Evaluation of food waste treatment techniques using aczel alsina based MAGDM model in the q-rung orthopair fuzzy soft structure – Nature

 

Executive Summary

Food waste represents a significant impediment to achieving global sustainability, directly undermining progress on key Sustainable Development Goals (SDGs), including SDG 2 (Zero Hunger), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). The management of food waste impacts food security, economic development, and environmental stability. The inherent complexity and uncertainty in evaluating food waste treatment technologies (FWTT) necessitate advanced decision-making frameworks. This report details a research initiative that proposes a novel multi-attribute group decision-making (MAGDM) model based on Aczel–Alsina operational laws within a q-rung orthopair fuzzy soft set (q-ROFSS) structure. This approach is designed to handle the ambiguity and imprecision in sustainability assessments. Two new aggregation operators, the q-ROFSAAWA and q-ROFSAAWG, were developed to consolidate expert data with greater accuracy. A case study was conducted to identify the most sustainable FWTT, applying the model to a real-world scenario. The results identified incineration as the most effective technology under the specified criteria, highlighting the model’s capacity to provide clear, data-driven guidance for policymakers. A comparative analysis confirms the methodology’s validity and superior feasibility, offering a robust tool for advancing sustainable food waste management in line with the 2030 Agenda for Sustainable Development.

1.0 Introduction: The Global Challenge of Food Waste and the Sustainable Development Goals

Food waste is a critical global issue with profound socioeconomic and environmental consequences. It represents a misuse of natural resources such as water, land, and energy, and directly conflicts with the principles of sustainable development. The management of food waste is intrinsically linked to several SDGs:

• SDG 2 (Zero Hunger): While millions face starvation, vast quantities of edible food are wasted, exacerbating food insecurity and inequality.
• SDG 12 (Responsible Consumption and Production): Food waste is a primary indicator of unsustainable production and consumption patterns. Target 12.3 specifically calls for halving per capita global food waste by 2030.
• SDG 13 (Climate Action): Food decomposing in landfills is a major source of methane, a greenhouse gas significantly more potent than carbon dioxide, contributing to climate change. Globally, uneaten food generates 8-10% of all greenhouse gas emissions.
• SDG 11 (Sustainable Cities and Communities): Inefficient food waste management places a heavy burden on municipal waste systems and landfills, particularly in growing urban areas.
• SDG 6 (Clean Water and Sanitation) & SDG 15 (Life on Land): Leachate from landfilled food waste can contaminate soil and groundwater, threatening terrestrial and aquatic ecosystems.

In developing nations such as Pakistan, the challenge is particularly acute. A rising population and cultural practices contribute to high levels of food waste, straining infrastructure and hindering progress toward the SDGs. Selecting an appropriate Food Waste Treatment Technology (FWTT) is therefore a complex multi-attribute decision-making (MAGDM) problem, requiring a balanced assessment of environmental, economic, and social factors. While various fuzzy logic-based models have been proposed to aid this process, they often struggle to manage the high degree of uncertainty and ambiguity inherent in expert opinions and real-world data. This report presents a novel approach designed to overcome these limitations and provide a more robust framework for sustainable decision-making.

2.0 Research Rationale and Methodology

2.1 Objectives

This research was motivated by the urgent need for a more sophisticated, flexible, and reliable decision-making framework to guide FWM strategies in alignment with the SDGs. Existing methods often fail to adequately capture the complexities and uncertainties involved. The primary objectives of this study were:

1. To develop a decision-making model using the q-rung orthopair fuzzy soft set (q-ROFSS) structure, which offers greater flexibility in representing complex and uncertain sustainability data compared to traditional fuzzy sets.
2. To introduce and validate new Aczel–Alsina operational laws for the q-ROFSS context, creating aggregation operators (q-ROFSAAWA and q-ROFSAAWG) that enhance the accuracy and reliability of consolidating expert judgments.
3. To construct and apply a comprehensive MAGDM algorithm to the real-world problem of selecting the optimal FWTT, thereby providing a practical tool for policymakers and managers.
4. To address critical gaps in the literature by systematically evaluating FWTTs through a robust framework that can handle the nuanced trade-offs between different sustainability criteria.

2.2 The Aczel-Alsina Based MAGDM Model

To address the stated objectives, a novel MAGDM model was formulated. This model provides a structured and systematic approach for evaluating complex alternatives against multiple, often conflicting, criteria. The methodology integrates the strengths of q-ROFSS for handling uncertainty with the flexibility of Aczel-Alsina operators for aggregating data.

The procedural steps of the proposed model are as follows:

1. Data Collection: Assemble decision matrices based on expert evaluations of each alternative (FWTT) against a set of sustainability criteria. Expert opinions are captured as q-rung orthopair fuzzy soft numbers (q-ROFSNs) to reflect ambiguity.
2. Data Normalization: Standardize the data to ensure comparability between different types of criteria (e.g., cost-based vs. benefit-based). This step is crucial for an unbiased assessment, ensuring all factors contribute appropriately to the final decision.
3. Data Aggregation: Utilize the newly developed q-ROFSAAWA or q-ROFSAAWG operators to aggregate the normalized data from all experts into a single, comprehensive value for each alternative. These operators provide a more nuanced aggregation than traditional methods.
4. Scoring and Ranking: Calculate a score for each aggregated alternative to determine its overall performance. The alternatives are then ranked from most to least preferable based on these scores.
5. Selection: The alternative with the highest rank is identified as the optimal choice, providing a clear, evidence-based recommendation for implementation.

3.0 Case Study: Selection of a Sustainable Food Waste Treatment Technology

3.1 Alternative Technologies Evaluated

Four common FWTTs were evaluated as alternatives in this case study, each with distinct implications for the Sustainable Development Goals.

• Composting (S1): A natural process that decomposes organic waste into a nutrient-rich soil conditioner. It supports SDG 12 and SDG 15 by promoting a circular economy and improving soil health, and SDG 11 through sustainable waste management. However, it requires space and management.
• Anaerobic Digestion (S2): A biological process that breaks down organic matter in the absence of oxygen, producing biogas. This directly supports SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) by creating renewable energy and capturing methane.
• Incineration (S3): A thermal treatment process that reduces waste volume and can generate energy (waste-to-energy). This contributes to SDG 7 and SDG 11 by producing energy and managing waste volume in land-scarce areas. However, it raises concerns regarding air emissions, potentially conflicting with SDG 3 (Good Health and Well-being).
• Landfill (S4): The disposal of waste by burial. This is the least sustainable option, directly undermining SDG 11, SDG 12, and SDG 13 due to methane emissions, land use, and potential for soil and water contamination (conflicting with SDG 6 and SDG 15).

3.2 Sustainability Assessment Criteria

A panel of five experts evaluated the four technologies against ten sustainability criteria, weighted according to their perceived importance for achieving sustainable FWM. These criteria serve as proxies for measuring progress towards the SDGs.

• Environmental Impact (Weight: 0.15)
• Resource Recovery Potential (Weight: 0.07)
• Waste Reduction Efficiency (Weight: 0.09)
• Economic Feasibility (Weight: 0.10)
• Energy Requirements and Output (Weight: 0.12)
• Scalability and Flexibility (Weight: 0.06)
• Regulatory Compliance (Weight: 0.05)
• Technical Complexity and Management (Weight: 0.08)
• Social Acceptance and Community Impact (Weight: 0.12)
• By-product Management (Weight: 0.16)

3.3 Findings and Results

The MAGDM model was applied to the expert evaluation data. After normalization and aggregation using the proposed operators, the final scores and rankings for each technology were calculated.

Results using the q-ROFSAAWA Operator:

1. Incineration (S3) – Score: 0.053081
2. Anaerobic Digestion (S2) – Score: 0.025915
3. Composting (S1) – Score: 0.018252
4. Landfill (S4) – Score: -0.036378

The ranking is S3 > S2 > S1 > S4. Incineration was identified as the most favorable option.

Results using the q-ROFSAAWG Operator:

1. Incineration (S3) – Score: -0.555141
2. Landfill (S4) – Score: -0.568486
3. Composting (S1) – Score: -0.578591
4. Anaerobic Digestion (S2) – Score: -0.613640

The ranking is S3 > S4 > S1 > S2. Again, Incineration emerged as the optimal choice.

Both operators consistently identified Incineration (S3) as the most effective FWTT based on the weighted criteria provided by the expert panel. This demonstrates the model’s ability to produce a decisive and stable outcome, providing clear guidance for strategic investment in waste management infrastructure that aligns with sustainability objectives.

4.0 Validation and Comparative Analysis

To ensure the robustness and validity of the proposed model, a comprehensive sensitivity and comparative analysis was performed. The model was tested against variations in its parameters and compared with existing decision-making methodologies.

4.1 Sensitivity Analysis

The sensitivity of the model was tested by varying the rung parameter (q). The analysis confirmed that while the absolute scores of the alternatives changed, the optimal choice—Incineration (S3)—remained consistently ranked first. This stability is crucial, as it demonstrates that the model’s recommendations are not arbitrary but are robust to minor variations in its underlying mathematical structure, making it a reliable tool for long-term policy planning.

4.2 Comparative Analysis

The proposed model was compared with other decision-making frameworks, including those based on different fuzzy set structures and other aggregation operators. The key advantages of the Aczel-Alsina based MAGDM model include:

• Greater Flexibility: The q-ROFSS structure allows for a wider range of membership grades, enabling a more accurate representation of the uncertainty and ambiguity common in sustainability assessments.
• Enhanced Accuracy: The Aczel-Alsina operators provide a more nuanced way to aggregate information, minimizing data loss and distortion compared to simpler averaging or geometric operators.
• Consistency: While rankings from other methods like TOPSIS and VIKOR varied, the proposed model consistently identified the most robust option, highlighting its reliability for making high-stakes decisions related to sustainable infrastructure.

This comparative analysis validates that the proposed methodology is not only effective but also superior in its ability to handle the complex, uncertain, and multi-faceted nature of selecting sustainable technologies.

5.0 Conclusion and Policy Implications for the SDGs

The challenge of food waste management is inextricably linked to the global pursuit of the Sustainable Development Goals. This report has detailed a novel, data-driven system designed to address the shortcomings of existing decision-making tools. By introducing Aczel–Alsina aggregation operators within a q-rung orthopair fuzzy soft set framework, this research provides a powerful and flexible MAGDM model for evaluating Food Waste Treatment Technologies.

The case study successfully demonstrated the model’s practical application, identifying incineration as the optimal FWTT under the specified conditions. This result provides a strong, evidence-based foundation for policymakers and stakeholders in the FWM sector to develop administrative mechanisms and investment strategies that are both economically viable and environmentally sound.

The broader implications of this research are significant. The proposed model serves as a robust analytical tool that can help decision-makers navigate the complex trade-offs inherent in sustainability challenges. It empowers them to move beyond simplistic assessments and make informed choices that advance multiple SDGs simultaneously, including those related to hunger, clean energy, climate action, and sustainable cities. Future research should focus on applying this versatile framework to other critical sustainability issues and integrating dynamic, real-time data to further enhance its predictive power and utility for global development.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on food waste management (FWM) and the evaluation of food waste treatment technologies (FWTT) addresses several interconnected Sustainable Development Goals (SDGs). The core issues of food waste, resource utilization, environmental impact, and economic effects create direct and indirect links to the following SDGs:

• SDG 2: Zero Hunger: The article connects food waste to inequality and “the world’s most starving economies.” It mentions that managing food waste can improve “food supply” and “nutritional safety,” which are central to ending hunger.
• SDG 7: Affordable and Clean Energy: The text explicitly discusses the potential of FWTTs to contribute to energy goals. It mentions “generating renewable energies” and evaluates technologies like “Anaerobic digestion” for its ability to produce “biogas” and “Incineration” for its use in “waste-to-energy facilities.”
• SDG 8: Decent Work and Economic Growth: The article highlights how food waste management influences “financial maturation” and “economic prosperity.” The evaluation of FWTTs based on “Economic feasibility” and their potential to stimulate “profitable business growth” connects waste management to sustainable economic models.
• SDG 11: Sustainable Cities and Communities: The management of municipal waste is a key component of sustainable cities. The article discusses the problems of “dumps, waste bins,” and “landfills,” which are primarily urban challenges. Effective FWM contributes to reducing the environmental impact of cities.
• SDG 12: Responsible Consumption and Production: This is the most central SDG to the article. The entire paper revolves around managing “food waste,” which is a key aspect of unsustainable consumption and production patterns. It discusses promoting “sustainable food consumption arrangements” and developing a “circular economic system” through resource recovery.
• SDG 13: Climate Action: The article establishes a clear link between food waste and climate change. It states that wasted food generates “8–10% of the releases of greenhouse gases” and that the “decomposition of food in landfills emits methane, a greenhouse gas.” The evaluation of FWTTs is presented as a strategy for climate change mitigation.
• SDG 15: Life on Land: The article points out that food waste represents an “improper utilization of natural resources such as… land.” It also discusses the negative impact of landfills on land and the positive impact of composting, which “strengthens fertilization and layout,” contributing to soil health and restoration.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the issues discussed, several specific SDG targets can be identified as directly relevant to the article’s focus on food waste management:

1. Target 2.1: By 2030, end hunger and ensure access by all people… to safe, nutritious and sufficient food all year round. The article supports this by discussing how reducing food waste can “preventing food supply” issues and help feed “starving economies.”
2. Target 7.2: By 2030, increase substantially the share of renewable energy in the global energy mix. The article directly addresses this by evaluating FWTTs like anaerobic digestion and incineration for their capacity to generate “renewable energy,” “biogas,” and electricity from waste.
3. Target 8.4: Improve progressively, through 2030, global resource efficiency in consumption and production. The article’s focus on FWM as a way to optimize the use of “energy, water, and land” and promote a “circular economic system” aligns perfectly with this target.
4. Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to… municipal and other waste management. The research aims to find the most effective technology for managing food waste, a major component of municipal waste, thereby reducing the negative environmental footprint of urban areas.
5. Target 12.3: By 2030, halve per capita global food waste at the retail and consumer levels and reduce food losses along production and supply chains. The article’s entire premise is built around the problem of “worldwide food waste” and finding technological solutions to manage it, which is the goal of this target.
6. Target 12.5: By 2030, substantially reduce waste generation through prevention, reduction, recycling and reuse. The evaluation of FWTTs is based on criteria like “Waste reduction efficiency” and “Resource recovery potential,” which are direct strategies for achieving this target.
7. Target 13.2: Integrate climate change measures into national policies, strategies and planning. The proposed MAGDM model for selecting an optimal FWTT is a strategic tool that can be integrated into policies to mitigate climate change by reducing methane and other greenhouse gas emissions from food waste.
8. Target 15.3: By 2030, combat desertification, restore degraded land and soil… and strive to achieve a land degradation-neutral world. The article mentions composting as a technology that produces a “nutritious” by-product to “strengthen fertilization and layout,” contributing to the restoration of degraded soil.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

The article mentions and implies several quantitative and qualitative indicators that can be used to measure progress towards the identified SDG targets:

• Indicator for Target 12.3: The article directly refers to the measurement of food waste in “yearly tons of wasted food generated globally” and provides a figure illustrating this data. This aligns with Indicator 12.3.1 (a) Food loss index and (b) Food waste index.
• Indicator for Target 13.2: A direct quantitative indicator is provided when the article states that uneaten food “generates approximately 8–10% of the releases of greenhouse gases all over the world.” The emission of “methane, a greenhouse gas that is twenty-five times more potent than carbon dioxide” from landfills is another key metric.
• Indicator for Target 7.2: The “Energy requirements and output” criterion used to evaluate FWTTs serves as a proxy indicator. It measures the net energy produced (e.g., biogas, electricity from waste-to-energy facilities), which contributes to the share of renewable energy.
• Indicator for Target 12.5: The criteria for evaluating FWTTs, such as “Waste reduction efficiency” and “Resource recovery potential,” are direct qualitative and potentially quantitative indicators for measuring waste reduction and recycling/reuse rates. The amount of “by-product” (e.g., compost, digestate) that is recycled is also a relevant metric.
• Indicator for Target 11.6: The volume and mass of food waste diverted from “landfills” is a key indicator. The management of by-products like “leachate” from landfills is another implied indicator for the environmental impact of municipal waste.

4. Table of SDGs, Targets, and Indicators

SDGs	Targets	Indicators Identified in the Article
SDG 2: Zero Hunger	2.1: End hunger and ensure access to safe, nutritious and sufficient food.	Discussion of improving “food supply” and addressing the needs of “starving economies” through food waste reduction.
SDG 7: Affordable and Clean Energy	7.2: Increase substantially the share of renewable energy in the global energy mix.	Generation of “biogas” and electricity through “waste-to-energy facilities” (anaerobic digestion, incineration). Evaluation criterion: “Energy requirements and output.”
SDG 8: Decent Work and Economic Growth	8.4: Improve global resource efficiency in consumption and production.	Analysis of “Economic feasibility” and “financial maturation” through better FWM, promoting a “circular economic system.”
SDG 11: Sustainable Cities and Communities	11.6: Reduce the adverse per capita environmental impact of cities, including waste management.	Focus on managing waste from “dumps, waste bins,” and reducing reliance on “landfills,” which are key urban waste management issues.
SDG 12: Responsible Consumption and Production	12.3: Halve per capita global food waste. 12.5: Substantially reduce waste generation through prevention, reduction, recycling and reuse.	Measurement of “yearly tons of wasted food.” Evaluation criteria for FWTTs include “Waste reduction efficiency” and “Resource recovery potential.”
SDG 13: Climate Action	13.2: Integrate climate change measures into policies, strategies and planning.	Quantification of food waste’s contribution to climate change (“8–10% of the releases of greenhouse gases”; “methane” emissions from landfills).
SDG 15: Life on Land	15.3: Combat desertification, restore degraded land and soil.	Use of “composting” to create a by-product that “strengthens fertilization and layout,” contributing to soil restoration.

Source: nature.com