A Sustainable Approach to Waste Management: Selecting the Optimal Landfill Site in Saskatchewan, Canada ^†

Abstract

Solid waste management is a crucial task for municipalities in disposing of city waste. Overcoming socioeconomic obstacles in finding appropriate landfill sites involves a multifunctional team using a process that includes selecting criteria and alternatives. In this study, the FUZZY Analytical Hierarchy Process (AHP) and FUZZY TOPSIS were used to rank five landfill alternatives based on seven criteria. Additionally, Interpretive Structural Modeling (ISM) was employed to establish hierarchical relationships between criteria. MICMAC analysis identified dominant and dependent factors. The study found that Land Capacity carries the highest weight, and the Central Landfill site is the most suitable location. Land Capacity is the dominant factor, while land surface temperature has minimal impact. Roads and communication networks have the highest driving power. The project’s findings can guide the selection of landfill sites and contribute to the development of new sites based on the criteria discussed and their relationships.

1. Introduction

In Canada, choosing a landfill location is a complex and important procedure that entails considering several variables to guarantee both environmentally friendly disposal practices and ethical garbage management. Canada, recognized as one of the world’s largest and most geographically diverse nations, faces unique opportunities and challenges in selecting suitable landfill locations. As the world’s highest per capita producer of municipal solid waste, the majority of Canada’s waste finds its way to landfills, setting it apart from other developed nations that have embraced more sustainable waste management practices [1]. The total amount of solid waste generated in Canada in 2020 was 36.0 million tons, marking a 17% increase from 2002. Only 27.5% of this waste was diverted for recycling or composting, while the remaining 72.5%—amounting to approximately 26.1 million tons—was subject to disposal in landfills, incinerators, or exportation [2]. Therefore, site selection requires careful consideration of social, economic, and environmental considerations due to the growing population, urbanization trends, and changing waste management techniques. When selecting and establishing appropriate waste treatment facilities or landfills, considerable consideration is needed because of possible hazards and negative public perceptions toward these establishments. Canada is a nation dedicated to environmentally responsible growth and stewardship, and as such, it places a high priority on selecting landfill locations that have the least negative effects on local residents, ecosystems, and water resources. The selection process is complex and site-specific due to the immensity of the Canadian environment, which necessitates careful consideration of regional variations in climate, geology, and land use patterns.

The paper aims to address three primary research objectives utilizing a FUZZY AHP-TOPSIS method: effectively employing criteria such as land capacity, water resources, slope and elevation, road networks, net cost, hydrology, and land surface temperatures to enhance energy efficiency in landfill site selection; determining the most significant energy efficiency criteria for landfill site selection and accurately weighting them within the proposed framework; assessing the adaptability and robustness of the framework for prioritizing energy efficiency in various landfill site selection scenarios. The study’s methodology integrates advanced decision-making techniques and Geographic Information Systems (GIS) to analyze various criteria and their interrelationships, facilitating the selection of landfill sites and contributing to the development of sustainable waste management practices. This incorporation of cutting-edge technologies, including GIS, is crucial due to the complexity of the task at hand. Through these tools, a comprehensive evaluation of elements such as land capacity, water resources, geological stability, and proximity to population centers is made feasible. Confirming suitable landfill sites is integral to achieving Canada’s national objectives of waste management, environmental impact reduction, and the promotion of sustainable practices. This introduction lays the groundwork for a more thorough examination of the complex factors and selection processes that go into selecting landfill locations across Canada’s diverse and ecologically conscious geography. Multi-criteria decision analysis (MCDA) involves assessing and prioritizing various factors to make informed choices. In the context of land-use planning and infrastructure development, several critical factors, including land capacity, water resources, slope and evaluation, road networks, human density, net cost, and hydrology, play pivotal roles. Multiple MCDA processes are involved in selecting appropriate locations for landfill sites.

2. Literature Review

The literature review for the research paper on landfill site selection methodologies and criteria draws upon a range of scholarly sources to provide a comprehensive overview of the field. Ref. [3] Conducted a thorough analysis of 89 scientific papers to examine methodological frameworks and criteria used in municipal solid waste landfill selection. They highlighted the importance of geographical information systems (GIS) and multi-criteria decision analysis in considering environmental, economic, and social factors. Similarly, Ref. [4] focused on identifying and evaluating landfill site selection criteria using a hybrid Fuzzy Delphi, Fuzzy AHP, and DEMATEL approach, emphasizing the complexity of the decision-making process. Ref. [5] Applied multi-criteria decision analysis techniques like the Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to locate regional landfills within a specified area. While [6] focused on transfer station site selection, their discussion of mathematical optimization and multi-criteria decision analysis methodologies remains relevant to municipal solid waste management. Additionally, Ref. [7] conducted a literature review on assessment methods for solid waste management, offering insights into various assessment approaches. Ref. [8] provided a scoping review of global waste management strategies, shedding light on alternative landfilling approaches and their sustainability implications. Additionally, Ref. [9] identified road networks, protected areas, and water resources as critical factors, integrating the GIS Method and highlighting the GIS-based approach using nighttime light data for landfill siting. These studies collectively contribute to our understanding of the diverse methodologies and criteria employed in landfill site selection, informing the research framework and approach adopted in this paper.

3. Methodology and Approach for Selecting a Site

This research aims to identify the optimal landfill site for solid waste management in Saskatchewan, Canada, using an integrated methodology combining Multiple Criteria Decision Analysis (MCDA) and Fuzzy logic. The study employs four primary methods: Fuzzy Analytic Hierarchy Process (FAHP), Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM). Expert consultations with five professionals in the field provide valuable insights to refine criteria and alternative selection. Criteria selection involves identifying seven key factors for landfill site selection: land capacity, water resources, slope and elevation, road networks, net cost, hydrology, and land surface temperatures. Alternatives selection extracts five landfill site locations from Saskatchewan’s geo hub website, enhancing realism and applicability. FAHP determines criteria weights using expert opinions expressed through linguistic variables and triangular Fuzzy numbers (TFNs). TOPSIS ranks alternatives using linguistic variables and TFNs, calculating closeness coefficients by comparing them with Fuzzy positive and negative ideal solutions (FPIS and FNIS). DEMATEL reveals causal relationships among criteria, constructing a causal diagram. Direct and total influence matrices are computed using TFNs, facilitating the determination of centrality and causality, visually presented on a graph. ISM establishes a hierarchical structure of criteria, classifying them into different levels based on driving power and dependence power. The reachability matrix undergoes transitivity checks, identifying reachability sets, antecedent sets, and criterion levels. The relationship diagram is drawn by eliminating indirect links. The research integrates FAHP-TOPSIS and DEMATEL-ISM methods, offering a robust framework for landfill site selection. Geographic Information Systems (GIS) and night-time satellite imagery support spatial analysis and visualization of alternatives, enriching the methodology. Expert opinions and advanced decision-making techniques further substantiate the site selection process, providing a comprehensive approach to landfill site selection in Saskatchewan, Canada.

4. A Roadmap: Comparative Analysis for Landfill Site Prioritization

Commencing with literature and expert input, the study integrates a dynamic flowchart, serving as a visual guide, combined with a roadmap for a comparative analysis of landfill site prioritization. This integrated approach incorporates Fuzzy AHP, Fuzzy TOPSIS, DEMATEL, and ISM methods. Figure 1: Unveiling Decision-Making Processes in Landfill Site Prioritization. Fuzzy AHP determines criteria weights via pair-wise comparison, Fuzzy matrix conversion, GEOMEAN aggregation, and Fuzzy weight calculation, ultimately establishing Best Normalized Priority (BNP). Simultaneously, Fuzzy TOPSIS employs a Fuzzy decision matrix, proceeding through normalization, FPIS and FNIS determination, distance calculation, and closeness coefficient derivation, concluding in a ranked order. Concomitantly, DEMATEL-ISM initiates with factor identification and expert insights, advancing through direct and total relation matrices, MICMAC analysis, and reachability matrix preparation, concluding with inter-factor relationship establishment. This visual framework enhances study clarity, providing a concise path through the sequential study phases.

5. Objectives

Considering the outlined challenges in identifying the optimal landfill site for solid waste management in Saskatchewan, Canada, this study aims to answer key research questions (RQs):

RQ1: What criteria should be effectively employed to evaluate and enhance energy efficiency in landfill site selection, specifically within the processes of land capacity, water resources, slope and elevation, road networks, net cost, hydrology, and land surface temperatures, utilizing a fuzzy AHP-TOPSIS method?

RQ2: What are the key energy efficiency criteria deemed most significant for landfill site selection, and how can they be accurately weighted utilizing the fuzzy AHP method within the proposed framework?

RQ3: How adaptable and robust is the proposed framework for prioritizing energy efficiency measures in diverse landfill site selection scenarios, emphasizing various criteria, by applying a fuzzy AHP-TOPSIS method?

6. Calculation

Five different landfill sites were selected as alternatives with seven different criteria (

Table 1. Landfill site alternatives and location.

Landfill Location Name	Permit Number	Permit Expiry Date	Longitude	Latitude
Central Regional Landfill, Hendon	PO21-130	08/31/2026	−103.84	52.04
Swift Current East	PO20-055	03/31/2025	−107.75	50.30
Unity	PO20-150	08/01/2025	−109.18	52.45
Pinehouse	PO21-110	07/01/2026	−106.60	55.61
Yorkton	PO22-010	01/31/2025	−102.44	51.42

).

7. FAHP Calculation

The pair-wise comparison matrix was prepared. The decision-makers gave a score of 1 to 9 based on which factor was more important than the other. The combined pair-wise matrix (

Table 2. Combined pair-wise comparison matrix of the seven attributes of landfill site selection.

Criteria	Short Code	Symbol	LC	WR	SE	RN	LT	NC	HD	GEOMEAN	WEIGHT
Land capacity	F1	LC	1.00	2.70	2.82	2.35	2.92	2.35	2.40	2.25	0.27
Water resources	F2	WR	0.39	1.00	2.55	2.35	2.39	1.99	1.82	1.54	0.18
Slope and elevation	F3	SE	0.36	0.42	1.00	2.85	2.35	1.63	1.01	1.08	0.13
Roads and Networks	F4	RN	0.46	0.46	0.37	1.00	2.18	1.57	1.61	0.89	0.11
Net cost	F5	NC	0.46	0.51	0.63	0.66	0.73	1.00	2.35	0.78	0.09
Hydrology	F6	HD	0.48	0.59	1.01	0.51	0.70	0.50	1.00	0.65	0.08
Land surface temperatures	F7	LT	0.35	0.43	0.46	0.48	1.00	1.45	1.45	0.68	0.08
		Sum	3.50	6.11	8.83	10.21	12.27	10.49	11.62	8.37

), which originated from the individual decision-makers pair-wise matrix, is consistent, CR = 0.02 < 0.1.

We found the Fuzzy weight from the calculation (Lower, Middle, and Upper) (

Table 3. Fuzzy weight and ranking of the attributes of landfill site selection.

Criteria	Symbol	Fuzzy Weight			LW	MW	UW	BNPW	RANK
Land capacity	LC	1.95	3.12	4.25	0.36	0.36	0.34	0.34	1.00
Water resources	WR	1.07	1.80	2.69	0.20	0.21	0.21	0.22	2.00
Slope and elevation	SE	0.70	1.08	1.59	0.13	0.13	0.13	0.13	3.00
Roads and Networks	RN	0.53	0.80	1.33	0.10	0.09	0.11	0.10	4.00
Net cost	NC	0.44	0.67	1.07	0.08	0.08	0.09	0.08	5.00
Hydrology	HD	0.38	0.53	0.79	0.07	0.06	0.06	0.06	7.00
Land surface temperatures	LT	0.39	0.56	0.88	0.07	0.07	0.07	0.07	6.00

).

8. Fuzzy TOPSIS

For Fuzzy TOPSIS, decision-makers used linguistic judgment, and it was converted to a Fuzzy scale as below: [10]. After converting the linguistic data to a numerical Fuzzy matrix from FPIS and FNIS, the separation is calculated, and the closeness coefficient is prepared (

Table 4. Closeness coefficient of five alternative locations and rankings.

	di*	di-	CCi	Rank
Central	1.50	3.22	0.68	1
Swift Current	2.08	2.59	0.55	2
Unity	3.19	1.64	0.34	4
Pinehouse	3.78	1.30	0.25	5
Yorkton	2.17	2.54	0.53	3

). Based on the closeness coefficient, the ranking is performed.

9. DEMETAL Calculation

We have taken the decision-makers’ judgment into consideration on a scale of 0 to 4. A rating of 0 signifies no influence, while 1 indicates low, 2 denotes moderate, 3 suggests high, and 4 represents extreme. Then, we prepared the normalized relationship matrix (

Table 5. Normalized direct influence matrix.

Factor	Land Capacity	Water Resource	Slope and Elevation	Roads and Communication	Net COST	Hydrology	Land Surface Temperature	Sum
Land capacity	0	1	2.8	1.6	2.6	2.4	1.4	11.8
Water resource	3.2	0	2.6	1.2	1.6	1.2	0.8	10.6
Slope and elevation	1.8	1.6	0	0.6	1.8	2.6	2	10.4
Roads and Communication	2	3	2.8	0	1.6	2	3	14.4
Net Cost	2.6	2	2.4	2	0	0.8	2	11.8
Hydrology	2.2	3.4	2.6	2.6	2.2	0	1.8	14.8
Land Surface Temp	2.8	2.4	2.2	0.8	1.6	2	0	11.8
							Max	14.8

). We have determined the threshold value (0.634473) and prepared the cause–effect relationship rank.

Table 6. Centrality mapping and causality of criteria and ranking.

	Factors	r	c	r + c	r − c	Rank
F1	Land capacity	4.36	5.23	9.59	−0.87	1
F2	Water resource	3.87	4.69	8.57	−0.81	4
F3	Slope and elevation	3.88	5.48	9.37	−1.60	3
F4	Roads and Communication	5.12	3.28	8.41	1.84	6
F5	Net cost, NC	4.27	4.26	8.54	0.008	5
F6	Hydrology, HD	5.27	4.14	9.42	1.13	2
F7	Land Surface Temp	4.28	3.97	8.26	0.30	7

highlights the centrality mapping and causality of criteria and ranking.

From these data, a cause–effect diagram is prepared (Figure 2).

ISM analysis: First, the overall influence matrix is prepared (

Table 7. Total influence matrix.

Influence Matrix, H		F1	F2	F3	F4	F5	F6	F7
F1	Land capacity	1.61	0.61	0.80	0.48	0.65	0.62	0.55
F2	Water resource	0.72	1.48	0.72	0.41	0.54	0.51	0.46
F3	Slope and elevation	0.65	0.58	1.57	0.38	0.55	0.58	0.53
F4	Roads and Communication	0.84	0.81	0.91	1.44	0.68	0.69	0.71
F5	Net cost, NC	0.75	0.65	0.77	0.49	1.49	0.53	0.57
F6	Hydrology, HD	0.87	0.85	0.93	0.61	0.73	1.58	0.67
F7	Land Surface Temp	0.76	0.67	0.76	0.43	0.59	0.59	1.45

).

Upon completion of the reachability matrix calculation, a pivotal step in our analysis, the examination of transitivity ensues (* highlighted in

Table 8. Transitivity check.

	F1	F2	F3	F4	F5	F6	F7	DRIVING POWER
F1	1	1*	1	0	1	0	0	4
F2	1	1	1	0	1*	0	0	4
F3	1	0	1	0	1*	0	0	3
F4	1	1	1	1	1	1	1	7
F5	1	1	1	0	1	0	0	4
F6	1	1	1	0	1	1	1	6
F7	1	1	1	0	1*	0	1	5
DEPENDENCE POWER	7	6	7	1	7	2	3

).

The criteria were categorized into five levels, and their inter-relation was illustrated (Figure 3).

10. MICMAC Analysis

The primary goal of the MICMAC analysis is to determine the extent of impact on variables. To categorize the impact of the factors, the driving and dependency powers are divided for this reason [11]. The graph is divided into four sections.

• Autonomous Variables: These variables require less force and dependability;
• Dependency variable: A variable with less driving force and greater dependency;
• Linkage variables: Higher driving force and dependence variables;
• Independent variables: Those that have more driving force and less dependency.

We conducted a MICMAC analysis to segment the dependence and driving factors (Figure 4).

11. Results and Discussion

From the calculation the analysis indicates that land capacity (LC) holds the highest weight among the criteria considered for selecting potential landfill sites for solid waste management. While planning for landfill site land capacity is of vital importance to use the site for many years in the future, upon audit and licensing, these facilities are continuously used. The higher the land capacity, the longer it can usually provide a valuable landfill location. The second highest weight is water resources, and the third highest is slope and elevation of the location. Hydrology, however, has the lowest weight among the criteria. From the analysis of five decision-makers, the consistency is 0.02, which is lower than 0.1, meaning that the decisions are consistent and are acceptable to use for further calculation. The five locations have been analyzed and ranked to check which one has the most potential, based on decision-maker input, to be the best choice for a landfill site. Among the five locations selected from the province of Saskatchewan, the Central location has ranked top with a closeness coefficient of 0.6812. In second place in the ranking is Swift Current, and Unity is in third position. The least optimum location for a landfill site is found to be Pinehouse, which has bad road conditions. Through the DEMATEL method, the inter-relationship effect on one another is checked based on the input from decision-makers of the influence factors an attribute has over others. From the calculation, it is seen that Hydrology is the most important factor, with the highest value of centrality of 9.43. From the causality calculation, it is seen that factors Land Capacity (F1), Water Resource (F2), and Slope and Elevation (F3) are effect factors, and the other four factors, Roads and Communication Network (F4), Net Cost (F5), Hydrology (F6) and Land Surface Temperature, (F7) are the cause factors. The causal diagram depicts the cause-and-effect factors in the plot. To evaluate the driving power and dependence power interpretive model is used, and from the MICMAC analysis, factors Land Capacity (F1), Water Resources (F2), Net Cost (F5), and Slope and Elevation (F3) are in the top left quadrant, which indicates the independent variable that usually affects others most. The following factors, Roads and Networks (F4), Hydrology (F6), and Land Surface Temperature (F7) are effect factors that are usually affected by other factors. The inter-relationship diagram shows Land Capacity, Slope, and Net Cost at level 1, Water Resources at level 2, and Roads and Communication at the bottom. Future Bayesian Network analysis with known probabilities could enhance clarity. Despite Central’s selection as the top choice for a landfill site, using real data in the Bayesian Network could provide a more confident and optimal location selection.

12. Limitations

The research focuses on Saskatchewan, Canada, and the findings may not be directly applicable to other regions due to geographical and environmental differences. The conclusion suggests that future Bayesian Network analysis with known probabilities could enhance the clarity and confidence in optimal location selection, indicating room for further research. Additionally, the study relies on expert consultations, which may introduce subjectivity or bias, impacting the objectivity of criteria selection and decision-making processes.

13. Conclusions

In conclusion, this article provides a comprehensive methodology for selecting the optimal landfill site in Saskatchewan, Canada. By integrating the Fuzzy Analytic Hierarchy Process (FAHP), the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM), the study establishes a robust decision-making framework. Expert consultations and the use of linguistic variables and triangular Fuzzy numbers enhance the reliability of criteria selection. Geographic Information Systems (GIS) and night-time satellite imagery aid in spatial analysis and visualization, ensuring practical applicability. The analysis highlights land capacity as the most critical criterion, identifying the Central Landfill site as the most suitable option. DEMATEL and ISM elucidate causal relationships and hierarchy among criteria, while MICMAC analysis identifies dominant factors. These findings offer valuable guidance for landfill site selection and future waste management practices, contributing to advancing sustainable waste management in Saskatchewan and beyond.