Application of GIS in Introducing Community-Based Biogas Plants from Dairy Farm Waste: Potential of Renewable Energy for Rural Areas in Bangladesh

Abstract

Dairy production is one of the most important economic sectors in Bangladesh. However, the traditional management of dairy cow manure and other wastes results in air pollution, eutrophication of surface water, and soil contamination, highlighting the urgent need for more sustainable waste management solutions. To address the environmental problems of dairy waste management, this research explored the potential of community-based biogas production from dairy cow manure in Bangladesh. This study proposed introducing community-based biogas plants using a geographic information system (GIS). The study first applied a restriction analysis to identify sensitive areas, followed by a suitability analysis to determine feasible locations for biogas plants, considering geographical, social, economic, and environmental factors. The final suitable areas were identified by combining the restriction and suitability maps. The spatial distribution of dairy farms was analyzed through a cluster analysis, identifying significant clusters for potential biogas production. A baseline and proposed scenario were designed for five clusters based on the input and output capacities of the biogas plants, estimating the location and capacity for each cluster. The study also calculated electricity generation from the proposed scenario and the net greenhouse gas (GHG) emissions reduction potential of the biogas plants. The findings provide a land-use framework for implementing biogas plants that considers environmental and socio-economic criteria. Five biogas plants were found to be technically and spatially feasible for electricity generation. These plants can collectively produce 31 million m³ of biogas annually, generating approximately 200.60 GWh of energy with a total electricity capacity of 9.8 MW/year in Bangladesh. Implementing these biogas plants is expected to increase renewable energy production by at least 1.25%. Furthermore, the total GHG emission reduction potential is estimated at 104.26 Gg/year CO2eq through the annual treatment of 61.38 thousand tons of dairy manure.

1. Introduction

In the context of the economy of Bangladesh, the dairy sector is highly beneficial in terms of producing jobs, ensuring food security, and providing protein to people’s diets, and has enormous potential to contribute to the national economic value that was estimated at 73,571 Crore BDT in 2023 [1]. In 2023, the livestock sub-sector provided 3.23% of our total gross domestic product (GDP) [2]. The dairy population is rising daily and was estimated at approximately 24.85 million cattle in 2023 in Bangladesh [1] (Figure A1). Manure is frequently dumped in lagoons, slurries, or piles, which can result in substantial methane emissions, environmental damage, adverse health effects, and the loss of essential nutrients that could be contributed to the soil. Bangladesh produced over 216.97 million tons of livestock manure in 2023, which resulted in 16.61 million tons of CO2eq in total GHG emissions. Additionally, nearly 62.19 million tons of manure leached out into the water, giving the country a total EP of 295.22 kg N-eq/ha/year [3].

Despite having lots of potential in Bangladesh, the dairy sector negatively affects the environment and public health due to the lack of proper dairy waste management systems and treatment facilities. Waste management is now an eminent aspect of ensuring a sustainable environment [4]. Dairy waste management is important because dairy effluents contain a large amount of organic matter, and dairy waste can be recycled into biogas, fiber, and energy [5]. Throughout the production process, the dairy industry emits a large volume of greenhouse gasses (GHGs) [6].

In Bangladesh, dairy cattle are responsible for about 52.2 million tons of CO2eq [7]. Livestock farming practices affect the environment, particularly in the air and water, which can be reduced by implementing efficient farm management systems [8]. Dairy farms are responsible for significant GHG emissions associated with CH₄ from enteric fermentation and poor manure management and N₂O from soil management [9] (Figure A2). Different manure management strategies in Bangladesh were projected to emit 6.77 kg of methane (CH4) per dairy cow and 6.41 kg per head of other livestock [10]. As a result, managing dairy cow manure requires the application of an effective option, such as a decomposition of organic matter to the slurry, to reduce carbon footprint and substitute natural gas with biogas produced on the farm (

Table A1. Livestock manure management practices.

Criteria-1	Collection Efficiency
	Road distance	Flood prone area	Slope
Road distance	1	7	5
Flood prone area	1/7	1	1/3
Slope	1/5	3	1
Criteria-2	Safety
	Road distance	Flood prone area	Slope
Road distance	1	1/7	1/3
Flood prone area	7	1	4
Slope	3	4	1
Criteria-3	Cost Minimization
	Road distance	Flood prone area	Slope
Road distance	1	7	5
Flood prone area	1/7	1	1/4
Slope	1/6	4	1

).

In rural Bangladesh, dry manure is sometimes directly burned for cooking purposes, and wastewater or discharge from dairy farms pollutes the nearby rivers, ponds, or other water sources, which is a risk to public health, aquatic life, and waterway-related ground species [11]. So, sustainable management of dairy waste is in growing demand to minimize the threat of diseases, convert waste to bioenergy, reduce GHG emissions, stop offensive odors, and prevent surface water pollution for environmental and social safety. Cattle manure can be used as a raw material for biogas generation, an effective alternative for producing green energy in the country [12]. Manure is typically utilized in the facility for waste management and biogas production. For sustainable waste management, biogas is regarded as one of the most effective technologies for processing organic waste to recover both resources and energy [13].

Dairy farms produce several types of organic wastes, such as manure, urine, wasted feed, bedding material, wastewater from washing, dead animals, and other materials usually produced on dairy farms [14]. In this research, dairy cattle manure is considered dairy waste in the scenario analysis. Farm animals produce 156 million tons of fresh manure annually in Bangladesh [10]. The government of Bangladesh is concerned with waste management and has taken the initiative to introduce policies on commercial and public-owned dairy farms [7]. The Bangladesh Biogas Development Foundation (BBDF) aspires to build community-based biogas facilities to provide green energy to a community. According to the BBDF, accessible cattle manure can produce 77.4 million m³ of biogas per year, 170 × 103 MWh of power per year, and 121.89 million equivalents to 2.95 million tons of kerosene or 5.9 million tons of coal per year in Bangladesh [7]. Most cattle manure is used for burning fuel preparation and solid storage, with 37.30% and 56.20%, respectively. Only 1.65% of cow dung is used for liquid slurry, and anaerobic digestion is used in 4.80% of cattle manure in Bangladesh [10]. The vast amount of animal waste generated in Bangladesh has increased interest in potential waste management applications. As a result, biofuel generation has been proposed as a solution for dairy waste management and a potential new source of green energy. The 229 million tons of livestock waste had a production capacity of 16,988.97 million m³ of biogas, which could be transformed into 16.68 × 107 MWh of electricity, and 29 million tons of biofertilizer might have reduced net CO₂ emissions by 4.42 million tons as compared to diesel power generation [15].

Biogas technologies are important for energy availability, energy security, reducing fossil fuel dependency, climate change mitigation, sustainability, and socio-economic benefits. The goal of community-based dairy manure management practices is to reduce the significant negative impact on the environment and health risks posed by poor management of livestock manure, focusing on the livestock production sector in Bangladesh. The community takes care of animal waste, which is important for human and livestock health, cleanliness, economics, and environmental factors. The global environmental goal is to eradicate cattle-related pollution and environmental deterioration. The geographic information system (GIS) is a computer program that improves the collection, verification, archiving, integration, analysis, and display of earth-related data in a geographically-linked manner. Numerous scientific and engineering domains have adopted and used GIS, making it a cross-disciplinary application. Finding the ideal location for one or more facilities to fulfill a specified function is part of the land suitability analysis, which also entails determining the suitability level of the land resource and determining whether it is suitable for the intended usage [16]. A GIS analysis was used to determine the site’s suitability for anaerobic digestion system (ADS) development and identify clusters of suitable areas in Addison County by considering environmental, socio-economic, and political factors [17]. Using GIS to map out the bio-resources and then identify the best locations appears to be a very engaging and practical technique in the context of the possible development of collective biogas plants [18].

Previous research has highlighted GIS as a beneficial tool for resource management, decision-making, and feasibility assessments in several countries. Rakpong Saikaew used GIS to show the feasible sites for installing the biogas plant from cow manure in Thailand as well as the ideal placement of the biogas plant in terms of risk assessment and financial analysis depth [19]. Florencia Venier applied GIS to assess the clean energy recovery opportunities for sustainable livestock waste management in Buenos Aires, identifying possible sites for biogas plant locations based on geographical, environmental, and socio-economic variables [20]. Applying GIS techniques, Khue Minh Dao investigated the quantity of biogas produced from pig manure that helps decrease GHG emissions in Hanoi [21]. Diego Diaz-Vazquez used a GIS-based methodology to evaluate biogas potentials from livestock manures and multicriteria site selection in Jalisco, Mexico [22]. Several articles have been found about dairy farm waste management options through theoretical methods in Bangladesh, including economic feasibility and determinants of biogas technology adoption [23,24]. However, a suitability study for developing community-based biogas plants from dairy cow manure through GIS and potential environmental impact assessments will be a new perception for Bangladesh.

Based on the background information, this study on community-based biogas production from dairy cow manure in Bangladesh proposes several research questions and hypotheses. The key research questions include: What is the current potential of dairy cow manure for community-based biogas production in Bangladesh? Considering transportation, socio-economic conditions, and environmental impact, what are the most suitable locations for community-based biogas plants? How does the spatial distribution of dairy farms influence the potential biogas production from dairy cow manure? What are the potential environmental benefits of community-based biogas production in terms of energy generation and greenhouse gas emission reductions in Bangladesh? Additionally, how can GIS facilitate the site selection process for biogas plants? The study also examines the following hypotheses: H1 suggests that dairy cow manure has significant potential for biogas production, reducing dependence on traditional energy sources. H2 posits that GIS can effectively identify optimal locations for biogas plants based on proximity to dairy farms, transportation access, and socio-economic needs. H3 indicates that analyzing the spatial distribution of dairy farms will reveal high-potential areas for biogas production that maximize energy output and minimize environmental risks. H4 asserts that implementing community-based biogas plants will lead to measurable reductions in greenhouse gas emissions, contributing to climate change mitigation in Bangladesh. Lastly, H5 argues that community-based biogas production can provide sustainable energy solutions, improve waste management, and reduce the environmental footprint of dairy farming. The study conducts a spatial analysis with specific objectives: to evaluate the current potential of dairy cow manure for community-based biogas production; to identify feasible sites for biogas plants while considering transportation, socio-economic factors, and environmental impacts; to analyze the spatial distribution of dairy farms and calculate potential biogas production; and to assess the environmental benefits of community-based biogas production in terms of energy generation and reduced greenhouse gas emissions in Bangladesh.

2. Materials and Methods

2.1. Selection of the Study Area

The study was conducted in Sirajganj district, a northern part of the Rajshahi division in Bangladesh, and the area is approximately 2497 square kilometers (sq. km.) Geographically, this area is located between 89°15′ and 89°59′ east longitudes and between 24°01′ and 24°47′ north latitudes [25] (Figure 1). Economically, Sirajganj is one of the most important districts in Bangladesh because of its unique economic structure. Sirajganj is the milk pocket area or hub for milk production in Bangladesh [26]. The highest number of dairy cattle populations are distributed in this district, and the district livestock office of Sirajganj estimated about 0.934 million dairy population, with a concentration in the Ullapara, Shahjadpur, and Chauhali areas. The largest amount of dairy manure can be found in the Bangladesh Milk Producers’ Cooperative Union Limited (BMPCUL’s) commercial dairy region, particularly in the Shahjadpur and Sirajganj milk pocket areas, and according to livestock manure management policy, it was estimated at 3.0 to 4.0 million tons [7].

2.2. Research Framework and Data Sources

This research investigated the production of potential community-based biogas from dairy farm waste. ArcGIS software version 10.8.1 was used to select suitable sites for community-based biogas plants by identifying restricted areas and considering different geographic criteria such as transport networks, surface water, important places, residential areas, and unstable areas. The site suitability map was identified, considering socio-economic factors such as road networks, flood-prone areas, and slopes, using the analytical hierarchy process (AHP). Afterward, the constraint map and site suitability map were combined to create a final feasibility map with available sites for siting community-based biogas plants. The summary of the research method framework is presented in Figure 2. Later, the cluster farms were identified, and the farm scale selected via cluster analysis of dairy farms was considered to maximize the available manure input, location of dairy farms, and capacity for energy potential. From this part, this study estimated the location and capacity of community-based biogas plants for the clusters. Finally, the study assessed the potential biogas-based electricity generation for the clusters and estimated each cluster’s greenhouse gas emission reduction potential. The spatial analysis in this research was conducted using ArcGIS software version 10.8.1. The geolocation data of dairy farms with heads were collected from the Department of Livestock Services (DLS), Ministry of Fisheries and Livestock, Bangladesh, and research-related data were collected from different secondary sources like the Bangladesh Economic Review (BER), Bangladesh Bureau of Statistics (BBS), Geofabric, Google Earth data, books, journals, research publications, and government survey reports [27].

2.3. Suitability Analysis

The site selection or suitability analysis determines the best location for developing community-based biogas plants from dairy waste in Bangladesh. For the suitability analysis in this study, the weighted linear combination (WLC) model was used in ArcGIS 10.8.1 software by considering several criteria and alternatives based on economic, social, physical, biological, and ecological aspects. For the suitability analysis of community-based biogas plants, the study used Equation (1):

$$S=\sum _{i=1}^n{W}_i{C}_i.\prod _{j=1}^m{R}_j$$

(1)

where, $W_i$ stands for weights for criteria $i$; $C_i$ is the criteria for suitability; $i$ is the road distance, flood-prone areas, and slope; $R_j$ depicts the restricted areas; and $j$ is the transport network, surface water, important places, residential areas, and unstable areas. The restriction model uses the Boolean intersection process, and restriction factors are classified into Boolean suitable or unsuitable areas in the restriction map. The suitability model uses the AHP to weigh the relative importance of criteria.

2.3.1. Restriction Analysis

A restriction analysis is used to identify some areas that must be avoided while establishing community-based biogas plants. For the planning of the spatial analysis, restriction mapping is important to restrict some areas that are based on specific restriction factors considering natural and artificial places. For restriction mapping, the study used Equation (2):

$$R=\prod _{j=1}^m{R}_j$$

(2)

where, $R$: restriction area; $R_j$: restriction criteria include transport networks, surface water, important places, residential areas, and unstable areas.

The restriction process is followed by specific steps, including identifying the restriction factors and making buffers for the specified places. After that, they are converted from feature to raster, and finally, a restriction map is obtained by combining all specific criteria-based restriction areas. The restriction factors in this study consist of transport networks (roads and railways), surface water (rivers, ponds, and wetlands), important places (parks, hospitals, and education facilities), residential areas (towns and villages), and unstable areas (river erosion areas and island). The buffer criteria for various constraint variables, which were based on secondary sources from previous studies, were applied in this research. The buffering criteria used in various restricted places for implementing different types of waste treatment plants are exposed in

Table 1. Buffering criteria for restriction areas used in different articles.

Transport Network	Surface Water	Important Places	Residential Area	Unstable Area	References
100 m	200 m	300 m	300 m	-	[28]
100 m	500 m	500 m	600 m	300 m	[29]
30 m	200 m	-	1 km	500 m	[30]
30 m	100 m	500 m	1 km	-	[31]
30–100 m	200 m	500 m	500 m–2 km	500 m	[32]
200 m	1 km	-	600 m	-	[33]
100 m	500 m	-	2–5 km	-	[34]
100 m	100 m	100 m	500 m	100 m	[35]

.

Based on these values, the buffering criteria used for restricted places for this research have been considered as 30 m for transport, 200 m for surface water, 500 m for important places, 1 km for residential areas, and 500 m for unstable areas (

Table 2. Buffering criteria used for restriction areas.

Transport Network (Road, Railway)	Surface Water (River, Pond, Wetland)	Important Places (Park, Hospital, Education Facilities)	Residential Area (Town, Village)	Unstable Area (River Erosion Area, Island)
30 m	200 m	500 m	1 km	500 m

). The buffer criteria are a zone that surrounds the restricted places and expands at a specific safe distance.

The use of the extract, overlay, and proximity toolboxes for processing spatial vector layers results in the development of a map showing the restricted areas. This procedure converts all features (polygon or point) to a raster using the Arc Toolbox. For every constraint feature, a binary GIS grid was developed in which “0” is attributed to cells within a buffer area, and “1” is attributed to cells outside the buffer area [31]. Finally, the spatial analyst tools multiplied all the constrained maps to produce a final restriction map.

2.3.2. Site Suitability Analysis

Several weighted criteria were used in the site suitability analysis for sitting biogas plants. This analysis is based on Equation (3):

$$C=\sum _{i=1}^n{W}_i{C}_i$$

(3)

where, $W_i$: weights for criteria $i$; $C_i$: criteria for selectivity analysis; $i$: roads, flood-prone areas, and slopes.

This study analyzed three important criteria: road distance, flood-prone area, and slope. These criteria were selected by experts based on the literature review of related studies on environmental, economic, and safety issues considerations for determining suitable locations for biogas plants. All criteria were classified using the Arc Toolbox. For transport networks, the best places are easily identified for biogas plants near the road for transportation and to maximize the collection of dairy cow manure. It is also important for resource distribution and minimizing the collection cost. Transportation costs may be cheaper if the dairy farms are located near the road networks. For flood-prone areas, preferences are given to areas that are not flood-prone or less flood-prone and far from rivers. In Bangladesh, the slope is not even; few areas are high land, and few are low land. Normally, high-slope areas are not feasible for biogas plants. That is why the reclassification of slope criteria was based on manure collection efficiency and low-cost collection facilities in the study areas. Reclassify tools were used to classify roads, flood-prone areas, and slopes with the quantile method that equally distributes the values into groups. Based on the distance from roads for road networks and the distance of flood-prone areas from rivers, areas were classified into five values: most, high, moderately, marginally, and low suitable values. Regarding road networks, the distance was distributed from 0 to 25,277 m as most to low suitable values, respectively. For rivers, the distance was classified from 5288 to less than 793 m as most suitable to low suitable values, respectively. The slope was classified into five groups from 0 to 8.17 m as most to low suitable values, respectively.

The AHP was used to estimate this study’s weighted preferences of specific criteria. The AHP is especially helpful for decision-making when several criteria are involved in the decision. The AHP combines math and psychology, which compares the different options and selects the best one [36]. It is a powerful tool that removes partiality from the decision-making process and ensures it reflects values and priorities [37]. Using several pairwise comparisons and assessing the synthesized results, the AHP assists the decision-maker in setting priorities and selecting the best decision [21]. The AHP is applied in four fundamental steps: (i) consider a set of assessment criteria and alternative solutions to separate the specific issue into several sub-issues; (ii) use a mathematical scale to assess specific elements in pairs; (iii) compare and evaluate the consistency of the values; (iv) analyzing the results to obtain at a final ranking [37]. Firstly, the study aimed to determine the most suitable location for installing community-based biogas plants. Three criteria were chosen: collection efficiency, safety, and cost minimization. After that, three alternative criteria were selected for the suitability model: road network, flood-prone areas, and slope, which helps to solve the multicriteria decision-making problems. These criteria were related to the suitability of the sitting biogas plant (Figure 3).

In the second step, a pairwise comparison of the criteria identifies the priorities for each factor pair that are presented in a significant way. Questionnaires were used to gather expert opinions on the relative importance of the factors considered. The AHP survey included a total of 10 respondents, whose qualifications were specifically chosen based on their expertise relevant to the application of GIS in introducing community-based biogas plants from dairy farm waste. The comparative results (for each factor pair) were described as integer values of 1 (equal value) to 9 (extremely different), where a higher number denotes that the chosen factor was more important than the other factor to which it was compared. A rating of 9 indicates that the row factor is more important in relation to the column factor. On the other hand, a rating of 1/9 indicates that relative to the column factor, the row factor is less important [38,39,40]. In cases where the column and row factors are equally important, they have a rating value of 1. For example, when comparing elevation and slope angle factors, a score of 1 indicates that both were equally relevant to evaluating suitability, and a score of 9 indicates that elevation is more critical than slope angle. All scores were assembled in a pairwise comparison matrix with diagonal and reciprocal scores located in the lower left-hand triangle. Reciprocal values (1/3, 1/5, 1/7, and 1/9) were used where the row factor was found to be less important than the column factor (

Table 3. AHP’s fundamental scale [21,41,42].

Definition	Explanation	Importance Intensity Scale
Equal importance	Two actions are equally important in achieving the goal.	1
Moderate importance	One action is slightly preferred over another.	3
Significant importance	One action has a significant advantage over another.	5
Extremely importance	One action is heavily valued, and its dominance is apparent.	7
Extremely significant	One action has the highest level of affirmation compared to another.	9
Intermediate values between two judgments	Need to make a compromise	2, 4, 6, 8

).

Third, we calculated the matrix and ensured the consistency of the pairwise comparison factor. The AHP also provided the measurements to calculate the normalized values of each factor and to determine the normalized principal eigenvalue and priority vectors. The pairwise matrix was calculated and is given by the following expression:

$$\left[\begin{array}{ccc}{C}_{11}& C_{12}& {...C}_{1n}\\ C_{21}& C_{22}& ...C_{2n}\\ {\begin{array}{c}.\\ .\\ C\end{array}}_{n1}& {\begin{array}{c}.\\ .\\ C\end{array}}_{n2}& \begin{array}{c}.\\ .\\ .\end{array}{\begin{array}{c}.\\ .\\ C\end{array}}_{nn}\end{array}\right]$$

(4)

The sum of each column of the pairwise matrix was denoted as follows:

$$C_{ij}=\sum _{i=1}^n{C}_{ij}$$

(5)

We then divided each element of the matrix by its column total to generate a normalized pairwise matrix as follows:

$$X_{ij}={\displaystyle \frac{C_{ij}}{\sum _{i=1}^n{C}_{ij}}}=\left[\begin{array}{ccc}{X}_{11}& X_{12}& {...X}_{1n}\\ X_{21}& X_{22}& ...X_{2n}\\ {\begin{array}{c}.\\ .\\ X\end{array}}_{n1}& {\begin{array}{c}.\\ .\\ X\end{array}}_{n2}& \begin{array}{c}.\\ .\\ .\end{array}{\begin{array}{c}.\\ .\\ X\end{array}}_{nn}\end{array}\right]$$

(6)

Finally, we divided the sum of the normalized matrix column by the number of factors used (n) to generate the weighted matrix of priority factors as follows:

$$W_{ij}={\displaystyle \frac{\sum _{j=1}^n{X}_{ij}}{n}}=\left[\begin{array}{c}{W}_{11}\\ W_{12}\\ {\begin{array}{c}.\\ .\\ W\end{array}}_{1n}\end{array}\right]$$

(7)

The initial consistency vectors were derived by multiplying the pairwise matrix by the vector of weights as follows:

$$\left[\begin{array}{ccc}{C}_{11}& C_{12}& {\dots C}_{1n}\\ C_{21}& C_{22}& \dots C_{2n}\\ {\begin{array}{c}.\\ .\\ C\end{array}}_{n1}& {\begin{array}{c}.\\ .\\ C\end{array}}_{n2}& \begin{array}{c}.\\ .\\ .\end{array}{\begin{array}{c}.\\ .\\ C\end{array}}_{nn}\end{array}\right]\times \left[\begin{array}{c}{W}_{11}\\ W_{12}\\ {\begin{array}{c}.\\ .\\ W\end{array}}_{1n}\end{array}\right]=\left[\begin{array}{ccc}{C}_{11}{W}_{11}+& C_{12}{W}_{11}+& \dots +C_{13}{W}_{11}\\ C_{21}{W}_{12}+& C_{22}{W}_{12}+& \dots +C_{23}{W}_{12}\\ \begin{array}{c}.\\ .\\ C_{n1}{W}_{1n}\end{array}& \begin{array}{c}.\\ .\\ C_{n1}{W}_{1n}\end{array}& \begin{array}{c}.\\ .\\ .\end{array}\begin{array}{c}.\\ .\\ C_{n1}{W}_{1n}\end{array}\end{array}\right]=\left[\begin{array}{c}{V}_{11}\\ V_{12}\\ {\begin{array}{c}.\\ .\\ V\end{array}}_{1n}\end{array}\right]$$

(8)

The principal eigenvector (λ_max) was then calculated by averaging the values of the consistency vector as follows:

$${\mathsf{\lambda }}_{max}=\sum _i^n{CV}_{ij}$$

(9)

Eigenvalues were calculated by averaging the rows of each matrix. Eigenvalues were also referred to as relative weights. The largest eigenvalue was equal to the number of factors, and when λ_max = n, judgments were consistent. Normalized eigenvalues were generated as weights of priority factors. The judgments were also checked to determine the consistency index (CI), which was calculated as follows:

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

(10)

here, n is the total number of criteria. Saaty introduced consistency ratio (CR) and compared it to the CI and random index (RI) values, which is the calculated value for matrices of different sizes (

Table 4. Random inconsistency indices for n = 10 [40].

n	1	2	3	4	5	6	7	8	9	10
Random consistency index (RI)	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49

). The consistency ratio was calculated as follows:

The consistency ratio was calculated as:

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

(11)

A lower CR indicates more consistency. If the value of CR is 0.10 or more, then the weight values of the matrix indicate inconsistencies, and the method (AHP) may not provide a meaningful result [42]. After that, we checked the consistency of the given values and found the consistency ratio (CR) at 0.056740, 0.030325, and 0.087347 for three criteria, respectively. If the CR value is less than 0.10, it is considered satisfactory [43]. Then, the raster cells were multiplied by their weighted values for three sub-criteria (road distance, flood-prone areas, and slope) to develop a suitability map using the weighted overlay tools in the GIS Arc toolbox (Figure A3). The suitable map was ranked on four scales: most suitable, highly suitable, moderately suitable, and low suitable areas for sitting biogas plants. A suitability model is presented in Figure 4.

2.3.3. Final Suitability Analysis

Using the Times function, the constraint and suitability maps were combined in spatial analysis tools to form the final suitable site. The different areas of the suitability map include the most suitable, highly suitable, moderately suitable, low suitable, and restricted areas. A final suitability model is presented in Figure 5.

2.4. Cluster Analysis of Dairy Farms

The cluster analysis is a statistical method for detecting cluster patterns that utilize analyzing patterns toolsets, spatial statistic tools, and spatial autocorrelation functions. This research identified spatial autocorrelation using the Global Moran’s I statistic based on feature positions and attribute values. This tool determines whether the pattern expressed is clustered, scattered, or random based on a collection of features and an associated attribute. A positive Moran’s I index value indicates a propensity toward clustering, and a negative value indicates a tendency toward dispersion. The z-score or p-value indicates statistical significance.

The nearest neighbor index (NNI) was then calculated using the average distance between each feature and its nearest adjacent feature. The ratio of the observed mean distance to the expected mean distance was used to construct the NNI. The expected distance in a hypothetical random distribution is the average distance between neighbors. The pattern was clustered if the index is less than one; if the index is greater than one, the pattern is dispersed or competitive. A line graph of the distances and their respective z-scores was generated for a series of distances. Peak z-scores indicate distances between dairy farms where spatial processes that indicate clustering were most pronounced, and statistically significant peak z-scores obtain distances between dairy farms where spatial processes that express clustering are most pronounced. These peak distances are usually good values for tools with a distance band or distance radius parameter.

Finally, the spatial statistics toolset’s cluster and outlier analysis was used to identify statistically significant hot areas, cold spots, and spatial outliers in the high density of dairy cow manure. This application produced new output feature classes such as local Moran’s I index, z-score, pseudo-p-value, and cluster/outlier type (COType). HH refers to a statistically significant cluster of high values and LL to low values in the output feature class. The COType field revealed statistically significant clusters and outliers at a 95% confidence level. When features with high values are surrounded by low values (HL) or low values are surrounded by high values (HL), clusters and outliers are seen at a 95% confidence level (LH). The cluster analysis produced a map that highlighted spatially significant farm clusters with high-density dairy farms and merged all significant cluster farms, including HH, HL, and LH clusters. The study then used a buffer of all significant cluster farms within a 10 kilometer distance to determine the ideal place for sitting community-based biogas facilities. The final suitability map and the cluster map were then intersected to indicate acceptable areas as well as the locations of selected high-density farms. Finally, the study designed a scenario that contains the selected cluster farms for biogas production and calculated the potential biogas production and electricity generation capacity.

2.5. Proposed Scenarios

Current dairy cow manure management situations have negative environmental consequences, and society suffers from issues such as stink, flies, and mosquitoes. On the other hand, cow manure can help the environment by producing sustainable energy in biogas. Two scenarios were considered for this study:

Scenario 1: This is the baseline scenario that represents all selected dairy farms’ existing manure management systems after the cluster analysis. It means that selected dairy farm animals release their manure directly into the environment, and different types of manure management systems are practiced without any treatment, such as solid storage, deposited on pasture, paddocks, and rangelands, burning for fuel, etc. However, there is no community-based biogas plant in this scenario. As a result, without any treatment of cow manure, GHG is entirely released into the natural environment.

Scenario 2: This study proposed a scenario for the development of community-based biogas plants that create economic and social benefits for the local community in the study area. After the cluster analysis, five clusters are found (A, B, C, D, and E) in the research area. In this scenario, the study assumed that manure is treated by anaerobic digestion and calculated the potential biogas production that is used for electricity generation.

2.6. Estimating Dairy Manure and Biogas Production Capacity

Geolocation data of dairy farms and their head numbers were collected from the Department of Livestock Services under the Ministry of Fisheries and Livestock, Bangladesh. The data were used to estimate the available dairy cow manure, but surplus availability and residue dryness parameters were removed from the estimate of accessible livestock residue, as in a prior successful study. Calculate the available dairy cow manure using Equation (12), which was modified from the work of Rahman and Paatero [44].

$$R_D=N\ast Y_D$$

(12)

where, $R_D$ is the total amount of dairy cow manure (kg d⁻¹), $N$ is the number of dairy cows (unit), and $Y_D$ (kg d⁻¹) is the dry matter generation rate.

$$A_D=R_D\ast C_D$$

(13)

where $A_D$ (kg d⁻¹) is the total available dairy cow manure and $C_D$ (kg d⁻¹) is the manure collection factor.

$$B_D=A_D*V_D*G_D$$

(14)

where, $B_D$ (m³ d⁻¹) is the biogas potential from dairy cow manure, $A_D$ (kg d⁻¹) is total available livestock residue, $V_D$ (VS) is the ratio of vs. to dry matter and $G_D$ (m³ kg⁻¹) is the rate of biogas generation from VS.

The amount of animal waste depends on the type of animal species, body weight, process of food, and type of breed, and it was calculated that cattle and buffalo produce about 10–20 kg of manure per day [45]. This study calculated the dairy cow manure at 9% of the cow’s body weight based on the average of the 250 kg live mass of dairy animals and determined an average manure volume of 22.5 kg per day [46,47]. This research used the collection factor for dairy cow manure as 0.50, the ratio of volatile solid to dry matter as 0.93, and the biogas production rate of 0.66 m³/kg VS [48,49,50].

The $A_D$ of animal dung revealed that methane content percentage depends on the manure source, accounting for 50–70% of the biogas [51]. The present study considered 61.70% of the methane content of the biogas derived from dairy manure. Then, CH4 energy production was converted to kWh by multiplying the methane output by 10.5, which is the electric energy conversion rate [48]. The conversion efficiency of a power plant is determined by its size. Generally, a plant with a large turbine system has a conversion efficiency of 35–42%, while a facility with modest generators has a conversion efficiency of 25% [52]. The conversion efficiency value was considered to be 35% in this study, and the plant’s annual operation capacity was 300 days, which was used to calculate potential energy capacity [48].

2.7. Calculation of GHG Emissions Reduction Potential

The study designed five community-based biogas plants that generate energy from biogas, potentially reducing GHG emissions while also reducing the consumption of fossil fuels in Bangladesh. The finding of the GHG emissions reduction potentials of community-based biogas plants compared to the effects of fossil fuels in our country. Several research findings have calculated the net GHG emissions from various fuels across their lifecycle assessment [53,54,55,56]. The following equation was used for the calculation of GHG emissions reduction potential for electricity generation from biogas:

$$GR=Gf-Gb$$

(15)

where, $GR$ is the GGHG emission reduction, $Gf$ is the average GHG emission from fossil fuel-based electricity generation and $Gb$ is the GHG emission rate from biogas-based electricity production.

$$Gf=\Sigma Pfi\cdot Gii=ni=1n$$

(16)

where, $Pf$ is the power generation using individual fossil fuels in Bangladesh and $Gi$ is the GHG emission for electricity generators run by individual fossil fuels, $i$ = 1, 2, …n; for example, coal, natural gas, diesel oil, etc.

Using Equation (15), the average GHG emissions for power generation from different fossil fuels in Bangladesh were calculated and found to be 544.74 g CO2eq/kWh. This value is quite close to the findings of a 2013 study conducted by the International Energy Agency (IEA). According to this institution, the average emission factor for power production in Bangladesh from non-biogas sources is 588 g CO2eq/kWh [50]. Laura et al. [57] conducted a study in the European Union (EU) on biogas burning to create energy and estimated that the marginal lifetime GHGs of biogas-based electricity values from—335 to 25 g CO2 per kWh. The GHG emissions of biogas-based energy generation in Bangladesh were assumed to be 25 g CO2eq/kWh, considering this finding [57]. So, utilizing Equation (16), the study estimated the electricity generation from community-based biogas plants.

Biogas-based electricity facilities can lower GHG emissions by about 519.74 g CO2eq/kWh when compared to other fossil fuels in Bangladesh. The GHG emission reduction potential was estimated in gigagram CO2eq per year (Equation (17)).

$$GT=Pb\ast GR$$

(17)

where $Pb$ is the total electricity generation potential using community-based biogas in this research.

3. Results and Discussions

3.1. Restriction Map for Development of Community-Based Biogas Plants

The restriction model was used to ensure the feasible areas for sitting community-based biogas plants that avoid the unsuitable areas in the final map. The study created a restricted map that removed the transport network (roads and railways), surface water (rivers, ponds, and wetlands), important places (parks, hospitals, and education facilities), residential areas (towns and villages), and unstable areas (river erosion areas and island) and their surrounding buffer areas. Figure 6a–e depicts the restriction maps for five restricted zones.

The study made the final restriction map by combining all these restriction maps, as shown in Figure 7.

Most of the restricted areas were in the east part of the Sirajganj district because this region has residential areas, water bodies, and river erosion areas.

3.2. Weighted Preferences for Site Selection by AHP

The study conducted the AHP analysis that calculated the matrix normalization, average values, index consistency, and consistency ratio (CR) by Microsoft Excel 2016 after checking the consistency of the given values and found the CR at 0.056740, 0.030325, and 0.087347 for three criteria such as road distance, flood-prone area, and slope, respectively. The weight preferences were calculated, and the highest priority was found to be the road network distance (49%), the next priority was flood-prone areas (29%), and the lowest priority was the slope (22%). Finally, the study used these weighted preferences for the suitability analysis for sitting community-based biogas plants (

Table 5. Suitability criteria with their weighted preferences.

Criteria	Collection Efficiency (%)	Safety (%)	Cost Minimization (%)	Final Weighted Preferences (%)
Road distance	72.35	12.68	63.25	49.00
Flood prone area	8.33	72.08	6.94	29.00
Slope	19.32	15.24	29.81	22.00

).

3.3. Site Suitability Map Based on Suitable Criteria

The site suitability map illustrates criteria such as road distance, flood-prone areas, and slope, taking into account collection efficiency, safety, and cost minimization, as shown in Figure 8. Four zones have been identified based on their suitability for constructing community-based biogas plants: most suitable, highly suitable, moderately suitable, and least suitable. The most suitable locations are near transport networks for easy dairy manure transportation from farms to the biogas plants, situated away from flood-prone areas and on appropriate slopes. These areas are deemed ideal for developing community-based biogas plants in the Sirajganj district.

3.4. Final Suitability Areas for Sitting Community-Based Biogas Plants

The final suitability map was created by combining the restriction and site suitability maps, clearly distinguishing suitable zones from prohibited areas. It features four distinct suitability categories: most suitable, highly suitable, moderately suitable, and least suitable, along with a restricted zone. Figure 9 displays the final suitability map for developing community-based biogas plants in the Sirajganj district of Bangladesh, with blue and red colors indicating the most and highly suitable locations for biogas plants, respectively. Figure 10 highlights the most suitable parcel, which constitutes only 7.41% of the total area of the Sirajganj district.

3.5. Identified High-Density Dairy Farms by Cluster Analysis

The study identified statistically significant hot spots, cold spots, and spatial outliers of dairy farms in the Sirajganj district using cluster and outlier analysis from spatial statistics tools. The distance band was calculated using the utility function “Calculate the Distance Band from Neighbor Count”, determining that the maximum distance at which any dairy farm had at least one neighbor was 4814.09 m. Spatial autocorrelation was measured using the incremental spatial autocorrelation function, which revealed a set of distances with associated z-scores; the peak z-score reached 4.2 km. Figure 11 illustrates the statistically significant peak z-scores that indicate pronounced clustering of dairy farms. Additionally, the study statistically identified significant hot spots, cold spots, and spatial outliers (at a 95% confidence level) with the cluster and outlier analysis tool. The results of the cluster analysis of dairy farms are presented in Figure 12.

The study ultimately identified a statistically significant cluster of dairy farms by combining all significant clusters (High–High, High–Low, and Low–High), revealing a range of potential farms from 747 to 145 that exhibit strong spatial correlation. Figure 13 illustrates the significant potential cluster of dairy farms suitable for community-based biogas production.

3.6. Scenarios Analysis

The study generated a map of high-density dairy farms by intersecting it with the final feasible map for siting biogas plants. The potential biogas capacity of each dairy farm was estimated using mathematical calculations, assuming that 50% of dairy cow manure is collected for community-based biogas plants based on the available manure supply. These findings enabled the estimation of the total biogas production capacity resulting from the strong geographical clustering of dairy farms. Biogas plants with a power capacity of 250 kW or more are considered economically viable investments [58]. Consequently, only biogas facilities with capacities exceeding 250 kW were included in the study. To establish community-based biogas plants, a minimum suitable area is necessary. According to previous research, land requirements are 1 hectare for a 200 kW plant, 1.5 hectares for a 500 kW plant, and 2.5 hectares for a 1 MW plant [54]. This study set the minimum required area for biogas plant sites at 2.5 hectares. Based on the theoretical potential of dairy farm manure for biogas and energy production, the study developed two scenarios: Scenario 1 (the baseline scenario) and Scenario 2 (the alternative scenario), the latter focusing on community-based biogas plants from the five identified dairy farm clusters.

3.6.1. Scenario 1: A Baseline Scenario

After conducting the cluster analysis, 145 dairy farms were identified as having a strong correlation from an initial pool of 747 farms. Among these, 70 dairy farms formed High–High (HH) clusters, 47 formed High–Low (HL) clusters, and 28 formed Low–High (LH) clusters based on the selected COType (HH, HL, LH). In this scenario, the selected dairy farms manage their manure through various untreated systems, such as solid storage, direct deposition on pasture and rangelands, and burning for fuel. Consequently, the baseline scenario does not include any biogas facilities, resulting in the emission of all greenhouse gasses (GHG), including methane (CH4) and nitrous oxide (N2O), into the environment without treatment. This research used the baseline scenario as a reference to assess GHG emission reductions in the alternative scenario.

3.6.2. Scenario 2: Community-Based Biogas Plants

The study developed a scenario for establishing community-based biogas plants using available dairy cow manure. To identify the optimal locations for these plants, a buffer was applied around all significant cluster farms intersecting with suitable areas. This analysis revealed five potential clusters (A, B, C, D, and E) for siting community-based biogas plants within a 10 km radius (Figure 14). These clusters are intended to generate electricity for local consumption, leveraging the significant concentration of dairy farms in the five subdistricts of the Sirajganj district.

The study determined that five biogas plants would be feasible when considering collection and transportation costs, as well as the efficiency of dairy cow manure collections. It was found that the total available dairy cow manure, estimated at 61,376 tons per year, has the potential to produce 31 million m³ of biogas, which can generate an energy potential of 200.60 GWh annually, with a total electricity capacity of 9751 kW per year (

Table 6. Results from Scenario 2 (community-based biogas plants).

Parameters	Cluster A	Cluster B	Cluster C	Cluster D	Cluster E	Total
Total residue (ton/year)	14,191	32,185	18,552	26,469	31,355	122,752
Total available residue (ton/year)	7096	16,092	9276	13,234	15,678	61,376
Potential biogas (300 days) (million m3/year)	3.6	8.1	4.7	6.7	7.9	31
Area (subdistricts)	Shahjadpur	Tarash	Raiganj	Sadar	Belkuchi
Total energy potential (GWh/year)	23.19	52.60	30.32	43.25	51.24	200.60
Electricity capacity (KW/year)	1127	2557	1474	2103	2491	9751

).

3.7. Estimation of GHG Emission Reduction Potential

Using Equation (15), the net GHG emission reduction potential from community-based biogas plants was calculated. In Cluster A, the biogas plant generates a total of 23.19 GWh of energy annually, leading to a reduction of approximately 12.05 gigagrams of CO2eq per year in greenhouse gas emissions in Bangladesh. Similarly, Clusters B, C, D, and E produce 52.60, 30.32, 43.25, and 51.24 GWh of energy per year, resulting in GHG reductions of approximately 27.34, 15.76, 22.48, and 26.63 gigagrams of CO2eq per year, respectively. Overall, the study estimates that establishing five community-based biogas plants from dairy cow manure in the Sirajganj district will reduce GHG emissions by a total of 104.26 gigagrams of CO2eq annually, with a capacity to generate 200.60 GWh of energy (Figure 15).

This study highlighted the importance of dairy manure treatment facilities in minimizing environmental impacts while generating electricity. Utilizing spatial analysis tools, it identified feasible locations for community-based biogas plants using dairy cow manure and evaluated the potential environmental benefits, ultimately supporting local electricity demand. Identifying suitable sites for these biogas plants is crucial for sustainable waste management. The suitability analysis provided a significant land-use concept that determines optimal locations for dairy manure treatment plants based on natural and socio-economic criteria. This methodology can also be applied to identify suitable sites for treating other livestock manure, such as buffalo and chicken, as well as agricultural residues and to determine concentrations of livestock clusters. Additionally, it can be adapted for areas with high cattle populations, including the Mymensingh, Rangpur, Pabna, and Dinajpur districts of Bangladesh. The approach may also extend to studies involving solar, wind, hydro energy, and urban waste management.

The study proposed a method for estimating the GHG emission reduction potential from electricity generation, aiding in the calculation of biogas potential from livestock manure, food waste, and other organic materials by determining net GHG emissions. Five clusters were identified for developing community-based biogas plants utilizing dairy cow manure. The cluster analysis considered various factors, including the availability of dairy cow manure, collection efficiency, moisture content, and excess availability.

Table 7. Summary of results.

Parameters	Values
Total manure production (ton/year)	122,752
Number of plants (≥1 MW)	5
Total biogas potential (million m3/year)	31
Total potential energy (GWh/year)	200.60
Total production capacity (MW/year)	9.8
GHG emission reduction (gigagram CO2eq)	104.26
Contribution to renewable energy (%)	1.25
Disposed amount of dairy waste (ton/year)	61,376

presents the total outcomes of all clusters, highlighting the environmental and socio-economic benefits for the rural areas of the Sirajganj district.

The study developed maps by GIS analysis that represent the feasible location of community-based biogas plants with the production of potential biogas and estimated the capacity of the power plants. These maps could help support the policymakers in developing the dairy manure management policy and the management of GHG emissions for a sustainable environment.

The study found five community-based biogas plants that could be established as biogas-based power generation plants with a total capacity of 9751 KW per year, which helps meet the demand for electricity in the dairy farms and the local area of the Sirajganj district in Bangladesh. These community-based biogas plants can produce around 200.60 GWh of energy annually, which is a small amount compared to the total generation of electricity in Bangladesh but helps to enhance renewable energy generation. Today, Bangladesh has 788.78 MW of renewable energy generation capacity, including off-grid, with biogas accounting for only 0.69% of total renewable energy generation capacity. By implementing these projected 9.8 MW plants in the Sirajganj district, Bangladesh may raise its renewable energy production while increasing biogas contribution to 1.25% of overall renewable energy production. The community-based biogas project is a sustainable solution to the ongoing energy crisis in small communities or villages in Bangladesh [59]. Nationally Determined Contributions (NDCs) state that 107,000 micro biogas plants should be constructed by 2030 to improve manure management. The Renewable Energy Policy (Draft) 2022 also highlights the idea of introducing biogas plants. The construction of biogas facilities is consistent with the policy’s objectives of increasing “green” energy in the overall energy mix because manure is not only a waste but can also be converted into highly calorific fuels [60].

Despite having some challenges connected with the operational capability of plants and economic issues, as well as limited biogas produced from dairy cow manure, which has discouraged many dairy farmers from adopting this technology, community-based biogas plants support the local community and have positive environmental effects by reducing GHG emissions. These biogas plants can cut off 104.26 gigagrams of CO2eq greenhouse gasses annually by managing 61,376 tons of dairy manure. Bangladesh commits in its 2021 NDCs to cut greenhouse gas emissions by 21.8% by 2030, primarily focusing on energy-related initiatives. Achieving these NDC targets will require significant efforts to reduce methane emissions and improve energy efficiency [61]. Community-based biogas is also used as an alternative fuel source for cooking in rural communities, and biogas plant effluent can be utilized as an organic fertilizer for crops and vegetable growth.

Valuable insights and experiences from other countries have successfully promoted renewable energy, including biogas production and electricity generation in Bangladesh. In the EU, specifically, biogas can help a number of nations to meet their renewable energy goals. Based on collected manure, the biogas potential was estimated to be 18 billion m³ of biomethane, and 13,866–19,482 biogas plants might be constructed, with an average capacity of 315–515 kWe power generation in Europe [62]. Biomass is a key renewable energy resource in India, with a bioenergy potential of 25 GW. The country generates 687 million tonnes of agro-residue biomass annually, making bioenergy the second-largest renewable energy source with an installed capacity of 4.3 GW as of December 2014. Additionally, India’s large livestock population offers substantial potential for biogas production from manure [63]. After 2003, China implemented financial support and enhanced biogas service systems to increase the number of newly constructed biogas units across all provinces. Various measures were adopted, including direct investments and promoting international cooperation through initiatives like an international carbon trading system [64]. The exploration of energy potential from livestock manure in Turkey gained traction following the rise in feed-in tariffs for renewable energy in 2011. Between 2013 and 2019, high-priority districts for installing new biogas plants were identified, with power capacities ranging from 6.30 MWe to 22.54 Mwe [65,66,67].

4. Conclusions and Policy Implications

Biogas is an important renewable energy source for generating electricity and reducing GHG emissions. The dairy industry is a major source of greenhouse gas emissions and has a negative impact on the environment due to poor manure management systems in Bangladesh. Dairy manure is used in the community-based biogas plant as a part of waste management in dairy farms and biogas production that helps the rural community. The study conducted a spatial analysis to introduce community-based biogas plants for the sustainable management of dairy cow manure, considering the current situation of manure management and the energy demand of the local people. This study’s findings on suitable sites for community-based biogas plants will aid in formulating legislation in the dairy industry and renewable energy sector in Bangladesh that will help improve socio-economic and environmental conditions. The study proposed suitable places and the capacity of community-based biogas plants for the production of potential energy from dairy cow manure in the Sirajganj district of Bangladesh. This research identified five significant clusters by cluster analysis that intersected with the most suitable areas, suggesting five community-based biogas plants in the Shahjadpur, Tarash, Raiganj, Sadar, and Belkuchi subdistricts. The total electricity generated from these biogas plants was estimated at 9.8 MW per year, which could meet the local electricity demand and contribute to increasing renewable energy output by at least 1.25 percent. The research results show the environmental contribution of biogas plants. The estimated total greenhouse gas reduction potential was 104.26 gigagrams of CO2eq per year by disposing of 61,376 tons of dairy waste annually in Bangladesh.

The study revealed the feasible site for sitting the waste treatment plants by GIS suitability analysis. These research findings could help to take similar steps throughout the country for sustainable waste management in Bangladesh. Many studies have been conducted on dairy waste management using theoretical approaches; however, selecting feasible sites for establishing a dairy manure treatment plant using GIS is a new concept for a developing and emerging economy like Bangladesh. Bangladesh is a middle-income generating country with a significant demand for power for commercial activity. As a result, this study first recommends the installation of biogas plants to generate electricity, considering sustainable management of dairy manure. The best locations for proposed plants are chosen based on the demand for the plant’s outputs. The installation of plants also depends on government initiatives, technical support, farmer’s capacity, and other key factors. The findings of this study can be used as a guidance document for improving dairy waste management in Bangladesh while contributing to a reduction in environmental effects from dairy farm waste. It can also help develop manure treatment facilities to generate energy, which will aid in socio-economic development and climate change resistance in Bangladesh.

The current research has several limitations. It focuses solely on the Sirajganj district of Bangladesh, which restricts the generalizability of the findings to other regions. Different areas may have unique socio-economic, environmental, and manure management conditions that could influence the applicability of the results. While the study identifies suitable sites for biogas plants, it does not thoroughly address the technical and economic feasibility of implementing these plants, including costs related to construction, operation, and maintenance. Additionally, the study does not consider the socio-cultural factors or the level of acceptance within the local community, which could significantly impact the success of community-based biogas plants. Understanding the community’s willingness to adopt these waste management systems is crucial.

Future research should include a detailed economic analysis to assess the cost-effectiveness, return on investment, and financial sustainability of biogas plants in rural areas, helping to identify the most economically viable locations for plant installation. Investigating socio-cultural factors and community acceptance regarding the use of dairy manure for biogas production could lead to more effective stakeholder engagement strategies. Moreover, a comprehensive climate impact assessment could quantify the long-term environmental benefits of biogas production from dairy manure, including potential reductions in methane emissions and improvements in soil health from digestate use.