Prioritization of waste management strategies in sugar industry using Fuzzy Analytic Hierarchy Process (FAHP): a sustainable approach

Abstract

The sugar industry is one of the most important agro-industrial sectors in Iran, generating substantial amounts of organic waste including sugarcane bagasse (SCB), bagasse fly ash (BFA), filter mud (FM) and sugar beet pulp (SBP). Existing studies are predominantly descriptive, focusing on waste types, compositions, or recycling pathways, but lack a systematic framework for prioritizing strategies while simultaneously considering economic aspect and operational/technical feasibility. This study employed the Fuzzy Analytic Hierarchy Process (FAHP) using linguistic variables converted to triangular fuzzy numbers. Two main criteria were economic aspect and feasibility (with the environmental criterion used as an initial filter). Waste management strategies were extracted from a review study and were considered as sub-criteria. Pairwise comparison matrices were completed by 20 experts (11 professors, 7 PhD students, and 2 waste management organization professionals). Weights were calculated via the geometric mean method, with consistency ratios (CR < 0.1) verified. Calculations were cross-validated using Excel 2021 and the FuzzyAHP package (v0.9.5) in R (v4.3.2). The Economic aspect criterion received higher weight (0.61) than Feasibility (0.39). The sub-criteria ranking from two point of view (economic aspect and feasibility) was fuel & energy production (0.25, 0.19), agricultural applications (0.24 − 0.20), industrial application (0.15–0.16), food/pharmaceutical/cosmetic production (0.14–0.14), construction materials production (0.11–0.16) & pollutant purification (0.11–0.15). The Gray Relational Analysis (GRA) was also performed, and the outcomes were seen to be consistent with those of FAHP. Under current economic and technical conditions of Iranian sugar factories, fuel & energy production and agricultural applications emerge as the most suitable strategies.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-42671-0.

Introduction

The sugar industry is one of the most important agro-industrial sectors in Iran, generating significant amounts of organic waste from sugarcane and sugar beet processing. Although these wastes have high potential for revaluation, their improper management leads to environmental burdens and the loss of economic value of these materials^1,2. Despite the growing trend toward sustainable waste management, Iranian sugar factories still lack an evidence-based framework for selecting appropriate and cost-effective management options³.

Existing studies on waste management in the sugar industry in Iran and other countries remain largely descriptive, reporting the types, compositions, or recycling pathways of waste without systematically comparing existing approaches. So far, there has been little prioritization of these strategies, and a simultaneous assessment of economic feasibility and operational viability is not fully present in the studies. Additionally, despite the widespread use of Multi-Criteria Decision-Making (MCDM) methods in environmental planning⁴, Fuzzy Analytic Hierarchy Process (FAHP) has not been applied to waste management decision-making in the Iranian sugar industry.

Prioritizing waste management strategies inherently involves numerous criteria that may conflict with each other and heavily relies on expert judgment⁵. The traditional Analytic Hierarchy Process (AHP) method has limitations when expert opinions are expressed verbally or involve uncertainty. FAHP overcomes these limitations by incorporating fuzzy scales, allowing for a more realistic representation of expert preferences⁵. Therefore, FAHP is a suitable tool for identifying the most appropriate value-added pathways for sugar industry waste in areas with limited quantitative data⁶.

This study makes several contributions to the literature: (1) It presents an FAHP-based prioritization framework for waste management strategies in the Iranian sugar industry. (2) This study integrates the findings of a comprehensive systematic review with expert-based FAHP modeling, enabling a comprehensive comparison of all major valuation paths. (3) Simultaneously evaluates economic and feasibility criteria, providing insights for policymakers seeking cost-effective and technically feasible solutions.

It is necessary to mention that selecting the best management option is essential due to the abundance and geographic spread of sugar mills in Iran⁷. So, the purpose of this study is to prioritize waste management approaches for sugar industries from two perspectives: the economic aspect and the feasibility of using these management approaches for sugar mills in Iran. Finally, the following questions arise: (1) Which criterion should be given more weight; the economic aspect or feasibility? (2) Considering feasibility and economic aspect, which approach should take precedence?

This article is organized as follows. Research gap is presented in section “Research gap”. Section  “Background of sugar factory waste” provides essential background information on the main waste streams produced in the processing of sugarcane and sugar beets in sugar factories. Section  “Materials and methods” describes the research methodology, including the background of FAHP, details of the FAHP implementation steps, and the prioritization of strategies. The results of FAHP and the prioritization outputs are presented in section “Results”. Section  “Discussion”, Findings, also examines priority solutions and discuss about policy implication. Additionally, section  “Conclusion” is the final conclusion of the study.

Research gap

Biowaste from the sugar industry faces significant opportunities and challenges. All biomass from sugar production is a potential raw material for the production of valuable materials⁸. Waste valorization, on the other hand, is gaining steam and is playing a key role in advancing circular economy and sustainable development policies and targets⁹. Nonetheless, it is clear that economic considerations and environmental consequences are at odds, making it challenging to prioritize and effectively implement sustainable waste management strategies and to make choices¹⁰. As a result, systematic research employing techniques like multi-objective optimization or MCDM is necessary to make policy/investment recommendations¹⁰. From the aforementioned, there are numerous significant gaps in figuring out the optimal method for waste management in the sugar industry:

Lack of systematic prioritization frameworks: Most studies focus on evaluating or improving individual waste streams or technologies separately, instead of comparing and ranking a wide range of alternative or complementary strategies against various often conflicting sustainability criteria, such as resource efficiency, economic viability/cost-benefit analysis, social acceptance/job creation, technical feasibility, environmental impact, and regulatory compliance¹⁰.

The failure to use MCDM in choosing the best waste management strategy: AHP and its variants (such as AHP-BWM and Delphi-AHP) have been used in the sugar industry for related tasks like identifying IT/supply chain barriers, prioritizing sustainability risks, assessing biomass/fuel mixtures for energy sustainability, or maintenance policies. Despite their demonstrated value in other waste management or industrial decision-making domains, FAHP extensions—which use triangular or trapezoidal fuzzy numbers to better understand ambiguity, subjectivity, and uncertainty in expert opinions, variability in production waste, and fluctuating markets—have not yet been applied in this field^11–13.

Implementation and policy gaps: Although the circular economy’s classifications and practical versus suggested approaches have been thoroughly examined, there are few useful tools for factory managers to prioritize tasks, particularly in developing countries where seasons, fluctuating sugarcane quality, and conflicting resource demands are common. The need for decision support models that direct investment and policy toward the best and most suitable methods for valuing production waste is becoming more and more apparent due to the ongoing pollution brought on by inefficient practices and capital waste^7,14.

To address these gaps, this research uses FAHP to rank waste management strategies for the sugar industry. It establishes a thorough, multi-criteria framework that takes uncertainty into account and is based on sustainability dimensions, giving realistic decision support for moving toward sustainability. It produces reliable rankings that traditional methods miss by dealing with ambiguous expert opinions.

Background of sugar factory waste

In sugar factories, several types of solid waste are produced during the processing of sugarcane and sugar beets. These wastes differ in quantity, composition, and potential uses, making their proper management essential to prevent pollution burdens on the environment and recover valuable resources^10,15,16.

Sugarcane-derived wastes

Sugarcane sector waste is described as follows:

Sugarcane Bagasse (SCB): The sugarcane (sucrose) is extracted during the sugar manufacturing process after the stalks of sugarcane have been chopped into little pieces. The fibrous remains of the sugarcane stalks, known as SCB, are the solids that are left behind after this process^17,18.

Bagasse Fly Ash (BFA): The burning of bagasse to create energy and steam (for energy generation) also results in another waste product: BFA. Particulate control equipment, which is situated upstream of the steam flow from the boilers and before the stack, collects bagasse ash^19,20.

Filter Mud (FM): The residue that is left behind after filtering the syrup produced from sugarcane. It is called FM²¹. The syrup is separated during the filtering process into the mud, which gathers at the bottom, and the clear syrup, which stays on the surface of the syrup stream²².

Sugar beet -derived wastes

The waste produced by the beet sugar factories is described below.

FM: The sugar juice is divided into clear juice and FM during the clarifying step of sugar manufacturing. While the latter is further filtered to extract the suspended matter (fine plant particles and insoluble salts) as an FM, the former is utilized in the manufacturing of sugar²³.

Sugar Beet Pulp: Following the extraction of sucrose from sugar beets, a waste product known as SBP is created that consists of the remaining elements of the processed sugar beets²⁴.

Materials and methods

FAHP and theoretical background

One of the most precise methods of decision-making, Multi-Criteria Decision Analysis (MCDA), or MCDM, can be thought of as a revolution in the field of different decision-making techniques^25,26. Different quantitative and qualitative criteria are taken into account by this approach in order to determine the optimal answer²⁷. Additionally, based on the importance of each criterion in a specific instance, expert panels give varying weights to the criteria²⁸. Over the last few decades, various authors have created or improved different forms of MCDM techniques. The primary distinctions between these techniques concern the complexity of the algorithms utilized in each approach, the methods for weighting the criteria, the representation of the evaluation criteria, the likelihood of data uncertainty, and the kind of data aggregation²⁹. The FAHP is one of these approaches.

In 1980, Thomas El-Saati, an Iraqi researcher, suggested the AHP approach³⁰. Decision-makers may use this approach to turn complicated decision-making challenges (particularly when the decision problem includes numerous alternatives and competing criteria) into a hierarchical structure and evaluate a vast amount of quantitative and qualitative data in the context of a multi-criteria problem³¹. The inability of the AHP technique to regulate uncertainty and inconsistency when making pairwise comparisons is mostly caused by its use of a set judgment scale³². The FAHP approach, which converts linguistic variables into fuzzy numbers in order to compare the priority of each decision variable to the others, is put out as a solution to the issue of establishing the consistency of priorities in the AHP method. Additionally, waste management criteria such as technical applicability, infrastructure readiness, and implementation feasibility are highly dependent on expert experience and specific industry conditions, making precise numerical comparisons unrealistic. FAHP allows for modeling these uncertainties via fuzzy numbers, providing a more realistic and flexible representation of expert knowledge^33–36.

Some of the unique fuzzy numbers that are useful in a variety of decision-making models are triangular, trapezoidal, L-R triangular fuzzy, and L-R trapezoidal fuzzy numbers³⁷. In this work, we utilize triangular fuzzy numbers.

Generally, FAHP enhances the robustness and reliability of the decision-making process by integrating qualitative and quantitative indicators, mitigating fluctuations between expert opinions, and maintaining consistency in complex multi-criteria evaluations. Its application in environmental management, sustainability planning, and waste valuation studies is well-documented, demonstrating its effectiveness in areas characterized by incomplete information and operational diversity^35,36,38,39. Given the heterogeneous nature of sugar mill waste streams and the operational constraints of the Iranian sugar industry, FAHP provides a more suitable and reliable framework for prioritizing waste management strategies compared to traditional AHP or other fuzzy multi-criteria decision-making techniques.

Based on a review of waste management approaches in the sugar industry¹⁴, we decided to prioritize sugar waste management approaches for Iran from the experts’ perspective. For this purpose, the FAHP method was used. This objective necessitates a number of actions, such as determining criteria and sub-criteria, creating a paired comparison matrix between criteria and sub-criteria, assigning weights to each criterion, and ultimately prioritizing or ranking. Here are descriptions of each of the actions.

Determining criteria and sub-criteria

Choosing appropriate evaluation criteria is a crucial step in any MCDM study, as it directly impacts the prioritization results. A systematic review of waste management literature in the agro-industrial and sugar processing sectors led to the identification of two primary criteria for this study: economic aspects and feasibility. Previous studies often emphasize economic returns, implementation feasibility, and technical applicability as key determinants in the successful revaluation of waste^14,40,41.

In addition, expert consultations with waste management professionals and sugar industry experts confirmed that, given financial constraints, infrastructural limitations, and the absence of advanced waste processing technologies, economic viability and feasibility are the most relevant and practically decisive criteria for Iranian sugar factories. Since all waste management strategies for the sugar industry are included in this study as long as they lower environmental pollution, the environmental criterion was left out of the list of chosen criteria. Therefore, the environmental criterion was considered as the initial filter for waste management solutions in sugar industries.

To select the sub-criteria for this study, which includes management strategies, a comprehensive systematic review article was used. In this study, all existing strategies worldwide for managing sugar industry waste were collected, and all available strategies were categorized into 6 general groups, which include (A) fuel and energy production, (B) construction materials production, (C) industrial application, (D) food, pharmaceutical, and cosmetic industries application, (E) agriculture application, and (F) pollutants purification¹⁴.

Creating a paired comparison matrix between criteria and sub-criteria

Paired comparison matrices were created between the criteria and sub-criteria after they were identified. The survey was distributed to 20 professionals and specialists in the field of waste management in order to identify the optimum waste management approach for the sugar industry. These 20 individuals consist of 2 waste management organization workers, 7 PhD students, and 11 professors in the waste management discipline. It should be noted that the basis for selecting the expert panel to fill out the pairwise comparison matrix was having a research and implementation field related to waste.

The pairwise comparison matrix was prepared in Word software and sent to the mentioned experts via email for completion. To complete the matrices, the criteria must first be compared with each other, and then the sub-criteria must be compared and scored. The linguistic variables for pairwise comparison are presented in Table 1.

Table 1.

Linguistic variables and triangular fuzzy numbers.

Linguistic variables	Definite number	Triangular fuzzy numbers	Inverse triangular fuzzy numbers
Equal importance	1	(1-1-1)	()
A bit important	3	(2-3-4)
Moderate important	5	(4-5-6)
Very important	7	(6-7-8)
Absolute superiority	9	(8-9-10)

After completing the matrices (by experts), conducting pairwise comparisons of the criteria and sub-criteria, and assigning scores, data from each matrix were extracted, and final equivalent or combined matrices of expert opinions were prepared.

Assigning weights to each criterion

Equivalent matrices were created in this investigation using the geometric mean method. The following equation (Eq. 1) illustrates the formula for geometrical mean:

1

Saati has proposed this method as the best technique for combining pairwise comparisons³⁰. The geometric mean of all pairwise comparison matrices derived from the experts’ opinions in the list of matrices was used to compute the final pairwise comparison matrix.

Formation of pairwise comparison matrices

First, based on Table 1, the equivalent triangular fuzzy numbers and inverse triangular fuzzy numbers are written in place of each of the definite numbers provided by the specialists in the matrices before beginning the computations pertaining to FAHP. Table 2 (A, B) gives a full illustration of the pairwise comparison matrix that the experts filled out and after replacing the fuzzy numbers, respectively.

Table 2.

Pairwise comparison matrix completed by experts (A) and completed by fuzzy numbers (B).

A
Sub-criteria	Sub-criteria 1	Sub-criteria 2	Sub-criteria 3	Sub-criteria 4
Sub-criteria 1	1
Sub-criteria 2		1
Sub-criteria 3			1
Sub-criteria 4				1

B
Sub-criteria	Sub-criteria 1	Sub-criteria 2	Sub-criteria 3	Sub-criteria 4
Sub-criteria 1	1
Sub-criteria 2		1
Sub-criteria 3			1
Sub-criteria 4				1

The aforementioned are represented in a formulaic manner as follows (Eqs. 2–4):

2

3

4

Calculation of compatibility coefficient

The calculation of the Consistency Ratio (CR) is a crucial step at this point, and it can be used to evaluate how desirable the outcome is based on its value. Saati has proposed utilizing the consistency ratio in decision-making. By dividing the Consistency Index (CI) by the Random Index (RI), one may calculate the consistency ratio. The judgment should be modified if this ratio is more than 0.1, and consistency in judgments is preferred if it is less than or equal to 0.1⁴². To determine the consistency ratio, five actions must be followed.

1. A new vector known as the weighted sum vector is created in the pairwise comparison matrix by multiplying each row by the column vector of the indicators’ relative weights. By dividing the geometric mean of each indicator by the sum of the geometric means of all indicators, one may determine the relative weight of each indicator (Eq. 5).

5

A: Pairwise comparison matrix. W_i: Relative weight column vector.

• 2.In this step, the compatibility vector is obtained by dividing the relative priority vector (A_w) by the elements of the weighted sum vector (Eq. 6).

6

• 3.In this step, λ_max is calculated by averaging the values of the compatibility vector resulting from dividing the weighted sum vector by the relative weight (Eq. 7).

7

• 4.Then proceed to calculate the compatibility index; the formula used to calculate the compatibility index is presented below (Eq. 8):

8

The random index can be obtained using Table 3 based on the number of criteria (n).

Table 3.

Calculating the random index based on criteria.

n	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
RIm	0	0	0.4890	0.7937	1.0720	1.1996	1.2874	1.3410	1.3793	1.1095	1.4181	1.4462	1.4555	1.4913	1.4986
RIg	0	0	0.1796	0.2627	0.3597	0.3818	0.4090	0.4164	0.4348	0.4455	0.4536	0.4776	0.4691	0.4804	0.4880

• 5.Finally, the CR is calculated using the following formula (Eq. 9).

9

Si calculation

In this step, the magnitude degree (Si) is determined for each row of the matrix created for pairwise comparison. To accomplish this, the sum of the row values is initially determined using the following formula (Eq. 10):

10

Now Si, which is itself a triangular fuzzy number, is calculated using the following equation (Eq. 11):

11

It should be noted that in these two relations, i represents the row number and j represents the column number. Given that , it can be concluded that (Eq. 12):

12

Now, after calculating the magnitude for each row of the pairwise comparison matrix, the magnitude is calculated relative to each other. In order to calculate the probability of for two fuzzy numbers and relative to each other, the following formula is used (Eq. 13).

13

The numerical value d represents the intersection point of the membership function of two fuzzy numbers and , which is visualized in the diagram below (Fig. 1).

Fig. 1.

The point of intersection of the membership function of two fuzzy numbers.

K is a triangular fuzzy number that represents the magnitude of a triangular fuzzy number and is obtained using the following equation (Eq. 14):

14

Calculating the weights of criteria in pairwise comparison matrices

To achieve this goal, the following formula is used (Eq. 15):

15

The above formula yields unnormalized weights, which are as follows (Eq. 16):

16

To normalize and finalize the weight of each criterion, the following formula is used to normalize the unnormalized weights (Eq. 17):

17

The weights resulting from the above formula are deterministic and non-fuzzy weights, and by doing this repeatedly, the weights of all matrices are obtained.

Ultimate prioritizing or ranking

Finally, based on the calculated non-fuzzy weights, prioritization of sugar industry waste management approaches was carried out, separated into two criteria: feasibility and economic aspect.

It should be noted that Excel 2021 software was used once, and the FuzzyAHP package version 0.9.5 in R software version 4.3.2 was used once to verify that the findings were the same when carrying out the FAHP.

Results

FAHP was used to prioritize waste management options for the sugar industry, and as a consequence, criteria and sub-criteria that are connected to one another were identified. The connection between the goal, criteria, and sub-criteria of this research is illustrated in Fig. 2 for better understanding.

Fig. 2.

The relationship between the FAHP criteria and sub-criteria.

As shown in Fig. 2, the criteria are related to the sub-criteria, and the sub-criteria are compared with each other once considering the first criterion and again considering the second criterion. Finally, the two criteria considered are compared with each other to determine the importance of each. To carry out this comparison, the completed matrices were analyzed, and, by forming an equivalent matrix, the relevant calculations were performed for prioritization.

Calculating criteria weights and CR

Table S1 displays the equivalent matrix formed for the criteria, along with the calculation results, which shows that the actual weight of the economic aspect is greater than feasibility.

The Boucher method was utilized in this research to determine the CR⁴³. As previously stated, this technique creates two distinct matrices, one based on the matrix’s middle numbers and the other on the geometric mean of the top and bottom numbers. The key thing to remember in this method is to compute the inverse values for each matrix individually. Since a new matrix has been established, particularly in the geometric mean matrix of the top and bottom numbers, this is significant. The matrices are shown in the table below (Table S2).

The compatibility index, which is equal to zero for both matrices, was computed by averaging the λ values and also using the formula (Eq. 8). Additionally, the random compatibility index for both matrices is zero, as shown in Table 3. Considering the two values of CI and RI are zero for both matrices as well as the equation (Eq. 9), the compatibility rate is likewise zero.

Table S3 displays the equivalent matrix formed for the sub-criteria, along with the calculation results for the two feasibility and economic aspects, which are the sub-criteria of the study. Table S4, also shows the process of calculating CR.

The CI value was determined by computing the average of λs, or λ_max in other words, and plugging the values into Eq. (8). The CR value was then computed using the values in Table 3 and Eq. (9). The computations revealed that the CR values were below 0.1, hence there was no need to enhance the matrices.

The normalization of the weights, which is the additional step done to get the true weights of the criteria and sub-criteria, should also be mentioned. This step is carried out when the total of the weights exceeds one. In this study, the total of the weights is equal to one, so there is no need to normalize them. As a result, the weights that were obtained are the true weights of the criteria and sub-criteria. The Tables S3 are briefly summarized in the following graphs, which show the relative weight of each criterion and sub-criteria. As a result, policymakers can compare the criterion and sub-criteria (from various perspectives) and make choices based on the graphs below.

As shown in Fig. 3 (A), the economic viability of the approach is given greater consideration than its feasibility. Additionally, the top two ranks for both economic aspects and feasibility are given to approaches for fuel and energy production and usage in agriculture, as shown in the ranking of the sub-criteria in terms of economic aspect and feasibility in Fig. 3 (B), respectively. The distinction is that, in terms of economic factors, they hold the first and second positions, whereas, in terms of feasibility, they hold the second and first positions.

Fig. 3.

Criteria ranking (A), sub-criteria ranking in terms of economic aspect and feasibility (B).

The economic aspect of the approach, according to specialists, takes precedence over its feasibility, with a weight of 0.61 compared to 0.39 for the latter. It’s important to keep in mind that every approach is implementable; nevertheless, the feasibility of the approach refers to the possibility of implementing it in real dimensions.

The ranking of the approaches in terms of economic aspects is as follows: fuel and energy production (weight 0.25), agricultural application (weight 0.24), industrial application (weight 0.15), food, pharmaceutical, and cosmetic application (weight 0.14), construction material production (weight 0.11), and pollutant purification/refining (weight 0.11) groups are in the first to sixth ranks, respectively.

The order of the approaches in terms of feasibility is as follows: the groups of applications in agriculture (weight 0.20), fuel and energy production (weight 0.19), construction materials production (weight 0.16), industrial application (weight 0.16), purification and refining of pollutants (weight 0.15), and food, pharmaceutical, and cosmetic industry’s applications (weight 0.14) are in positions 1 to 6, respectively.

Discussion

Within the framework of sustainability, the economic dimension acts as the primary filter, determining not only profitability but also the systemic feasibility of any proposed strategy^44,45. Scientific studies consistently confirm the central role of financial metrics in evaluating strategic transitions. Systematic reviews of MCDM applications have shown that financial factors—such as cost assessment and economic impact assessment—are the most important or sensitive factors in determining the success of a strategy⁴⁶. This finding is crucial because it shows that financial risk often serves as an aggregate measure of overall project vulnerability. This means that every non-financial challenge (such as technical complexity, market fluctuations, or regulatory uncertainties) ultimately crystallizes into financial risk—like increased operating costs or project delays⁴⁷. Therefore, the high weight assigned to the economic criterion in prioritization models reflects the severity of underlying systemic constraints, making economic analysis essential for strategic implementation in the real world.

First approach: fuel and energy production

This strategy ranked first in this study, aligning with the main economic policies in fossil fuel-dependent regions. The main goal is not only to reduce dependence on fossil fuels but also to provide clean and affordable alternatives that contribute to energy security and economic sustainable development goals^48,49.

Bioenergy generation has a complex relationship with the agricultural sector, which is known as the food-energy-land nexus. The expansion of biofuel crop cultivation leads to competition for scarce resources such as land, water, and labor. This competition can lead to trade-offs and conflicts in land allocation between bioenergy production and vital uses such as food production and environmental conservation^50–53. However, if waste is converted into bioenergy, this issue is solved.

The main obstacle to the scalability of this strategy is the need for a high initial investment to develop infrastructure and install technology⁵⁴. Bioenergy systems face significant market risks, including high sensitivity to market price fluctuations and policy instability⁵⁵. These policy instabilities can severely disrupt investor confidence and market development and eliminate the possibility of developing this technology and management solution.

Second approach: agricultural application

This strategy was ranked second in priority due to the prevalence of agriculture in Iran and the country’s suitable climate^56,57. Studies show that using compost made from waste leads to increased production efficiency, cost savings, profitability, and a reduction in the introduction of chemical fertilizers into the environment⁵⁸.

Implementing sustainable agricultural practices requires managing important factors; often, this involves balancing conflicting environmental goals⁵⁹. For example, while using FM as organic fertilizer reduces the need for chemical fertilizers, improper management carries the risk of pollutants entering the environment^60,61. Policymakers should consider that if waste is used in agriculture, they must identify and manage these conflicting impacts on economic and environmental sustainability.

Widespread adoption of this strategy is facing challenges due to high financial and structural barriers. The most significant financial challenge is the high initial costs of setting up the necessary infrastructure⁶². Other structural barriers include the lack of uniform standards for technologies (which increases the complexity and cost of integration) as well as limitations related to farm size, soil conditions, and waste characteristics^63,64. To put it better, the soil conditions are very important for using the waste as fertilizer.

Policy implications and strategic recommendations

Strategic prioritization based on FAHP in waste management for the sugar industry showed that the economic aspect (profitability and cost) has a dominant weight compared to feasibility. This result confirms the need to design policy interventions whose primary goal is to reduce financial risk^10,12,65. For the first strategy, fuel and energy production, which faces high capital intensity and vulnerability to market price fluctuations, policies should focus on stabilizing income and encouraging initial investment. To guarantee a return on investment, long-term Power Purchase Agreements (PPAs) or feed-in tariffs for waste-derived bioenergy must be implemented⁶⁶. Additionally, the development of necessary infrastructure for biorefineries should be supported through government-led financial facilities or Public-Private Partnership (PPP) models to overcome the significant initial investment barriers⁶⁷.

Despite the agricultural application strategy being ranked second due to the high economic and environmental benefits of biofertilizers in Iran, as well as the high level of cultivation in the country, structural barriers prevent its full implementation. Policies in this area should focus on removing adoption barriers at the farm level and ensuring product quality. In the first step, the government should take action to develop uniform national standards for the safe and effective use of processed waste to ensure environmental safety (risk of pollutants) and product quality. In the second step, providing financial assistance to the sugar industry is crucial to address the high initial costs of waste management equipment⁶⁸.

Comparison of FAHP with Grey Relational Analysis (GRA)

GRA was used as an alternative MCDM technique to evaluate the resilience of the FAHP findings⁶⁹. The relational degree between each alternative and an ideal reference sequence is measured by the GRA⁷⁰, which Deng introduced and is especially helpful for dealing with ambiguous and incomplete data^71,72. The decision matrix (Table 4) was created using the clear normalized weights produced by FAHP. Both standards (economic aspect and feasibility) were handled as benefit qualities. The Grey Relational Coefficient (GRC) was then determined using the following equation after min-max normalization (Eq. 18):

18

Table 4.

Decision matrix for GRA (crisp weights derived from FAHP) and Comparison of rankings (FAHP vs. GRA).

Alternative	Economic aspect (weight = 0.61)	Feasibility (weight = 0.39)	FAHP (economic aspect)	FAHP (Feasibility)	GRA grade	GRA rank
Agricultural application	0.24	0.20	2	1	0.9237	1
Fuel & energy production	0.25	0.19	1	2	0.9025	2
Industrial application	0.15	0.16	3	4	0.4183	3
Construction materials production	0.11	0.16	5	3	0.3705	4
Food, pharmaceutical & cosmetic	0.14	0.14	4	6	0.3672	5
Pollutants purification	0.11	0.15	6	5	0.3496	6

where represents the absolute difference from the ideal reference sequence x₀= (1, 1), Δ_min and Δ_max are the minimum and maximum differences, and ρ = 0.5 is the distinguishing coefficient⁷³. The Grey Relational Grade (GRG) for each option was then calculated as the average of the coefficients.

The GRA results were highly consistent with the original FAHP ranking (Table 4). The greatest GRG was achieved in agricultural application (0.9237, Rank 1), followed by fuel and energy production (0.9025, Rank 2). With just a small shift in position, the two best strategies maintained their dominance under both approaches. The prioritization is consistent across different MCDM methods, as shown by the modest changes in the lower-ranked options. The comprehensive GRA computations are presented in Supplementary Table S5.

Conclusion

This study used the FAHP method to evaluate and prioritize feasible waste management strategies for sugarcane and sugar beet waste. By combining expert judgment and multi-criteria evaluation, the results indicate that fuel and energy production and agricultural applications are the most suitable options under the current economic and technical conditions of Iranian sugar factories. These findings reflect the relative advantages of these pathways in terms of economic viability and feasibility. Also, the findings of FAHP were compared with those of GRA, and it was discovered that the results of the two approaches were consistent. Additionally, by adopting these approaches as their primary strategy, industries can create double benefits for themselves and reduce their environmental pollution burden. Also, if the first solution is chosen as the waste management solution, they can provide part of their energy needs.

On the other hand, the biggest obstacle to carrying out this research was the dearth of practical knowledge about industrial-scale approaches in the nation. Despite the implementation of a variety of waste management techniques, none of them were based on scientific evidence, and there was no monitoring to see if they were effective. Or, in general, little effort was made in waste management, and the generated waste was just sold; the efficacy of those approaches was assessed along with their benefits and drawbacks.

It should also be noted that, considering the prioritization of approaches and the identification of the best approach for waste management in the country’s sugar industry, it is recommended that before making a major decision on adopting approaches, a pilot be conducted in a sugar factory to examine the effects of the approach on the environment and a cost-effectiveness analysis and also to optimize the parameters affecting the process.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

“A.S. led the software, performed data analysis, and wrote the main manuscript text. M.A. contributed to the manuscript’s analysis and interpretation. N.D. assisted in data analysis and manuscript revisions. F.C.K. provided supervision and critical revisions. K.Y. assisted with additional data analysis and reviewed the manuscript. All authors reviewed and approved the final manuscript.”

Funding

The budget of this project has been provided by the Institute for Environmental Research and Vice-Chancellor of Education, Tehran University of Medical Sciences.

Data availability

The file associated with the retrieved articles for this study is available through the corresponding author for a suitable reason.

Competing interests

The authors declare no competing interests.