered the best solar irradiance receiving location in the world [17]. The survey report added that Pakistan is suitable for developing solar power plants because of its vast available land spaces, which are unaffected in terms of air pollution, cloud coverage, irradiance diffusion, and aerosol content [18]. Tables 3 and 4 present the data acquired from the Atmospheric Science Data Centre of the NASA Surface meteorology and Solar Energy. The ten-year averaged data of Pakistan shows that the average insolation in the country varies from 5 to 7 kW h/m2/day, whereas 30% areas receive more than 6 kW h/m2/day solar insolation. Figures in Table 4 translated the availability of 150,000 km2 area hydrogen production. According to an estimate, less than 2% of the available area can accommodate more than 100 solar plants of 200 MW capacity, and nearly 20 GW can be generated from this small area [21]. Accordingly, the estimate of hydrogen production can be made according to the area available for solar energy generation [22]. Studies show that 2.5 to 7.5 acres are required to establish solar plant of 1 MW capacity. The studies further refer to these values as a benchmark, and the real values can change depending on the use of different technologies [23].

Table 1 e The capacity of wind resources [12]. Wind power class

1 2 3 4 5 6 7

Wind potential

Poor Marginal Moderate Good Excellent Outstanding Superb

10 m

50 m

Wind speed (m/s)

Wind power density (W/m2)

Wind speed (m/s)

Wind power density (W/m2)

0e4.4 4.4e5.1 5.1e5.6 5.6e6.0 6.0e6.4 6.4e7.0 >7.0

0e100 100e150 150e200 200e250 250e300 300e400 >400

0e5.4 5.4e6.2 6.2e6.9 6.9e7.4 7.4e7.8 7.8e8.6 >8.6

0e200 200e300 300e400 400e500 500e600 600e800 >800

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Fig. 2 e Global horizontal irradiance map of Pakistan [19].

Table 3 e Values of minimum-maximum solar radiation [20]. Minimum average radiation Maximum average radiation

5.2275 (kWh/m2/day) 7.0016 (kWh/m2/day)

Table 4 e Area-wise (%) of solar radiation [20]. Solar Insolation 2

5e6 (kWh/m /day) >6.0 (kWh/m2/day)

Area in percentage 69.31 30.69

Biomass energy potential Agriculture is the main source of livelihood for rural people in Pakistan due to the unavailability of other income generation opportunities in those areas. Subsequently, agriculture activities produce an enormous amount of crop residue, which can be used as biomass energy source. Sugarcane is one of the main crops produced in Pakistan with an average annual production of over 55 million tons, which makes Pakistan the fifth largest sugarcane producing country in the world. Sugarcane residue produces an estimate of more than 18 million tons of bagasse every year, which is a huge power-generating resource. Using these bagasse resources, the high-pressure

cogeneration plants at 84 sugar mills in the country can produce 1844 MW [24]. Another major sector that contributes to biomass is livestock, and as per [25], the animal population in Pakistan is growing at a rate of 8%. Livestock is one of the primary sources of livelihood for most of the people living in rural areas. Approximately 30e35 million people in rural areas earn 30e40% of their livelihood from livestock. In 2012e13, 72 million livestock animals in the country produced over 1140 million tons of dung and 338 million tons of urine. This massive amount of excreta has the potential to produce 19.125 million m3/day biogas by anaerobic fermentation of dung with the use of approximately 3 million family-size biogas plants. The energy generated through this process could meet the cooking and heating needs of 50 million people. Approximately 68% of Pakistan’s total population of 207 million resides in rural areas. Thus, using this single source of energy, the country can meet the cooking and heating needs of more than half of its rural areas. In addition, this system can produce up to 57.4 million kg/year nitrogen-enriched bio-fertilizer, which is necessary to sustain the fertilization of agriculture lands [26]. The above paragraphs confirm the enormous potential of biomass for the purpose of hydrogen production in Pakistan. It is given in the literature that 13 kg dry crop residue can produce 1 kg of hydrogen [23]. Accordingly, Pakistan has potential to produce 6633 thousand tonnes of hydrogen from biomass.

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The estimate of hydrogen production from different kinds of crop residue is given in Table 5.

Geothermal energy potential Geological studies confirm that Pakistan is located on the junction of tectonic plates, and therefore the country has an abundant amount of hydro geothermal energy. A map developed by the geological survey of Pakistan is given in Fig. 3, which shows the location of the thermal spring in different areas of Pakistan [27]. According to [27], Pakistan can generate 100,000 MW costeffective electricity using these available geothermal resources. Table 6 provides details of areas, temperature summary of those areas, and various applications of available geothermal energy. These areas and the temperature in these areas are best for hydrogen production. Further, urban areas of Sindh province and other prominent industrial centers have a substantial amount of hot dry rock [28] which provides great opportunity for hydrogen production.

MSW energy generation potential Municipal solid waste (MSW) can also contribute largely to the renewable hydrogen generation in Pakistan. Approximately 30 million tons of MSW is produced annually by the country’s major cities [29]. MSW is increasing at an alarming rate due to the rapid increase in urbanization. However, Pakistan lacks the effective recycling of MSW into valuable material. Poor waste management and conventional way of waste disposal not only degrade the environment but also leads to the socioeconomic and public health problems [30,31]. Thus, promoting waste to energy is the need of time. On the one hand, it helps the government to tackle the challenge of managing increasing MSW in the country. While on the other, it can be a clean source of generating green hydrogen. This increasing amount of MSW is useful for the production of hydrogen. The recent technological advancement has

Table 5 e Estimation of hydrogen production from crop residue. Crop

Type of residue

The available residue (1000 MT)

Availability for hydrogen potential (1000 tonnes)

Wheat

Stalks Husks Stalks Husk Boll Shell Stalks Straw Stalks Cobs Husk Stalks Husks Cobs Stalks Stalks

7200 36,000 600 90 10,400 1400 10,400 3300 11,900 3300 110 152 950 142 285

554 2769 46 7 800 108 800 254 915 254 8 12 73 11 22

Maize Rice

Cotton

Barley Bajra

Dry Chilly Total 86,229

6633

5

made MSW-to-hydrogen technologies economically and technically viable. Areas with high population density mount the chances of producing a higher amount of hydrogen from waste, and most of Pakistan’s urban areas densely populated where waste-to-hydrogen can reach 1 million tonnes. MSW in only one metropolitan city of Karachi reaches up to 13,000 tonnes every day. MSW generated in rural areas is also beneficial for MSW-to-hydrogen; however, low quantity of combustible material available in rural areas may decrease amount of hydrogen production. Nonetheless, rural areas can produce 814 million kgs of hydrogen per day [32].

Micro-hydro potential In 1925, Sir Ganga Ram built a small hydropower station at BRB canal in Lahore having the capacity of 1.1 MW and is still operational. Micro-hydro potential in the country is estimated to be 3100 MW at 815 different natural waterfalls and run of river sites. Potential of these sites varies from 0.2 MW to 40 MW [33]. Table 7 details the total available potential of micro-hydro at the different sites of the country measured by WAPDA in collaboration with AEDB and provincial governments. Currently, 9 micro-hydro projects, listed in Table 8, are generating 98.41 MW. Provincial governments are taking micro-hydropower projects with the help of AEDB. The provincial government of KPK has taken six projects with a cumulative capacity of 118 MW expected to be started by 2018 [34]. Further, several projects of 2500 MW capacity are at different phases of the feasibility study in the province. Similarly, 4 projects of 20 MW capacity have been undertaken by the Punjab government. These projects are expected to be completed by the time since the country has expertise in hydropower generation.

Research framework To achieve the objective of this study, the research framework is divided into four phases. In the first phase, criteria and subcriteria to evaluate RES for hydrogen production are identified through an exhaustive literature survey. The study conducted a systematic review of articles published in English-language journals using databases such as Science Direct, Google Scholar, Springer, Web of Science, Wiley, Emerald, Taylor & Francis, and Scopus. In the second phase, the Fuzzy Delphi is applied in which experts were requested to score each criterion and subcriterion according to their importance and relevance to the topic of study. During this phase, the study screened out unimportant criteria and sub-criteria. The experts were also requested to categorize sub-criteria into input sub-criteria, desirable output sub-criteria, and undesirable output subcriteria. The third phase uses FAHP to compute the relative weights of the selected criteria and sub-criteria. In the final phase, relative weights of sub-criteria (input sub-criteria, desirable output sub-criteria, and undesirable output sub-criteria) were used in environmental DEA to measure the relative efficiency of alternatives. The environmental DEA also computed slacks to quantify necessary

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Fig. 3 e Map of hydro-geothermal resources in Pakistan [27].

Table 6 e Areas, temperature and applications of geothermal resources in Pakistan. Area

Temperature

Murtazabad

185e230  C

Karachi

70e145  C

Chagai

200e300  C

Chakwal

60e90  C

Kotli, Tatta Pani, 100e200  C and Tato

Applications
Production of ethanol and biofuels
Binary power plant
Green Housing
Fruit & vegetable drying
Food processing
Pulp & paper processing
Soft drink carbonation
Concrete Block curing
Hydrogen production
Dry and Flash steam power plant
Heat pump HVAC
Aquaculture
Biogas production
Mushroom culture
Binary power plant
Fabric Dyeing
Refrigeration & Ice making
Cement & Aggregate Drying
Pulp drying
HVAC
Lumber drying

reductions in input sub-criteria and undesirable sub-criteria, and an increase in desirable output sub-criteria. Fig. 4 illustrates the schematic diagram of research framework.

Methodology Fuzzy Delhi Dalkey and Helmer developed the Delphi method in 1963. The Delphi method is a survey-based method that obtains the opinion of experts. The method has three fundamental features including anonymous feedback, iteration and controlled-response, and statistical group opinion [35]. The expert opinion cannot always be transformed into quantitative values. In other words, the human way of thinking is subjective in nature and therefore their judgement can be imprecise and vague. In order to overcome subjectivity and vagueness in expert opinion, [36] introduced Fuzzy Delphi in 1993 which combined the conventional Delphi with the fuzzy set theory. Noorderhaben (1995) suggested that the use of fuzzy Delphi method can solve fuzziness involved in experts’ feedback. Fuzzy Delphi uses triangular fuzzy numbers (TFNs) to adjust fuzziness in the decision making process [35]. This study employs Fuzzy Delphi for the screening and finalization of initial criteria obtained from the literature review. The method is applied in following steps:

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Table 7 e Micro hydro potential in Pakistan [34]. Province

No. of Potential Sites

Potential Range (MW)

Total Potential (MW)

Type

300 150 125 40 200

0.2e40 MW 5e40 MW 0.2e32 MW 0.2e40 MW 0.1e38 MW

560 120 750 280 1300

Canals Canal Falls Natural Falls/Flow Natural Falls Natural Falls

Punjab Sindh Khyber Pakhtunkhwa Azad Jammu & Kashmir Gilgit-Baltistan

Step 1. Obtain expert opinion of decision group. This step collects experts’ feedback to assess the significance of criteria and sub-criteria identified through literature survey. The experts were requested to score each criteria and sub-criteria using linguistic variables. Step 2. Set up values of TFNs. This step computes TFNs values given by experts. The geometric mean model of Klir and Yuan (1995) [35] is applied to find the common understanding of expert group decision. The computing formula is as below: Assume number j expert out of n experts assigns evaluation value of the significance to number j element as:   fij ¼ aij ; bij ; cij where ði ¼ 1; 2; 3; …; nÞ and ðj ¼ 1; 2; 3; …; mÞ w Then, the fuzzy weightage of j expert is:   fj ¼ aj ; bj ; cj ; where ðj ¼ 1; 2; 3; …; mÞ w Among which, n     1X bij ; cj ¼ Max cij aj ¼ Min aij ; bj ¼ i i n i¼1

Step 3. Transformation of fuzzy numbers. This step employs center of gravity technique to transform fj of each criteria and sub criteria into fuzzy numbers weight w definite values Sj using following equation: Dj ¼

aj þ bj þ cj ; where ðj ¼ 1; 2; 3; …; mÞ 3

Table 8 e Micro hydro operational power projects in Pakistan [34]. S. No 1 2 3 4 5 6 7 8 9

Location

Capacity

Dargai, Malakand, KPK Pehur HES, Swabi, KPK Nandipur, Upper Chenab Canal, Punjab Rasul, Upper Jhelum Canal, Punjab Shadiwal, Upper Jhelum Canal, Punjab Chichoki Upper Chenab Canal, Punjab Reshun HES, Chitral, KPK Shishi HES, Chitral, KPK Renala, Lower Bari Doab Canal, Punjab

20 18 13.8 13.8 13.5 13.2 4.2 1.8 1.1

Fig. 4 e Schematic diagram of research framework.

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Step 4. Finalization of important criteria and sub-criteria. The final step selects important criteria and sub-criteria and screens out those which are not important for the study. It does so by comparing weights of criteria with the threshold ‘a’. The principle of screening is given as: If Dj  a, then criteria or sub-criteria j is selected. If Dj  a, then criteria or sub-criteria j is rejected. Fig. 5 shows the schematic of Fuzzy Delphi threshold.

Table 9 e Fuzzy scale. Linguistic variables Equal preference Moderate preference Strong preference Very strong preference Extreme preference

Fuzzy number

Membership function

~ 1 ~ 3 ~ 5 ~ 7

(1, 1, 1) (2/3, 1, 3/2) (3/2, 2, 5/2) (5/2, 3, 7/2)

~ 9

(7/2, 4, 9/2)

FAHP The earliest work on FAHP appeared in [37], which combined AHP with fuzzy set theory. The method is powerful in dealing with the uncertainty involved in the decision making process. Decision-makers often prefer to give interval judgements than fixed-valued judgments. This is because decision-makers are usually unable to precisely tell their preference due to vague nature of comparison processes. The initial step of FAHP develops a hierarchical structure of the problem. The hierarchical structure comprises of goal, criteria, and sub-criteria. The second step constructs pairwise comparison matrices to compare components with respected to their relevance and importance to goal, criteria, and subcriteria. Later, TFNs are used to define relative importance values to consider impression in human judgement. This ~ 5; ~ where 1 ~ 3; ~ 7; ~ 9) ~ shows equal study uses five TFNs (1; ~ shows extreme preference. Table 9 provides preference and 9 more details on TFNs, their linguistic variables, and the membership function. Reciprocal values are given to inverse comparisons, for instance, aji ¼ a1ij where aij shows the importance of ith component to the importance of jth ~ can be given component. Thus, the subsequent fuzzy matrix A by: 2

1 6a 6 ~21 ~¼ 6 : A 6 4 : ~n1 a

~12 a : : : ~n2 a

3 : a~1n : : 7 7 : : 7 7 : : 5 : 1

: : : : :

ða~ij ¼ 1Þ,

where 1

1

1

if

ði ¼ jÞ

are: aggregating individual priorities (AIP), and aggregating individual judgements (AIJ). The former is applicable in a situation where group members act together as a unit while the latter is applicable in a situation where individuals act separately [38]. This study uses AIJ method to aggregate individual judgements because the method handles expert judgements at earlier stages and avoids any re-evaluations by experts required in case if inconsistencies arise at later stage of alternative rankings. Let us denote TFN score given by expert i ~ ij ¼ ðaij ; bij ; cij Þ where ði ¼ 1; 2; ::; n) and on component j as w ~ ij ¼ ðai ; bi ; ci Þ where (j ¼ 1; 2;::;mÞ. The aggregate judgement w n P bij ; (j ¼ 1; 2; ::; mÞ of a group is given by aj ¼ Min faij g; bj ¼ n1 i

i¼1

~ ij ¼ ðai ; bi ; ci Þ and cj ¼ Maxfcij g. The crisp value of TFN w i   a þ ð4bj Þþ cj . where (j ¼ 1; 2; ::; mÞ is obtained by wj ¼ j 6 The next step is to check the consistency of pairwise comparisons. The consistency is checked using consistency index CI ¼ ðlmax nÞ=ðn 1Þ where n is the size of the matrix. The consistency of judgement is checked using consistency ratio CR ¼ CI=RI where RI denotes random consistency index whose values are listed in Table 10. The judgement matrix is accepted if the value of CR is greater than or equal to 0.1; otherwise, the matrix is considered as inconsistent.

Environmental DEA

and

~ 5; ~ ~ 3; ~ 7; ~ 9Þ ~ij ¼ 1; ða

or

1

~ ; 5 ~ ; 3 ~ ; 7 ~ ; 91 Þ if ði sjÞ. ~ij ¼ 1 ða The third step aggregates individual judgment and generates group priority vectors. Studies show two common approaches for aggregating the judgements. These approaches

DEA has been widely recognized as a powerful nonparametric method for frontier estimation. It is more suitable for measuring performance than conventional econometric techniques such as ratio analysis and regression analysis. DEA uses linear programming to compute the relative efficiency of decision-making units (DMUs) and provide targeted values for the improvement of DMUs. DMUs are free in DEA to prefer any combination of inputs and outputs to maximize their efficiency. Due to high recognition and advantages, DEA has been used in a variety of fields, for instance, banking sector [39], supply chain management [40,41], energy and environmental assessment [42e44] and other performance assessments. The conventional DEA, as discussed in [45], carries the assumption that the number of inputs should be minimized and the number of outputs should be maximized. However, as

Table 10 e Random Index values. Fig. 5 e Fuzzy Delphi threshold.

n RI

1 0

2 0

3 0.52

4 0.89

5 1.11

6 1.25

7 1.35

8 1.40

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Koopman’s [46] mentioned that undesirable outputs such as waste and pollutants are also produced as a byproduct during the production process. These undesirable outputs are not taken account by conventional DEA models leading to erroneous and biased performance assessment. As we know that the production of hydrogen from RES can also produce undesirable outputs; therefore, this study employs environmental DEA which also considers undesirable outputs while measuring relative efficiency performance. Incorporating undesirable outputs requires the redefinition of the production function. Thus, the initial output vector

efficient. If bCRS is greater than zero then the observation is inefficient. The environmental DEA model also computes slacks to quantify how much increase in desirable outputs and decrease in inputs and undesirable outputs is required to achieve an efficient level.

(y 2Rsþþ Þ where (i ¼ 1; 2; 3; 4;…;s) is redefined into (y ¼ yd þ yu )

Fuzzy Delphi was applied to select only relevant and most important sub-criteria. Out of 26 identified sub-criteria, 9 subcriteria were rejected due to being unimportant in the context of Pakistan, while 17 were selected for the evaluation. The selected sub-criteria were further classified into inputs, desirable outputs, and undesirable outputs sub-criteria in order to use them in environmental DEA. Six sub-criteria were categorized as inputs, eight as desirable outputs while remaining three were categorized as undesirable outputs. Table 11 lists the selected criteria and classification.

q 2Rþþ ). d u

with ðy

d

The corresponding reference technology

reprePCRS ¼ f ðx; y ; y Þ jx  Xl; yd  Yl; yu  Yl; l  0g sents weak disposability of undesired outputs, more details on weak disposability can be found in [47]. In this scenario, the directional efficiency measure ðxο ; ydο ; yuο Þ along a preassigned direction of output vector ðgy ¼ yd yu s0mþs ) gives solution of the given model: max b Subject to

Results Finalization of criteria and sub-criteria using Fuzzy Delphi

FAHP

Xl  xο Y d l  ydο þ bydο Y u l  yuο þ byuο   max yui  yuο  byuο l 0 bCRS

where denotes the optimal solution. If bCRS is equal to zero with lο ¼ 1; lj ¼ 0 (js0) then the observation is directional

After finalizing criteria and sub-criteria for evaluation, the FAHP was applied to compute relative weights. Fig. 6 shows the hierarchal structure of FAHP applied in this study. The FAHP initially computed the relative weights of the main criteria. The pairwise comparison matrix of the main criteria is provided in Appendix Table A-1. The pairwise matrix was solved to obtain the relative weights of main criteria which are shown in Fig. 7.

Table 11 e Selected criteria and sub-criteria for the evaluation. Criteria

Sub-criteria

Sub-criteria code

Selected/ Rejected

Classification of selected sub-criteria

Technical

Technical maturity Deployment time Resource potential Grid availability Performance prediction Local technicians Feasibility Investment cost O&M cost Electricity generation cost Feed-in tariff rate Economic value Funds availability R&D cost Economic viability Social acceptance Job creation Social benefits CO2 emission Stress on environment Land requirement Waste generation Political acceptance Compatibility with national energy policy National energy security Foreign dependency

T-1 T-2 T-3 T-4 T-5 T-6 T-7 E-1 E-2 E-3 E-4 E-5 E-6 E-7 E-8 S-1 S-2 S-3 Ev-1 Ev-2 Ev-3 Ev-4 P-1 P-2 P-3 P-4

Selected Selected Selected Rejected Rejected Selected Rejected Selected Selected Selected Rejected Rejected Rejected Selected Rejected Selected Selected Selected Selected Selected Selected Selected Selected Selected Rejected Rejected

Desirable output Input Desirable output e e Desirable output e Input Input Input e e e Input e Desirable output Desirable output Desirable output Undesirable output Undesirable output Input Undesirable output Desirable output Desirable output e e

Economic

Social

Environmental

Political

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Fig. 6 e The hierarchical structure of output and input criteria. After obtaining relative weights of the main criteria, relative weights of sub-criteria were computed. Table A-2eA-6 in Appendix present the pairwise comparison of sub-criteria under respective criteria categories. After solving pairwise comparisons, the relative weights of sub-criteria were obtained. Later, relative weights of sub-criteria were multiplied by the relative weights of their respective categories to compute the global weights for sub-criteria. Table 12 presents relative weights and global weights of each sub-criterion. In order to use sub-criteria relative weights in the environmental DEA, I initially obtained the weights of each subcriterion with respect to the alternatives. This means that weights of sub-criteria were solved individually for the alternatives. A number of 17 pairwise comparisons were constructed and subsequently solved. The weights of sub-criteria with respect to the alternatives are shown in Table 13. In order to obtain the relative weights of sub-criteria with respect to the alternatives, the weights of sub-criteria given in Table 13 were multiplied by their respective global weights. Finally, the relative weights of sub-criteria for the alternatives were obtained which are presented in Table 14.

Environmental DEA In the final phase, the environmental DEA was applied to compute the relative efficiency of RES for hydrogen

Fig. 7 e Relative weights of criteria.

production in Pakistan. Before employing environmental DEA, I prepared data to be used in the efficiency analysis. Relative weights of all input sub-criteria were summed to form single input. Similarly, relative weights of desirable outputs and undesirable outputs were added to respectively form single desirable output and single undesirable output. Finally, the data was used to measure the relative efficiency of alternatives. Table 16 presents the aggregated input data and results of relative efficiency analysis; where X denotes sum of relative weights of input sub-criteria with respect to the alternatives, Y shows the sum of relative weights of desirable outputs subcriteria with respect to the alternatives, Yu represents the sum of relative weights of undesirable outputs sub-criteria with respect to the alternatives, Beta shows the relative efficiency scores, while SlackX, SlackY, and SlackYu present slacks of inputs, desirable outputs, and undesirable outputs respectively. Finally, the RES were ranked according to their efficiency score presented in Table 15. Table 16 shows the relative efficiency score and final ranking of RES for hydrogen production in Pakistan.

Table 12 e Relative weights of criteria and sub-criteria. Criteria

Criteria Subweight criteria

Technical

0.232

Economic

0.285

Social

0.127

Environmental

0.259

Political

0.098

T-1 T-2 T-3 T-6 E1 E2 E3 E7 S-1 S-2 S-3 Ev-1 Ev-2 Ev-3 Ev-4 P-1 P-2

Subcriteria weight

Global weight of subcriteria

0.154 0.08 0.566 0.2 0.302 0.207 0.397 0.094 0.249 0.453 0.298 0.37 0.256 0.101 0.274 0.562 0.438

0.036 0.019 0.131 0.046 0.086 0.059 0.113 0.027 0.032 0.058 0.038 0.096 0.066 0.026 0.071 0.055 0.043

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Table 13 e Weight of sub-criteria with respect to alternatives.

T-1 T-2 T-3 T-6 E1 E2 E3 E7 S-1 S-2 S-3 Ev-1 Ev-2 Ev-3 Ev-4 P-1 P-2

Wind

Solar

Biomass

Geothermal

MSW

MH

0.27 0.159 0.297 0.212 0.199 0.098 0.048 0.147 0.105 0.23 0.25 0.08 0.192 0.305 0.184 0.265 0.242

0.239 0.17 0.228 0.228 0.098 0.148 0.089 0.139 0.24 0.214 0.216 0.101 0.147 0.254 0.203 0.217 0.218

0.124 0.254 0.117 0.271 0.103 0.312 0.149 0.088 0.107 0.188 0.146 0.372 0.251 0.072 0.188 0.156 0.177

0.06 0.047 0.074 0.047 0.304 0.197 0.404 0.298 0.058 0.044 0.047 0.108 0.195 0.064 0.157 0.038 0.047

0.104 0.119 0.112 0.086 0.15 0.202 0.206 0.21 0.243 0.153 0.193 0.253 0.107 0.107 0.128 0.122 0.118

0.202 0.251 0.173 0.157 0.146 0.042 0.104 0.119 0.247 0.171 0.148 0.085 0.109 0.199 0.141 0.202 0.199

Table 14 e Relative weights of sub-criteria in respect to alternatives.

T-1 T-2 T-3 T-6 E1 E2 E3 E7 S-1 S-2 S-3 Ev-1 Ev-2 Ev-3 Ev-4 P-1 P-2

Wind

Solar

Biomass

Geothermal

MSW

MH

0.010 0.003 0.039 0.010 0.017 0.006 0.005 0.004 0.003 0.013 0.009 0.008 0.013 0.008 0.013 0.015 0.010

0.009 0.003 0.030 0.011 0.008 0.009 0.010 0.004 0.008 0.012 0.008 0.010 0.010 0.007 0.014 0.012 0.009

0.004 0.005 0.015 0.013 0.009 0.018 0.017 0.002 0.003 0.011 0.006 0.036 0.017 0.002 0.013 0.009 0.008

0.002 0.001 0.010 0.002 0.026 0.012 0.046 0.008 0.002 0.003 0.002 0.010 0.013 0.002 0.011 0.002 0.002

0.004 0.002 0.015 0.004 0.013 0.012 0.023 0.006 0.008 0.009 0.007 0.024 0.007 0.003 0.009 0.007 0.005

0.007 0.005 0.023 0.007 0.013 0.002 0.012 0.003 0.008 0.010 0.006 0.008 0.007 0.005 0.010 0.011 0.009

Table 15 e Relative efficiency analysis of RES. DMU Wind Solar Biomass Geothermal MSW MH

X

Y

Yu

Beta

SlackX

SlackY

SlackYu

0.0432 0.0408 0.0531 0.0940 0.0588 0.0399

0.1095 0.0984 0.0683 0.0243 0.0580 0.0801

0.0335 0.0338 0.0656 0.0344 0.0404 0.0254

0.0000 0.0491 0.5175 0.6451 0.3905 0.0179

0.0000 0.0000 0.0122 0.0783 0.0269 0.0077

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

0.0000 0.0006 0.0000 0.0000 0.0000 0.0000

Sensitivity analysis

Table 16 e Final ranking of RES. Alternatives Wind Solar Biomass Geothermal MSW MH

Relative efficiency score

Final Ranking

0 0.0491 0.5175 0.6451 0.3905 0.0179

1 3 5 6 4 2

Sensitivity analysis investigates whether any changes in criteria weights influence the results. In order to know any impact on the results, we varied criteria weights in six more cases. The different weights used in seven cases are given in Table 17, while the results of these cases are illustrated in Fig. 8.

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Table 17 e Weights of criteria under different cases.

Technical Economic Social Environmental Political

Case-1 (Fuzzy AHP weights)

Case-2

Case-3

Case-4

Case-5

Case-6

Case-7

0.232 0.259 0.127 0.285 0.098

0.2 0.2 0.2 0.2 0.2

0.4 0.15 0.15 0.15 0.15

0.15 0.4 0.15 0.15 0.15

0.15 0.15 0.4 0.15 0.15

0.15 0.15 0.15 0.4 0.15

0.15 0.15 0.15 0.15 0.4

After obtaining criteria weights, the environmental DEA was employed to compute the relative efficiency of RES. Finally, I ranked RES based on their respective efficiency scores. Wind achieved efficiency score of 0 which translates full efficiency level. Micro-hydro achieved 0.0077 which is closed to 0 and therefore was ranked second. Solar achieved 0.0491 and was subsequently ranked third. MSW ranked fourth with efficiency score of 0.3905. Biomass ranked fifth by achieving 0.5175 efficiency score whereas geothermal ranked last by obtaining efficiency 0.6451. The environmental DEA also presented analysis of slacks which helps RES to achieve efficient level.

Conclusions

Fig. 8 e Sensitivity analysis.

From Fig. 8, we can see that varying weights have no influence on the final rankings and therefore it can be said that the results are robust.

Discussion This study conducted the feasibility of six RES (wind, solar, biomass, geothermal, MSW, and micro-hydro) in the Pakistani context. These RES were evaluated under five main criteria and seventeen sub-criteria. Firstly, the study employed the Fuzzy Delphi to select important criteria and sub-criteria which were identified using literature survey. The selected sub-criteria were further classified into inputs, desirable outputs, and undesirable outputs sub-criteria. It can be seen from Table 11, that six sub-criteria were categorized as inputs, eight sub-criteria as desirable outputs, and three as undesirable outputs. In the next step, FAHP was applied to obtain weights of criteria and sub-criteria. Environmental criteria received highest weight of 0.285 followed respectively by Economic (0.259), Technical (0.232), Social (0.127), and Political (0.098). Resource potential sub-criteria achieved the highest global weight of 0.131 whereas the Deployment time sub-criteria received least global weight of 0.019. Later, relative weights of sub-criteria with respect to the alternatives were found and presented in Table 14.

Renewable hydrogen is recognized as a green and sustainable energy future. Several countries around the world are focusing on developing hydrogen economies. There is also a great potential for Pakistan to produce green hydrogen from copious RES available in the country. The green hydrogen production can play a crucial rule in curbing Pakistan’s energy crisis and further strengthening its future energy security. However, in case to develop a successful hydrogen economy, it is essential that the green hydrogen is produced using the most optimum energy sources. In this study, we assessed the feasibility of six RES in Pakistan and found that wind energy best suits for green hydrogen production. Also micro-hydro and solar energy provide feasible options. However, MSW, biomass, and geothermal technologies in Pakistan have yet not matured to be considered for hydrogen production. The study is useful for energy planners and policymakers to make effective decision-making regarding green hydrogen production. The results of the study are limited to Pakistan as we received feedback from the experts in the context of Pakistan. Also, other factors such as economic status, renewable energy potential, and other socio-political conditions can be substantially different from other countries. Nonetheless, the integration of MCDA established in this study can be replicated for related studies in other countries.

Acknowledgement The authors acknowledge anonymous reviewers for their valuable comments to improve this manuscript to a higher standard.

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Appendix

Table A1 e Paired matrix main criteria.

Technical Economic Social Environmental Political Technical

Technical

Economic

1, 1, 1 0.728, 1.096, 1.633 0.598, 0.833, 1.183 0.958, 1.36, 1.868 0.339, 0.443, 0.624 1, 1, 1

0.612, 0.913, 1.373 1, 1, 1 0.305, 0.411, 0.609 0.407, 0.575, 0.913 0.51, 0.724, 1.096 0.612, 0.913, 1.373

Social 0.845, 1.201, 1.642, 2.434, 1, 1, 1 1.134, 1.642, 0.649, 0.891, 0.845, 1.201,

Environmental 1.673 3.281 2.263 1.23 1.673

0.535, 0.735, 1.043 1.096, 1.74, 2.458 0.442, 0.609, 0.882 1, 1, 1 0.323, 0.443, 0.684 0.535, 0.735, 1.043

Political 1.603, 2.255, 0.913, 1.381, 0.813, 1.122, 1.463, 2.255, 1, 1, 1 1.603, 2.255,

2.949 1.961 1.541 3.097 2.949

Technical 1, 1, 1 0.728, 1.096, 1.633 0.598, 0.833, 1.183 0.958, 1.36, 1.868 0.339, 0.443, 0.624 1, 1, 1

Table A2 e Paired matrix of sub-criteria under Technical category.

Technical maturity Deployment time Resource potential Local technicians

Technical maturity

Deployment time

Resource potential

Local technicians

1, 1, 1 0.498, 0.7, 1.034 1.878, 2.667, 3.521 0.833, 1.303, 1.961

0.967, 1.428, 2.009 1, 1, 1 2.058, 3.006, 3.879 0.639, 0.861, 1.201

0.284, 0.375, 0.533 0.258, 0.333, 0.486 1, 1, 1 0.552, 0.742, 1.034

0.51, 0.767, 1.201 0.833, 1.161, 1.565 0.967, 1.348, 1.813 1, 1, 1

Table A3 e Paired matrix of sub-criteria under Environmental category.

CO2 emission Stress on environment Land requirement Waste generation

CO2 emission

Stress on environment

Land requirement

Waste generation

1, 1, 1 0.429, 0.578, 0.841 0.484, 0.633, 0.848 0.601, 0.761, 0.967

1.189, 1.73, 2.332 1, 1, 1 0.442, 0.598, 0.813 0.798, 1.059, 1.373

1.179, 1.579, 2.065 1.23, 1.673, 2.263 1, 1, 1 1.065, 1.571, 2.242

1.034, 1.315, 1.664 0.728, 0.944, 1.253 0.446, 0.636, 0.939 1, 1, 1

Table A4 e Paired matrix of sub-criteria under Economic category.

Investment cost O&M cost Electricity generation cost R&D cost

Investment cost

Investment cost

Investment cost

Investment cost

1, 1, 1 0.498, 0.7, 1.034 0.771, 1.096, 1.541 0.461, 0.661, 0.976

0.967, 1.428, 2.009 1, 1, 1 1.878, 2.702, 3.575 0.421, 0.564, 0.813

0.649, 0.913, 1.296 0.28, 0.37, 0.533 1, 1, 1 0.475, 0.643, 0.874

1.024, 1.513, 2.168 1.23, 1.772, 2.376 1.144, 1.555, 2.105 1, 1, 1

Table A5 e Paired matrix of sub-criteria under Social category.

Social acceptance Job creation Social benefits

Social acceptance

Job creation

Social benefits

1, 1, 1 1.069, 1.455, 2.035 0.779, 1.122, 1.541

0.491, 0.687, 0.936 1, 1, 1 0.527, 0.711, 1.019

0.649, 0.891, 1.284 0.982, 1.407, 1.896 1, 1, 1

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