Carbon Footprint Assessment of Food Waste Disposal Methods in a Thai Hypermarket’s Fresh Food Department

Abstract

The global urgency to mitigate environmental degradation and promote sustainable resource use necessitates effective waste management strategies, particularly in the retail sector, which is a significant contributor to food waste. This study explores the carbon ramifications of food waste disposal methods within a hypermarket’s fresh food department in Bangkok, Thailand. Using the method of life cycle assessment (LCA) under the CML2001 framework, this study evaluates three food waste management methods: anaerobic digestion (AD), sanitary landfill, and mechanical and biological waste treatment (MBT). The analysis is structured to quantify the carbon footprint associated with each waste management strategy, measured in kilograms (kg) of carbon dioxide (CO₂) equivalent (eq.) per kg of food waste. The estimated carbon footprint is 0.0066 kg CO₂ eq./kg of food waste for MBT, 0.1221 kg CO₂ eq./kg of food waste for AD, and 1.4667 kg CO₂ eq./kg of food waste for sanitary landfill. These values were derived from defined system boundaries, modeling assumptions, and available operational data used to construct the life cycle inventory (LCI). In addition, a formal sensitivity analysis was not conducted in this study. Therefore, the reported values should be interpreted within the context of the modeling assumptions and data sources applied.

1. Introduction

Food waste is a major societal challenge, as approximately 1.3 billion tons of food are wasted globally each year [1,2,3,4]. This results in profound environmental, economic, and social repercussions [5,6]. For example, according to the UN Environment Program [7], food waste is responsible for 8–10% of global greenhouse gas (GHG) emissions, making it a significant contributor to climate change. The main greenhouse gases (GHGs) contributing to global warming potential (GWP) include not only carbon dioxide (CO₂) but also methane (CH₄), nitrous oxide (N₂O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF₆) [8,9,10]. Moreover, the issue of food waste highlights a major inefficiency in global food distribution and consumption practices, further exacerbating the imbalance between food production and accessibility [11,12,13].

The grocery retail sector, represented by convenience stores, malls, supermarkets, and hypermarkets, although not being the world’s largest producer of food waste when compared to households and agriculture, still significantly impacts the environment due to its position at the end of the food supply chain [13,14]. Hypermarkets, in particular, are pivotal in food waste generation and their management [11,15]. They are the last link in the supply chain before food reaches the consumer, inheriting the cumulative environmental impacts from upstream activities, such as food production, transportation, packaging, and processing [16,17]. These activities contribute to food waste and generate a substantial carbon footprint due to the energy consumed during storage, transportation, and handling, and the release of GHGs when food waste biodegrades [18,19,20]. Managing this waste, therefore, becomes crucial, requiring effective strategies to mitigate its environmental consequences [21,22].

In Thailand, there is a growing concern about the environmental and socio-economic implications of food waste [23,24]. This applies to such economic sectors as households [25,26,27], hospitality [24,27,28], and, increasingly, grocery retail [29,30]. This concern is particularly pronounced among Thai branches of international grocery retailers that have started working in the direction of food waste reduction [29,31,32]. The initiatives to reduce food waste implemented by these entities are critical in setting benchmarks for waste management practices across the retail sector of Thailand. Given this context, the sector of grocery retail in Thailand, especially the local branches of international chains, offers a valuable vantage point for studying food waste management strategies. They present a unique opportunity to examine the patterns of food waste generation and investigate the implementation of practices that can lead to a significant reduction in food waste in specific retailers and across the entire retail sector [29,33].

This study examines food waste and its management practices in a hypermarket in Thailand, representing a local branch of an international grocery retailer, using the life cycle assessment (LCA) methodology to analyze and compare the efficiency and environmental impacts of various waste disposal approaches. By focusing on three primary methods of disposal, namely anaerobic digestion (AD), sanitary landfill disposal, and mechanical–biological waste treatment (MBT), the study aims to identify the most environmentally sustainable and economically viable practices for disposing of food waste at the retail level.

The study aims to explore the relationship between food waste generation and GHG emissions, as food waste contributes significantly to environmental degradation when not managed effectively. The decomposition of organic waste in sanitary landfills produces methane, a potent GHG, while AD and MBT offer varying levels of environmental efficiency depending on their implementation. The study strives to broaden the understanding of sustainable food waste management practices and advocate for systemic changes in the retail sector of a developing economy that can align with global sustainability targets. Moreover, the study’s findings emphasize the critical policy implications for other developing countries, where the grocery retail sector is expanding rapidly. Most studies focus on developed countries with established waste management systems, while limited research has been conducted in developing countries, especially Thailand [34,35,36,37,38,39,40,41,42,43]. In Thailand, food waste management practices differ due to infrastructure limitations and economic constraints. The novelty of this study is to apply the retail-specific LCA framework to evaluate food waste disposal pathways within a large urban hypermarket under Thai operating conditions. Although several previous studies in Thailand have examined municipal organic waste management systems, these studies have focused on municipal solid waste (MSW) streams rather than retail-generated food waste. In contrast, this current study establishes a clearly defined hypermarket system boundary and incorporates primary operational data. In addition, the study bridges the gap between broad municipal waste assessments and retail-level operational decision-making to provide empirically grounded and policy insights for sustainable food waste management in Thailand and other developing urban contexts.

This study is organized as follows: Section 2 reviews the relevant literature and describes the research methodology of the study. Section 3 presents the results. Section 4 discusses the findings. Section 5 concludes the study by highlighting the limitations and outlining future research directions.

2. Materials and Methods

2.1. Literature Review

This study incorporates a literature-based LCA review as an initial methodological step to identify and compare the environmental impacts of food waste management options in the retail, household, and catering sectors. The review focuses on GWP as a key impact category, reflecting its relevance to climate mitigation in Thailand.

Peer-reviewed LCA studies published between 2010 and 2024 were selected from an academic database of Scopus. The scope includes anaerobic digestion (AD), composting (CC), incineration (IT), landfilling (LF), animal feed (AF) and dry feed (DF). Studies were included only if system boundaries, functional units, and GWP results were explicitly reported.

The reviewed studies applied gate-to-gate, gate-to-grave and cradle-to-grave system boundaries, with functional units typically defined per kilogram or ton of food waste. Where necessary, results were normalized to facilitate cross-study comparison.

GWP values were extracted from the selected studies to support comparative analysis. Variations in reported impacts were attributed to the differences in system boundaries, technology configurations, energy recovery assumptions, and geographical contexts. The characteristics of the selected studies are summarized in

Table 1. Summary of selected LCA studies evaluating the GWP of food waste management approaches. These studies provide a comparison that focuses on food waste management in a hypermarket context in Bangkok, Thailand.

Study	Country	Unit Analysis	Process in Food Waste Management					Scope	Functional Unit	Finding
			AD	CC	IT	LF	Others
[34]	South Korea	Household	-	✓	-	✓	✓ (DF and WF)	Gate-to-Gate	1 ton of food waste	- DF and WF produced the highest GWP value from all methods, which was 155.22 kg CO2 eq./ton of food waste. - WF produces the lowest GWP value of 52.82 kg CO2 eq./ton of food waste.
[19]	Sweden	Large size supermarket (2300–4900 m2)	-	-	-	-	-	Cradle-to-gate (Production > Supermarket)	1 ton of food waste	- Food waste of 6 supermarkets was 2500 kg CO2 eq./ton of food waste.
[35]	Sweden	Mid-size urban supermarket (approximately 410 m2)	✓	✓	✓	✓	✓ (MRF, AF)	Gate-to-Grave	1 ton of food waste	- AD produced the highest GWP value from all methods, which was 1027 kg CO2 eq/year−1. - AF produces the lowest GWP value of −1549 kg CO2 eq/year−1.
[36]	Sweden	Large size supermarket (~1209 m2) Small size supermarket (<500 m2)	✓	✓	✓	✓	✓ (DN)	Gate-to-Grave	1 kg of food waste	- DN produced the highest GWP value of all methods, which was 0.35–0.98 kg CO2 eq./kg of food waste. - IT and AD produced the lowest GWP value of all methods, which was 0.04–0.23 kg CO2 eq./kg of food waste.
[37]	Italy	Retail stores (N/A)	✓	✓	✓	✓	✓ (H)	Gate-to-Grave	1 ton of food waste	- LF produced the highest GWP value equal to 1243.98 kg CO2 eq./ton of food waste. - AD produced the lowest GWP value from all methods, equal to 66.07 kg CO2 eq./ton of food waste.
[38]	United Kingdom	Mid-size retail (~410 m2)	✓	✓	✓	✓	✓ (DF)	Gate-to-Grave	1 ton of food waste	- LF without gas collection produced the highest GWP value from all methods, which was 2969 kg CO2 eq./ton of food waste. - DF produces the lowest GWP value of −5583 kg CO2 eq./ton of food waste.
[39]	France	Retail sector (larger than 400 m2)	✓	-	✓	-	✓ (Prevention)	Cradle-to-Grave	1 ton of food waste	- AD and IT produced the highest GWP value from all methods, which was −400 to −3900 kg CO2 eq·t−1/ton of food waste. - Prevention produces the lowest GWP, which was −65 to −200 kg CO2 eq·t−1/ton of food waste.
[41]	Sweden	Supermarket	✓	-	✓	-	-	Cragle-to-grave	1 kg of food waste	- LF produced the highest GWP value from all methods, which was 0.9 kg CO2 eq./kg of food waste. - CC produced the lowest GWP value from all methods, which was 0.033 kg CO2 eq./kg of food waste.
[42]	United state	Retail sector	✓	✓	✓	✓	-	gate-to-grave	1 kg of food waste	- AD produced the highest GWP value from all methods, which was −0.23 kg CO2 eq./kg of food waste. - IT produced the lowest GWP value from all methods, which was −0.11 kg CO2 eq./kg of food waste.
[43]	China	Canteen	✓	✓	-	-	-	Gate-to-grave	1 ton of food waste	- AD produced the highest GWP value from all methods, which was 38.87 kg CO2 eq./ton of food waste. - CC produced the lowest GWP value from all methods, which was 38.87 kg CO2 eq./ton of food waste.

Note: “-” = No process; “✓” = Process included.

and provide the methodological basis for subsequent analyses.

As Table 1 suggests, studies have focused on developed countries with more established waste disposal systems, and there is limited research on developing nations like Thailand, where food waste management practices differ due to infrastructural and economic constraints. This current study will fill this gap in research.

2.2. LCA Study of Food Waste Disposal Methods in Thai Hypermarkets

Building on the literature review, this section conducts an LCA of food waste disposal methods in a Thai hypermarket. The assessment evaluates environmentally relevant disposal scenarios under Thai operating conditions using consistent system boundaries and functional units, with GWP as the primary impact indicator.

2.2.1. Goal and Scope Definition

This study aims to comprehensively assess the carbon footprint associated with selected food waste disposal methods used by a hypermarket in the Ladkrabang District of Bangkok, Thailand. The goal of the study is to delineate and understand the carbon-related consequences of different food waste disposal methods, thereby aiding in the identification and implementation of more environmentally sustainable practices. The choice of the Ladkrabang District was strategic, as it was selected for its representative hypermarket, which featured a robust, accessible management system that facilitated efficient data collection (Figure 1).

The ABC hypermarket used as a case study has a sales area of approximately 5000–15,000 m², serves around 7100 customers per day, and generates an estimated 13,040.86 kg (13.040 tons) of food waste annually [47]. The selected hypermarket was chosen as a case study because its store area, customer volume, and food waste generation characteristics are comparable to those of other large retail stores in Bangkok. While the analysis is based on a single case study, the store provides a useful example for food waste management practices and environmental impacts within large retail operations in an urban Thai context. These operational indicators fall within the reported ranges for large urban hypermarkets in Bangkok and therefore reflect typical operational characteristics and food waste generation patterns for this retail format.

This study is conducted on a gate-to-grave basis. This choice is justified because this approach aligns with the lifecycle of food waste generated in hypermarkets in Thailand and beyond. Food waste originates at the point of the fresh food department (the gate) through to its final disposal (the grave). This approach encompasses food waste collection, transportation, and food waste management, such as sorting, processing, and disposal methods. This approach, however, excludes upstream processes, such as production or packaging.

In this study, GWP is selected as the primary impact category because food waste management is closely associated with GHG emissions, particularly CH₄ generated from landfill decomposition. This helps to study the GWP of different food waste disposal scenarios. While the focus on GWP presents a clear assessment of climate impacts, it may not capture other environmental impacts associated with different food waste management methods. Therefore, future research could extend the assessment to provide a more comprehensive environmental evaluation.

Food waste at ABC hypermarket is disposed of at dedicated disposal facilities using three main food waste management methods, including AD, sanitary landfill disposal, and MBT. Given the significance of these waste treatment modalities, three scenarios were selected for a comprehensive LCA analysis, facilitating a quantitative evaluation of their respective environmental impacts:

AD Scenario: This process tracks the transportation and handling of food waste from ABC hypermarket to the On Nut Garbage Disposal Plant (Figure 1), where it undergoes composting. This scenario helps evaluate the environmental load involved in managing hypermarket waste through anaerobic digestion using a biodigester system.

Sanitary Landfill Scenario: This process involves the collection of food waste from the hypermarket and its transport to both the On Nut Garbage Disposal Plant for initial processing and subsequently to the final disposal site located in Phanom Sarakham District, Chachoengsao Province, Thailand (Figure 2), which serves as a sanitary landfill for ultimate waste disposal.

MBT Scenario: In this scenario, food waste is managed through a combination of mechanical sorting and biological treatment. Waste is collected from the ABC hypermarket and transported to the On Nut Garbage Disposal Plant (Figure 1), where MBT processes are applied.

These three food waste management processes are shown in Figure 2.

Functional Unit

In this study, the functional unit, a quantifiable measure allowing for the comparison of environmental impacts and used in past studies [36,48], is defined as 1 kg of food waste. It was selected as the functional unit because mass-based units are commonly used in LCAs of food waste. This specific measurement was chosen to standardize the assessment of various food waste disposal methods, ensuring that each method’s impact on the environment is evaluated on a consistent basis.

System Boundary

This study is a gate-to-grave study in which the environmental effects are associated with all activities from the collection of food waste in the hypermarket to its final treatment. Each scenario is designed to illuminate the environmental consequences of different food waste management strategies, providing a basis for comparison and aiding in the identification of the most sustainable practices. To ensure comparability, waste collection, transportation to food waste management facilities, and the final waste treatment process were included for each scenario.

To accurately design and compare the different scenarios, several estimations and assumptions were necessary, particularly regarding transportation and treatment facility locations. All facilities involved in the AD and MBT processes are located at the On Nut Garbage Disposal Plant in Thailand. For the sanitary landfill scenario, the disposal site is situated in the Phanom Sarakham district, Chachoengsao, Thailand, where a sanitary landfill is utilized. Additionally, since specific data related to the treatment processes are scarce within local contexts, this study relies on international literature to fill in these gaps and provide a comprehensive analysis of each food waste management scenario.

Food Waste Composition

As part of the primary data collection, the composition of food waste in the hypermarket’s fresh food department was recorded over a one-year (September 2022 to August 2023) monitoring period. Waste was sorted and categorized into five major groups: fruits and vegetables, meat and seafood, dairy, bakery products, and ready-to-eat meals (

Table 2. The average composition of food waste by weight (%).

Food Waste Category	Average Share by Weight (%)
Fruits and vegetables	57.16%
Meat and seafood	21.41%
Dairy products	6.31%
Bakery	12.99%
Ready-to-eat meals	1.61%

).

Table 2 shows that the food waste composition reflects a typical food waste profile of a fresh food department in a Thai supermarket. Fruits and vegetables account for a large portion of food waste, totaling over half (57.16%) of all food. This is caused by perishability, overstocking, and fluctuating consumer demand. Meat and seafood are the second largest category (21.41%), primarily due to short shelf life, high perishability, slow sales, and inconsistent customer demand. Bakery products are the third-largest category (12.99%) because of their short shelf life and high turnover rate, which is influenced by promotional planning. Finally, dairy and ready-to-eat meals contribute less (6.31% and 1.61%) due to their longer shelf life.

2.2.2. Life Cycle Inventory (LCI) Data Quality, Sources, and Assumptions

Qualitative data used in this study were collected from interviews with 3 managers, taking care of food waste (25–30 min each), and structured questionnaires with 21 fresh food department employees, whose roles provide critical insights into the daily practices and challenges associated with food waste handling. An interview was also conducted with an engineer involved in the design and implementation of the food waste disposal process (a 1 h interview), offering food waste management to fertilizer and energy, and secondary data from other data sources compiled from various databases, as well as the GaBi 9 Database.

Because interview data may contain uncertainty due to differences in respondents’ operational experiences and reporting accuracy, the collected information was used primarily to define operational parameters for LCI. Process calculations were first developed using engineering equations in MS Excel and subsequently implemented in GaBi software version 9 for LCI modeling. While the GaBi database provides emission factors, other secondary sources were used for contextual data:

a.

ChaiRatchakarn [49]; HINO.th [50]; Sukmak et al. [39]; Tejada et al. [51] (for electricity consumption and emissions from different types of vehicles)

b.

Pollution Control Department [52]; Sukmak et al. [39]; Teerawattana et al. [53]; Grzesik and Malinowski [54] (for waste disposal).

Model Description

In this study, all gathered data and process calculations, including those for each disposal method, were created using engineering equations and an MS Excel spreadsheet (trademark of Microsoft). The models were then transferred to GaBi software version 9. Since the study aims to evaluate the carbon impacts associated with transportation and selected food waste management processes, specifically focusing on three disposal methods, the evaluation needs to be conducted using accurate techniques, which must be represented by the life cycle impact assessment (LCIA) methodology best suited for the Thai environment. The CML 2001 methodology, developed by the Institute of Environmental Sciences at Leiden University in the Netherlands [55], has been widely recognized and utilized by the Thailand Environment Institute (TEI) and endorsed by the Thai government [56]. Given its acceptance and successful application within the context of Thai environmental studies, CML 2001 is considered highly appropriate and the optimal choice for the current study. It is a midpoint approach associated with less uncertainty than damage approaches, since the environmental impacts are considered at an early stage of the cause-and-effect chain.

Description of the Three Disposal Processes

In this study, staff scanned unsold food items into the company’s internal database, generating daily records of food waste quantities by category. These records were used to determine the composition of food waste presented in Table 2. While the LCA modeling was conducted using the total aggregated mass of food waste as the functional flow, the composition data derived from the internal database were used to illustrate the proportional distribution of waste categories and to verify that the modeled waste stream reflects a typical fresh food retail waste stream. These composition data were therefore used for descriptive and validation purposes rather than as direct input parameters in the LCI model.

To manage perishable goods and minimize waste, the case study hypermarket implements a dynamic pricing strategy in which products nearing their sell-by dates are discounted throughout the day. Items that remain unsold at the end of the day are classified as food waste. After recording, the unsold food is cut into smaller pieces and temporarily stored in designated waste containers within the hypermarket’s waste storage area. A waste collection vehicle then transports this processed food waste for final disposal.

2.2.3. LCI Modeling

Description of AD

The On Nut Garbage Disposal Plant in Thailand, a sprawling 929,600-square meter facility located in Bangkok, plays a crucial role in waste management for the city. Established under the Bangkok Metropolitan Administration, this mechanical and biological treatment center handles a staggering 3800–4000 tons of waste daily [57]. It utilizes a combination of methods, including landfilling (1200 tons/day), composting (1000 tons/day), the AD method, MBT (800 tons/day), and wastewater treatment (700 cubic meters/day) [55,58]. The fermentation process is carried out over a period of 40 days and separates larger particles (exceeding 80 mm) for sanitary landfill disposal. The primary inputs for composting are food waste, electricity, and liquefied petroleum gas (LPG). While this process successfully produces valuable byproducts such as soil conditioner and bio-fermented water, rich in essential nutrients nitrogen (N), phosphorus (P), and potassium (K), it also generates air pollutants such as methane (CH₄), CO₂, nitrogen oxides (N₂O), sulfur dioxide (SO₂), and carbon monoxide (CO) [39,53]. Manual sorting is carried out to separate food waste from non-biodegradable materials.

The AD process includes a two-step process, as suggested in

Table 3. Secondary data and emission factors used in LCA. The table summarizes key parameters obtained from the GaBi 9 database and supporting literature sources to estimate GHG emissions in the food waste management scenarios.

Process	Stage	Parameter	Value	Unit	References
Anaerobic digestion	Manual Sorting	Input
		Food waste	1	kg	-
		Output
		Food waste	1	kg	-
	Anaerobic digestion	Input
		Food waste	1	kg	[39]
		Electricity	0.058	kWh	[39]
		Liquefied petroleum gas	0.010	kg	[39]
		Output
		Bio-fermented water	0.001	kg	[39]
		Soil conditioner	0.0003	kg	[39]

, which begins with manual sorting to separate food waste from non-biodegradable material (such as plastics, metals, glass, paper and others) before the food waste enters the AD. This sorted food waste then enters the AD. This system utilizes the natural process of AD to decompose the food waste and produce bio-fermented water and soil conditioner as by-products, similar to composting [39,53]. After manual sorting, food waste is processed in a biodigester through a 40-day anaerobic fermentation. During this period, rotating drums and sieves are used to separate larger particles (>80 mm), which are subsequently disposed of in a sanitary landfill.

In terms of transportation, the transportation of food waste begins at the hypermarket and proceeds to the On Nut Garbage Disposal Plant, where it is loaded onto 6-wheel HINO 500 VICTOR NEO FG diesel trucks. The round-trip distance covered is 35.6 km with a capacity of 5000 kg. Subsequently, the food waste is transported by trailer trucks from the hypermarket to the On Nut Garbage Disposal Plant [39,49]. The input parameters for transportation are shown in

Table 4. Transportation parameters between the hypermarket and the On Nut Garbage Disposal Plant.

Parameter	Route	References
	Hypermarket to On Nut Garbage Disposal Plant
Vehicle Type	6-wheel HINO 500 VICTOR NEO FG	[39,49]
Fuel type	Diesel	[49,59]
Weight of the load (kg)	5000	[39,49]
Distance (Departure-return) (km)	35.6	[60]

.

Following the AD process, food waste that is larger than 80 mm that cannot be composted is sent to a designated sanitary landfill site in Phanom Sarakham District, Chachoengsao Province, Thailand. These oversized items are transported by trailer trucks with a 50-ton maximum load capacity.

Description of Sanitary Landfill

The On Nut Garbage Disposal Plant utilizes sanitary landfilling for various waste types, including food scraps exceeding the size limit for composting (AD and MBT methods). This method involves meticulously layering waste in a designated area. Heavy machinery then compacts and levels the waste, alternating layers of soil and waste material. Over time, the organic components naturally decompose [52]. The sanitary landfill inputs include food waste, electricity, fuel, water, plastic, and wire. The outputs include wastewater, which contains indicators of organic matter pollution (BOD and COD), as well as air pollutants such as CH₄ and biogenic CO₂ [39]. However, emissions to water, which are important indicators for assessing the extent of water pollution, were not used for carbon footprint calculation. Since landfills operate in open spaces, these CH₄ emissions contribute to environmental concerns. The input parameters for the sanitary landfill can be seen in

Table 5. The input parameters for the sanitary landfill.

Process	Stage	Parameter	Value	Unit	References
Landfill	Sanitary Landfill	Input
		Food waste	1	kg	[39]
		Electricity	0.004	kWh	[39]
		Fuel	0.009	kg	[39]
		Water	0.057	liter	[39]
		Output
		Waste water	0.086	liter	[39]
		Biochemical oxygen demand: BOD	0.00007	kg	[39]
		Chemical oxygen demand: COD	0.00012	kg	[39]

.

In terms of transportation, the transportation of food waste begins at the hypermarket and proceeds to the On Nut Garbage Disposal Plant, where it is loaded onto 6-wheel HINO 500 VICTOR NEO FG diesel trucks. The round-trip distance covered is 35.6 km with a load capacity of 5000 kg. Subsequently, the food waste is transported by trailer trucks from the hypermarket to the On Nut Garbage Disposal Plant. This involves a round-trip distance of 174.2 km, and the trailer trucks have a load capacity of 50,000 kg [39,52]. The input parameters for transportation are shown in

Table 6. Transportation parameters from the hypermarket to the Phanom Sarakham landfill.

Parameter	Route		References
	Hypermarket to On Nut Garbage Disposal Plant	On Nut Garbage Disposal Plant to Phanom Sarakham Landfill
Vehicle Type	HINO 500 VICTOR NEO FG	Trailer truck	[39,49]
Fuel	Diesel	Diesel	[49,59]
Distance (Departure-return) (km)	35.6	174.2	[60]
Weight of the load (kg)	5000	50,000	[39,49,59]

.

Table 6 shows that the sanitary landfill method for food waste disposal involves a two-step transportation process. First, 6-wheel trucks (HINO 500 VICTOR NEO FG) fueled by diesel transport food waste from hypermarkets to the On Nut Garbage Disposal Center, covering a total distance of 35.6 km per round trip. Each truck carries up to 5000 kg of food waste. Upon arrival, the waste is sorted, with any pieces larger than 80 mm diverted for landfill disposal. The second step utilizes trailer trucks running on diesel to transport the sorted food waste to the designated sanitary landfill site in Phanom Sarakham District, Chachoengsao Province. This journey covers a 174.2 km round trip, with each truck carrying up to 50,000 kg of food waste.

Description of MBT

The MBT method includes four processes:

a.

Manual and Magnetic Sorting: This process involves the manual sorting of waste to remove large and recyclable items, followed by the use of a magnetic separator to extract metallic impurities.

b.

Initial Shredding and Trommel Screen: After sorting, the waste undergoes initial shredding and passes through a trommel screen to filter out particles smaller than 80 mm, preparing them for further processing.

c.

Composting in Reactor: The waste then enters the aerated composting in reactor phase, where it undergoes a controlled decomposition process with the help of oxygen, lasting approximately 60 days.

d.

Composting in Windrows: Following aerated composting, the waste undergoes windrow composting, a process that further accelerates decomposition and transforms the waste into a nutrient-rich soil conditioner. This stage not only speeds up the decomposition process but also prevents anaerobic decay [54].

Table 7. The input parameters for MBT.

Process	Stage	Parameter	Value	Unit	References
Mechanical and Biological Waste Treatment: MBT	Manual and Magnetic Sorting	Input
		Food waste	1	kg	[54]
		Diesel	0.0009	liter	[54]
		Output
		Food waste	1	kg	[54]
	Initial Shredding and Trommel Screen	Input
		Food waste	1	kg	[54]
		Diesel	0.0009	liter	[54]
		Output
		Food waste	1	kg	[54]
	Composting in reactors	Input
		Food waste	0.34378	kg	[54]
		Diesel	0.00068	liter	[54]
		Electricity	0.00349	kWh	[54]
		Output
		Soil conditioner	0.34378	kg	[54]
	Composting in windrows	Input
		Soil conditioner	0.34378	kg	[54]
		Diesel	0.00071	liter	[54]
		Electricity	0.00044	kWh	[54]
		Output
		Soil conditioner	0.16624	kg	[54]

shows that inputs to this process include food waste and the use of diesel fuel to power machinery. Outputs consist of processed food waste, composting in reactors, composting in windrows, electricity generated from the process, residual diesel fuel, and the soil conditioner produced as a final product.

For transportation in this scenario, the same mode of transportation used in the first scenario (AD scenario) is applied to this scenario, as shown in Table 4.

2.2.4. Global Warming Potential (GWP) Results

GWP refers to the elevation of the average atmospheric temperature, leading to detrimental impacts on the environment. The impact on GWP in the “MBT” scenario, compared to the “AD” and “sanitary landfill” scenarios, respectively, is shown in Figure 3.

The most carbon-benign option is the MBT scenario, which generates a total GWP of 0.0066 kg CO₂ eq./kg food waste. The environmental impacts are primarily attributed to electricity consumption associated with mechanical sorting, biological treatment, and transportation processes. This scenario is the most environmentally friendly option, demonstrating a reduction in GWP of 94.55% compared to AD and 99.55% compared to sanitary landfill.

The AD scenario produces a total GWP of 0.1221 kg CO₂ eq./kg food waste, with electricity consumption being the dominant contributor at 96.52% and transportation accounting for 3.47%. Notably, the transportation impact in the AD scenario matches that of MBT at 0.0042 kg CO₂ eq., suggesting similar logistics requirements.

The sanitary landfill scenario exhibits the highest carbon with a total GWP of 1.4667 kg CO₂ eq./kg food waste. This substantial impact is largely due to food waste disposal, which accounted for 99.31% with a total GWP of 1.4565 kg CO₂ eq. of the total GWP. The high emissions are largely due to methane and biogenic CO₂ emissions, along with additional emissions from electricity and fuel consumption during the landfill process, particularly because of the long distances between hypermarkets, the On Nut Waste Disposal Center, and the Phanom Sarakham landfill. The remaining 0.69% (0.0102 kg CO₂ eq.) is attributed to transportation emissions, mainly from combustion of fuel during waste collection and transfer.

Overall, the data demonstrate that MBT represents the most environmentally sustainable option, with significantly lower GWP compared to both AD and sanitary landfill scenarios, while the landfill scenario emerges as the least environmentally favorable due to its extensive transportation requirements and decomposition-related emissions.

3. Results

This study provides a comprehensive assessment of selected food waste management options by integrating a literature-based LCA review with a case-specific LCA of a Thai hypermarket. Across all evaluated scenarios, clear differences in environmental performance were observed. MBT consistently demonstrated the lowest GWP, reflecting its relatively low energy demand and limited emissions from waste treatment processes. AD showed moderate environmental performance, with its impacts largely driven by electricity, exhibiting the highest environmental burden due to decomposition-related emissions and extended transportation distance.

The results highlighted the critical role of system configuration, electricity use, and logistics in determining overall environmental impacts. Transportation distance and treatment efficiency were found to be the key factors influencing GWP under Thai operating conditions. These findings emphasized the importance of selecting context-appropriate food waste management strategies that minimize process-related emissions, and they provided a useful basis for decision-making in the grocery retail sector and related waste management policy in Thailand.

4. Discussion

4.1. Interpretation of Results

The results from the GWP of food waste management within the hypermarket retail sector in Bangkok showed that sanitary landfill has the largest environmental impact of 1.4667 kg CO₂ eq./kg of food waste compared to the other two methods of food waste disposal due to the AD of organic waste within the landfill, which generated CH₄ and biogenic CO₂. These potent greenhouse gases have a significant impact on GWP [39].

The MBT method demonstrated the lowest GWP at 0.0066 kg CO₂ eq./kg of food waste, showcasing it as the most environmentally sustainable option among all methods. A key observation was the consumption of electrical energy necessary for each waste treatment process, which directly correlates with GWP. This highlights the necessity of optimizing energy use within these systems to minimize environmental impacts. Moreover, the transportation of waste from the hypermarket to the disposal center significantly affects emission rates.

Among the scenarios, sanitary landfills had the longest transportation route. Food waste traveled from the hypermarket to the On Nut Waste Disposal Center and then to the faraway Phanom Sarakham landfill, contributing to its GWP. Factors such as truck size and transportation routes were found to directly impact fuel consumption. These insights align with broader environmental research linking transportation logistics to increased GHG emissions and underscore the importance of enhancing logistical and operational efficiencies [61,62]. These findings are consistent with the LCA results summarized in Table 1, which report higher GWP for landfill and lower impacts for alternative treatments. Improvements in these areas, particularly through more efficient routing and truckload optimization, could lead to substantial reductions in GWP.

Based on the findings of this study, policymakers can promote the most carbon-friendly disposal method, especially MBT. In addition, optimizing waste collection and transportation networks can help to reduce travel distances and associated emissions. Improving energy efficiency within treatment facilities is important because electricity consumption is a key contributor to environmental impacts. In terms of retailers, systematic food waste monitoring and data-driven waste management can support more effective waste reduction and disposal strategies. Together, these measures provide practical guidance for improving food waste management systems in Thailand and other developing countries.

4.2. Comparative Analysis of This Study’s Findings

The comparative analysis of GWP impacts across different food waste disposal methods reveals the following insights. For the MBT method, this study shows a GWP of 0.0066 kg CO₂ eq./kg food waste, which aligns closely with findings from other studies, particularly in Thailand. This value falls within the range reported by presenting a comparison of GHG emissions from food waste management across different countries and systems. The result for AD, showing 0.1221 kg CO₂ eq./kg food waste, is consistent with findings from other studies, including Padeyanda et al. [63] in South Korea (1.33 kg CO₂ eq./kg food waste), Mondello et al. [37] in Italy (0.999 kg CO₂ eq./kg food waste), Corona et al. [42] in the United States (0.042 kg CO₂ eq./kg food waste) and is slightly higher than the Thai National LCI database [64] (0.1102 kg CO₂ eq./kg food waste). In the case of sanitary landfill, the estimated emission of 1.4667 kg CO₂ eq./kg food waste aligns with previous studies, such as Hyung Kim and Wk Kim [34] in South Korea (1.497 kg CO₂ eq./kg food waste), Mondello et al. [37] in Italy (1.243 kg CO₂ eq./kg food waste) and Corona et al. [42] in the United States (0.900 kg CO₂ eq./kg food waste), while exceeding the Thai National LCI database [64] (0.7933 kg CO₂ eq./kg food waste). For other food waste management, this study shows 0.0066 kg CO₂ eq./kg food waste, which is within the range of reported values by Abeliotis et al. [65] in Greece (0.0015 kg CO₂ eq./kg food waste), Grzesik and Malinowski [54] in Poland (~0.00025 kg CO₂ eq./kg food waste), and Chomchiangkham [66] in Thailand (0.0011 kg CO₂ eq./kg food waste). These comparisons validate this study’s findings while highlighting regional variations in waste management system efficiencies, with MBT consistently demonstrating the lowest environmental impact across different geographical contexts. These findings are summarized in

Table 8. Research on the impact of food waste disposal methods on GWP.

No.	Study	Country	GWP (kg CO2eq./kg Food Waste)
			AD	Sanitary Landfill	MBT
1	[65]	Greece	-	-	0.0015
3	[54]	Poland	-	-	~0.00025
4	[34]	South Korea	1.494	1.497	-
5	[37]	Italy	0.999	1.243	-
6	[42]	United state	0.042	0.900	-
7	[63]	South Korea	1.330	-	-
8	[64]	Thailand	0.1102	0.7933	-
9	[66]	Thailand	-	1.200	0.0011
10	This Study	Thailand	0.1221	1.4667	0.0066

.

These findings are further supported by the literature, which highlights the increasing use of LCA as a valuable tool for evaluating and comparing food waste management strategies across different contexts. More specifically, recent studies emphasize that the environmental performance of waste treatment systems depends not only on the technology itself but also on operational factors such as energy consumption, transportation logistics, and system boundaries [41]. This further supports the importance of context-specific assessments, particularly in developing countries where infrastructure and operational conditions differ from those in developed regions.

4.3. Policy Framework

The findings of this study have several important implications for food waste management policies, particularly in the context of Thailand and other developing countries with growing retail sectors:

Government incentives: The results of this study show that MBT can significantly reduce global warming potential (GWP) compared with conventional sanitary landfilling. However, despite its environmental advantages, MBT systems often require higher initial investment and operational costs than traditional landfill-based waste management systems. This economic barrier may discourage adoption in the absence of supportive policy mechanisms. Several studies indicate that although MBT can reduce GHG emissions compared to conventional landfilling, its economic performance remains weaker without supportive policy frameworks [67,68]. Life cycle and techno-economic analyses show that MBT systems frequently require additional financial support due to higher combined treatment and disposal costs. Some case studies reported up to a 30% increase in total costs relative to landfill scenarios [67]. Moreover, economic feasibility assessments suggest that MBT facilities often lack sufficient revenue streams and depend on appropriate pricing mechanisms. This highlights a potential mismatch between environmental benefits and market incentives [67,68]. Therefore, policy incentives are necessary to address market failures and encourage wide adoption, bridging the gap between environmental performance and economic viability.

Mandatory requirements: Regulatory requirements play a crucial role in ensuring effective waste management practices. Stricter landfill regulations and higher taxes serve as economic drivers for reducing GHG emissions from organic waste, while early gas recovery and waste reduction strategies significantly mitigate CH₄ emissions [69,70]. Mandatory waste reporting systems for large retailers enable better monitoring and management through accurate data collection, allowing for targeted measures such as optimized storage conditions to reduce waste and carbon footprints [48,71].

Public–private partnerships (PPPs): PPPs form a crucial framework for implementing sustainable waste management practices. These partnerships require effective stakeholder communication and strategic planning, supported by clear regulatory frameworks to protect public interests [72,73]. The success of PPPs in Thailand’s food industry development demonstrates their potential for advancing sustainable waste management practices while balancing public and private sector interests [74].

4.4. Management Implications

The findings have several important implications for environmental management in the retail sector, particularly for hypermarkets dealing with significant volumes of food waste. The implications can be shown as follows:

4.4.1. Adoption of MBT Systems

Given the significantly lower GWP of MBT (0.0066 kg CO₂ eq./kg of food waste) compared to AD (0.1211 kg CO₂ eq./kg of food waste) and sanitary landfill (1.4667 kg CO₂ eq./kg of food waste), hypermarket managers should prioritize the implementation of MBT systems for food waste management. Implementation strategies for MBT in retail settings require several key components. Feasibility studies for on-site MBT facilities demonstrate environmental benefits through reduced transportation emissions and local waste processing [75], while providing sustainable solutions through recyclable sorting and organic waste processing [76]. Partnerships with waste management companies offering MBT services can decrease landfill waste by converting biodegradable materials into reusable outputs [77] and demonstrate lower environmental impact through optimized treatment processes, as confirmed by LCA studies [54].

4.4.2. Transportation Optimization

This study reveals that transportation contributes to GWP, particularly in the sanitary landfill scenario. Transportation optimization can be achieved through several integrated approaches. Geo-referenced databases and advanced optimization algorithms, including machine learning models and dynamic route optimization (DRO) systems, enable more accurate modeling and real-time optimization of waste collection routes. These modern approaches incorporate real-time traffic data, vehicle capacity constraints, and temporal restrictions, achieving up to 30% reduction in transportation costs [78]. Integration of geographic information systems (GIS) with optimization algorithms has demonstrated superior performance in route planning and resource allocation compared to traditional methods [79].

4.4.3. Continuous Improvement

Effective continuous improvement in food waste management requires an integrated approach combining regular monitoring, assessment, and strategic planning. Regular and accurate tracking of food waste through daily data collection enables retailers to identify specific problematic products and implement targeted reduction interventions [48,71]. LCA serves as a crucial tool for periodic reassessment, helping identify environmental benefits and trade-offs across different waste management options, including recycling, composting, and landfilling [80,81]. Setting and reviewing quantifiable waste reduction targets provide organizations with a structured framework for implementing sustainable strategies throughout the food supply chain [82], enabling stakeholders to track progress and adjust strategies effectively [83]. While these continuous improvement strategies require initial investments in infrastructure and training, they enable hypermarket managers to significantly reduce environmental impact while realizing potential cost savings through more efficient waste management practices.

5. Conclusions

The objective of this study was to examine the carbon footprint of three disposal scenarios: AD, sanitary landfill, and MBT. The LCIA method of CML2001 was selected for this study due to its comprehensiveness and widespread use. The results established the MBT method as the most environmentally friendly option, outperforming both sanitary landfill and AD methods, with the lowest GWP at 0.0066 kg CO₂ eq. The primary contributors to GWP across these methods were identified as open landfill, electrical energy used during the treatment processes, and transportation distance. Notably, larger truck sizes and inefficient routes could significantly increase fuel consumption and, consequently, greenhouse gas emissions. These findings underscore a critical policy framework for waste management in developing countries with expanding retail sectors, particularly in Thailand, emphasizing the need for comprehensive strategies to address growing environmental challenges. Successful implementation of sustainable waste management practices in the retail sector requires coordinated efforts from diverse stakeholders, including retail managers, waste management companies, local authorities, consumers, environmental NGOs, employees, and suppliers, each with unique interests and varying degrees of influence. Retail managers balance cost-efficiency and sustainability in waste strategies, while waste management companies offer expertise. Local authorities support sustainable practices through subsidies, and consumers drive demand for eco-friendly products. Environmental NGOs advocate sustainability, and employees contribute insights into waste management. Suppliers reduce waste through better packaging and minimizing overstocking. Effective collaboration among all stakeholders is key to optimizing waste management and achieving sustainability goals. As this study is limited to GWP as one impact category, future studies could examine other environmental impacts.