Scenario-Based LCA of Kitchen Waste Management Incorporating Transport Logistics: A Case Study of Aya Town, Japan

Abstract

Kitchen waste management strongly affects greenhouse gas (GHG) emissions, especially in small municipalities with limited treatment options. This study assessed alternative strategies for Aya Town, Japan, by integrating life cycle assessment (LCA) with Geographical Information System (GIS)-based transport analysis. Six scenarios were designed, ranging from mandatory composting with frequent collection to full incineration at a regional waste-to-energy (WtE) facility. Emissions were estimated from transport, composting, and incineration processes, with sensitivity tests on composting electricity use (20, 50, and 90 kWh per ton) and WtE efficiency (15%, 17.9%, 20%, and 25%). The results showed that reducing collection frequency lowered emissions by about 9% relative to the current system, while decreasing composting participation further reduced emissions. Full incineration yielded the lowest emissions, whereas sensitivity analyses confirmed that facility parameters influenced absolute values but not the relative ranking of scenarios. These findings emphasize the importance of transport logistics, participation rates, and infrastructural context. High-quality compost may justify limited voluntary composting; however, WtE incineration remains the most robust option for climate mitigation in Japan’s incineration-based waste management system.

1. Introduction

Food waste has emerged as a growing environmental issue on a global scale, contributing significantly to greenhouse gas (GHG) emissions and the inefficient use of natural resources. Food waste occurs at every stage of the food supply chain, including the disposal of substandard produce in agriculture and fisheries (production), processing residues in food manufacturing (processing), unsold products in retail (distribution), and food scraps or leftovers from restaurants, schools, and households (consumption). Among these, kitchen waste from households is particularly challenging to collect and manage due to its dispersed generation and the labor required for separation and transportation. According to the 2024 Food Waste Index Report by the United Nations Environment Program, an estimated 1052 million tons of food waste were generated globally in 2022. Of this total, approximately 60% originated from households [1], highlighting the critical role of domestic food waste in the overall waste stream. These figures highlight the substantial role of household-level management in global food waste and underscore the importance of developing effective treatment and reduction strategies.

In terms of treatment methods for household kitchen waste, the main approaches are commonly adopted: incineration, landfilling, composting, and anaerobic digestion (AD). Each method has distinct environmental implications, particularly in relation to GHG emissions and resource recovery.

In Japan, municipal solid waste management has some distinctive features compared with many other countries. The predominant treatment method is incineration. Upstream measures along the food supply chain are regulated by the Food Recycling Law, which aims to promote recycling of food waste mainly in upstream sectors such as food manufacturers, retailers, and the food service industry. Since household food waste is largely outside the scope of this law, in many municipalities it is collected as combustible waste, and treated in waste-to-energy (WtE) incineration plants.

Life cycle assessment (LCA) has therefore become a widely adopted tool to evaluate the environmental and economic impacts of food waste treatment options. Previous case studies have provided valuable insights into food waste management options using LCA. Inaba et al. [2] demonstrated that improving the energy efficiency of incineration plants could reduce sector-wide carbon dioxide (CO₂) emissions by 13–14%, while AD provided more limited benefits. Matsuda et al. [3] analyzed scenarios for Kyoto City and found that food waste prevention achieved the greatest GHG reduction, whereas household composting was found to potentially increase emissions. Yoshikawa et al. [4] examined centralized and on-site composting systems and showed that centralized composting had lower environmental impacts while also providing socio-economic benefits such as fertilizer savings and job creation. Similarly, Yano and Sakai [5] assessed over 1000 waste facilities and concluded that combining AD with high-efficiency incineration could achieve a 27% reduction in GHG emissions by 2030. These studies emphasize the importance of technology choice and facility efficiency but generally neglect the influence of transport and participation behavior. Morais and Ishida [6] analyzed composting policies across 152 Japanese municipalities and identified diverse institutional arrangements, stressing that Japan’s unique hybrid model of household composting and municipal collection reflects cultural and institutional factors. However, their analysis was mainly qualitative and did not quantify emissions and thus could not assess the quantitative implications for GHG reduction.

International case studies also provide important insights. Muhammad and Rosentrater [7] compared multiple technologies, including composting, incineration, AD, landfilling, gasification, and hydrothermal carbonization, showing that fermentation-based AD significantly reduced GHG emissions compared to landfilling. Keng et al. [8] analyzed municipal solid waste treatment in Malaysia and demonstrated that WtE incineration had advantages in terms of climate change mitigation, while composting contributed to resource circulation but with limited GHG reductions. Bastin and Longden [9] integrated Geographical Information System (GIS)-based transport modeling into their analysis of centralized versus decentralized WtE incineration in the UK, revealing that decentralized facilities could reduce transport distances and associated CO₂ emissions. Although innovative, their analysis focused solely on WtE technology and did not compare it with other waste treatment.

In addition to these individual case studies, review articles have synthesized broader trends. Amicarelli et al. [10] reviewed 33 LCA studies on food waste and highlighted that food waste contributes around 6% of global GHG emissions. Their analysis emphasized that AD and WtE are generally more favorable than landfilling, but their review was largely technology-focused and centered on global warming potential (GWP), with limited consideration of logistics or behavior. Batool et al. [11] conducted a more comprehensive critical review of 2005–2023 studies and explicitly pointed out the lack of research on small municipalities, transport-related emissions, participation rates, and cost-effectiveness. These observations directly resonate with the methodological challenges addressed in the present study.

Based on the literature reviewed above,

Table 1. Summary of previous studies on food waste management.

Category	Reference	Treatment Methods	Scope/ Case	Key Indicators	Main Findings	Limitations
Review studies	Amicarelli et al. [10]	Multiple (composting, AD, incineration, landfill, etc.)	33 LCA studies (global)	GWP, water use, land occupation	Food waste ≈ 6% of global GHG emissions; AD and WtE consistently lower impacts than landfilling	Focused mainly on GWP; little attention to logistics, small municipalities, or behavior
	Batool et al. [11]	Multiple including emerging technologies	Critical review (2005–2023)	GWP, acidification, eutrophication, toxicity, land use	AD more favorable than incineration or landfill; research on new technologies (gasification, HTC) is lacking	Explicitly highlighted gaps: small municipalities, transport, participation, cost-effectiveness
Case Studies—Japan	Inaba et al. [2]	Incineration, AD	National (Japan)	CO2 (hybrid LCA)	Improving incineration efficiency reduces CO2 substantially; AD effect limited	Focused only on CO2; no logistics or behavior aspects
	Matsuda et al. [3]	Incineration, AD, composting, prevention	Kyoto City	GHG	Food waste prevention achieves the largest reduction; household composting may even increase emissions	Urban case; no transport analysis
	Yoshikawa et al. [4]	Centralized vs. on-site composting, incineration	University campus/farmers (Japan)	GHG, socio-economic	Centralized composting yields lower impacts; agricultural use leads to fertilizer savings and job creation	Small-scale case; limited generalization
	Yano and Sakai [5]	Incineration, AD, combined	1068 facilities (Japan)	GHG, energy recovery	AD + high-efficiency incineration reduces GHG by 27% and triples energy recovery	Focus on facilities; no citizen behavior or logistics
	Morais and Ishida [6]	Centralized, decentralized, hybrid composting	152 municipalities (Japan)	Policy/institutional (SWOT)	Highlighted diversity in institutional designs; hybrid systems (household composting + collection) unique to Japan	Mainly qualitative; no quantified LCA
Case studies -International	Muhammad and Rosentrater [7]	Composting, incineration, AD, landfill, gasification, HTC	U.S.	GHG, energy recovery	AD achieves much lower GHG than landfill; new technologies (gasification, HTC) promising	No cost or behavioral aspects
	Keng et al. [8]	Composting, AD, incineration	Malaysia (urban)	GHG, acidification, eutrophication	WtE incineration has the lowest climate impacts; composting contributes to resource circulation but is less effective in GHG reduction	Data constraints; focused on urban setting
	Bastin and Longden [9]	WtE (centralized vs. decentralized)	UK (Cornwall, Warwickshire)	GHG, transport distances	Decentralized facilities significantly reduce transport distances and CO2 (up to 5.5-fold difference in Warwickshire)	Limited to WtE; no comparison with other waste treatments

provides an overview of representative LCA studies on food waste management, including their scope, indicators, main findings, and limitations.

In light of these gaps, the present study combines scenario-based LCA with GIS-informed transport modeling in Aya Town, a small Japanese municipality. By incorporating collection frequency and voluntary composting participation into the analysis, this study responds to the concerns raised by Batool et al. [11] and contributes both locally relevant evidence and methodological innovation with broader applicability to small-scale municipal waste management.

2. Materials and Methods

2.1. Study Area and Current Status of Food Waste Treatment

Aya Town is a small rural municipality located in Miyazaki Prefecture, Japan, with a population of approximately 6800. It is situated at the junction of Kyushu Central Mountains and the Miyazaki Plain. It is characterized by a humid subtropical climate and abundant forest cover. The town is known for its rich biodiversity, including extensive areas of lucidophyllous (evergreen broadleaf) forest, which support a wide variety of flora and fauna.

In recognition of its longstanding efforts to harmonize human activity with natural ecosystems, Aya Town was designated as a UNESCO Biosphere Reserve in 2012. This designation reflects the town’s integrated approach to conservation and sustainable land use, which includes organic agriculture, low-impact forestry, and environmental education. The core areas of the biosphere reserve preserve valuable natural forest ecosystems, while the surrounding buffer and transition zones support environmentally responsible human activities. This unique environmental setting provides the context for the town’s food waste composting policy, which is closely tied to its commitment to local resource recycling and ecological sustainability.

Aya Town has engaged in organic waste recycling for several decades. Although earlier initiatives focused on small-scale practices, the town significantly formalized and strengthened its system in 1997 with the construction of a composting facility and the launch of a formal source-separated collection program for household kitchen waste [12,13]. This system contributed to the town’s resource circulation model and supported the reduction of synthetic fertilizer use, consistent with its promotion of organic agriculture. However, as of 2024, the composting facility has become increasingly outdated, and rising maintenance and repair costs have raised concerns about its long-term sustainability.

In contrast, many municipalities in Japan designate household kitchen waste as combustible waste and incinerate it together with other combustible waste. Combustible waste from Aya Town is transported to an incineration facility operated by neighboring Miyazaki City (population approx. 400,000). The plant, known as Eco-Clean Plaza Miyazaki, has a daily processing capacity of 579 tons and an electricity generation efficiency of 17.9%. The facility commenced operation in 2005 and, despite partial refurbishments in recent years, it is now approaching 20 years of service. More importantly, Aya Town exemplifies a common arrangement in Japan, in which depopulating small municipalities depend on large-scale incineration facilities operated by neighboring urban centers.

For a small municipality such as Aya Town, constructing and operating an AD facility independently is economically and technically unrealistic, given its population size and limited financial resources. Therefore, in practice, the primary feasible options for handling kitchen waste are either to continue composting or to incinerate it together with other combustible waste. These constraints provide the rationale for the scenario design described in Section 2.2.

2.2. Goal and Scope Definition

The goal of this study is to assess the environmental impacts, particularly GHG emissions, associated with different household kitchen waste management strategies in Aya Town, Japan. The analysis is intended to support local policy decisions by comparing multiple treatment scenarios, including composting, incineration, and hybrid approaches with varying levels of participation in waste separation. The assumed participation rates (75%, 50%, and 25%) are not derived from survey statistics but are instead introduced as sensitivity-style assumptions to capture a plausible range of household engagement levels. This approach enables comparison across scenarios while acknowledging the absence of locally measured participation data.

The functional unit is defined as the total amount of household kitchen waste generated annually in Aya Town. All GHG emissions are calculated based on this unit, with emissions from related processes—such as changes in combustible waste collection due to different separation rates—normalized accordingly.

The system boundary includes (1) the collection and transportation of both kitchen waste and combustible waste, (2) the treatment of kitchen waste by composting and combustible waste by incineration, and (3) direct emissions from facility operations. The detailed categorization of emission sources within this boundary is presented in Section 2.3.

The substitution effects, such as avoided emissions from reduced use of chemical or organic fertilizers due to compost application, are not considered in this study. This is because compost derived from kitchen waste in Aya Town is not systematically marketed or distributed as fertilizer, and its application does not clearly substitute for specific commercial fertilizers. By excluding uncertain substitution effects, the analysis focuses on direct and measurable GHG emissions.

Upstream processes, such as the manufacturing of collection vehicles or the construction of treatment facilities, are excluded, as they are assumed to be equivalent across all scenarios. The study focuses on direct GHG emissions, while the detailed system boundary and emission calculation methods are described in Section 2.3.

2.3. System Boundary and GHG Emission Calculation

Figure 1 illustrates the system boundary for this study. Within this boundary, GHG emissions are generated primarily through the collection and transportation of household waste and the treatment of kitchen and combustible waste by composting or incineration.

Six emissions components were identified and quantified (X₁–X₆), representing the main sources of CO₂, methane (CH₄), and nitrous oxide (N₂O) emissions from the processes shown in Figure 1. Each component and its calculation procedure are described in detail below.

2.3.1. CO₂ Emissions from Waste Collection and Transportation (X₁ and X₂)

CO₂ emissions from waste collection and transportation were calculated separately for kitchen waste (X₁) and combustible waste (X₂). Both waste streams were modeled using the Vehicle Routing Problem (VRP) solver implemented in ArcGIS Pro (ver. 3.5.3) Network Analyst. The population distribution of Aya Town was represented on a 500 m grid, and the center point of each mesh was defined as a waste collection node. The actual road network data were used to generate optimized collection routes.

For each mesh, the amount of waste generated per collection day ($W_i$) was estimated from the local population ($P_i$), the per-capita daily waste generation rate ($g$), and a coefficient ($f$) representing how many days’ worth of waste is collected on each collection day, as expressed by the following:

$$W_i=P_i\times g\times f$$

Here, $f$ indicates how many days of accumulated waste are collected at one collection day. $f=7$ for once-weekly collection; $f=3.5$ for twice-weekly collection; and for five collections per week, we modeled two types of collection days separately—three days with $f=1$ (one day’s accumulation) and two days with $f=2$ (two days’ accumulation)—and then summed their routing results to obtain the weekly total. The corresponding collection frequency was defined in the analysis (see Section 2.4 for scenario definitions).

The VRP analysis incorporated vehicle loading constraints. Kitchen waste trucks were assumed to depart from the composting facility with an effective loading capacity of 1.92 tons per trip, while combustible waste trucks departed from the Eco-Clean Plaza Miyazaki incineration facility with a loading capacity of 1.5 tons. In both cases, once the truck reached its full capacity, it returned to the corresponding facility before starting the next route.

The total annual driving distance was obtained by multiplying the weekly distance by 52 weeks. The VRP output provided total driving distances for each waste stream, which were subsequently converted to CO₂ emissions using the following equations.

Annual CO₂ emissions from diesel fuel use during waste collection and transportation were calculated as follows:

$$X_n=D_n÷FC\times {EF}_{diesel}$$

Here, $X_n$ represents the annual CO₂ emissions (ton-CO₂/year) from collection and transportation for waste type $n$ (kitchen waste or combustible waste), $D_n$ is the total annual driving distance (km), $FC$ is the fuel consumption rate (5.5 km/L), and ${EF}_{diesel}$ is the CO₂ emission factor for diesel fuel (2.62 kg-CO₂/L).

2.3.2. CH₄ and N₂O Emissions from Composting Process (X₃ and X₄)

CH₄ (X₃) and N₂O (X₄) emissions from the composting process were estimated using the default emission factors provided by the Japanese Ministry of the Environment under the Greenhouse Gas Accounting and Reporting System [14]. These factors are also applied in Japan’s national GHG inventory.

Annual CH₄ and N₂O emissions from composting were obtained by multiplying the total amount of composted kitchen waste by the corresponding emission factors (0.96 kg-CH₄/ton and 0.27 kg-N₂O/ton of wet kitchen waste).

All emissions were assumed to be proportional to the total tonnage of composted kitchen waste, consistent with the national inventory methodology. In reality, CH₄ and N₂O generation during composting may vary depending on aeration, moisture content, and pile management; however, such facility-specific data were unavailable for Aya Town.

The resulting CH₄ and N₂O values were converted into CO₂ equivalents (CO₂-eq) using the Global Warming Potentials (GWP) provided by the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), with a 100-year time horizon and without climate-carbon feedback—27 for CH₄ and 273 for N₂O [15]. This standardized approach enables transparent comparison with other Japan-specific studies and ensures methodological consistency across all waste management scenarios.

2.3.3. Electricity-Related CO₂ Emissions and Sensitivity Parameters (X₅ and X₆)

Electricity-related GHG emissions arise from two processes: electricity consumption at the composting facility (X₅) and the electricity balance associated with WtE incineration (X₆). Both were quantified using Japan-specific parameters and were examined in a sensitivity analysis to capture variability in facility energy performance.

Based on previous studies of Japanese composting systems, the baseline electricity demand was set at 50 kWh per ton of wet kitchen waste [16], while 20 kWh per ton and 90 kWh per ton were used as lower and upper bounds to reflect inter-facility variation, respectively [17]. CO₂ emissions were calculated by multiplying electricity consumption by the regional grid emission factor of Kyushu Electric Power (0.258 kg-CO₂/kWh) [18].

In the case of WtE incineration, the incineration of kitchen waste affects the facility’s electricity balance by changing both power generation and internal consumption.

Electricity generation was estimated from its calorific value of kitchen waste and the assumed facility power generation efficiency of the plant, whereas electricity consumption for incineration was treated as a fixed operational requirement based on the recent literature [19]. When the electricity generated from incineration exceeds the additional consumption, the surplus is exported to the grid, displacing grid electricity and yielding avoided CO₂ emissions (negative X₆). Conversely, when generation is lower than consumption, additional grid electricity is purchased, leading to positive CO₂ emissions (positive X₆).

To represent realistic variation in facility performance, four WtE power-generation efficiencies were tested—15%, 17.9% (actual efficiency of Eco-Clean Plaza Miyazaki), 20%, and 25%—converting the range observed in Japanese plants from current to upgraded conditions. The combination of three composting electricity settings (20, 50, and 90 kWh/ton) and four WtE efficiencies (15%, 17.9%, 20%, and 25%) produced twelve possible parameter combinations. For each combination, all other emission components (X₁–X₄) were held constant. This integrated treatment of X₅ and X₆ ensures a consistent and transparent assessment of electricity-related CO₂ emissions and their influence on overall results.

2.4. Scenario Description

To assess the environmental impacts of different municipal food waste management strategies, six scenarios (A–F) were developed, reflecting both the current practice and plausible future policy alternatives in Aya Town. These scenarios differ in terms of waste separation policies, participation (cooperation) rates in composting, and collection frequencies. The total annual amount of household kitchen waste was assumed to be constant across all scenarios to allow for fair comparison.

In Japan, where most municipal solid waste is incinerated, only a limited number of municipalities have introduced kitchen waste separation schemes. Aya Town is one of these rare cases.

Because few Japanese municipalities have publicly available data on household cooperation rates for kitchen waste separation, it is difficult to derive realistic participation values from domestic statistics. However, International surveys such as the WRAP Recycling Tracker [20] have shown large variations in household participation depending on the presence and convenience of collection services.

To capture this realistic behavioral variability, the cooperation (participation) rate was therefore set at 0%, 25%, 50%, 75%, and 100% as a policy-sensitive parameter. This range represents a plausible spectrum of resident engagement under voluntary participation conditions, from full compliance to complete non-participation.

Scenario A represents the current food waste management policy, in which all residents are required to separate kitchen waste for composting, and collection occurs five times per week. Combustible waste is collected once per week.

Scenario B explores the possibility of reducing the frequency of kitchen waste collection while maintaining the same composting policy and participation rate as Scenario A.

Scenarios C–E assume that kitchen waste is no longer officially designated as a separately collected waste type. Instead, a certain proportion of residents voluntarily participate in composting. Participation rates of 75%, 50%, and 25% are considered, respectively. Composting participants separate and dispose of kitchen waste twice per week, while the remainder treat kitchen waste as combustible. These participation rates were not derived from survey data but were introduced as sensitivity-analysis settings to reflect a range of plausible outcomes under voluntary participation. Such stepwise reductions are consistent with potential policy discussions in small Japanese municipalities, where voluntary household participation is often the determining factor in waste separation.

Scenario F assumes the complete termination of the composting program, with all kitchen waste treated as combustible and collected once per week for incineration.

Table 2. Description of food waste management scenarios in Aya Town.

Scenario	Policy on Kitchen Waste Management	Composting Participation Rate	Collection Frequency
A: Baseline	Source separation and composting required	All residents participate (100%)	Kitchen waste: 5 times/week Combustible waste: 1 time/week
B: Reduced Collection Frequency	Source separation and composting required	All residents participate (100%)	Kitchen waste: 2 times/week Combustible waste: 1 time/week
C: 75% Composting	Composting optional; waste classified as combustible	75% of residents participate	Kitchen waste: 2 times/week (for 75%) Combustible waste: 1 time/week
D: 50% Composting	Composting optional; waste classified as combustible	50% of residents participate	Kitchen waste: 2 times/week (for 50%) Combustible waste: 1 time/week
E: 25% Composting	Composting optional; waste classified as combustible	25% of residents participate	Kitchen waste: 2 times/week (for 25%) Combustible waste: 1 time/week
F: Full Incineration	Composting terminated; waste treated as combustible	No residents participate (0%)	Combustible waste: 1 time/week

summarizes the characteristics of each scenario, including policy orientation, composting participation, and collection frequencies.

Other technological options, such as constructing an AD facility or upgrading to combined heat and power, were not included in this analysis. For a small municipality such as Aya Town, the construction and operation of these systems would be economically and technically unrealistic. Although the Eco-Clean Plaza Miyazaki does recover heat for limited local use, the amount corresponds to only about 10% of the generated electricity on an energy basis, and no official specification values are available. For this reason, heat recovery was not incorporated into the scenario settings. Therefore, the scenarios were limited to composting, incineration, or a combination thereof, which represent the feasible options under current institutional and financial constraints.

2.5. Constant CO₂ Emissions from Plastics in Combustible Waste (C_p)

In addition to the variable components (X₁–X₆), this study also identified a constant term, C_p, representing CO₂ emissions from the incineration of plastics contained in combustible waste. Plastics are routinely mixed in combustible waste streams and are oxidized to CO₂ during incineration.

However, these emissions were not included in the comparative analysis among scenarios because the quantity of plastic waste—and therefore the amount of plastic-derived CO₂—remains constant regardless of how kitchen waste is managed. Whether kitchen waste is composted, partially separated, or fully incinerated, the plastic fraction within combustible waste does not change under the policy settings considered in Aya Town.

The C_p term was therefore treated as a fixed background emission that does not affect the relative results of the scenario comparison. Its exclusion from the comparative totals ensures that the analysis focuses exclusively on the changes directly attributable to kitchen waste management strategies.

3. Results

3.1. GHG Emissions by Scenario

Table 3. Net GHG emission (tons-CO₂-eq/year) under different sensitivity settings. Rows represent composting electricity consumption (20, 50, and 90 kWh/ton); columns represent WtE efficiency (15%, 17.9%, 20%, and 25%). All values represent net emissions after accounting for both composting electricity use (X₅) and WtE electricity balance (X₆).

Scenario	Composting Electricity [kWh/ton]	WtE Efficiency
		15%	17.9%	20%	25%
A: Baseline	20	48.953	48.953	48.953	48.953
	50	51.057	51.057	51.057	51.057
	90	53.706	53.706	53.706	53.706
B: Reduced Collection Frequency	20	44.513	44.513	44.513	44.513
	50	46.616	46.616	46.616	46.616
	90	49.265	49.265	49.265	49.265
C: 75% Composting	20	38.963	38.963	38.963	38.963
	50	41.067	41.067	41.067	41.067
	90	43.716	43.716	43.716	43.716
D: 50% Composting	20	32.105	31.596	31.227	30.349
	50	33.157	32.648	32.279	31.401
	90	34.482	33.972	33.603	32.725
E: 25% Composting	20	26.769	26.005	25.452	24.135
	50	27.295	26.531	25.978	24.660
	90	27.957	27.193	26.640	25.323
F: Full Incineration	20	16.410	15.392	14.654	12.897
	50	16.410	15.392	14.654	12.897
	90	16.410	15.392	14.654	12.897

summarizes the net GHG emissions of Scenarios A–F under different parameter settings. The values reflect combinations of composting electricity consumption (20, 50, and 90 kWh per ton of wet kitchen waste) and WtE power generation efficiency (15%, 17.9%, 20%, and 25%). X₁–X₄ remain constant across all conditions, while variations in X₅ (composting electricity use) and X₆ (WtE electricity balance) determine the ranges. Here, X₆ can be either negative, representing avoided emissions when generation exceeds consumption, or positive, representing additional emissions when consumption exceeds generation.

Following the tabulated results, Figure 2 visualizes the scenario-based net GHG emissions. In this figure, the stacked bars represent the constant contributions from X₁–X₄ (collection, transportation, and direct composting process emissions), while the error bars show the variation arising from the sensitivity analysis of X₅ (composting facility electricity consumption) and X₆ (WtE efficiency balance). This representation highlights both the relative importance of the different emission components and the uncertainty associated with facility-specific parameters.

The figure also allows for straightforward comparison across scenarios. Specifically, reducing the collection frequency (Scenario B) yielded approximately 9.3% lower GHG emissions compared with the baseline (Scenario A). Emissions decreased progressively as voluntary participation in composting declined (Scenarios C–E), reflecting the reduced contribution of composting-related CH₄ and N₂O emissions. The lowest GHG emissions were observed under Scenario F, in which all kitchen waste was incinerated together with combustible waste.

3.2. Component-Wise Analysis of GHG Emissions

3.2.1. Transportation-Related Emissions (X₁ and X₂)

Figure 3 illustrates a sample output from the GIS-based VRP analysis. The composting facility is located within Aya Town, and in this study, its current location was explicitly adopted as the basis for the routing analysis. In contrast, the incineration plant (Eco-Clean Plaza Miyazaki) is situated in a neighboring city outside Aya Town. As a result, the VRP results highlight clear differences in driving distances between kitchen waste collection destined for the composting facility within the town and combustible waste collection transported to the incineration facility.

These differences in distance directly translate into CO₂ emissions from fuel consumption (X₁ and X₂), which were calculated using the parameters described in Section 2.3. In practice, kitchen waste collection involves shorter routes concentrated within the town, while combustible waste requires longer transport to the regional facility. Although total driving distances vary by scenario depending on the participation rate in composting and the frequency of collection, the relative contrast between the two destinations remains evident. The detailed numerical results for X₁ and X₂ across all scenarios are provided in Supplementary Table S1.

3.2.2. Composting Process Emissions (X₃ and X₄)

The combined emissions of CH₄ (X₃) and N₂O (X₄) from composting showed substantial variation across scenarios, in contrast to the relatively stable emissions from collection and transportation (X₁ + X₂). In Scenarios A and B, composting-related emissions exceeded those from collection and transportation. In Scenario C, the magnitudes of X₃ + X₄ and X₁ + X₂ were nearly equivalent, while in Scenarios D and E, X₃ + X₄ became substantially lower than X₁ + X₂. In Scenario F, where composting was entirely absent, X₃ + X₄ dropped to zero.

These results indicate that composting-related emissions are highly sensitive to changes in kitchen waste management strategies. Consequently, X₃ and X₄ represent the most influential components driving differences between scenarios and highlight a key target for reducing overall GHG emissions. Scenario-specific values of X₃ (CH₄) and X₄ (N₂O) are also summarized in Supplementary Table S1, highlighting their variation across composting participation levels.

3.2.3. Electricity Consumption at Composting Facilities (X₅)

The sensitivity analysis of electricity consumption (20, 50, and 90 kWh per ton of wet kitchen waste) revealed that higher electricity demand substantially increases emissions in scenarios dominated by composting (A and B), while the effect is progressively smaller in scenarios with lower composting participation (C–E). In Scenario F, where composting is absent, X₅ is zero. Although electricity use can alter the absolute values of emissions, its overall impact remains less significant compared to composting process emissions (X₃ and X₄), indicating that the latter constitutes the more critical factor in determining scenario outcomes. The detailed numerical results of X₅ combined with X₆ are provided in Supplementary Table S2.

3.2.4. Waste-to-Energy Electricity Balance (X₆)

The sensitivity analysis of WtE efficiency (15%, 17.9%, 20%, and 25%) demonstrated that the electricity balance associated with kitchen waste incineration can vary from additional emissions (deficit) to avoided emissions (offset). At lower efficiencies (15% and 17.9%), electricity generation from kitchen waste is insufficient to cover the additional energy consumed by incineration, resulting in positive X₆ values. Conversely, at higher efficiencies (20% and 25%), generation surpasses consumption, leading to negative X₆ values that represent avoided emissions. The magnitude of this effect is relatively small compared to direct composting emissions (X₃ and X₄), contributing less than 5% of total GHG emissions across all scenarios. Nevertheless, the analysis highlights that improvements in incineration efficiency could marginally enhance the environmental performance of incineration-oriented scenarios, particularly Scenario F. Scenario-wise results of the combined X₅ and X₆ terms under different sensitivity settings are summarized in Supplementary Table S2.

4. Discussion

4.1. Policy Implications for Aya Town

The findings of this study highlight that GHG emissions from Aya Town’s current kitchen waste composting system are relatively high compared to alternative scenarios. From a climate mitigation perspective, full incineration (Scenario F) emerges as the most favorable option, given that energy recovery at a large-scale incineration facility is more efficient than composting. However, considering Aya Town’s history of promoting composting for more than two decades and its strong identity as a model of sustainable resource circulation, a complete termination of composting may not be the most realistic or socially acceptable pathway.

Instead, the results suggest that a smaller-scale, voluntary composting practice could remain meaningful, particularly if it enables the production of high-quality compost. In practice, the demand for kitchen waste compost is often limited, as its quality tends to be lower and more heterogeneous than that of other organic amendments available on the market. Nevertheless, some organic farmers place particular value on high-quality food compost, and niche demand may exist if the product can meet stricter quality requirements. This indicates that while general demand is constrained, quality-focused composting pathways may still contribute to local organic farming in a selective manner.

Moreover, Aya Town is unusual in the Japanese context, having maintained source separation and composting of kitchen waste for more than 25 years. As a result, residents are already accustomed to waste separation, which provides a stronger basis for sustaining selective composting practices compared to many other municipalities in Japan.

Importantly, the potential for combining climate benefits with agricultural contributions should not be overlooked. For example, if approximately 25% of Aya Town’s kitchen waste were diverted to produce high-quality compost, as illustrated in Scenario E, the associated GHG emissions could be reduced by roughly half compared to the current system, while also supporting local organic farming through the provision of higher-value compost. This indicates that a selective and quality-focused composting pathway may provide both environmental and agricultural benefits, balancing emission reductions with meaningful resource recovery.

4.2. Cost Assumptions

Reliable cost data for municipal waste treatment in Japan are extremely limited, and available figures often vary widely among regions due to differences in facility scale, accounting methods, and inclusion of capital costs. Therefore, the cost estimates presented here should be regarded as indicative reference values intended to illustrate approximate economic tendencies rather than precise financial accounting.

Based on publicly available government data, we applied representative unit treatment costs for incineration and composting. These values already include facility electricity use and other operating expenses. According to the Ministry of Agriculture, Forestry and Fisheries of Japan [21], the average treatment costs for municipal facilities in Tochigi Prefecture were approximately 56,000 JPY per ton for incineration and 11,700 JPY per ton for composting, calculated from comprehensive accounts that include both capital and operation expenditures. Because the scales of the Aya Town systems ($\approx$600 tons per day for incineration and $\le$300 tons per year for composting) are comparable to those surveyed, these values were adopted as representative unit costs in this study.

Collection and transport costs were estimated by multiplying modeled diesel consumption by a local diesel price of JPY 160 per L, referring to the most recent prefectural average retail fuel price reported by the Miyazaki Prefectural Government [22].

Table 4. Estimated annual treatment and transport costs by scenario.

Scenario	Incineration Cost	Composting Cost	Transport Cost	Total Cost	X1 + X2 + X3 + X4 (Ton-CO2/Year)	Cost per Ton-CO2-eq Reduced (JPY/Ton)
	(103 JPY/Year)
A: Baseline	0	3136	1274	4410	47.596	-
B: Reduced Collection Frequency	0	3136	1003	4138	43.155	−61,067
C: 75% Composting	3752	2352	1072	7176	37.605	276,867
D: 50% Composting	7504	1568	1053	10,124	30.604	336,330
E: 25% Composting	11,256	784	1131	13,170	25.196	391,113
F: Full Incineration	15,008	0	902	15,910	14.765	350,286

summarizes the annual costs for each scenario, disaggregated into incineration, composting, and transport components. It also presents the total annual GHG emissions calculated under the same system boundary and the cost per ton of CO₂-eq reduction relative to the baseline (Scenario A). In this table, GHG emissions were expressed as the sum of X₁–X₄, representing CO₂ from waste collection and transportation (X₁, X₂) and CH₄ and N₂O from composting (X₃, X₄). This definition of system boundary was adopted to ensure consistency with the cost framework: since the simplified cost analysis did not explicitly monetize electricity consumption or electricity recovery (X₅, X₆) at composting or incineration facilities, the emission inventory was correspondingly limited to direct fuel use and composting process emissions.

As shown in Table 4, Scenario B—maintaining full composting participation while reducing the collection frequency—achieved simultaneous reductions in both total cost and GHG emissions relative to the baseline. This represents a cost-saving abatement, in which improved collection efficiency yields environmental and economic benefits at the same time.

In contrast, Scenarios C–F, where the composting participation rate decreases and incineration increases, show progressively higher total costs and lower abatement efficiency. The cost per ton of CO₂-eq rises sharply as the share of incineration grows, reflecting the higher treatment cost of incineration compared with composting. Interestingly, Scenario E (25% composting) exhibits an even higher marginal abatement cost than full incineration (Scenario F), implying that maintaining only a small fraction of household composting participation may result in both economic and operational inefficiencies.

4.3. Contribution in the Context of Previous Research

This study contributes to the literature on food waste management by addressing a small rural municipality context, which has been underrepresented compared to urban-focused LCA studies. By integrating GIS-based routing analysis with an LCA framework, the study demonstrates how spatial and behavioral factors influence the environmental outcomes of kitchen waste treatment. In particular, the findings suggest that in the context of rural Japan—where population density is low and incineration infrastructure is already in place—factors such as collection distances and voluntary household participation rates can exert stronger influences on overall GHG emissions than the treatment technology itself. This perspective extends the existing literature, which often emphasizes large-scale systems or assumes uniform participation in composting or AD programs.

4.4. Broader Relevance and International Context

Although this study focuses on Aya Town, its findings have broader implications for waste management in small municipalities, particularly in countries like Japan, where incineration is the dominant treatment pathway. Aya Town illustrates a structural feature common in Japan: small, depopulating municipalities often lack the financial capacity to introduce alternative technologies such as AD and therefore rely on large-scale WtE facilities operated by nearby urban centers. This dependency highlights that, in such contexts, GHG emissions are influenced not only by treatment technologies themselves but also by collection logistics and residents’ participation in source separation.

This insight resonates with international literature. Previous reviews (e.g., Amicarelli [10] and Batool [11]) have noted the limited attention to transport distances and behavioral factors in LCA studies of waste management. By integrating GIS-based routing analysis and voluntary participation scenarios, this study demonstrates how these factors can be decisive in small-scale systems. Thus, the results contribute not only to the Japanese debate on composting versus incineration but also to a broader understanding of how local infrastructure and community behavior shape environmental outcomes.

At the same time, the findings underline a trade-off between climate mitigation and local resource circulation. From a GHG perspective, full incineration (Scenario F) clearly yields the lowest emissions. However, voluntary composting remains relevant for specific stakeholders, such as organic farmers who value high-quality compost. While this role may be limited, it demonstrates that small-scale composting can coexist with a primarily incineration-based system, providing social or agricultural benefits beyond the narrow focus on emissions. In this sense, Aya Town serves as a representative case for many small municipalities worldwide that must balance climate goals, economic constraints, and community values in designing sustainable waste management strategies.

4.5. Strengths and Limitations

The study provides several strengths. By combining LCA with GIS-based routing analysis, it highlights the importance of collection logistics in determining GHG emissions from kitchen waste management. Moreover, by applying sensitivity analyses to composting facility electricity consumption and WtE power generation efficiency, the study explicitly accounts for facility-specific uncertainties that are often overlooked in small-scale municipal contexts.

Nevertheless, some limitations should be acknowledged. First, the study includes only a simplified economic comparison of treatment and transport costs, rather than a full cost-benefit or financial feasibility assessment. Second, compost quality, agricultural utilization, and potential substitution effects were excluded, which restricts the evaluation of composting beyond GHG emissions. Third, other technological options, such as small-scale AD or combined heat and power, were not analyzed. Finally, the findings are closely linked to the Japanese context, where incineration dominates municipal waste management, and may not be directly generalizable to regions with different infrastructures and policy frameworks. These limitations suggest important avenues for future research, including the integration of economic analyses, broader environmental indicators, and comparative assessments across different national contexts.

5. Conclusions

The study integrated LCA with GIS-based transport analysis to evaluate kitchen waste management scenarios in a small Japanese municipality. The results showed that reducing collection frequency and shifting from mandatory composting toward greater reliance on WtE incineration can significantly lower GHG emissions. Sensitivity analyses confirmed that facility-specific parameters influence absolute values but not the relative ranking of scenarios. These findings underscore the importance of transport logistics, participation rates, and infrastructural context in designing sustainable waste management strategies for small municipalities. Additionally, a simplified cost comparison suggested that optimizing collection frequency under full composting can reduce both emissions and operating costs, while partial voluntary composting may lead to higher costs and limited benefits.