Adoption of circular economy for food waste management in the context of a developing country

Abstract

Food wastage is a global concern with high economic, social and environmental impacts. Pakistan, a developing country, is also significantly affected by the adverse impacts of food wastage. For overcoming this problem, the transition from a Linear to a Circular Economy (CE) for the management of food wastage can serve as a viable strategy. However, there are barriers of political, technical and cultural nature, which are impediments in the path of this transition. This study aims to identify and prioritize these barriers in order of their significance. This research study evaluated and ranked these barriers using a Fuzzy Multi-Criteria Decision Making (MCDM) technique, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A total of 15 barriers were analyzed, and the ‘complicated intrinsic nature of CE’, ‘misleading information about shelf-life leading to waste rather than distribution’, ‘the poor economic viability of start-ups with CE model’, ‘corporate and organizational hesitance to change/innovate’ and ‘technological backwardness of farmers/growers on the agricultural production side’ were ranked as the most significant hurdles. The novelty of this study lies in its application. This study is unique as it has focused on developing countries and proposed policy recommendations for the transition towards a CE. In light of the above-mentioned results, this study provides policy recommendations for public and private sector policymakers that would facilitate the food industry in shifting towards the CE model.

Keywords

Food wastage circular economy identification of barriers fuzzy MCDM

Introduction

Food wastage is one of the grave issues that affect a significant population of the world, resulting in food deprivation and adverse environmental impacts. Annually, almost 30% of globally produced food is reported as wasted (Parfitt et al., 2010). The Food and Agricultural Organization (FAO), a subsidiary of the United Nations Organization, has classified the issue into two categories, Food Wastage and Food Loss. Food wastage is primarily concerned with the food loss that happens at the supply side of the supply chains. This wastage may be caused by the decrease in the amount of food during harvesting, accumulation of agricultural yield, processing, postprocessing and packaging. The reduction in the amount of ‘fit for consumption’ food at distribution, retail and consumption phases of the supply chain is referred to as Food Wastage. These two categories of food wastage are characterized by the indicators of the ‘Food Loss index’ and ‘Food Waste index’. The former is a country-specific index and uses a base period of1 year to measure the percentage loss for five main food groups including fruits and vegetables, roots and tubers, miscellaneous crops, grains and pulses, fish and fish products and animal products. The food waste index is still in the proposal phase, and its criteria are yet to be formulated by the respective organizations.

Food wastage is associated with adverse environmental impacts. The most common practice for food waste disposal is to dump it into landfills. However, it is detrimental to the environment as it releases methane gas into the atmosphere, which is at least 21 times stronger as compared to carbon dioxide (Roka, 2019). Landfilling also leads to a decrease in land capacity, which otherwise can be used more productively. The alternative for this practice can be the waste disposal in the form of a compost, which would result in the reduction of the harmful gases, leading to reduced contribution to greenhouse gases (Seberini, 2020). Moreover, another staggering concern is that food wastage has a significant carbon footprint. The global annual carbon footprint of the food sector roughly accounts for twice the share associated with the US transport sector.

Pakistan and other developing countries are more concerned with food loss than food wastage, whereas the developed world primarily faces the dilemma of food wastage. In the developing world, food loss can be ascribed to technical limitations, lack of infrastructure and proper storage, climate and natural disasters, lack of government policies, poor standards and protocol and cultural behaviour (Vittuari et al., 2019). This food loss phenomenon has numerous social, economic (de Gorter et al., 2021) and environmental impacts (Hall et al., 2009). These countries face financial challenges for the minimization of food wastage and they also have to suffer from the detrimental financial impacts as the aftermath of the wastage. The investment in terms of financial capital, labour, raw material, land and machinery for food production is wasted as the food fails to reach the consumers. However, if the food reaches the consumers and is still wasted, the cost of transport and storage is also added causing greater financial impacts. This results in the rise of food prices. Excessive food wastage contributes to the food insecurity of a population. According to FAO, 26.1 million people were suffered as a result of food insecurity in Pakistan during 2017–2019. The food loss issue combined with the high prices also exacerbates the malnourishment problem, a common phenomenon in citizens of developing countries. Malnourishment also contributes to the deterioration of health for the low-income class of society, causing additional investment in the healthcare sector (Christina Gayton; The Economics Review).

The massive scale of food wastage and its harmful impacts demand a sustainable food wastage management practice. This can be achieved by the implementation of the circular economy (CE) approach, as adopted by the European Union (EC, 2015). Currently, developing countries such as Pakistan make use of the linear economy practice, which follows a make-use-dispose model. On the contrary, the CE follows a make-use-recycle/reuse model and is aimed at maximization of the utility of the resource (WEF, 2020; the Ellen MacArthur Foundation, 2019).

The proposed transition towards a CE model has the capability to decrease food loss in Pakistan. However, Pakistan, being a developing country, has to face hindering barriers in the path of the transition from a linear to a CE. These barriers need to be identified and dealt with to make the transition process accelerated. It is important to understand that not all barriers can be eliminated once. Hence, the prioritization of these barriers is necessary in order to eliminate them systematically. The prioritization of these barriers would enable the respective authorities to formulate a dedicated strategy aimed at the effective transition towards the CE. For this purpose, this research study has employed Fuzzy TOPSIS, an MCDM method, for prioritization of these barriers.

In this research paper, there are a total of five sections. ‘Introduction’ provides a brief introduction of the nature and aim of the study. It is followed by the ‘Literature review’, which includes a review of previous research studies conducted on the CE and the Fuzzy TOPSIS method. Subsequently, in the ‘Data collection and methodology’ section the process of data collection and analysis has been discussed. In the ‘Results and discussion’ section, the results are interpreted, and their implications are discussed. The last part, ‘Conclusion’ concludes the study and discusses its limitations.

Literature review

The concept of the CE is a comparatively modern concept; however, numerous research studies have focused on it from multiple perspectives. A research study surveyed 47 companies involved in food processing in Belgium to locate the stages, predominantly associated with food loss (Dora et al., 2020). The study prioritized the food processing stage in terms of food loss, followed by transportation, shifting, fluctuating production schedules, inventory and storage and staff errors. Another research study reviewed 31 companies to locate the food loss spots for different food groups in the various steps of the supply chain (Beretta et al., 2013). The results indicated that the causes of food loss industry-specific and depend on indigenous factors. However, the common and leading causes of food loss were determined to be the lack of quality standards, pest infestations, delayed harvesting and insufficient storage issues. In addition, (Principato et al., 2019) analyzed the pasta production process in Italy, from the perspective of food loss. The researchers also recommended the mechanism for the introduction of a CE in pasta production. Similarly, a study investigated barriers in the implementation of the CE and focused on eight companies (Veleva et al., 2017). It identified inefficient waste disposal, inadequate calculations and reporting lack of awareness in the workforce as the most significant barriers.

Furthermore, Araujo Galvão et al. (2018) performed a literature review for the identification of obstacles with respect to transition towards a CE. The study identified regulatory barriers, economic barriers, management-related barriers, non-standard performance indicators and other barriers of consumer, social, technological nature as the leading hurdles for implementation of the CE. Another study adopted a qualitative approach for the identification of barriers (Ritzén and Sandström, 2017). The researchers interacted with the managers of manufacturing companies and highlighted various barriers in the adoption of the CE. The identified barriers were classified in technological, financial, operational, structural and attitudinal categories. Moreover, Kirchherr et al. (2017) focused on the identification of obstacles to the CE in the context of the European Union. The results indicated that the lack of infrastructure, lack of adequate policy framework, economic cost and lack of awareness as the most important hurdles for the adoption of a CE. The author recommended an initially gradual and then accelerated transition from the linear economy.

Few research studies also discussed the mechanisms for the disposal of food waste. A research study recommended the adoption of anaerobic digestion as an economically favourable technique as compared to alternatives, such as composting and landfilling and incineration (Slorach et al., 2019). Similarly, Oldfield et al. (2016) assessed the effect of anaerobic digestion, composting and landfilling and incineration on global warming. These results also prioritized the anaerobic digestion treatment as the most effective waste disposal method.

This research study relies on an MCDM technique for the evaluation of these barriers. The MCDM techniques are utilized in scenarios, which involve multiple criteria and alternatives. The alternatives in this research study are the barriers that are hindering the transition towards the CE. The MCDM techniques are employed to prioritize or rank the alternative solutions, in light of predetermined criterion importance weights. (Nădăban et al., 2016). The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was pioneered by Hwang and Yoon (1981). The technique ranks the alternatives on the basis of their proximity to the ideally positive alternative and their distance from the ideally negative solution. However, in a decision-making problem, there lies an intrinsic ambiguity as decision-makers are involved in assigning qualitative weights to the criteria or alternatives. Hence in such a situation, the fuzzy set theory is involved. The theory was initially developed by Zadeh in 1970 (Zadeh, 1965). The Fuzzy TOPSIS technique has found widespread applications in decision-making problems.

A research study employed Fuzzy TOPSIS for choosing a supplier for a manufacturing firm, amongst a pool of three suppliers (Sevkli et al., 2010). Similarly, another study used Fuzzy TOPSIS for prioritizing barriers that were faced by the automobile sector in the adoption of the CE (Agrawal et al., 2021). Furthermore, Mahpour (2018) used Fuzzy TOPSIS for the evaluation of barriers that threaten the application of CE for reducing construction waste. A research study employed Fuzzy TOPSIS for prioritization of the areas for use of treated and refined wastewater (Kim et al., 2013). In addition, Lee et al. (2020) developed a combined framework comprised of Fuzzy TOPSIS, Analytical Hierarchy Process (AHP), Linear Programming (LP) and Semi-structured interviews for the selection of tire recycling outlets.

It is evident from the discussion that several studies have been conducted regarding food waste management and CE as a potential solution. However, these studies have mainly focused on the developed world and these studies are scarce, pertaining to developing countries. This research study has tried to fill this gap by focusing on Pakistan, a developing country, from the perspective of food waste management and CE. This study aims to determine and prioritize the barriers to adopting a CE approach in food waste management in Pakistan. For this purpose, the Fuzzy TOPSIS technique has been employed. Thus, the scope, utility and choice of the technique are the novelties of this study. The results of this study and the formulated policy recommendations will help the managers in companies and government bodies to incorporate the CE model in the food industry of Pakistan.

Data collection and methodology

As stated previously, this study has employed the Fuzzy TOPSIS technique for evaluation of barriers to adoption of CE. The Fuzzy TOPSIS methodology requires a set of criteria, alternatives and qualitative judgements assigned by the experts to these criteria and alternatives for analysis. Therefore, the first step of the data collection was the identification of barriers, the alternatives that are hindering the adoption of the CE in Pakistan’s food industry. For this purpose, initially, a total of 25 barriers were identified from a thorough and extensive literature review. In order to reduce redundancy, in the second phase, the determined barriers were further shortlisted to 15 barriers, based on their relevancy to the study, frequency of utility in research studies and the expert opinion. The list of these barriers is presented in Table 1. Moreover, these criteria were gauged on the basis of three criteria, that is, technical, cultural and political difficulty.

Table 1.

Identified barriers.

S. No.	Description	References
A1	Lack of awareness and knowledge about applying CE towards food waste management	Kirchherr et al. (2017), Ritzén and Sandström (2017), Veleva et al. (2017)
A2	Lack of experimentation and successfully established CE business models	Govindan and Hasanagic (2018), Kirchherr et al. (2017)
A3	Lack of government policies to promote CE	Kirchherr et al. (2017)
A4	Non-standardized waste indicators, i.e. no set standard to measure the amount of waste	Govindan and Hasanagic (2018)
A5	Technological backwardness of farmers/growers on the agricultural production side	Zhang et al. (2019)
A6	Lack of technology to utilize waste food, e.g. waste-to-biofuel technology	Principato et al. (2019)
A7	Lack of government funding towards CE	Araujo Galvão et al. (2018), Kirchherr et al. (2017), Ritzén and Sandström (2017)
A8	Misleading information about shelf-life leading to waste rather than distribution	Stangherlin and de Barcellos (2018)
A9	Ineffective sorting/utilization of food waste on site and preference to landfilling	Dumlao-Tan and Halog (2017)
A10	Complicated intrinsic nature of CE	Govindan and Hasanagic (2018), Ritzén and Sandström (2017)
A11	Relying on non-environment friendly practices to reduce waste, e.g. incineration	Oldfield et al. (2016), Slorach et al. (2019)
A12	Lack of proper supply chain channels for redistribution of unconsumed food to food banks/food-deprived people	Ritzén and Sandström (2017)
A13	The poor economic viability of start-ups with CE model	Kirchherr et al. (2017)
A14	Corporate and organizational hesitance to change/innovate	Govindan and Hasanagic (2018)
A15	Cultural nature of people, i.e. hesitation to reuse/recycle	Araujo Galvão et al. (2018), Kirchherr et al. (2017)

CE: circular economy.

In the next phase, the expert opinion was incorporated as they were asked to assign weights to the identified criteria and alternatives as per their significance. For this purpose, a survey was conducted in which 39 experts were asked to assign relative importance weights to the criteria then to the alternatives, concerning each criterion. The expert pool comprised of the policymakers working in the government, food security specialists from FAO, sustainability and CE consultants, food supply chain managers, environmental engineers, project managers from waste management companies in different cities of Pakistan and researchers (Table 2). Moreover, these responses were collected using a 5-point Likert scale and the linguistic variables ranged from insignificant to extremely significant. The member functions of each linguistic variable are presented in Table 3.

Table 2.

Expert’s profile.

Experts’ profile	Number
Food security specialists	8
Policymakers from government	4
Sustainability and CE consultants	7
Food supply chain managers	9
Project managers from waste management	8
Environmental engineers	3

CE: circular economy.

Table 3.

Linguistic variables and their member functions (Mahpour, 2018).

Linguistic variable	Membership function
Extremely insignificant	(1, 1, 3)
Very insignificant	(1, 3, 5)
Significant	(3, 5, 7)
Very significant	(5, 7, 9)
Extremely significant	(7, 9, 9)

Fuzzy TOPSIS

Fuzzy TOPSIS is a multi-criteria decision-making approach that ranks the alternatives through their Euclidean distances. Initially, Fuzzy TOPSIS determines the ideally positive and negative solutions and then measures distance of each alternative from both these solutions. It ranks the alternatives based on their proximity to the positive ideal solution and their distance from the negative ideal solution, thus maximizing the benefits and minimizing the costs (Wang and Lee, 2009). The steps associated with the fuzzy TOPSIS technique are as follows.

Step 1: A distinct set of criteria and alternatives, for evaluation, is determined. Moreover, a pool of experts is also identified who are then consulted for the evaluation of the criteria and alternatives.

Step 2: In the next step, the scale for linguistic variables is determined. This study has incorporated fuzzy set theory for mitigation of the uncertainty, inherent in the expert opinion. This research study has used triangular fuzzy numbers; therefore, three fuzzy numbers $a 1$ , $a 2$ and $a 3$ are used to assign estimated value to the linguistic judgements. The adopted linguistic variables and their membership functions are presented in Table 3.

Step 3: After the conduction of surveys, the expert opinion is utilized to calculate the criteria weights. If the fuzzy assessment by $z^{t h}$ expert for the $y^{t h}$ a criterion is taken as

a_{y}^{z}, b_{y}^{z}, c_{y}^{z}

where $z = 1, 2, \dots, E$ ; $y = 1, 2, \dots, N$ , $E$ and $N$ are the total number of experts and criteria respectively. Then the fuzzified weight for $y^{t h}$ criterion will be calculated by using equation (1).

P_{y} = (\frac{1}{E} \sum_{E}^{z = 1} a_{y}^{z}, \frac{1}{E} \sum_{E}^{z = 1} b_{y}^{z}, \frac{1}{E} \sum_{E}^{z = 1} c_{y}^{z})

(1)

Step 4: In step 4, alternative weights are calculated, representing the final weights of each alternative, concerning each criterion. If the assessment of $z^{t h}$ expert for the $x^{t h}$ alternative against $y^{t h}$ a criterion is taken as

a_{x y}^{z}, b_{x y}^{z}, c_{x y}^{z}

where $z = 1, 2, \dots, E$ ; $x = 1, 2, \dots, M$ ; $y = 1, 2, \dots, N$ ; and $E$ , $M$ and $N$ are the total number of experts, alternatives and criteria, respectively. Then the fuzzified weight for $x^{t h}$ alternative against $y^{t h}$ criterion will be calculated by using equation (2).

Q_{x y} = (\frac{1}{E} \sum_{E}^{z = 1} a_{x y}^{z}, \frac{1}{E} \sum_{E}^{z = 1} b_{x y}^{z}, \frac{1}{E} \sum_{E}^{z = 1} c_{x y}^{z})

(2)

Step 5: In the next step, the decision matrix is constructed by using the fuzzified weights calculated in step 4. The decision matrix is made using equation (3).

D_{M \times N} = \begin{matrix} \begin{matrix} A_{1} \\ A_{2} \\ ⋮ \\ A_{x} \end{matrix} & \begin{matrix} \begin{matrix} C_{1} & C_{2} & \dots & C_{y} \end{matrix} \\ [\begin{matrix} Q_{11} & Q_{12} & \dots & Q_{1 N} \\ Q_{21} & Q_{22} & \dots & Q_{2 N} \\ ⋮ & ⋮ & \dots & ⋮ \\ Q_{M 1} & Q_{M 2} & \dots & Q_{M N} \end{matrix}] \end{matrix} \end{matrix}

(3)

where $Q_{x y}$ is the fuzzified weight of the $x^{t h}$ alternative for $y^{t h}$ criterion where $x = 1, 2, \dots, M$ and $y = 1, 2, \dots, N$ .

Step 6: In the next step, the decision matrix is normalized. For this purpose, the maximum value for $y^{t h}$ criterion, $c_{y}^{*} = m a x c_{x y}$ is determined. Then, the normalized values are calculated using equation (5).

N o r m D_{M x N} = {[n Q_{x y}]}_{M \times N}

(4)

{[n Q_{x y}]}_{M \times N} = \frac{1}{m a x c_{x y}} \times {[Q_{x y}]}_{M \times N}

(5)

N o r m D_{M \times N} = [\begin{matrix} \frac{Q_{11}}{m a x c_{x y}} & \frac{Q_{12}}{m a x c_{x y}} & \dots & \frac{Q_{1 N}}{m a x c_{x y}} \\ \frac{Q_{21}}{M a x_{2} c_{21}} & \frac{Q_{22}}{M a x_{2} c_{22}} & \dots & \frac{Q_{2 N}}{m a x c_{x y}} \\ ⋮ & ⋮ & \dots & ⋮ \\ \frac{Q_{M 1}}{m a x c_{x y}} & \frac{Q_{M 2}}{m a x c_{x y}} & \dots & \frac{Q_{M N}}{m a x c_{x y}} \end{matrix}]

(6)

where, $x = 1, 2, \dots, M$ (total number of alternatives) and $y = 1, 2, \dots, N$ (total number of criteria).

Step 7: The weighted normalized decision matrix $V_{M \times N}$ is found by using equation (7).

V_{M \times N} = {[v_{x y}]}_{M \times N}

(7)

where v_{x y} = n Q_{x y} \times P_{y}

(8)

$x = 1, 2, \dots, M$ (total number of alternatives) and $y = 1, 2, \dots, N$ (total number of criteria).

Step 8: The Fuzzy positive and negative ideal solutions (FPIS and FNIS) are determined by using equations (9) and (10), respectively.

Fuzzy P I S_{y} = (v_{1}^{+}, v_{2}^{+}, \dots, v_{N}^{+})

(9)

Fuzzy N I S_{y} = (v_{1}^{-}, v_{2}^{-}, \dots, v_{N}^{-})

(10)

where, v_{y}^{+} = (1, 1, 1) and v_{y}^{-} = (0, 0, 0)

(11)

$x = 1, 2, \dots, M$ (total number of alternatives) and $y = 1, 2, \dots, N$ (total number of criteria).

Step 9: The distance of each alternative from FPIS and FNIS is calculated using equations (12) and (13), respectively.

d_{x}^{+} = \sum_{y = 1}^{N} d (v_{x y}, v_{y}^{+})

(12)

d_{x}^{-} = \sum_{y = 1}^{N} d (v_{x y}, v_{y}^{-})

(13)

where, $x = 1, 2, \dots, M$ (total number of alternatives) and $y = 1, 2, \dots, N$ (total number of criteria).

Step 10: In the final step, the closeness coefficients for all the alternatives are calculated by equation (14).

C C_{A g g_{x}} = \frac{d_{x}^{-}}{d_{x}^{+} + d_{x}^{-}}

(14)

where, $d_{x}^{+}$ is the distance of the alternative x from the FPIS, whereas $d_{x}^{-}$ is the distance of the alternative x from the FNIS. The alternative, which is at farthest distance from the FNIS and nearest to the FPIS, is ranked as the best alternative. Similarly, the alternative at the farthest distance from the FPIS and nearest to the fuzzy negative ideal solution is ranked at the lowest place.

Therefore, the alternatives are ranked in the decreasing order of the values of closeness coefficients. The alternative having the highest value of closeness coefficient is ranked first and the alternative with the lowest closeness coefficient value is ranked last.

Results and discussion

This aim of this research study was to evaluate a total of fifteen barriers to adoption of CE in Pakistan’s food industry, on the basis of three criteria. Therefore, after the identification of barriers and criteria, expert opinion was sought for assigning the importance weights to the criteria and alternatives. The equations (1) and (2) were used to find the fuzzified weights of the criteria and alternatives concerning each criterion. The obtained fuzzified weights of alternatives were used to construct a fuzzified decision matrix according to equation (4). Furthermore, the decision matrix was normalized by dividing it with the maximum value for each respective criterion, as instructed in equation (6). Lastly, equation (8) was employed for the calculation of a weighted normalized decision matrix. This weighted normalized decision matrix is presented in Appendix Table A1. Then, the calculations were carried out to find the distance of each alternative from the FPIS and FNIS using equations (13) and (14), respectively. Using these distances, finally, the closeness coefficients were computed using which the ranking of alternatives was done. FPIS and FNIS for all alternatives are presented in Appendix Table A2. All closeness coefficients are presented in the form of a bar chart as shown in Figure 1. Finally, the 15 alternatives were ranked on the basis of their closeness coefficients, presented in Table 4.

Figure 1.

Closeness coefficients.

Table 4.

Ranking of identified alternatives.

Rank	Barrier
1	Complicated intrinsic nature of CE
2	Misleading information about shelf-life leading to waste rather than distribution
3	The poor economic viability of start-ups with a CE model
4	Corporate and organizational hesitance to change/innovate
5	Technological backwardness of farmers/growers on the agricultural production side
6	Relying on non-environment friendly practices to reduce waste, e.g. incineration
7	Lack of proper supply chain channels for redistribution of non-consumed food to food banks/food-deprived people
8	Cultural nature of populous, i.e. hesitation to reuse/recycle (linear make-use-dispose mindset)
9	Ineffective sorting/utilization of food waste on-site and preference to landfilling
10	Lack of technology to utilize waste food, e.g. waste-to-biofuel technology
11	Non-standardized waste indicators, i.e. no set standard to measure the amount of waste
12	Lack of government funding towards CE
13	Lack of experimentation and successfully established CE business models
14	Lack of awareness and knowledge about applying CE towards food waste management
15	Lack of government policies to promote CE

CE: circular economy.

The Fuzzy TOPSIS analysis resulted in the prioritization of ‘complicated intrinsic nature of CE’, ‘misleading information about shelf-life leading to waste rather than distribution’, ‘the poor economic viability of start-ups with CE model’, ‘corporate and organizational hesitance to change/innovate’ and ‘technological backwardness of farmers/growers on the agricultural production side’ as the most significant barriers.

The complicated intrinsic nature of the CE is the most significant barrier in the developing countries like Pakistan as in these countries conventional methods are more preferred rather than the innovative ideas. Therefore, it is difficult to adopt the complicated and innovative CE principles as an alternative to the traditional models. The public perception is that the adoption of CE models would lead to increased requirement of efforts and investments, whereas the associated gains are often underestimated. This particular barrier can be overcome if the experts in the field are incentivized and brought on board to work in dedicated departments established with the sole purpose of working towards a shift towards the CE. The aim should be to evaluate the current economic and social scenario of the country on the basis of which a CE model should be established, and budgets should be allocated accordingly. In addition, information campaigns should be launched, aimed at increasing awareness regarding potential benefits of the adoption of CE.

Moreover, the analysis ranked the ‘misleading information about shelf-life leading to waste rather than distribution’ as the second most significant barrier to the adoption of the CE. According to a study by Eppink et al. (2011), a good extent of the food wastage on the consumer side can be attributed to non-standardized labels on edibles. The retailers and other business entities are usually engaged in the practice of removing the labels from edibles, especially non-perishables, before the expiry dates, leading to food wastage. In addition, the labels assigned by the producers are usually inadequate in terms of their description or assignment of the useability period. This problem requires input from both the public and private sectors. As regulations and policy framework of assigning due dates should be formulated by the government bodies and the private sector should incorporate technically sound and ethically viable measures for minimizing food wastage due to inefficient labelling. In addition, the private sector should be encouraged to aid in the collection of the food items that are near the expiry dates and their redistribution at low costs and or through charitable organizations (Symmank et al., 2018). This practice is already being followed by some start-ups in Pakistan, such as the ‘Robin Hood Army’, ‘Aitebaar’ and ‘Rizq’ In a conversation with their representatives, they stressed the dire need for facilitation and help from the government authorities. It is also pertinent to educate consumers about anti-wastage habits and behaviours (Borrello et al., 2017; Stangherlin and de Barcellos, 2018).

Another significant barrier is the ‘poor economic viability of start-ups with a CE model’. As the primary purpose of the start-ups and other business entities is to generate maximum revenue and to minimize the associated costs. Therefore, the adoption of the CE does not fit in the framework as currently for an individual start-up, adoption of the CE is not an economical strategy. This issue is also the major reason behind the corporate reluctance to its adoption, which has been ranked at the fourth place. Furthermore, most of the large companies in Pakistan dealing in food products such as biscuits, cakes, crisps and other related snacks do not have a dedicated department to recycle or reuse the leftover waste instead of disposing of the waste. The reason behind this practice is that these practices serve an additional cost for business owners and do not contribute to an increase in revenue. This barrier can be addressed through designing and implementation of government regulations, which should ensure incorporation of CE in business plans. Moreover, the introduction of CE principles should be incentivized in form on monetary and other benefits.

In addition, the barrier ranked at fifth place is associated with the agriculture industry of Pakistan. Pakistan is an agrarian country whose economy largely depends on agricultural products (Rehman et al., 2015). However, this sector is also a major contributor of food wastage in the country (Johnson et al., 2018). The major reasons for food wastage are lack of awareness and technological limitations. Most of the farming community has little or no education regarding sustainable agricultural practices, quality output production, and reduction of food wastage (Naveed and Anwar, 2013; Rehman et al., 2013). The governmental bodies should focus on the enhancement of awareness and provision of upgraded technology, which can aid in the reduction of food wastage and improvement of waste disposal mechanisms. In addition, special emphasis should be laid on the provision of technical means to the farming community. The technical apparatus, which can aid in reduction of food shortage, should be subsidized and provided on easy installments.

Furthermore, transitioning from a linear to a CE in Pakistan will require extensive efforts to overcome the barriers and hurdles. However, it is not possible to address all the barriers at once. Therefore, corporate companies, start-ups, food waste organizations, the government authorities and consumers must work together to address the barriers prioritized in this research study to ensure a steady and effective transition.

Conclusion

Food wastage is a global concern, with detrimental environmental and socioeconomic impacts, especially in developing countries. The associated adverse effects include food insecurity, greenhouse emissions, inflation and many others. Thus, there is a necessity of transitioning from linear to CE.

This research study aims to identify and highlight various political, cultural and technical impediments that have threatened the food sector’s transition towards CE in Pakistan, a developing country. For this purpose, a total of 15 technical, cultural and political potential barriers were identified from literature, and with Fuzzy TOPSIS, these barriers were prioritized. The MCDM analysis resulted in ranking of ‘complicated intrinsic nature of CE’, ‘misleading information about shelf-life leading to waste rather than distribution’, ‘the poor economic viability of start-ups with CE model’, ‘corporate and organizational hesitance to change/innovate’ and ‘technological backwardness of farmers/growers on the agricultural production side’ as the top five important barriers.

For overcoming these barriers, consolidated efforts are required from government bodies, business entities and consumers. In addition, financial incentives, research and development, promotion of zero waste behaviors amongst citizens and provision of adequate technology to farmers and other similar measures should be ensured. This research study can be further enhanced through selection of more barriers and employment of a diverse set of techniques for evaluation of similar barriers.

Footnotes

Appendix A

Table A2.

Fuzzy positive and negative ideal solutions (FPIS and FNIS) and percentage closeness coefficients.

Alternative	D+	D−	CC%
1	13.926	16.659	54.47
2	13.825	16.550	54.49
3	14.119	16.873	54.44
4	13.365	16.089	54.62
5	12.736	15.426	54.78
6	13.296	16.011	54.63
7	13.931	16.677	54.49
8	11.943	14.606	55.02
9	13.112	15.826	54.69
10	11.209	13.841	55.25
11	12.907	15.616	54.75
12	13.053	15.764	54.70
13	12.215	14.883	54.92
14	12.764	15.467	54.79
15	13.088	15.801	54.70

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Yousaf Ali

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