Land Suitability Investigation for Solar Power Plant Using GIS, AHP and Multi-Criteria Decision Approach: A Case of Megacity Kolkata, West Bengal, India

Abstract

Renewable energy sources are the most necessitated natural energy to reduce fossil fuels globally. Fossil fuel is the most valuable and limited resource on the planet, but on the other hand, renewable energy creates less pollution. Solar energy is the most effective renewable resource for daily use. Solar power plants are necessary for domestic and daily use. Remote sensing and geographic information technology (GIS) were used for this study to delineate the possible site selection of solar power plants in Kolkata and the surrounding area in West Bengal, India. The analytical hierarchy process (AHP) and the multi-criteria decision-making process (MCDA) were used for each weight calculation and ArcGIS v10.8 was applied for weighted overlay analysis (WOA) for delineation of the result. The site suitability map was developed using a pairwise comparison matrix and the weights were calculated for each criterion. The suitability map was divided into five categories, from not suitable to very highly suitable. A total of 474.21 km² (10.69%) of the area was classified as very highly suitable whereas 249.54 km² (5.62%) area was classified as not suitable because of the water area and east Kolkata wetland. A total of 1438.15 km² (32.43%) of the area was classified as highly suitable for a solar power plant. The Kolkata megacity and water body locations were identified as moderate to not suitable sites. Very high and high-potential sites were identified 2 to 5 km from the central business district (CBD) location, which is Dharmotala. Renewable energy source is needed in the megacity of Kolkata. If solar power plants are contracted then the demand for fossil fuel will be reduced one day, and that will help the environment as well as the society in terms of sustainable development. This study result is helpful for administrators, urban planners, developers, and other stakeholders for the implementation and development of a new solar power plant in the study area.

1. Introduction

Renewable energies are essential for future transportation, industrial work, and daily demands for people [1]. Nowadays, fossil fuel has a huge amount of uses and has led to increased health problems like respiratory disease, asthma, lung infection, and cancer [2,3,4]. In addition, fossil fuel is limited on the planet and one day it will be finished, leading to the need for alternative energy for sustainable development. Solar energy is the most valuable natural energy source, and can fulfill the energy demands on Earth. Solar power stations are essential because of future energy demand, and the present energy supply comes from many sources [5,6,7]. Arid and semi-arid regions have a huge amount of solar energy received from the sun. If we were to store that energy for uses of daily needs, the demand for fossil fuels would be progressively reduced. Environmental degradation and global warming are caused by fossil fuel, and renewable energy sources represent an alternative replacement for the demand for energy in the future [6] and alternative for sustainable development [8]. Solar energy remains unique as a free, clean, and profuse energy source that provides electric energy to many parts of the world [8,9,10]. The energy of the solar system is frequently communicated in terms of the global horizontal irradiance (GHI), which is the entire quantity of the shortwave radiation received from above by a surface horizontal to the earth’s crust. The GHI comprises both the diffuse horizontal irradiance (DHF) and the direct normal irradiance (DNI) [5,11]. Solar energy is now distributed to energy systems worldwide, which is gradually increasing the demand [12,13]. Solar power plants are necessary worldwide, and this new renewable technology is being built in many parts of the world.

The particular slope–land topography and surface aspect are affected by the solar radiation scattering on the earth’s crust and deflect an equal distribution of solar energy globally. Solar energy is cost effective and has low operational cost, which increases accessibility, and has low contraction costs and a high rate of distribution from the power line [14]. Solar transmitters have been planted near road networks, power lines, high solar energy-assessable areas, and human settlements [1]. Remote sensing (RS) and the geographic information system (GIS) remained widely applied for site-selection purposes and delineation of new potential sites for solar power plants using handling, modeling, analyzing, and visualizing of spatial data sources [15]. Many researchers have used the GIS-based and MCDA methods to delineate the site selection of solar power plants [6,7,8,9,13,14,15,16,17,18].

The analytical hierarchy process (AHP) [19] was applied to categorize the site suitability process in the earth’s crust through the support of the GIS software [20,21]. This approach includes pair-wise comparison procedures and multi-criteria decision analysis (MCDA) to demarcate the biophysical, socio-economic, and physical principles [22]. The pair-wise comparison technique, analytical hierarchy process (AHP), and multi-criteria decision analysis (MCDA) were applied to explain the numerous site suitability estimation difficulties established on the basis of nominated principles or parameters (

Table 1. Some literature on solar power plants in different parts of the planet.

Sl. No.	References	Applied Methods	Renewable Energy Sources	Location	Summary
1	[34]	MCDA, WOA, TOPSIS	Photovoltaic power plant (PVPP)	China	10 MW sites for PVPP sites were observed. Research results indicate that export risk attitudes and flexible judgment of real decision-making are needed most.
2	[35]	AHP, MCDA	Photovoltaic power plant (PVPP)	Saudi Arabia	A total of 300,000 km2 (16%) of the study regions are suitable for PVPP.
3	[36]	AHP, MCSA, WOA	Photovoltaic power plant (PVPP)	Egypt	A total of 261.1747 km2 (24.9%) of the examination regions are suitable.
4	[37]	AHP, ELECTRE, TOPSIS, and VIKOR	Solar power plant	Turkey	Five cities in Turkey examined solar power plants, where the priority order for the explanation was, for example, Karaman > Konya = Niğde > Aksaray > Nevşehir.
5	[38]	GIS-MCDA, OWA	Solar power plant	Iran	Different methods like extremely optimistic, optimistic, neutral, pessimistic, and extremely pessimistic were used.
6	[39]	GIS-MCDA, AHP	Solar PV power plant	Algeria	A total of 25,286 km2 (17%) of the study area are suitable for a PVPP area.
7	[40]	RETScreen Model	Solar PV power plant	India	A total of 31 major cities in India were studied for solar PV where greenhouse gases (GHG) and air quality is being reduced.

) [23]. The weighted overlay method (WOM) through the AHP approach delivers exact, strong results for suitable site selection of an area [24,25]. This technique was used for suitable site selection, established with numerous criteria through quantitative valuation [26,27]. The AHP, MCDA, and WOA approaches were widely used for different purposes, like hospital-site selection [21], potential groundwater-zone selection [20,28,29,30], landslide-susceptibility analysis [25,31], potential plantation areas, potential sites for building contracts, paddy production, and many other environment-related investigations [7,12,32,33]. These methods were used to identify potential sites based on the particular criteria. Not only AHP, but many other methods, like the fuzzy analytical hierarchy process (FAHP), logistic regression, random forest, support vector machine, and artificial neural network (ANN), were also used.

Remote sensing and the geographical information system (GIS) were widely used for natural resource development and potential site selection with any environmental condition. AHP and the MCDA approaches were used to delineate the site selection of solar power plants in Kolkata and in adjacent rural and urban areas. The weighted overlay analysis (WOA) technique was used to calculate the weight of each criterion for selecting a solar power plant. Kolkata megacity and the extended parts of this megacity truthfully need an alternative energy source due to the lack of fossil fuel and other energy sources; therefore, a solar power plant can help reduce fossil fuel use and air pollution. Kolkata megacity and the surrounding areas do not have any solar power plant, and daily energy demands are increasing gradually. The results of this study can help with the site selection for a solar power plant near Kolkata megacity and maintain the energy demand for those regions or city areas. The main focus of the investigation was on identifying an appropriate site for a solar power station in Kolkata and the surrounding areas, with the division of sites as very highly suitable to not suitable for a solar power plant in Kolkata megacity and its surroundings. This study can be helpful for administrators, urban planners, developers, and shareholders to build some solar power plants to distribute energy during sunset and on cloudy days, and correspondingly the results will help improve the sustainable development of Kolkata megacity, Howrah Municipal Corporation, and adjacent urban towns and rural areas. Industrial areas, housing complexes, transportation systems, electricity, and many other essential areas can benefit from this natural energy source.

2. Identification of the Area

The megacity of Kolkata is situated in the eastern part of India, and is the capital city of West Bengal. Kolkata megacity is around 1851 km² and the study area is around 4436 km², including Howrah Municipal Corporation, Barrackpore, Chandannagar, and Kalyani municipalities. Kolkata megacity is an ex-capital of India. Kolkata is one of the riverine cities situated in the Hooghly river basin, and the elevation of the area varies between 1.5 and 11 m. The annual average summer temperature varies between 27.5 °C and 35 °C, and winter temperature varies between 14 °C and 22 °C.

As the seventh most populated city, Kolkata has an enormous quantity of the transportation development and industrial work. Those anthropogenic activities are the main reasons for the air pollution and green space losses. Carbon monoxide is also increasing because of the huge number of vehicles in this city; therefore, renewable energy demands are increasing gradually. This area receives an enormous amount of solar energy, which could be used for solar power-based energy transportation for vehicles and industrial works (Figure 1). The seventh most populated city of India has a population of 450,000, with a growth rate of 0.84% (https://censusindia.gov.in/census.website/, accessed on 12 February 2022). Urban amenities are being developed gradually due to residential expansion. Nearly 96,868 hectares of urban areas have been developed and have gradually expanded towards the fringe areas of locations like Rajpur-Sonarpur, Garia, and rural areas of Howrah (http://www.atlasofurbanexpansion.org/cities/view/Kolkata, accessed on 12 February 2022).

3. Materials

An investigation was carried out to select a solar power-station site in the Kolkata Municipal Corporation (KMC), the Howrah Municipal Corporation (HMC), and surrounding areas using AHP, a pair-wise comparison matrix, and the MCDA approach. Twelve parameters or criteria were applied to assess a solar power station in Kolkata and surrounding areas. ArcGIS software version 10.8 with the weighted overlay analysis (WOA) tool was applied for the investigation (Figure 2). For the WOA approach, all the criteria had the same geographic extension and the same projection system. The criteria were in raster format and the weighted values were calculated. The weighted values varied on a scale from low (1) to high (10) importance for identification of a solar power-station area. The values varied from low to high, and pair-wise comparison-based weighted values were used for the WOA ArcGIS tool.

3.1. Data Used

The criteria were derived from different websites and the reclassified criteria were estimated. The solar radiation data were derived from NASA website, and the digital elevation model (DEM) was applied to produce the slope, aspect, and altitude maps. The land-use and land-cover map was derived from Landsat 8 OLI/TIRS data, which were downloaded from the USGS website, and the residential area was derived from a LULC classification map. The land-surface temperature (LST) data used were calculated from Landsat 8 band 10 data using the indicated formulas. Highway data were digitized from Google Earth and the power supply line was derived from West Bengal State Electricity Transmission Co. Limited. The soil type was derived from the National Bureau of Soil Survey and Land Use and Planning (NBSSLUP).

Table 2. Data details for the solar power plant.

Criteria	Data Source	Website
Solar radiation	NASA	https://power.larc.nasa.gov/, accessed on 17 January 2022
LULC, water body, residential area, and LST	Landsat 8 OLI/TIRS	https://earthexplorer.usgs.gov/, accessed on 15 january 2022
Power supply Line	WBSETCT	http://www.wbsetcl.in/docs/power%20map.pdf, accessed on 15 january 2022
Soil type	NBSSLUP	https://www.nbsslup.in/, accessed on 18 January 2022
Highway, protected area	Google Earth
Slope, aspect, and altitude	DEM	https://earthexplorer.usgs.gov/, accessed on 22 January 2022

details the data acquisition of the site-selection information.

3.2. Preparation and Computation of Criteria

3.2.1. Solar Radiation

Solar radiation was essential and the most affected criterion for the area selection for the solar power plant. Solar radiation influences the temperature variation over the study area, and high solar radiation is most suitable for a solar power plant. The solar-radiation map was derived from the NASA website (https://power.larc.nasa.gov/, accessed on 17 January 2022), and due to the solar radiation rate the areas were separated into five groupings: very high, high, medium, low, and very low solar-radiation zones (Figure 3a). The northwest, west, and some parts in the northern area were located in high-solar-radiation zones, and the central–southeast parts of this study area were located in low-solar-radiation zones.

3.2.2. Land-Surface Temperature

To identify the location for the solar power plant, the most effective criterion was the land-surface temperature (LST). The temperature criterion indicated the variation in temperature and the distribution of temperature, which is a very important factor for delineating the potential zones for a solar power plant in Kolkata and the surrounding areas. To identify the solar power plant in the study area, the LST map was divided into three categories: high, medium, and low (Figure 3b). The high zones indicated that the temperature was very high and that it was a suitable area. The low zones indicated that the criterion was too low for a solar power plant. The land-surface temperature was calculated for the following formulas:

• Adaptation of the DN, or digital number, to the spectral radiance (L) [41,42,43] was carried out with Equation (1):

  $$L=\left(\frac{L_{max}-L_{min}}{D{N}_{max}}\right)\times Band+L_{min}$$

  (1)

  where L represents the SR OR atmospheric spectral radiance in watts/(m²*srad * μm), $L_{max}$ denotes the DN value-based maximum spectral radiance (SR), $Band+L_{min}$ represents the selected band-based minimum spectral radiance (SR), $D{N}_{max}$ denotes the maximum values of the digital number, and $Q_{cal max} – Q_{cal min}$ indicates the maximum and minimum difference of sensor calibration, respectively.
• With the thermal band-related coefficients specified in the satellite metadata files, the TIRS satellite dataset band was transformed from the SR to the BT when the DN significance values were transformed to the SR [44,45] (Equation (2)):

  $$BT=\frac{K2}{Ln\left(\frac{K1}{L\mathsf{\lambda }}+1\right)}-273.15$$

  (2)

  where $K_2$ and $K_1$ represent the specific band-based thermal alteration coefficient values, and BT denotes the brightness temperature values on the Celsius scale.
• Calculation of NDVI [46,47] (Equation (3)):

  $$NDVI=\frac{\left(NIR-R\right)}{\left(\mathit{NIR}+R\right)}$$

  (3)

  where NDVI denotes the Normalized Different Vegetation Index, the values of which vary between +1 and −1 based on the area.
• The Pv, or proportion of vegetation, was determined through the maximum and the minimum NDVI map values [48]. The formula for calculating the proportion of vegetation is denoted in Equation (4):

  $$Pv=(\frac{\mathit{NDVI} – NDV{I}_{min}}{NDV{I}_{max} – NDV{I}_{min}}){ }^2$$

  (4)
• The land-surface emissivity, or LSE, was determined through the $P_v$ value; the $P_v$ values were established through the NDVI values of the remote sensing-based satellite images of the earth’s crust. This approach was applied via NDVI thresholding approaches like NDVITHM through Equation (5) [46,49]:

  $$LSE=0.004\times Pv+0.986$$

  (5)
• Transformation of the Kelvin values to the Celsius values [46,48,49] was carried out with Equation (6):

  $$LST=\frac{BT}{\{1+\left[\frac{\mathsf{\lambda }\mathrm{BT}}{\mathsf{\rho }}\right]\mathrm{In}\left(\mathrm{LSE}\right)}$$

  (6)

  where λ denotes the emitted radiance wavelength.

3.2.3. Residential Area

Residential area was a major criterion for delineating the solar power plant in Kolkata and surrounding areas Howrah, Hooghly, part of North 24 Parganas, part of South 24 Parganas, and Purba Medinipur. This was identified because of the huge amount of population pressure, and the transportation system and industrial works have increased the deficiency of fossil fuel and natural resources (Figure 3c). If these areas had a solar power plant, the people in those areas would benefit more from the natural renewal resource. The nearest residential area is most suitable for a solar power plant because of the power supply line, which supplies power to the residential areas.

3.2.4. Power Line

The power supply lines of the location were suitable for delineating a solar power plant for the megacity of Kolkata and the surrounding areas. The distributed power lines were mostly suitable because of the power supply. The areal distributions were divided into four categories based on the areal condition (Figure 3d). The nearest locations of the power line were more suitable than other areas because of the purpose of the power supply. The power-line map of the study area was derived from West Bengal State Electricity Transmission Corporation Limited (http://www.wbsetcl.in/docs/power%20map.pdf, accessed on 15 January 2022).

3.2.5. Soil Types

Soil type was effective for the identification of potential solar power plant zones. The effectiveness of the porosity of the soil is influenced by the adsorbed water, grain size, grain shape, saturation degree, void ratio, and existing impurities [50]. Kolkata megacity and the surrounding areas have four different types of soil: fine–aeric haplaquepts, fine loamy–aeric haplaquepts, fine–vertic ochraqualfs, and fine loamy-typic haplaquepts (Figure 3e). The soil datasets were downloaded and digitized from the National Bureau of Soil Survey and Land Use and Planning, which was downloaded from the website https://www.nbsslup.in/, accessed on 18 January 2022.

3.2.6. Highway

The highway criterion mostly affected the potential solar power plant zones because distance from highways increased the distribution efficiency over the study area. Many researchers used highways to delineate the solar power plant area (Figure 3f). Based on the areal conditions and literature review, seven buffer zones were created for potential highway zones. The distance from highway areas were classified as 1 km, km, 4 km, 8 km, 10 km, 13 km, and 17 km.

3.2.7. Slope Map

The slope-map gradient is the predisposition to water infiltration into the earth’s crust. The vertical slope, infiltration rate, and descending water flowing were concentrated but the flat or plain zones had additional water infiltration rates of rainwater and influencing temperature variation (Figure 3g). The slope map of the study location was classified into four categories: high, medium, low, and very low. The delta region of Kolkata and the surrounding areas had a low slope gradient in most parts, especially the southern, eastern, and southwestern parts. Those areas had more potential as areas for a solar power plant.

3.2.8. Aspect

The aspect of this area was essential for implementation of the solar power plant. The aspect of this area was highly influenced by the areal condition. Due to the areal condition, the aspect of the study area was divide into four categories: high, medium, low, and very low (Figure 3h).

3.2.9. Altitude

Altitude was an essential criterion for generating a solar power plant in Kolkata megacity and the surrounding urban and rural areas. High-altitude areas received less solar energy received of cloud cover, and the summer season received more solar energy on the earth’s surface. Based on local conditions, the altitude map was divided into four categories: high, medium, low, and very low (Figure 3i).

3.2.10. Protected Area

Protected areas like the airport, administrative areas, playgrounds, parks, gardens, and railway stations are not suitable areas for a solar power plant, compared to the other parts of the study area that were more suitable for a solar power plant. The central parts of this study area, including an international airport, called Netaji Subhash International Airport; Victoria; Birla Planetarium; different administrative areas like the court, the C.M. office, and other administrative offices; and rail stations like Howrah railway station and Sealdha railway station, were not suitable for a solar power plant (Figure 3j). Other areas were suitable for a solar power plant. Based on the areal condition, this criterion was divided into two areas: protected area and non-protected area.

3.2.11. Water Body

Water body area was a criterion for the solar power plant because of the area identification. Water areas are not suitable for solar power plants because of their electric conductivity. That is why the water area was not suitable for the solar power plant (Figure 3k). The areal condition of the southern, southeastern, and eastern parts are in dense water areas, and those areas were not suitable for a solar power plant. The other parts were more suitable for a solar power plant.

3.2.12. Land-Use and Land-Cover (LULC) Map

RS- and GIS-based satellite datasets like Landsat 8 OLI/TIRS were downloaded using the United States Geological Survey Earth Explorer (https://earthexplorer.usgs.gov/, accessed on 15 January 2022) with a <10% cloud-cover image. On 6 April 2020 the Landsat 8 OLI/TIRS image was applied to classification of the LULC map. The supervised classification approach through the maximum likelihood algorithm was applied to establish the LULC of Kolkata and the surrounding areas. The satellite images were pre-processed, sub-setting the region of interest (ROI) or area of interest (AOI), and the classification procedures were managed in ERDAS Imagine software version 2014. Five features were identified in Kolkata and the surrounding areas, including settlement, vegetation, water body, agricultural land, and open space (Figure 3l). Open space was most suitable for a solar power plant, agricultural land and settlement area were moderately suitable for a solar power plant, and water bodies were least suitable for a solar power plant.

3.3. Euclidean Distance

The point, line, and polygon (vector features) were used for spatial analysis, and Euclidean distance was used for the spatial analysis toolbar in ArcGIS software (Version 10.8) for measuring the distances. The Euclidean distance estimates the vector or raster and measures the distance from each compartment or distance to the adjacent cradle, which was derived from the website https://desktop.arcgis.com/en/arcmap/10.3/tools/, accessed on 28 January 2022. The distance ranges were classified according to the factor map of each criterion [51]. This tool was used for the site-suitability maps, and the criterion data demonstrating the distance from certain object was needed [52].

3.4. Reclassification of Criteria

For site selection of the solar power plant it was necessary for the criteria to have been transformed into the same raster format, where the “reclassify” toolbar of ArcGIS v10.8 was used for the same unit for all site selection criteria. Reclassification is a technique that denotes the raster data by changing from single values to new significant values using the software. The tools used to countenance abundant value changes in the contributing raster datasets to the anticipated, quantified, or alternative values were derived from the website https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/understanding-reclassification.htm, accessed on 28 January 2022. In the investigation of the site selection for a solar power plant, the residential areas, high-radiation areas, and nearest road networks were most suitable and different land-use and land-cover classes, slope, and aspect were less suitable for site selection for a solar power plant. All criterion data for site selection for a solar power plant were reclassified in ArcGIS software v10.8. The criteria were reclassified into the numeral raster format, which is signified through the different possible heights of the solar power stations established based on the notified values of the threshold [52].

3.5. Weighted Value Estimation Using AHP Method

Potential site selection for a solar power station in Kolkata Municipal Corporation (KMC) and the surrounding areas based on the importance score values was applied through field experience and some previous research investigation. AHP is mostly applied for MCDA approaches [53]. The selected procedure can also be of assistance to the construction of the weighted criterion values of each particular criterion and use a hierarchy structure to established appropriate suitable sites for selection [54]. The AHP method includes important phases for enumerating the components in the pairwise comparison approach and the decision-making criteria technique [22]. The analytical hierarchy process (AHP) identifies the selected weighted scores of the criteria by applying the pairwise comparison technique to homogenize the influences to a unity sum [25,55,56]. Based on Saaty [57], the analytical hierarchy process (AHP) was applied to estimate each criterion weight and after that, the weighted values of each criterion were investigated, which were observed through the pairwise comparison matrix approach [57]. The weighted score was separated into 1–9 for scoring the criteria in the pairwise comparison matrix approach [57,58] (

Table 3. The important scale values and their clarification for the pairwise comparison matrix [19].

Importance Rank	Definition	Explanation
1	Equal importance	Two criteria enrich objective criteria equally.
3	Low importance of one over another	Judgments and experience slightly favor one criterion over another.
5	Strong or essential importance	Judgments and experience are slightly favored.
7	Established importance	A criterion is strongly favored and its dominance established in practice.
9	Absolute or high importance	The evidence favoring one criterion over another is of the highest probable order of affirmation.
2, 4, 6, 8	Intermediate values between the two adjacent importance or judgments	When adjustment is needed
Reciprocals	If criteria i has one of the above numbers designated to it when compared with criteria j, then j has the reciprocal value when compared with i.

), where low (1) and high (9) were observed (Table 3). The mathematically divided criterion assessment matrix is $n=\left(\frac{n-1}{2}\right)$ for n numbers of suitable sites of all criteria in the pairwise comparison matrix approach [6,19,32,55].

The technique was applied to calculate each criterion weight for the site selection for a solar power plant [19]. The AHP method was calculated through the consistency ratio (CR), which is calculated with Equation (7). The consistency ratio (CR) identifies the corrected logical inconsistency of the pairwise matrix approach, which is recognized through the export decision or understanding.

$$\mathrm{CR}=\frac{\mathrm{CI}}{\mathrm{RI}}$$

(7)

where the consistency ratio (CR) is calculated for site selection and RI denotes the random index values. The CI or consistency index is estimated using Equation (8).

$$\mathrm{CI}=\frac{({\lambda }_{max}-n)}{\left(n-1\right)}$$

(8)

where CI indicates the consistency index when ${\lambda }_{max}$ denotes the maximum values of the pairwise comparison matrix approach, and $n$ indicates the criterion number, which is assumed to identify the solar power station areas in KMC and the surroundings. The RI or random index indicates the CI dependent mean values based on the matrix instructions assumed by Saaty [19]. Based on Saaty’s observation and investigation, the RI value of the 12 criteria was 1.54, where the CR was <0.10. After that, the weighted-based calculated standards were not given significant outcomes. For the current observation, the estimated CR was around 0.08, which is beneath the satisfactory perimeter, and the weighted-based calculated values (

Table 4. The pairwise comparison matrix for the multi-criteria decision analysis (MCDA).

Criteria	Solar Radiation	LST	Residential Area	Power Line	Soil Type	Highway	Slope	Aspect	Altitude	Protected Area	Water Body	LULC
Solar radiation	1	4.5	4.5	4.00	3.50	3.50	4.50	3.50	7.00	3.00	4.50	7.00
LST	0.22	1	2.5	3.50	2.00	4.00	2.50	3.00	3.00	3.50	3.50	5.50
Residential area	0.22	0.4	1	4.50	2.50	2.50	2.00	3.00	3.50	5.00	4.50	4.50
Power line	0.25	0.28	0.22	1	3.50	2.50	3.00	3.00	3.00	3.50	4.50	4.00
Soil type	0.28	0.50	0.40	0.28	1	1.50	2.00	1.50	2.00	4.00	3.50	4.50
Highway	0.28	0.25	0.40	0.40	0.66	1	1.50	1.50	2.00	2.50	4.50	4.00
Slope	0.22	0.40	0.50	0.33	0.50	0.66	1	2.00	2.00	2.50	3.00	3.50
Aspect	0.28	0.33	0.33	0.33	0.66	0.66	0.50	1	2.50	2.50	2.50	3.00
Altitude	0.14	0.33	0.28	0.33	0.50	0.50	0.50	0.40	1	4.50	3.50	2.50
Protected area	0.33	0.28	0.20	0.28	0.25	0.40	0.40	0.40	0.22	1	2.00	1.50
Water body	0.22	0.28	0.22	0.22	0.28	0.22	0.33	0.40	0.28	0.50	1	2.50
LULC	0.14	0.18	0.22	0.25	0.22	0.25	0.28	0.33	0.40	0.66	0.40	1
Total	3.61	8.75	10.78	15.44	15.59	17.70	18.51	20.03	26.91	33.16	37.40	43.50

,

Table 5. The standardized matrix and the weighted dissemination for the multi-criteria decision analysis (MCDA).

Criteria	Solar Radiation	LST	Residential Area	Power Line	Soil Type	Highway	Slope	Aspect	Altitude	Protected Area	Water Body	LULC	Weight
Solar radiation	0.28	0.51	0.42	0.26	0.22	0.20	0.24	0.17	0.26	0.09	0.12	0.16	0.24
LST	0.06	0.11	0.23	0.23	0.13	0.22	0.13	0.15	0.11	0.10	0.09	0.13	0.14
Residential area	0.06	0.04	0.09	0.29	0.16	0.14	0.11	0.15	0.13	0.15	0.12	0.10	0.13
Power line	0.07	0.03	0.02	0.06	0.22	0.14	0.16	0.15	0.11	0.10	0.12	0.09	0.11
Soil types	0.08	0.06	0.04	0.02	0.06	0.08	0.11	0.07	0.07	0.12	0.09	0.10	0.07
Highway	0.08	0.03	0.04	0.03	0.04	0.06	0.08	0.07	0.07	0.07	0.12	0.09	0.06
Slope	0.06	0.04	0.05	0.02	0.03	0.04	0.05	0.10	0.07	0.07	0.08	0.08	0.06
Aspect	0.08	0.04	0.03	0.02	0.04	0.04	0.02	0.05	0.09	0.07	0.07	0.07	0.05
Altitude	0.04	0.04	0.03	0.02	0.03	0.03	0.03	0.02	0.04	0.13	0.09	0.06	0.05
Protected area	0.09	0.03	0.02	0.02	0.02	0.02	0.02	0.02	0.01	0.03	0.05	0.03	0.03
Water body	0.06	0.03	0.02	0.01	0.02	0.01	0.02	0.02	0.01	0.01	0.03	0.06	0.02
LULC	0.04	0.02	0.02	0.02	0.01	0.01	0.01	0.02	0.01	0.020102	0.01	0.02	0.02
	1	1	1	1	1	1	1	1	1	1	1	1	1

,

Table 6. Estimation of the consistency ratio.

Criteria	Solar Radiation	LST	Residential Area	Power Line	Soil Type	Highway	Slope	Aspect	Altitude	Protected Area	Water Body	LULC	Total	Cr
Solar radiation	0.24	0.64	0.58	0.43	0.27	0.23	0.26	0.18	0.32	0.09	0.11	0.13	3.51	14.34
LST	0.05	0.14	0.32	0.38	0.15	0.26	0.15	0.16	0.14	0.11	0.09	0.10	2.06	14.45
Residential area	0.05	0.06	0.13	0.48	0.19	0.16	0.18	0.16	0.16	0.15	0.11	0.08	1.87	14.45
Power line	0.06	0.04	0.03	0.11	0.27	0.16	0.18	0.16	0.14	0.11	0.11	0.07	1.44	13.37
Soil type	0.07	0.07	0.05	0.03	0.08	0.01	0.12	0.08	0.09	0.12	0.09	0.08	0.98	12.92
Highway	0.07	0.03	0.05	0.04	0.05	0.06	0.09	0.08	0.09	0.08	0.11	0.07	0.84	12.87
Slope	0.05	0.06	0.06	0.03	0.04	0.04	0.06	0.10	0.09	0.08	0.08	0.06	0.77	13.04
Aspect	0.07	0.05	0.04	0.03	0.05	0.04	0.03	0.05	0.12	0.08	0.06	0.06	0.67	13.06
Altitude	0.03	0.05	0.04	0.03	0.04	0.03	0.03	0.02	0.05	0.14	0.09	0.05	0.60	12.90
Protected area	0.08	0.04	0.02	0.03	0.02	0.03	0.02	0.02	0.01	0.03	0.05	0.03	0.39	12.69
Water body	0.05	0.04	0.03	0.02	0.02	0.01	0.02	0.02	0.01	0.01	0.02	0.05	0.33	12.72
LULC	0.03	0.02	0.03	0.03	0.02	0.01	0.02	0.08	0.02	0.02	0.01	0.02	0.25	13.40
														160.23

Maximum eigenvalue (${\mathsf{\lambda }}_{max}$) = 13.35 $n=15$; consistency index (CI) = $\left(\frac{{\mathsf{\lambda }}_{max}-n}{n-1}\right)$ = 0.12; random index (RI) = 1.54; consistency ratio (CR) = (CI/RI) = 0.08.

and

Table 7. The random inconsistency catalogues (RI) for $n=15$ [19].

Order	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
RI	0	0	0.52	0.89	1.11	1.25	1.35	1.4	1.45	1.49	1.52	1.54	1.56	1.58	1.59
First-order difference		0	0.52	0.37	0.22	0.14	0.1	0.05	0.05	0.04	0.03	0.02	0.02	0.02	0.01

) were effective at selecting a solar power station in Kolkata and the adjacent area (

Table 8. The weighted values of the criteria and scores of all sub-criteria.

Main Criteria	Weighted Values	Influence (%)	Sub-Criteria	Score	Potentiality
Solar radiation	0.25	25	High	7	Highly suitable
			Medium	6	Moderately suitable
			Low	5	Less suitable
			Very low	4	Suitable
LST	0.14	14	High	6	Highly suitable
			Medium	4	Moderately suitable
			Low	2	Less suitable
Residential area	0.13	13	Built-up area	2	Less suitable
			Other area	7	Highly suitable
Power line	0.11	11	0.5 km	8	Very highly suitable
			1 km	7	Highly suitable
			2 km	6	Very moderately suitable
			3 km	5	Moderately suitable
			4 km	4	Less suitable
			5 km	3	Not suitable
			7 km	2	Suitable
			11 km	1	Not suitable
Soil type	0.076	7.6	W037, W038, W039	6	Mostly suitable
			W049, W050, W051, W057, W061	4	Moderately suitable
			W079, W081, W082, W083	3	Less suitable
			W084, W085, W086, Alluvial	1	Not suitable
Highway	0.065	6.5	1 km	7	Very highly suitable
			2 km	6	Mostly suitable
			4 km	5	Very moderately suitable
			8 km	4	Moderately suitable
			10 km	3	Less suitable
			13 km	2	Not suitable
			17 km	1	Suitable
Slope	0.06	6	High	6	Highly suitable
			Medium	5	Moderately suitable
			Low	2	Less suitable
			Very low	1	Not suitable
Aspect	0.05	5	High	5	Highly suitable
			Medium	3	Moderately suitable
			Low	1	Less suitable
Altitude	0.046	4.6	High	5	Highly suitable
			Medium	4	Moderately suitable
			Low	3	Less suitable
			Very low	1	Not suitable
Protected area	0.03	3	Protected area	1	Not suitable
			Non-protected area	6	Highly suitable
Water body	0.026	2.6	Water-body area	1	Not suitable
			Non-water area	7	Highly suitable
LULC	0.019	1.9	Water body	1	Not suitable
			Vegetation	3	Less suitable
			Agricultural land	4	Moderately suitable
			Settlement	5	Highly suitable
			Open space	7	Very highly suitable

). The calculated weighted values were input in the ArcGIS weighted overlay analysis (WOA) tool to identify the potential zones for a solar power plant.

3.6. The Solar Power Station Areas Using Weighted Overlay Tool

WOA is an important technique to establish a suitable site selection and location selection through the overall dimension of the diverse and dissimilar impacts [59,60]. The analytical hierarchy process (AHP) was used for site suitability analysis using the weighted overlay analysis (WOA) process [61,62,63,64,65]. Using the WOA technique, all criteria were overlaid with each other in GIS software to calculate potential zones for a solar power station. To estimate a solar power station for Kolkata and the adjacent areas, the analytical hierarchy process (AHP) and MCDA were applied (Table 4, Table 5, Table 6 and Table 7). Overall criteria were transformed into the raster and most of the arrangements in the procedure of the WOA tool in ArcGIS software v10.8. The WOA tool in ArcGIS software v10.8 calculated the site selection for the pixel-based investigation through the increasing the site suitability score and the weighted value aimed at every pixel. The procedure aimed at solar power station site selection was investigated using the Equation (9) [49,51].

$$S={\displaystyle \sum }_{i-1}^{n}WiXi$$

(9)

where $S$ is the suitable solar power station site, $Wi$ is the solar power station weighted values, $Xi$ is the criterion of the $i^{\mathrm{th}}$ suitable site, and $n$ is the total number of criteria selected for the solar power stations.

4. Results

In this solar power plant site suitability study, the multi-criteria decision-making (MCDA) process and GIS were used for Kolkata and the surrounding area. The study area was located in Kolkata, Howrah, and some parts of the urban and rural areas in Hooghly, South 24 Parganas, and North 24 Parganas. The analytical hierarchy process (AHP) was used to calculate the weighted values for delineating a suitable site for a solar power plant. Based on related literature, areal condition, and our decision, 12 types of criteria were selected for this study. The solar radiation and land-surface temperature data were mostly used for this study because the high amount of solar radiation and LST area are most suitable for solar power plants.

Some research investigations have been applied to the solar power plants in different parts of the world, and some results indicating 10 MW sites for PVPP sites were observed in China [34]. Research results indicate that export-risk attitudes and flexible judgment of real decision-making are most needed in Saudi Arabia [35]. A total of 300,000 km² (16%) of the studied regions are suitable for PVPP in Egypt [36]. A total of 261.1747 km² (24.9%) of the examined regions are most suitable in Turkey [37], and 5 cities in Turkey were examined for solar power plants, where the priority order for the explanation was, for example, Karaman > Konya = Niğde > Aksaray > Nevşehir. A similar study was carried out in Iran [38]. Different methods like extremely optimistic, optimistic, neutral, pessimistic, and extremely pessimistic were used in Algeria [39], and 31 major cities in India were studied for solar PV to reduce greenhouse gasses (GHG) and improve air quality in India [40]. In the recent investigation of solar power plants in Kolkata and the adjacent areas five classes were determined, including very highly suitable, highly suitable, moderately suitable, less suitable, and not suitable. Some previous studies observed highly suitable and high–moderate–low suitability. Therefore, this investigation can help planners, management purpose, and others stakeholders with the implementation purpose of the solar power plants in Kolkata and the adjacent areas.

The consistency ratio (CR) in this study was 0.083, which was the acceptable limit for the pairwise comparison technique for delineating the solar power plant over the study area. The other criteria for solar power were the LULC area, distance from the power supply line, distance from the highway, slope of this area, aspect of this area, and distance from water bodies. Water bodies are not suitable for solar power plants and vegetation and settlement areas are less suitable. The highly suitable LULC classes were open space and agricultural land. Using the geographic information system (GIS) tools and MCDA process, the 12 criteria were selected and the final suitability map was developed in ArcGIS v10.8. The final suitability map was classified into five categories based on the results, which were not suitable, less suitable, moderately suitable, highly suitable, and very highly suitable areas. The Hooghly River, eastern water bodies and wetlands, and other water areas were not suitable areas for site selection for a solar power plant in Kolkata and the surrounding area.

The study area suitability map shows that 474.21 km² (10.69%) areas were very highly suitable because of the high rate of solar radiation recorded and LST, and because the power supply line was densely located in that area (Figure 4). A total of 10.69% of very highly suitable area was located in the northern and northwestern parts of this study area. Most of them were covered by highly suitable and moderately suitable areas, occupying 1438.15 km² (32.43%) and 2197.45 km² (49.54%), respectively (Figure 4). The highly suitable areas were located in the southern, southwestern, and southeastern parts like the Pujali, Maheshtala, Amtala, and Thakurpukur areas. The moderate areas were mainly located in the central part and northern part of this study area, which were Kolkata, Dhaulagiri, part of Howrah, and many other areas. The less suitable area occupied 76.64 km² (1.73%) and the not-suitable area occupied 249.45 km² (5.62%). The not-suitable area was the Hooghly River, water bodies (pond, lake, or water areas), east Kolkata wetland, and the brackish water fishery area of North 24 Parganas (

Table 9. The total areas and the percentage distribution of site-suitability areas based on investigation results.

Suitable Classes	Area of Suitability
	Area in km2	Area in Percentage (%)
Not suitable	249.45	5.62
Less suitable	76.64	1.73
Moderately suitable	2197.55	49.54
Highly suitable	1438.15	32.43
Very highly suitable	474.21	10.69
Total	4436	100

).

5. Discussion

Solar power plants are essential for urban and peri-urban areas due to the high use of fossil fuel and natural gases. Transportation systems and industrial works have mostly used petroleum, diesel, LPG, and natural gases. Due to overwhelming population pressure and transportation need, air pollution has increased and created environmental degradation. Due to urban green space deficiency, heat stress, thermal variation, climate change, and environmental degradation, the natural ecosystem is being hammered. Gradually the shortage of natural energy has increased the future demand for alternative energy for daily use and industrial works. Solar energy is essential to fulfilling the energy demand globally. The result indicates that 474.21 km² (10.69%) of the area is very highly suitable, 249.54 km² (5.62%) of the area is not suitable because of water areas and the eastern Kolkata wetland, and 1438.15 km² (32.43%) of the area is highly suitable for a solar power plant. The most influencing factors were solar radiation, LST, power supply line, and residential area, and solar radiation plays a vital role for solar power plants due to the sunshine availability area of this study region. The urban areas need renewable energy most due to the high fossil-fuel use and air quality gradually affecting the human body. High use of fossil fuels is damaging the ecosystem, and the results are air pollution, asthma, skin disease, lung cancer, and many more.

Some previous studies applied solar power plants in different parts of the earth’s surface, and different types of parameters were applied for solar power plant site selection [5,6,35,38]. Rainfall data, annual sunshine, and public demands were also applied for the solar power plants. Furthermore, urban areas have higher use of natural energy than rural areas. Transportation systems and industrial works need more energy for implementation of daily needs. Solar energy is fulfilling the energy demand, and this energy is environmentally friendly, creating fewer emissions and less pollution globally. Solar power plants are necessary to meet the future energy demand and to protect our environment.

6. Limitations and Recommendations

Site selection for solar power plants is needed on land where PVPPs are constructed. However, before the construction, some research is needed, such as site selection using different methods like AHP, TOPSIS, OWA, MCDA, and fuzzy AHP. Those techniques can help with cost and time, and are applied for site selection and implementation purposes. The limitation of these solar power plants in Kolkata and adjacent areas is that sunshine datasets were not used in this study and other techniques were not used. AHP was widely applied for site selection but other techniques would help with cross-verification of the results. The digital elevation model (DEM) has been available since 2014; therefore, if there is any undulation or fluctuation in elevation, then the aspect and elevation must be changed. Therefore, previous DEM information is presenting some difficulties with preparing recent elevation information. Some future research recommendations for these locations are applying rainfall data, annual sunshine data, public demands, and distribution facilities as criteria. Other investigations may be of help for solar power plant location selection, like surveys and area calculation. Furthermore, this study’s results can help with the implementation with and without modification based on the planners’ perspectives, land availability, and local administrative involvement.

7. Conclusions

The decision-making process was used for solar power plants in Kolkata and the surrounding area in West Bengal, India. Due to the huge amount of fossil fuels, the megacity and adjacent areas have high levels of pollution and the air quality is dangerous for human health. Public health is damaged due to the high rate of fossil-fuel use in transportation, industrial work, and daily demands. Limited fossil fuels are sold at a very high rate and increase the pollution, which is hammering the natural environment and methodological condition. In this case, renewable energy is more important for sustainable urban development and managing the daily demands of Kolkata and the surrounding rural areas. The model-based study of the solar power plant suitability map showed that 10.69% of the study areas were very highly suitable site for solar power plants, and those area are located in the northwestern and northern portions of the investigated area. A total of 5.62% of the study area was not suitable for a solar power plant. Furthermore, a solar power plant is the most-used source of renewable energy, which increases air quality and human health. Petrol, diesel, and liquefied petroleum gas (LPG) are limited on the earth, and one day they will be depleted. People need alternative fuel for transport, industrial works, electricity, and daily use. In this condition, solar power is an alternative fuel for daily use. In addition, urban areas are the most polluting and have the highest thermal variation because of the overwhelming population pressure, transportation system, and urbanization. Those problems create a need for the development of new renewable energy, and solar energy is the most effective energy on the earth’s surface. Solar power plant site selection is necessary for the sustainable development of energy distribution.