Integrated Approach for Offshore Wind Turbine Site Selection: Implications for Sustainability in Power Supply Chain

Abstract

Offshore wind turbine (OWT), a sustainable energy source, has recently gained wide attention. The energy demand for India is soaring high as it is a fast-developing nation in terms of industrialization; however, the interest shown by India toward renewable energy is low, especially for OWTs. This study aims to identify, categorize, and evaluate the criteria needed to be considered in the installation of OWTs and selection of potential locations in India. Based on literature analysis and exploratory interviews with experts, six aspects, namely, climatic conditions, regional features, investments and benefits, environmental impact, economic impact, and social and technical impact, with a total of twenty-six criteria, were identified and evaluated. An integrated approach of data envelopment analysis (DEA) with grey analytical hierarchy process (GAHP) and grey Complex proportional assessment (GCOPRAS) is used to evaluate the criteria and also to identify the locations for OWTs. Soil condition, extreme wind speed, seismic movement, tidal flow, and closeness to the power transmission grid have been identified as the top five criteria to be considered in the installation of OWTs. Gujarat, Tamil Nadu, Odisha, the Lakshadweep Islands, and the Andaman and Nicobar Islands have been identified as potential locations for installing OWTs in India. The outcomes of this study will deliver better insights for the practitioners about the criteria that need to be considered in OWTs. Further, this study sheds light on the importance of OWTs in an Indian context, which can possibly attract more investments.

1. Introduction

Energy is a vital commodity for a country to accomplish sustainable development. Energy demand increases with an increase in population, rapid urbanization, improved industrial activity, and economic competitiveness [1]. The prediction by Lowe and Drummond (2022) [2] indicates that the global energy demand in 2050 will increase by 50% against the installed capacity in 2019. Currently, in meeting the global energy demand, nearly 81% of energy is produced out of non-renewable resources (coal, oil, and gas), while the amount of energy produced through renewable resources (solar, wind, and biofuel) is modest only [3]. An estimation by the International Energy Agency (IEA) indicates that India’s energy demand will increase by nearly 50% between 2019 and 2030, and presently, nearly 80% of India’s energy requirements are fulfilled using coal and oil (IEA, 2021) [4]. From 2006 to 2016, the number of coal-fired thermal power plants in India has tripled [5]. Among the countries, China and India burn 60% of the world’s coal and, as a result, have been recognized as global emission hotspots. Realizing the need to lower the global warming impact by 2° Celsius, the Intergovernmental Panel on Climate Change (IPCC) has urged the global nations for a swift transition to renewable energy (RE), and it expects 70–85% of the world’s energy demand in 2050 to come from renewable resources [6]. Further, the concern regarding the energy security policy pushed the nations to pursue RE resources [7]. Also, to attain sustainable development goals (SDGs) 7 (sustainable energy) and 13 (climate action) proposed by the United Nations, energy from renewable sources (wind, biomass, hydropower, sunlight, geothermal, wave, and tide) is much needed [8].

Compared with other renewable sources, wind energy is more preferred for reasons like technological and levelized cost of maturity [9]. Considering the advantages, many nations have started installing onshore wind farms and later realized that such wind farms consume major land areas. Further, the nature of the terrain also influences the effectiveness of onshore wind turbines [10]. Also, restrictions on the distance between the onshore wind turbines and the number of wind turbines within a specific area hamper the installation of onshore wind turbines [11]. With technology development, the onshore wind turbine has moved toward offshore wind turbine (OWT), which consumes less area [12]. The first OWT was installed and operated in 1991 in Vindeby, Denmark [13]. Since then, thousands of megawatt (MW) capacity of OWTs has been installed for large-scale electricity generation. As of 2020, the total global offshore wind energy was 35.5 GW. Three countries, namely the United Kingdom (UK) (10.42 GW, i.e., 29%), China (9.99 GW, i.e., 28%), and Germany (7.68 GW, i.e., 22%), share more than 75% of installed global OWT capacity. Other important countries of OWT energy are the Netherlands (2.61 GW), Belgium (2.26 GW), and Denmark (1.70 GW) [14]. Likewise, Europe has increased the installed OWT capacity from 3.6 GW in 2000 to 22 GW in 2019 [9]. Regarding the installation of OWTs, the developed nations have shown more interest than the developing nations, which is evident from the earlier literature. In lowering the global warming effect, the role of developing countries is crucial as these countries hold considerable responsibility for increasing pollution. However, the RE practices received only a lukewarm response from the developing countries [5,15,16].

For India, the most polluted and populated country with high industrial activity, the transition toward RE resources is more compelling. Presently, India holds the fourth position in the global wind power industry [17]. The wind energy projects in India mainly focuse on onshore wind turbines, which consume more land areas. Though being bestowed with a 7516.6 km coastline (5422.6 km—mainland and 1197 km—islands), India has not fully explored the possibility of installing OWTs. Recently, the Government of India notified the “National offshore wind energy policy” to examine the possibility of OWT farms. Although the installation of OWTs is more complicated for reasons like corrosion by high salt fog, lightning strikes, and typhoons, countries could not bypass it as its contribution to RE could be vital [18]. A study carried out by Govindan and Shankar (2016) [12] evaluated the barriers to the installation of OWTs in India. It found that high capital cost investment is the critical barrier and also underlined that conceptual understanding is very low in India. A recent study by Dhingra et al. (2022) [19] also focused on the barriers to OWTs in the Indian context and explored the barriers under seven categories.

The role of India in lowering the climate crisis and global transition toward RE is very crucial; however, there are only very few studies from the Indian context on OWT installation [20]. To relish the ambitious objective of generating 450 GW from RE sources by 2030, India also has to focus on the OWTs in addition to other renewable resources. Further, earlier studies [10,11] on wind turbines have focused mainly on climatic conditions and regional features, while social and environmental impacts have not been given the desired attention. So, the social and environmental impacts have to be considered in the installation of OWTs, and selecting suitable locations in India is mandatory. Considering the significance of OWT energy in the Indian scenario, this study wishes to fill the critical void in the literature by answering the following research questions:

(a)

What are the criteria to be considered in the installation of OWTs in India and the classification of those criteria?

(b)

What are the possible locations where OWTs could be installed?

To answer the above research questions, this study uses an integrated approach of data envelopment analysis (DEA) with grey analytical hierarchy process (GAHP), and grey Complex proportional assessment (GCOPRAS). Initially, to identify the criteria for OWT installation, relevant literature was reviewed, followed by exploratory interviews with experts working in the wind energy area. Based on the interviews, the criteria are grouped into six categories (climatic conditions, regional features, investments and benefits, environmental impact, economic impact, and social and technical impact). Next, the integrated approach has been used to select the potential locations for OWTs from a list of locations.

The paper is organized in the following way. Section 2 explains the need and status of RE resources in developing countries, the criteria to be considered in OWTs, and research gaps. Section 3 gives details about the methods used in this study. Section 4 illustrates an empirical application of the methods in selecting locations for OWTs. Section 5 discusses and validates the results obtained. Section 6 concludes the study by providing the contributions, limitations, and future scope.

2. Literature Review

2.1. RE in Developing Countries

The roles and contributions of the developing countries are very critical in ensuring the global RE transition as these countries account for a large proportion of pollution [6]. The over-dependence of developing countries on fossil fuels has increased pollution levels, and as a result, seven out of ten most polluted cities are located in South Asia [16]. Pollution levels in major industrial cities of Bangladesh, China, India, and Pakistan exceed the standard level [21]. The given pollution scenario signifies the need for transition to RE production in these developing countries. According to Balakrishnan et al. (2020) [22], limited countries like China, Germany, and the USA have shown much interest in the transition toward RE while the rest of the countries are still relying on fossil fuels. Unpopularity, high implementation cost, and late return of capital have been mentioned as some of the major complexities in the transition toward RE resources. For a long time, developing countries have been depending on biomass and hydro-power for RE generation. Vanegas Cantarero (2020) [6] advocates that in 2019 the developed countries invested nearly 152 billion dollars in RE while the developing countries invested nearly 130 billion dollars.

India started investing in RE projects from the 1980s and is now ranked fourth among the global countries. It has set an ambitious objective of generating 450 GW from RE sources by 2030 [23]. Relevant research endorses that the goal is achievable as India is endowed with perennial sunshine and has a long coastline [24]. As of 31 December 2021, India ranks third in the RE country attractive index and has a total installed capacity for RE of 151.4 GW. India secures fourth and fifth rank in terms of installed wind and solar energy generation. To give more stimulus for RE generation, India has allowed 100% foreign direct investment (Invest India, 2021) [25]. Though India is offered many RE sources, it has not reached the benchmark growth in RE generation for myriad reasons [26]. Compared with RE resources (solar, wind, and hydro), the interest shown by India in OWT installation is very low. Having a large coastline, India must explore the possibility of installing OWTs.

2.2. Proposed Criteria

Though RE sources (solar and hydro) have been well-established and are being utilized, they have certain limitations. For instance, the efficiency of solar energy is questionable during winter days. Likewise, hydropower generation is limited by water storage and water release limits. Such limitations directed the energy practitioners to focus on wind energy [12]. As of now, wind energy is widely embraced due to its low-cost electricity generation. Owing to its various benefits, a number of onshore wind farms are established. However, onshore wind turbines consume more land areas, which is unaffordable for small countries [27]. This limitation of onshore wind turbines became the seed for OWTs. Appreciably, the energy produced by the OWTs is ten times higher than that of the onshore wind turbine [28]. As a result, swift initiatives were taken by European countries, North American nations, and China in installing OWTs, while other nations, especially developing nations (Brazil, India, and Russia), are struggling with the conceptual understanding of OWTs [29]. For the successful installation of OWTs, a number of criteria have to be considered and analyzed. Compared with onshore wind turbines, the installation of OWTs poses many technical challenges. A study by Dhingra et al. (2022) [19] indicated grid connections, public acceptability, and economic viability as the criteria to be considered in OWT installation. A similar study by Jiang (2021) [30] pointed out weather conditions and safety risks involved in lifting operations as the major factors in erecting OWT. Ma et al. (2021) [18] highlighted economic condition, maintenance cost, technical services, and operating performance as the important criteria in OWT installation. Gavériaux et al. (2019) [31] mention wind frequency and environmental impact as critical factors to be considered in OWTs. A study carried out by Shafiee and Adedipe (2022) [32] in analyzing the OWT decommissioning in the UK specified lifting and loading of the turbine as the major challenge. Similarly, a study by Chomać-Pierzecka [33] pointed out that social and environmental concerns restrict the OWT installation in Poland. Further, awareness related to RE resources for energy production has to be created by the government [34]. By reviewing the existing literature, the criteria to be considered in OWT installation are identified and are categorized into six aspects of the experts’ suggestions as given in

Table 1. Aspects and criteria to be considered in OWT installation.

Aspects	Criteria	References
Climate conditions	Wind speed	[9]
	Wave height	[31]
	Extreme wind speed	[18]
	Frequency window effects	[12,35]
	Tidal flow	[30]
Regional features	Water depth	[19]
	Soil condition	[18,19]
	Seismic movement	[35]
	Closeness to shore	[18]
	Closeness to power transmission grid	[31]
	Closeness to oil reserves	[9]
	Closeness to naval bases	[18]
Investments and benefits	Operation and maintenance costs	[30]
	Benefit-to-cost ratio	[12,19]
	Net energy production	[30]
Environmental impact	Noise impact	[35]
	Closeness to environmental conservation areas	[9,33]
	Impact of marine species	[19]
Economic impact	Economic outreaches	[18,33]
	Energy demand	[9]
	Local installation vessel availability	[30]
	Local fabrication capability	[35]
Social and technical impact	Local residents’ support and government laws	[30,34]
	Land acquirement	[9]
	Skilled workforce availability	[18,31]
	Tax deduction	[12,35]

.

2.3. Existing Multi-Criteria Decision-Making (MCDM) Approaches

Since the establishment of RE sources is influenced by numerous factors, an appropriate method capable of considering and evaluating numerous factors has to be used to select an ideal location. The MCDM technique appears to be an ideal method as it handles and evaluates many factors by considering the consolidated opinions of experts when arriving at a solution [36]. Earlier studies have used MCDM techniques for the selection of locations for various RE sources. For instance, Wang et al. (2022) [37] used an integrated approach comprising of DEA and GAHP—Technique for Order Preference by Similarity to Ideal Solution (GTOPSIS) to identify locations for solar plants in Vietnam. Abdel-Basset et al. (2021) [35] utilized a hybrid MCDM approach of AHP and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE)-II in a neutrosophic context for selecting the locations of OWTs in Egypt. Shahraki Shahdabadi et al. (2021) [38] used Simple Additive Weighting (SAW), TOPSIS, and method of elimination and choice translating reality (ELECTRE) methods with the Shannon entropy mass method for selecting locations for a biomass power plant in Iran. To select locations for solar and wind farms in India, Saraswat et al. (2021) [39] used a Geographical Information System (GIS) combined with fuzzy AHP (FAHP).

2.4. Research Gaps and Contributions

For India, being a highly populated and industry-dominant country, the energy demand is high. In meeting the energy demands, India mainly depends on coal and as a result, it remains one of the most polluted countries with poor air quality [40]. So, the transition toward the RE resource is indispensible. Realizing the need and urgency, India has started investing more in RE resources (Invest India, 2021) [25]. Also, India has been generating sufficient amount of energy from the wind sources, i.e., mainly from onshore wind turbines. However, as indicated by Ren et al., (2021) [27], the onshore wind turbines occupy more land areas and the space below the turbines becomes uninhabited. Such drawback of onshore wind turbines reminds one of offshore wind shore turbines. Kota et al. (2015) [41] highlighted that the southern coast of India is capable of producing 1 GW of power. Hence, India needs to explore the potential of generating energy from the OWTs. Sufficient studies examining the possibility of deploying OWTs in India are missing.

To fill this gap and contribute to the literature on OWT, an effort has been made in this study to identify the suitable locations for OWTs in India using an integrated approach of DEA, GAHP, and GCOPRAS. Regarding this, the criteria that need to be considered in the installation of OWTs are collected along with the possible locations in India where OWTs could be installed. With DEA, the potential locations for OWTs are filtered out based on the performance of quantifiable dimensions (wind speed, wave height, water depth, and energy demand). Next, using GAHP and GCOPRAS, the locations were analyzed and ranked based on criteria (climatic conditions, regional features, investments and benefits, environmental impact, economic impact, and social and technical impact).

3. Methodology

3.1. DEA

In this study, DEA is used to shortlist the locations by selecting the DMUs having an efficiency score equal to 1. The thumb rule of DEA is that the number of DMUs should be less than five times the total number of input and output indicators (Rashidi and Cullinane, 2019) [42,43]. Charnes et al. (1978) [44] proposed the DEA method by presenting a non-parametric frontier approach for calculating the relative efficiency of a set of homogeneous alternatives, DMUs, with multiple inputs and outputs. DEA does not require any prior assumptions regarding the relationships between inputs and outputs. Thus, DEA avoids subjectivity in weight determination (Giannakitsidou et al., 2020) [45]. The two most commonly used DEA models are the Charnes–Cooper–Rhodes (CCR) model and the Banker–Charnes–Cooper (BCC) model. Returns to scale are the basic difference between these two models. The BCC model is based on the effect of variable returns to scale, and the CCR model is based on constant returns to scale (Sharma et al., 2016) [46].

The symbols and notations used in this study are as follows: $n$—number of DMUs, $DM{U}_i$—$i^{th}$ DMU $(i=\mathrm{1,2},\dots ,n)$, $DM{U}_o$—DMU target, $a_o=\left(a_{o1},a_{o2},\dots ,a_{om}\right)$—input vector of $DM{U}_o$, $b_o=\left(b_{o1},b_{o2},\dots ,b_{on}\right)$—output vector of $DM{U}_o$, $a_i=\left(a_{i1},a_{i2},\dots ,a_{im}\right)$—input vector of $DM{U}_i$ $\left(i=\mathrm{1,2},\dots ,n\right)$, $b_i=\left(b_{i1},b_{i2},\dots ,b_{in}\right)$—output vector of $DM{U}_i$, $u\in R^{m\times 1}$—weight-input vector, $\nu \in R^{n\times 1}$—weight-output vector, $\theta$—efficiency for DMUs.

3.1.1. CCR Model

The CCR model was first introduced by Charnes et al. (1978) [44], and the steps are as follows:

$$\underset{u,v}{Max\xi }=\frac{v^T{b}_o}{u^T{a}_o}$$

(1)

such that

$$v^T{b}_e\le u^T{a}_e,e=\mathrm{1,2},\dots ,n, u\ge 0,v\ge 0$$

(2)

3.1.2. BCC Model

The BCC model was proposed by Banker et al. (1984) [47], and the following are the steps involved:

$$\underset{u,v,v_0}{Max\delta }=\frac{v^T{b}_o-v_o}{u^T{a}_o}$$

(3)

such that

$$\frac{v^T{b}_e-v_o}{u^T{a}_e}\le 1,e=\mathrm{1,2},\dots ,n, u\ge 0,v\ge 0$$

(4)

3.2. Grey System Theory

Ju-Long (1982) [48] proposed grey system theory (GST), which is capable of solving uncertain problems with discrete and partial data under fuzziness conditions. The grey numbers are converted into crisp numbers using the following steps [49]:

(a)

Normalization;

(b)

Standardization;

(c)

Computation.

3.2.1. GAHP

AHP, introduced by Saaty (1980) [50], is the most frequently used MCDM technique. AHP helps in breaking down a complex problem into simple criteria. In AHP, the factors under consideration are evaluated by making pairwise comparisons between the factors. In this study, GST is combined with AHP to avoid subjectiveness in the evaluation process. The grey scale and numbers used in this study are given in

Table 2. Linguistic terms and grey numbers.

Linguistic Terms	Grey Scale	Grey Numbers
Very high	4	[0.75, 1]
High	3	[0.50, 0.75]
Low	2	[0.25, 0.50]
Very low	1	[0.25, 0]
Neutral	0	[0, 0]

. The steps involved in GAHP are given below [51]:

(a)

Establish pairwise comparison matrix;

(b)

Normalize the grey comparison matrix;

(c)

Check the consistency of the matrix;

(d)

Calculate the weights of the criteria.

3.2.2. GCOPRAS

The COPRAS technique, introduced by Zavadskas and Kaklauskas (1994) [52], is used to determine the best alternative. Zavadskas et al. (2008) [53] was the first to propose the integration of GST with COPRAS, in which the indices are given in terms of intervals. GCOPRAS are efficient in handling problems with inaccuracy and non-deterministic [54]. The steps involved in GCOPRAS are discussed below [54]:

(a)

Calculate the weight importance of the criteria. Here, the weight importance of the criteria is estimated using the GAHP technique.

(b)

A comparison matrix is established by the experts, using linguistic terms given in Table 2, between each alternative with respect to the criteria.

(c)

Establish the normalized decision matrix

(d)

Construct the weight normalized decision matrix

(e)

Calculate the relative significance of alternatives

(f)

The relative significance of each alternative is calculated

(g)

Calculate the utility degree of each alternative

4. An Empirical Study in India for OWT

For India, the electricity demand has always been high. It has been estimated that the yearly electricity demand of India is expected to increase from 1160 TWh in 2016–2017 to 2531 TWh in 2031–2032 [55]. Hence, India is in a situation where it needs to ramp up its energy production. At present, India is 76% dependent on coal to meet its energy demand [5]. Overdependence on coal has resulted in severe environmental consequences. As a result, India has been listed as the second top carbon emitter, next to China, by the global environmental bodies. At the Paris Climate Summit, India has given its assent to the ambitious 1.5 degrees reduction strategy [56]. To act accordingly, India needs to reduce carbon emissions by giving up its dependence on coal and establishing more RE sources. Realizing the need for transformation to RE, India has devised a plan to install a non-fossil fuel-based energy system to contribute to 40% of the energy demand by 2030. As an initiative, the Ministry of New and Renewable Energy wishes to set up RE sources capable of generating 175 GW by 2022 [15]. Presently, India holds the fifth position in terms of RE consumption [7]. Although India has shown a large interest in RE resources, it has not explored energy from OWTs. Having a large coastline, India needs to explore the potential of OWTs.

Here, the proposed research framework (Figure 1) is utilized for identifying suitable locations to install OWTs. Criteria and alternatives were identified from earlier literature and experts’ input. An expert panel consisting of twelve experts with sufficient knowledge of OWTs has been used for the evaluation of the criteria and selection of suitable locations. The experts were selected by following the judgmental sampling technique as it only selects experts having sufficient knowledge on the specific research topic [57]. The profile of the experts is given in

Table 3. Profile of the experts.

Characteristics			n	%
Experts (n = 12)	Educational qualification	Doctorate	3	25
		Post-graduation	3	25
		Graduation	6	50
	Position	Energy engineer	3	25
		Sites analysis engineer	3	25
		RE consultant	3	25
		Project engineer	3	25
	Working experience	More than 15 years	3	25
		10–15 years	3	25
		<10 years	6	50

.

4.1. Phase 1: Selection of Potential Locations Using DEA Technique

In this phase, a total of thirteen locations are considered as DMUs. Three inputs (wind speed, wave height, and water depth) and one output (energy demand) is considered to determine the perfect efficiency scores (equal to 1) of DMUs. Input and output factors of 13 locations are given in

Table 4. Input and output factors of the DEA model [58,59].

Location	DMU	Wind Speed (m/s)	Wave Height (m)	Water Depth (m)	Energy Demand (MW)
Maharashtra	L01	8	1.1	123	16,113
Tamil Nadu	L02	6	0.9	99	25,147
Andaman and Nicobar Islands	L03	5	1.2	24	448
Karnataka	L04	6	1	110	13,585
Kerala	L05	6	1.2	40	3182
Goa	L06	6	1.2	90	14,569
Andhra Pradesh	L07	6	1.5	101	10,059
Odisha	L08	6	1.2	30	4170
West Bengal	L09	6	1.2	90	6111
Daman and Diu	L10	8	1.1	112	286
Puducherry	L11	6	1.2	45	328
Gujarat	L12	4	1.5	95	38
Lakshadweep Islands	L13	4	1	65	58

. Using these data, DEA (CCR and BCC) is carried out. The DMUs with an efficiency score of 1 are selected for second phase analysis and are evaluated using GAHP and GCOPRAS. The efficiency score of the DMUs obtained in the DEA technique is given in

Table 5. DEA model results of the locations.

DMU	Efficiency Score		Rank
	CCR	BCC
L01	52.43%	81.82%	11
L02	100.00%	100.00%	1
L03	7.35%	100.00%	2
L04	54.02%	92.31%	8
L05	31.32%	94.94%	6
L06	63.73%	88.05%	9
L07	40.00%	79.95%	13
L08	54.72%	100.00%	3
L09	26.73%	81.33%	12
L10	1.01%	83.33%	10
L11	2.87%	92.79%	7
L12	0.23%	100.00%	4
L13	0.35%	100.00%	5

. In the CCR model, only one DMU achieved a perfect efficiency score. However, in the BCC model, five DMUs achieved perfect efficiency scores, which are L02, L03, L08, L12, and L13. These five locations are considered as potential sites for OWTs and are evaluated in the second phase.

4.2. Phase 2: Ranking of Selected Locations Using G-MCDM

4.2.1. Weight Calculation Using GAHP

As shown in

Table 6. Aspects and criteria determining the suitability for OWT installation.

Aspects	Criteria
Climate conditions (C1)	Extreme wind speed (C11)
	Frequency window effects (C12)
	Tidal flow (C13)
Regional features (C2)	Soil condition (C21)
	Seismic movement (C22)
	Closeness to shore (C23)
	Closeness to power transmission grid (C24)
	Closeness to oil reserves (C25)
	Closeness to naval bases (C26)
Investments and benefits (C3)	Operation and maintenance costs (C31)
	Benefit-to-cost ratio (C32)
	Net energy production (C33)
Environmental impact (C4)	Noise impact (C41)
	Closeness to environmental conservation areas (C42)
	Impact of marine species (C43)
Economic impact (C5)	Economic outreaches (C51)
	Local installation vessel availability (C52)
	Local fabrication capability (C53)
Social and technical impact (C6)	Local residents support and government laws (C61)
	Land acquirement (C62)
	Skilled workforce availability (C63)
	Tax deduction (C64)

, a total of 22 criteria grouped under six aspects are considered for ranking of the locations. The initial comparison matrix between the aspects was obtained from the experts using the linguistic terms (Table 2). The ratings of the experts are aggregated, and the aggregated grey comparison matrix is provided in

Table A1. Aggregated grey comparison matrix of G-AHP.

Aspects	C1	C2	C3	C4	C5	C6
C1	[0, 0]	[0.75, 1]	[0.65, 0.9]	[0.55, 0.8]	[0.5, 0.75]	[0.70, 0.95]
C2	[0.75, 1]	[0, 0]	[0.65, 0.9]	[0.75, 1]	[0.65, 0.9]	[0.75, 1]
C3	[0.3, 0.55]	[0.25, 0.5]	[0, 0]	[0.25, 0.4]	[0.1, 0.2]	[0.10, 045]
C4	[0.75, 1]	[0.65, 0.9]	[0.75, 1]	[0, 0]	[0.65, 0.9]	[0.75, 1]
C5	[0.1, 0.45]	[0.1, 0.25]	[0.05, 0.1]	[0.1, 0.28]	[0, 0]	[0.15, 0.40]
C6	[0.1, 0.45]	[0.2, 0.45]	[0.05, 0.3]	[0.3, 0.6]	[0.1, 0.45]	[0, 0]

of Appendix A. The aggregated grey comparison matrix is converted into crisp numbers. Then, the consistency ratio is checked. The crisp matrix of the six aspects is given in

Table A2. Grey crisp matrix of G-AHP.

Aspects	C1	C2	C3	C4	C5	C6
C1	0	0.95	0.83	0.71	0.65	0.89
C2	0.95	0	0.83	0.95	0.83	0.95
C3	0.471875	0.40625	0	0.335714	0.130769	0.275
C4	0.95	0.83	0.95	0	0.83	0.95
C5	0.296875	0.1625	0.06	0.18	0	0.292857
C6	0.265789474	0.332353	0.138235	0.5	0.265789	0

of Appendix A. Then, the crisp matrix (

Table A3. Grey normalized matrix of G-AHP.

Aspects	C1	C2	C3	C4	C5	C6	Priority Value
C1	0	0.354332	0.295559	0.26535	0.240157	0.26505	0.236741
C2	0.323731	0	0.295559	0.355045	0.306662	0.282919	0.260653
C3	0.1608	0.151523	0	0.125467	0.048316	0.081897	0.094667
C4	0.323731	0.309574	0.338291	0	0.306662	0.282919	0.260196
C5	0.101166	0.060609	0.021366	0.067272	0	0.087215	0.056271
C6	0.090573	0.123961	0.049225	0.186866	0.098202	0	0.091471
Total	1	1	1	1	1	1	1

of Appendix A) is normalized. Then, the consistency ratio is estimated and it is 0.004739, which is acceptable (less than 0.10). Likewise, the weights of the criteria are also calculated. The aggregated grey comparison matrix and the normalized grey comparison matrix of all criteria are given in

Table A4. Aggregated grey comparison matrix of criteria.

Criteria	C11	C12	C13	C21	C22	C23	C24	C25	C26	C31	C32	C33	C41	C42	C43	C51	C52	C53	C61	C62	C63	C64
C11	[0, 0]	[0.65, 0.9]	[0.6, 0.85]	[0.7, 0.95]	[0.7, 0.95]	[0.55, 0.8]	[0.7, 0.95]	[0.65, 0.9]	[0.7, 0.95]	[0.65, 0.9]	[0.4, 0.65]	[0.7, 0.95]	[0.4, 0.65]	[0.7, 0.95]	[0.4, 0.65]	[0.7, 0.95]	[0.3, 0.55]	[0.7, 0.95]	[0.7, 0.95]	[0.4, 0.65]	[0.55, 0.8]	[0.7, 0.95]
C12	[0.6, 0.85]	[0, 0]	[0.7, 0.95]	[0.6, 0.85]	[0.55, 0.8]	[0.3, 0.55]	[0.3, 0.55]	[0.4, 0.65]	[0.4, 0.65]	[0.55, 0.8]	[0.3, 0.55]	[0.25, 0.5]	[0, 0.15]	[0.4, 0.65]	[0.7, 0.95]	[0.4, 0.65]	[0.55, 0.8]	[0.25, 0.5]	[0.25, 0.5]	[0.1, 0.45]	[0.6, 0.85]	[0.25, 0.5]
C13	[0.7, 0.95]	[0.55, 0.8]	[0, 0]	[0.65, 0.9]	[0.7, 0.95]	[0.55, 0.8]	[0.6, 0.85]	[0.7, 0.95]	[0.55, 0.8]	[0.3, 0.55]	[0.55, 0.8]	[0.4, 0.65]	[0.55, 0.8]	[0.25, 0.5]	[0.7, 0.95]	[0.55, 0.8]	[0.4, 0.65]	[0.55, 0.8]	[0.55, 0.8]	[0.6, 0.85]	[0.1, 0.45]	[0.7, 0.95]
C21	[0.75, 1]	[0.7, 0.95]	[0.55, 0.8]	[0, 0]	[0.65, 0.9]	[0.75, 1]	[0.7, 0.95]	[0.6, 0.85]	[0.7, 0.95]	[0.6, 0.85]	[0.65, 0.9]	[0.55, 0.8]	[0.65, 0.9]	[0.6, 0.85]	[0.55, 0.8]	[0.7, 0.95]	[0.65, 0.9]	[0.5, 0.75]	[0.75, 1]	[0.75, 1]	[0.65, 0.9]	[0.55, 0.8]
C22	[0.7, 0.95]	[0.65, 0.9]	[0.7, 0.95]	[0.75, 1]	[0, 0]	[0.65, 0.9]	[0.65, 0.9]	[0.55, 0.8]	[0.75, 1]	[0.55, 0.8]	[0.75, 1]	[0.65, 0.9]	[0.7, 0.95]	[0.55, 0.8]	[0.6, 0.85]	[0.65, 0.9]	[0.55, 0.8]	[0.45, 0.7]	[0.35, 0.6]	[0.55, 0.85]	[0.75, 1]	[0.65, 0.9]
C23	[0.65, 0.9]	[0.6, 0.85]	[0.75, 1]	[0.65, 0.9]	[0.55, 0.8]	[0, 0]	[0.65, 0.9]	[0.7, 0.95]	[0.55, 0.8]	[0.6, 0.85]	[0.4, 0.65]	[0.45, 0.7]	[0.75, 1]	[0.3, 0.55]	[0.55, 0.8]	[0.75, 1]	[0.65, 0.9]	[0.4, 0.65]	[0.15, 0.3]	[0.35, 0.6]	[0.1, 0.45]	[0.7, 0.95]
C24	[0.4, 0.65]	[0.25, 0.5]	[0.1, 0.45]	[0.3, 0.55]	[0.7, 0.95]	[0.65, 0.9]	[0, 0]	[0.6, 0.85]	[0.7, 0.95]	[0.7, 0.95]	[0.65, 0.9]	[0.7, 0.95]	[0.55, 0.8]	[0.7, 0.95]	[0.4, 0.65]	[0.55, 0.8]	[0.4, 0.65]	[0.7, 0.95]	[0.25, 0.5]	[0.7, 0.95]	[0.55, 0.8]	[0.4, 0.65]
C25	[0.65, 0.9]	[0.1, 0.45]	[0, 0.15]	[0.2, 0.45]	[0.15, 0.4]	[0, 0.15]	[0.6, 0.85]	[0, 0]	[0.65, 0.9]	[0.7, 0.95]	[0.55, 0.8]	[0, 0.15]	[0, 0.15]	[0.2, 0.45]	[0.15, 0.4]	[0.2, 0.45]	[0, 0.15]	[0.2, 0.45]	[0.15, 0.4]	[0.05, 0.3]	[0.2, 0.4	[0.1, 0.45]
C26	[0.55, 0.8]	[0.3, 0.55]	[0.2, 0.45]	[0.15, 0.4]	[0.2, 0.45]	[0.1, 0.45]	[0.3, 0.55]	[0.7, 0.95]	[0, 0]	[0.6, 0.85]	[0.3, 0.55]	[0.2, 0.35]	[0.05, 0.3]	[0.35, 0.6]	[0.05, 0.3]	[0.6, 0.85]	[0.1, 0.45]	[0.15, 0.4]	[0.6, 0.85]	[0.05, 0.3]	[0.35, 0.6]	[0.15, 0.4]
C31	[0.25, 0.5]	[0.7, 0.95]	[0.3, 0.55]	[0, 0.15]	[0.1, 0.45]	[0.25, 0.5]	[0.55, 0.8]	[0.55, 0.8]	[0.65, 0.9]	[0, 0]	[0.55, 0.8]	[0.6, 0.85]	[0.2, 0.45]	[0.6, 0.85]	[0.05, 0.3]	[0.35, 0.6]	[0.35, 0.6]	[0.35, 0.6]	[0.5, 0.2]	[0.2, 0.45]	[0.5, 0.2]	[0.35, 0.6]
C32	[0.7, 0.95]	[0.65, 0.9]	[0.6, 0.85]	[0.7, 0.95]	[0.4, 0.65]	[0.55, 0.8]	[0.3, 0.55]	[0.4, 0.65]	[0.7, 0.95]	[0.6, 0.85]	[0, 0]	[0.4, 0.65]	[0, 0.15]	[0.2, 0.45]	[0.35, 0.6]	[0.5, 0.2]	[0.2, 0.45]	[0.2, 0.35]	[0.1, 0.25]	[0.1, 0.25]	[0.15, 0.3]	[0.5, 0.2]
C33	[0.55, 0.8]	[0.3, 0.55]	[0.7, 0.95]	[0.1, 0.45]	[0.6, 0.85]	[0.05, 0.3]	[0, 0.15]	[0.2, 0.45]	[0.15, 0.4]	[0.6, 0.85]	[0.7, 0.95]	[0, 0]	[0.7, 0.95]	[0.1, 0.45]	[0.2, 0.45]	[0.2, 0.45]	[0.05, 0.3]	[0.5, 0.2]	[0.2, 0.45]	[0.5, 0.2]	[0.2, 0.45]	[0.2, 0.45]
C41	[0.1, 0.45]	[0.15, 0.4]	[0.6, 0.85]	[0.05, 0.3]	[0.2, 0.45]	[0.6, 0.85]	[0.05, 0.3]	[0.35, 0.6]	[0.5, 0.2]	[0.2, 0.35]	[0.2, 0.45]	[0.7, 0.95]	[0, 0]	[0.55, 0.8]	[0.4, 0.65]	[0.15, 0.4]	[0.6, 0.85]	[0.05, 0.3]	[0.5, 0.2]	[0.2, 0.45]	[0.2, 0.45]	[0.35, 0.6]
C42	[0.2, 0.45]	[0.35, 0.6]	[0.2, 0.45]	[0.5, 0.2]	[0, 0.15]	[0.15, 0.4]	[0.6, 0.85]	[0.35, 0.6]	[0.2, 0.45]	[0.2, 0.35]	[0.35, 0.6]	[0, 0.15]	[0.25, 0.5]	[0, 0]	[0.7, 0.95]	[0.25, 0.5]	[0, 0.15]	[0.2, 0.45]	[0.35, 0.6]	[0.2, 0.35]	[0.5, 0.2]	[0.05, 0.3]
C43	[0.05, 0.3]	[0.5, 0.2]	[0.5, 0.2]	[0.2, 0.35]	[0.15, 0.3]	[0.1, 0.25]	[0.2, 0.45]	[0.1, 0.25]	[0.5, 0.2]	[0.1, 0.25]	[0.1, 0.25]	[0.35, 0.6]	[0.1, 0.45]	[0.4, 0.65]	[0, 0]	[0.3, 0.55]	[0.4, 0.65]	[0.55, 0.8]	[0.15, 0.4]	[0.6, 0.85]	[0.15, 0.4]	[0.6, 0.85]
C51	[0.5, 0.2]	[0.05, 0.3]	[0.2, 0.45]	[0.1, 0.25]	[0.5, 0.2]	[0.2, 0.45]	[0.15, 0.3]	[0.25, 0.5]	[0.35, 0.6]	[0.1, 0.25]	[0.5, 0.2]	[0.1, 0.25]	[0.15, 0.4]	[0, 0.15]	[0.7, 0.95]	[0, 0]	[0.25, 0.5]	[0.1, 0.45]	[0.2, 0.45]	[0.15, 0.4]	[0.6, 0.85]	[0.2, 0.45]
C52	[0.7, 0.95]	[0.55, 0.8]	[0.3, 0.55]	[0.7, 0.95]	[0.55, 0.8]	[0.1, 0.45]	[0, 0.15]	[0.1, 0.45]	[0.2, 0.45]	[0.15, 0.4]	[0.6, 0.85]	[0.05, 0.3]	[0.05, 0.3]	[0.15, 0.4]	[0.25, 0.5]	[0.4, 0.65]	[0, 0]	[0.7, 0.95]	[0.4, 0.65]	[0.55, 0.8]	[0.25, 0.5]	[0.55, 0.8]
C53	[0.25, 0.5]	[0.2, 0.45]	[0.35, 0.6]	[0.1, 0.25]	[0.5, 0.2]	[0.2, 0.35]	[0.15, 0.4]	[0.6, 0.85]	[0.05, 0.3]	[0.5, 0.2]	[0.15, 0.3]	[0.55, 0.8]	[0.25, 0.5]	[0.1, 0.45]	[0.4, 0.65]	[0.55, 0.8]	[0.7, 0.95]	[0, 0]	[0.7, 0.95]	[0.1, 0.45]	[0.35, 0.6]	[0.15, 0.4]
C61	[0.35, 0.6]	[0.5, 0.2]	[0.35, 0.6]	[0.2, 0.45]	[0.15, 0.3]	[0.1, 0.25]	[0.5, 0.2]	[0.2, 0.45]	[0.2, 0.35]	[0.05, 0.3]	[0.1, 0.25]	[0.1, 0.25]	[0.5, 0.2]	[0.35, 0.6]	[0.5, 0.2]	[0.2, 0.45]	[0.1, 0.45]	[0.25, 0.5]	[0, 0]	[0.25, 0.5]	[0.55, 0.8]	[0.25, 0.5]
C62	[0.15, 0.4]	[0.6, 0.85]	[0.05, 0.3]	[0.5, 0.2]	[0.2, 0.35]	[0.35, 0.6]	[0.05, 0.3]	[0.35, 0.6]	[0.2, 0.45]	[0.35, 0.6]	[0.2, 0.35]	[0.15, 0.3]	[0.55, 0.8]	[0.2, 0.35]	[0.2, 0.45]	[0.2, 0.35]	[0.35, 0.6]	[0.25, 0.5]	[0.4, 0.65]	[0, 0]	[0.4, 0.65]	[0.55, 0.8]
C63	[0.6, 0.85]	[0.3, 0.55]	[0.7, 0.95]	[0.25, 0.5]	[0.1, 0.45]	[0, 0.15]	[0.2, 0.45]	[0.15, 0.4]	[0.2, 0.45]	[0.15, 0.4]	[0.5, 0.2]	[0.55, 0.8]	[0.1, 0.25]	[0.15, 0.3]	[0.1, 0.25]	[0.1, 0.25]	[0.5, 0.2]	[0.1, 0.45]	[0.6, 0.85]	[0.7, 0.95]	[0, 0]	[0.1, 0.45]
C64	[0.65, 0.9]	[0.6, 0.85]	[0.7, 0.95]	[0.55, 0.8]	[0.3, 0.55]	[0.7, 0.95]	[0.55, 0.8]	[0.1, 0.45]	[0, 0.15]	[0.2, 0.45]	[0.6, 0.85]	[0.05, 0.3]	[0.05, 0.3]	[0.5, 0.2]	[0.2, 0.45]	[0.1, 0.25]	[0.1, 0.25]	[0.1, 0.25]	[0.15, 0.3]	[0.2, 0.35]	[0.2, 0.45]	[0, 0]

and

Table A5. Grey crisp matrix of criteria.

Criteria	C11	C12	C13	C21	C22	C23	C24	C25	C26	C31	C32	C33	C41	C42	C43	C51	C52	C53	C61	C62	C63	C64
C11	0.00	0.83	0.77	0.89	0.89	0.71	0.89	0.83	0.89	0.83	0.53	0.89	0.53	0.89	0.53	0.89	0.41	0.89	0.89	0.53	0.71	0.89
C12	0.77	0.00	0.89	0.77	0.71	0.41	0.41	0.53	0.53	0.71	0.41	0.35	0.02	0.53	0.89	0.53	0.71	0.35	0.35	0.22	0.77	0.35
C13	0.89	0.71	0.00	0.83	0.89	0.71	0.77	0.89	0.71	0.41	0.71	0.53	0.71	0.35	0.89	0.71	0.53	0.71	0.71	0.77	0.22	0.89
C21	0.90	0.85	0.67	0.00	0.79	0.90	0.85	0.73	0.85	0.73	0.79	0.67	0.79	0.73	0.67	0.85	0.79	0.62	0.90	0.90	0.79	0.67
C22	0.85	0.79	0.85	0.90	0.00	0.79	0.79	0.67	0.90	0.67	0.90	0.79	0.85	0.67	0.73	0.79	0.67	0.56	0.45	0.71	0.90	0.79
C23	0.79	0.73	0.90	0.79	0.67	0.00	0.79	0.85	0.67	0.73	0.50	0.56	0.90	0.39	0.67	0.90	0.79	0.50	0.18	0.45	0.21	0.85
C24	0.53	0.35	0.22	0.41	0.89	0.83	0.00	0.77	0.89	0.89	0.83	0.89	0.71	0.89	0.53	0.71	0.53	0.89	0.35	0.89	0.71	0.53
C25	0.83	0.22	0.02	0.29	0.23	0.02	0.77	0.00	0.83	0.89	0.71	0.02	0.02	0.29	0.23	0.29	0.02	0.29	0.23	0.11	0.29	0.22
C26	0.71	0.41	0.29	0.23	0.29	0.22	0.41	0.89	0.00	0.77	0.41	0.25	0.11	0.47	0.11	0.77	0.22	0.23	0.77	0.11	0.47	0.23
C31	0.35	0.89	0.41	0.02	0.22	0.35	0.71	0.71	0.83	0.00	0.71	0.77	0.29	0.77	0.11	0.47	0.47	0.47	0.41	0.29	0.41	0.47
C32	0.89	0.83	0.77	0.89	0.53	0.71	0.41	0.53	0.89	0.77	0.00	0.53	0.02	0.29	0.47	0.41	0.29	0.25	0.13	0.13	0.19	0.41
C33	0.71	0.41	0.89	0.22	0.77	0.11	0.02	0.29	0.23	0.77	0.89	0.00	0.89	0.22	0.29	0.29	0.11	0.41	0.29	0.41	0.29	0.29
C41	0.22	0.23	0.77	0.11	0.29	0.77	0.11	0.47	0.41	0.25	0.29	0.89	0.00	0.71	0.53	0.23	0.77	0.11	0.41	0.29	0.29	0.47
C42	0.29	0.47	0.29	0.41	0.02	0.23	0.77	0.47	0.29	0.25	0.47	0.02	0.35	0.00	0.89	0.35	0.02	0.29	0.47	0.25	0.41	0.11
C43	0.13	0.43	0.43	0.28	0.21	0.15	0.33	0.15	0.43	0.15	0.15	0.53	0.25	0.60	0.00	0.47	0.60	0.80	0.26	0.87	0.26	0.87
C51	0.41	0.11	0.29	0.13	0.41	0.29	0.19	0.35	0.47	0.13	0.41	0.13	0.23	0.02	0.89	0.00	0.35	0.22	0.29	0.23	0.77	0.29
C52	0.89	0.71	0.41	0.89	0.71	0.22	0.02	0.22	0.29	0.23	0.77	0.11	0.11	0.23	0.35	0.53	0.00	0.89	0.53	0.71	0.35	0.71
C53	0.35	0.29	0.47	0.13	0.41	0.25	0.23	0.77	0.11	0.41	0.19	0.71	0.35	0.22	0.53	0.71	0.89	0.00	0.89	0.22	0.47	0.23
C61	0.57	0.44	0.57	0.36	0.23	0.16	0.44	0.36	0.30	0.14	0.16	0.16	0.44	0.57	0.44	0.36	0.27	0.43	0.00	0.43	0.86	0.43
C62	0.26	0.87	0.13	0.43	0.28	0.53	0.13	0.53	0.33	0.53	0.28	0.21	0.80	0.28	0.33	0.28	0.53	0.40	0.60	0.00	0.60	0.80
C63	0.77	0.41	0.89	0.35	0.22	0.02	0.29	0.23	0.29	0.23	0.41	0.71	0.13	0.19	0.13	0.13	0.41	0.22	0.77	0.89	0.00	0.22
C64	0.83	0.77	0.89	0.71	0.41	0.89	0.71	0.22	0.02	0.29	0.77	0.11	0.11	0.41	0.29	0.13	0.13	0.13	0.19	0.25	0.29	0.00

of Appendix A. The relative grey weights of the criteria are provided in

Table 7. Relative grey weights of GAHP.

Aspects	Criteria	Weights	Rank
C1	C11	0.74	2
	C12	0.51	7
	C13	0.66	4
C2	C21	0.75	1
	C22	0.73	3
	C23	0.63	6
	C24	0.65	5
	C25	0.31	21
	C26	0.38	16
C3	C31	0.46	9
	C32	0.47	8
	C33	0.40	13
C4	C41	0.39	14
	C42	0.32	20
	C43	0.38	17
C5	C51	0.30	22
	C52	0.45	10
	C53	0.40	12
C6	C61	0.37	18
	C62	0.42	11
	C63	0.36	19
	C64	0.39	15

.

4.2.2. Ranking the Locations Using GCOPRAS

In the GCOPRAS technique, the grey numbers are used to rate the five locations: L02, L03, L08, L12, and L13. These five locations are rated by considering the 22 criteria. The ratings between the locations and criteria are normalized and are given in

Table A6. Normalized decision matrix.

	C11	C12	C13	C21	C22	C23	C24	C25	C26	C31	C32	C33	C41	C42	C43	C51	C52	C53	C61	C62	C63	C64
Gujarat	0.27	0.39	0.41	0.37	0.23	0.25	0.15	0.11	0.20	0.03	0.09	0.27	0.31	0.32	0.26	0.22	0.14	0.04	0.19	0.10	0.03	0.09
Tamil Nadu	0.22	0.32	0.22	0.20	0.27	0.17	0.22	0.04	0.20	0.25	0.18	0.15	0.17	0.18	0.26	0.07	0.20	0.21	0.34	0.30	0.37	0.31
Odisha	0.15	0.21	0.22	0.30	0.08	0.03	0.27	0.32	0.20	0.31	0.27	0.27	0.17	0.09	0.03	0.27	0.20	0.21	0.19	0.30	0.37	0.26
Andaman and Nicobar Islands	0.15	0.04	0.11	0.03	0.15	0.25	0.15	0.21	0.20	0.25	0.27	0.23	0.17	0.32	0.26	0.22	0.25	0.32	0.09	0.20	0.20	0.17
Lakshadeweep Islands	0.22	0.04	0.04	0.10	0.27	0.31	0.22	0.32	0.20	0.17	0.18	0.08	0.17	0.09	0.18	0.22	0.20	0.21	0.19	0.10	0.03	0.17

of Appendix B. Next, using the weights of the criteria estimated in GAHP, the weight normalized matrix is established as provided in

Table A7. Weight normalized decision matrix.

	C11	C12	C13	C21	C22	C23	C24	C25	C26	C31	C32	C33	C41	C42	C43	C51	C52	C53	C61	C62	C63	C64
Gujarat	0.20	0.20	0.27	0.27	0.16	0.16	0.10	0.03	0.08	0.01	0.04	0.11	0.12	0.10	0.10	0.07	0.06	0.01	0.07	0.04	0.01	0.03
Tamil Nadu	0.16	0.16	0.15	0.15	0.20	0.10	0.14	0.01	0.08	0.12	0.09	0.06	0.07	0.06	0.10	0.02	0.09	0.09	0.13	0.12	0.13	0.12
Odisha	0.11	0.11	0.15	0.22	0.05	0.02	0.17	0.10	0.08	0.14	0.13	0.11	0.07	0.03	0.01	0.08	0.09	0.09	0.07	0.12	0.13	0.10
Andaman and Nicobar Islands	0.11	0.02	0.07	0.02	0.11	0.16	0.10	0.07	0.08	0.12	0.13	0.09	0.07	0.10	0.10	0.07	0.11	0.13	0.03	0.08	0.07	0.07
Lakshadeweep Islands	0.16	0.02	0.02	0.07	0.20	0.19	0.14	0.10	0.08	0.08	0.09	0.03	0.07	0.03	0.07	0.07	0.09	0.09	0.07	0.04	0.01	0.07

of Appendix B. Then, the relative significance of each location is calculated and is given in

Table A8. Relative significance of the locations.

	S + i	S-i	S-min/S-i
Gujarat	4.25	7.80	1.9252
Tamil Nadu	2.40	6.96	1.7182
Odisha	3.25	5.61	1.384253
Andaman and Nicobar Islands	3.45	4.05	1
Lakshadeweep Islands	2.91	5.34	1.31869

of Appendix B. The utility degree of locations is calculated and is given in

Table 8. Grey COPRAS output.

Location	${\mathit{Q}}_{\mathit{I}}$	${\mathit{U}}_{\mathit{I}}$(%)	Rank
L02	6.357	84.76916	2
L03	4.757	63.42842	5
L08	6.176	82.34391	3
L12	7.500	100	1
L13	5.977	79.69987	4

.

5. Discussions and Implications

5.1. Discussions

According to Table 7, the criteria Soil condition (C21) occupies the top position. In erecting OWTs, the role of soil condition is very significant. The interaction between the soil and the OWTs determines the lifetime and also has a major influence on the maintenance and repair costs. Also, the material properties of the soil at each layer must be studied. A study by [60] regarding the dissemination of OWTs underscored that soil–structure interaction must be given more importance in the design and installation stages. Further, it must be noted that, in general, the areas prone to earthquakes are not preferred for OWT installation. Also, it has to be taken into account that all areas at some times are susceptible to seismic movements. Under such circumstances, measuring the condition of soil has become mandatory. The second important criterion is Extreme wind speed (C11). Although the functioning and output of OWTs mainly rely on wind speed, the same turns out to also be a major challenge. Wind speed beyond a certain limit damages the turbine. Generally, wind turbines were designed to withstand a certain level of wind speed. When wind speed exceeds the designed limit, it damages the wind turbine [12]. So, during the selection of location for the OWTs, extreme wind speed must also be taken into consideration.

The next significant criterion to be considered in location selection for an OWT is Seismic movement (C22). Since the lifespan of OWTs largely depends on the foundation, it is important to consider the seismic movement of the locations. Depending on the seismic movement only, the type of foundation (gravity base, monopile, tripod, and jacket) to be laid is chosen. A study by [61] on the selection of foundations for OWTs revealed that the preference of the type of foundation mainly depends on the seismic movement of the locations. Similarly, the criteria of Tidal flow (C13) also influence the selection of the type of foundations. In general, tidal flow refers to the water movement in the sea. Although the occurrence of waves in the sea is a common phenomenon, the frequency of wave occurrence has to be taken into consideration while erecting OWTs. Frequent wave moment strikes the shaft of the OWT continuously and leads to damage. The continuous impact of the waves on the turbine sometimes collapses the turbine [62]. The above-discussed four criteria come under the first stage (location selection and formal consent) of OWT erection.

Closeness to the power transmission grid (C24) was the next important criterion in location selection for an OWT. Often, OWTs are located far away from densely populated areas for reasons like noise problems. However, the energy demand is high in these areas only. Hence, at the time of power transmission from the OWTs to the customers, power loss becomes unavoidable. Given this criterion, the OWTs must be located close to the power substations [63]. Similarly, the next important criterion is Closeness to shore (C23). This criterion must be taken into serious consideration in selecting locations for OWTs as the disturbance caused by OWTs may interrupt the functioning of the ports. Usually, the ports are engaged in handling economic activities by loading and unloading goods and freight. In these activities, the signal received from the ships is very crucial. When the OWTs are located close to ports, the noise from the turbines interrupts the signal of the ships and leads to miscommunication [64]. Apart from the criteria discussed, other criteria given in Table 7 must also be considered when selecting locations for OWTs.

5.2. Validation of Results

To check the reliability and robustness of the obtained results, the outcome is compared with results generated using other alternative ranking MCDM techniques such as GTOPSIS, Grey Evaluation based on Distance from Average Solution (GEDAS), Grey weighted aggregated sum product assessment (GWASPAS), and Grey Multi-Attributive Border Approximation area Comparison (GMABAC). The results obtained using five kinds of alternative selection techniques are provided in

Table 9. Comparison of rank with other methods.

Location	GAHP and GCOPRAS		GAHP and GTOPSIS		GAHP and GEDAS		GAHP and GWASPAS		GAHP and GMABAC
	Value	Rank	Value	Rank	Value	Rank	Value	Rank	Value	Rank
L02	0.847	2	0.621	2	0.639	2	0.845	2	0.562	5
L03	0.634	5	0.465	4	0.321	5	0.625	5	0.623	4
L08	0.823	3	0.565	3	0.608	3	0.812	3	0.863	1
L12	1	1	0.654	1	0.783	1	0.915	1	0.713	3
L13	0.796	4	0.325	5	0.584	4	0.715	4	0.754	2

and are visually presented in Figure 2. In four techniques, L12 occupies the top position, and only one technique, L08, secures the pole position. When viewed from a larger perspective, almost in all the five alternative selection techniques, the top three positions are variably occupied by the same locations (L12, L08, and L02). From this, it was understood that the integrated approach used in this study is robust, and the results obtained are also reliable. Hence, the outcome of this study can be used as a guideline by the policymakers and government in selecting the location for OWTs.

5.3. Implications

5.3.1. Theoretical Implications

This study has some significant implications in terms of theoretical contributions. First, this study offers a comprehensive framework for the identification of suitable locations for OWT installation by considering important criteria. Although several studies on OWTs exist, most are related to location identification [9,18,31,35] and barriers evaluation [12,19]; there is a gap in the literature regarding suitable location selection by considering the important criteria. The present study bridges this gap by evaluating the locations based on the consideration of the important criteria. Initially, a total of 26 criteria under six aspects were identified through literature and interaction with experts. Then, out of 13 possible locations, five suitable locations are identified by considering four criteria (three as inputs and one as output). Finally, these five locations are prioritized with respect to 22 criteria. Further, as it is essential to know the significance of each criterion, the weight importance of the criteria is calculated and ranked. Knowledge about the weight importance of the criteria will help the industrial practitioners in taking appropriate measures. The outcome of this study has the potential to help industrial practitioners and policy decision-makers with useful insights into the installation of OWTs.

5.3.2. Practical Implications

From a practical approach and perspective, this study provides a comprehensive framework intended to help industrial practitioners in OWT installation and provides the weights and importance of the criteria to help them take appropriate steps during OWT installation. To abate the carbon emission caused by conventional coal-based energy generation, global nations are looking to harness the potential of OWTs as much as possible as it has several advantages over other RE resources. However, in the installation of OWTs, numerous criteria have to be considered. Among the 22 criteria, soil condition was identified to have more influence on OWT installation. This outcome indicates that besides giving much focus to the wind speed, the condition of the soil has to be given more attention. Only the soil condition decides the type of foundation to be laid for the OWTs. Additionally, the susceptibility of the concerned location for OWTs to seismic movements has to be measured prior to installation activities. Thus, soil conditions and seismic movements may have a major influence on the financial budgeting and functioning of OWT installation.

6. Conclusions

This study intends to identify the potential locations for OWT installation in India by analyzing and evaluating the essential criteria. Through an extensive literature review and exploratory interviews with the experts, twenty-six criteria under six aspects were identified. Next, an integrated approach comprising DEA with GAHP and GCOPRAS was used to determine the potential locations and rank the criteria for OWTs. Outcomes indicate that soil condition, extreme wind speed, seismic movement, tidal flow, and closeness to the power transmission grid are the top five criteria to be considered in the installation of OWTs. Gujarat > Tamil Nadu > Odisha > Lakshadweep Islands > Andaman and Nicobar Islands have been identified as the potential locations for installing OWTs. Future works on OWTs may be carried out to reveal the hierarchical and causal relationship among the criteria.