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Article

Evaluation of Nutrient Loss and Greenhouse Gas Emissions Caused by Food Loss and Waste in China

by
Chun Chen
1,
Yiman Fang
2,3 and
Xiaozhong Wang
2,3,*
1
Department of Earth Sciences, The University of Hong Kong, Hong Kong 999077, China
2
Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, Southwest University, Chongqing 400715, China
3
Key Laboratory of Low-Carbon Green Agriculture in Southwestern China, Ministry of Agriculture and Rural Affairs, College of Resources and Environment, Southwest University, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(16), 7341; https://doi.org/10.3390/su17167341 (registering DOI)
Submission received: 1 July 2025 / Revised: 11 August 2025 / Accepted: 12 August 2025 / Published: 14 August 2025
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Abstract

Food loss and waste (FLW) impose major nutritional and environmental costs globally. This comprehensive China-wide study quantifies FLW-driven nutrient depletion and greenhouse gas (GHG) emissions across the entire supply chain. Using national-scale modeling with China-specific data, we found that FLW in 2022 reached 415 million tons (i.e., 21.4% of total production was lost/wasted), generating 281 Mt CO2-eq. Daily per capita FLW at 757 kcal (29.7% of recommended intake lost/wasted), 28.4 g protein (43.7%), and 114 mg vitamin C (14%) dissipated significant nutrients. Using the Wasted Nutrient Days metric, 72–416 days of varying nutrient adult needs were lost, worsening malnutrition burdens. The key node along the supply chain leading to high FLW is postharvest handling and storage (responsible for 49% of FLW mass and emissions), while vegetables/cereals (mass loss quantities) and meat-based foods (high emission intensity) were the most lost/wasted food types. Scenario analysis shows that combining optimized diets and supply chain improvements could reduce FLW by 503 g/capita/day and emissions by 62.2%, closing nutritional gaps and supporting carbon neutrality.

1. Introduction

Ensuring sustainable food security has become a major global challenge, driven by continued and uneven demographic growth and increasing stress on natural resources and the environment. Within this context, reducing food loss and waste (FLW) across the supply network has been recognized as a critical strategy to enhance food system efficiency, mitigate environmental impacts, and align human activities with planetary boundaries [1,2]. However, the scale of global FLW remains staggering: approximately one-third of food produced for human consumption—equivalent to 1.3 billion tons annually—is lost or wasted [3]. This inefficiency not only undermines food security but also drives the unnecessary consumption demand of 173 km3 of freshwater, 198 million hectares of land, and 28 million tons of fertilizers annually, in addition to contributing 2189 million tons of CO2-equivalent greenhouse gas (GHG) emissions [4,5]. In addition, nutrition security is now widely acknowledged to be in crisis globally [6], partly due to FLW. Recognizing its systemic implications, the United Nations has prioritized FLW reduction as a key target under Sustainable Development Goal 12 (SDG 12.3), aiming to halve per capita food waste and reduce supply chain losses by 2030 [7].
As the world’s largest food producer and consumer, China plays a pivotal role in global food system sustainability. In 2017, China accounted for 25% and 28% of global food production and available supply, respectively [3], with production reaching 1892 million tons (Mt) yet only 1312 Mt (69%) entering the retail supply chain, which is a 31% disparity. The significant gap between production and retail availability underscores severe inefficiencies, with FLW occurring at every stage of the supply chain. While FLW has gained attention in China, existing research exhibits critical gaps. First, studies disproportionately focus on consumer-stage waste [8,9], neglecting systematic quantification of upstream losses from production to retail. Second, assessments primarily emphasize resource and environmental impacts (e.g., GHG emissions and water use) [9,10] while failing to address the nutritional consequences of FLW for national food security. Third, most analyses are geographically fragmented, relying on provincial or municipal data [8,10], leaving a void in robust national-scale evaluations that could inform systemic policy interventions.
Addressing these gaps is critical for aligning China’s food system with its dual priorities of ensuring nutritional security and achieving carbon neutrality by 2060 [11]. This study provides the first comprehensive national assessment of FLW-associated nutrient losses and GHG emissions across China’s entire food supply chain. This work focuses on three goals as follows: (1) integration of a food system material flow model with China-specific loss/waste rates to quantify FLW magnitudes across five stages (production, postharvest handling and storage, processing, distribution, and consumption); (2) nutrient loss estimation using the China Food Composition Table [12] to evaluate impacts on 10 dietary components critical for public health (calories, protein, fiber, vitamins A/C/E, calcium, magnesium, iron, and zinc); and (3) GHG emission accounting through meta-analysis of China-specific carbon footprint parameters, enabling spatially resolved supply chain impact assessments. By coupling these analyses with scenario modeling, we further evaluate the co-benefits of FLW reduction for resource efficiency and climate mitigation, offering actionable insights for policymakers.

2. Materials and Methods

2.1. Material Flow Model of the Food System

2.1.1. Model Construction

The material flow analysis (MFA) framework forms the foundation of this study by considering stages of FLW from farm to table. Building on the Food and Agriculture Organization [3] integrated material flow model, we adapt its structure to China’s context by incorporating five critical FLW stages defined by the FAO: (1) agricultural production loss, (2) postharvest handling and storage loss, (3) food processing loss, (4) food distribution loss, and (5) food consumption waste. This model integrates dynamic systematic variables—including food trade (import/export), stock variation, and non-food diversions (e.g., seeds, feed, biofuels, etc.)—to reconcile discrepancies between production volumes and actual dietary consumption. Notably, the framework accommodates 10 major food categories (cereals, roots, pulses and nuts, vegetable oils, vegetables, fruits, various meats, eggs, milk, and fish), enabling granular analysis of nutrient and GHG emission impacts. To enhance spatial relevance, the model incorporates China-specific loss/waste rates derived from localized studies, addressing gaps in prior global [3,13], continental [4,5], and national-scale analyses [14,15,16].

2.1.2. Data Sources

Primary data on food production, trade, and multiple utilization paths (seeds, feed, and processing) were sourced from the 2022 FAO Food Balance Sheets (FAO-FBSs) [17] (Table S1). China-specific FLW rates for each stage of the supply chain were extracted from Li et al.’s [18] meta-analysis of peer-reviewed studies on Chinese FLW, ensuring regional accuracy for cereals, meats, vegetables, fruits, etc. For understudied categories (other cereals, eggs, and milk), localized data gaps were addressed using regionally analogous parameters from industrialized Asia, as calibrated by Porter et al. [5] (Table S2). This substitution reflects geographically comparable technological and infrastructural conditions in food processing and retail.

2.2. Evaluation of Nutrient Loss

2.2.1. Assessment of Individual Nutrient Loss

Nutrient loss quantification was performed using a multi-tiered approach. First, edible food losses for each commodity were multiplied by China-specific nutrient composition values sourced primarily from the China Food Composition Table [12], which provides standardized nutrient profiles for the 10 critical dietary components adapted in this study: calories, protein, dietary fiber, vitamin A, vitamin C, vitamin E, calcium, magnesium, iron, and zinc. Nutrient parameters for wheat, rice, maize, bovine meat, mutton, and pigmeat were directly extracted. For food groups with heterogeneous subcategories (other cereals, roots, vegetable oils, and fruits), weighted-average nutrient values were calculated based on FAO Food Balance Sheets [17] production volumes and subcategory distributions. In cases where FAO-FBSs lacked granular subcategory data (pulses, vegetables, poultry, eggs, milk, and fish), nutrient content was derived using historical methodology from the Calculation Table of Main Foods and Main Nutrients in China by the Food Development Research Group [19]. While this introduces potential temporal bias, it aligns with best practices for bridging data gaps in understudied food groups, as recommended in FAO guidelines [20]. A comprehensive overview of the nutrient content parameters for various foods is shown in Table S3.

2.2.2. Assessment of Wasted Nutrient Days (WNDs)

To quantify the dual impact of FLW on national nutrition security and individual dietary adequacy, we employed Wasted Nutrient Days, a method validated in global food security literature [13,21,22]. WND is defined as
W N D = A n n u a l   p e r   c a p i t a   n u t r i e n t   l o s s   ( g   c a p i t a − 1   y e a r − 1 ) R e c o m m e n d e d   d a i l y   I n t a k e   ( R D I ,   g   c a p i t a − 1   d a y − 1 )
This ratio represents the number of days the annual nutrient loss from FLW could theoretically meet an individual’s nutritional requirements. For example, a WND of 90 for protein implies that lost protein could fulfill one person’s needs for 90 days, or equivalently, 90 people’s needs for one day, highlighting systemic inefficiencies in food utilization. The recommended daily intakes (RDIs) were sourced from the Chinese Dietary Reference Intakes [23] (Table S4), ensuring alignment with China’s latest age and gender-specific nutritional guidelines.

2.3. GHG Emissions Assessment

2.3.1. Methodology

To quantify the climate impacts of FLW, this study employs a life cycle assessment (LCA)-based carbon footprint approach, which has been a principal tool in various food system assessments [24,25,26]. The carbon footprint metric integrates direct emissions (e.g., methane from rice paddies) and indirect emissions (e.g., energy used in fertilizer production) across life cycles of food commodities, expressed as CO2-equivalents (CO2-eq) using IPCC AR6 global warming potential (GWP) conversion factors [27]. GHG emissions attributable to FLW were calculated by multiplying China-specific carbon footprint coefficients (kg CO2-eq kg−1) by the mass of food lost or wasted at each supply chain stage.

2.3.2. Source of Food Carbon Footprint Parameters

To address spatial heterogeneity in greenhouse gas (GHG) emission factors, this study employs a China-specific meta-analysis of the carbon footprints of various foods, building on extensively documented global methodologies [28,29,30]. While global assessments provide foundational insights, regional disparities in natural resource endowments, agricultural practices, and input efficiencies create significant variability in emission factors for equivalent food items across production regions [31,32]. This necessitates regionally calibrated parameters, as generalized global values risk misrepresenting China’s unique agroecological and managerial contexts.
The carbon footprint assessment adopts a “cradle-to-farm gate” system boundary, encompassing emissions from (1) production and transportation of agricultural inputs (e.g., fertilizers, pesticides, and feed) and (2) on-farm activities (e.g., livestock enteric fermentation, nitrous oxide from soil management, etc.). Notably excluded are carbon absorption by farmland soil (whose inclusion would yield negative net emissions) and land-use change emissions, which are exogenous to FLW. Functional units are standardized as kg CO2-eq kg−1, with livestock and aquatic products calculated on a live-weight basis and dairy products converted to milk equivalents to ensure comparability.
A systematic literature review was conducted across CNKI, Google Scholar, Web of Science, and Elsevier using the keywords “life cycle assessment,” “carbon footprint,” and “greenhouse gas emissions.” Screening followed PRISMA guidelines: titles and abstracts of 2138 identified studies were reviewed, with 124 publications (83 journal articles: 27 Chinese, 56 English; 41 Chinese academic theses) meeting inclusion criteria. Carbon footprint values were directly recorded from tables or digitized from figures using GetData Graph Digitizer, with values cross-checked to ensure alignment with the defined system boundary.
The final dataset comprises 1698 carbon footprint values, 1676 (98.70%) of which were published within the past decade and 1353 (79.68%) within the past five years, ensuring relevance to contemporary Chinese agricultural practices. Results of the meta-analysis are shown in Figure S1.

2.4. Scenario Analysis

Having acknowledged the carbon footprints of FLW across various food groups in China, it would be a matter of course to discuss the potential improvements in the industry for optimizing efficiency accordingly. We have identified three current caveats in China’s food system, namely (1) actual dietary consumption habits deviate from the recommended diet, resulting in “hidden hunger” and inefficient provision of food bundles; (2) substantial quantities of vegetables and cereals are lost at the postharvest handling and storage stage, the primary contributor of GHG emissions from FLW; (3) inconsistent but persistent FLW of every food category along the supply chain, suggesting considerable capacity for overall improvement.
Therefore, we have designed the following three sets, a total of six scenarios, to showcase the expected efficiency boost under specific enhancement strategies. See Table S5 for the proportions of loss and waste of various foods in the supply chain in Scenarios 1–6.
  • Scenario 1: Baseline scenario. The current Chinese dietary pattern in 2022.
  • Scenario 2: Improved postharvest handling and storage. We have updated the previously established FLW database [6], with data now spanning from 2021 to 2025. Based on this database, we define the parameters for optimizing each food harvesting stage as the minimum values observed among developed countries (Table S5).
  • Scenario 3: Improved supply chain. Leveraging our established food loss and waste database [6], this scenario aims to reduce losses across all post-harvest stages (storage, processing, distribution, and food waste) by adopting globally optimized minimum values for China-specific food groups and supply-chain segments (Table S5).
  • Scenario 4: Optimal diet. Dietary consumption aligns with the diet recommended by the Dietary Guidelines for Chinese Residents (2022) [33].
  • Scenario 5: Optimal diet + improved postharvest and storage. Building upon Scenario 4, this scenario further reduces postharvest handling and storage losses by optimizing the FLW parameters defined in Scenario 2 (Table S5).
  • Scenario 6: Optimal diet + improved supply chain. Based on Scenario 4, this scenario integrates measures to reduce losses at all stages by optimizing the food loss/waste parameters from Scenario 3 (Table S5).

3. Results

3.1. Current Status of FLW in China

Material flow analysis revealed systemic inefficiencies in China’s 2022 food system, with daily per capita food production, available supply, and food loss and waste (FLW) quantified at 4001 g, 1895 g, and 781 g, respectively (Figure 1). At the national scale, these metrics translate to 2126 million tons (Mt) of annual production, 1006 Mt of available supply, and 415 Mt of FLW. Notably, FLW represented 19.5% of total food production and 41.2% of available supply, highlighting critical losses across the supply chain.
Three stages dominated FLW magnitudes: postharvest handling and storage (382 g capita−1 day−1, 49% of total FLW), agricultural production (198 g capita−1 day−1, 25%), and food consumption (133 g capita−1 day−1, 17%). Collectively, these stages accounted for 91% of FLW, while food distribution and processing contributed only marginally (9%, Figure 1). Vegetables, cereals, and roots constituted 83.9% of total FLW, with respective losses of 436 g (55.9%), 153 g (19.6%), and 66 g (8.4%) capita−1 day−1; the remaining 16.1% derived from other food groups, including meats, fruits, and dairy (Figure 1). Stage-specific vulnerabilities varied by food category (Figure 2). However, collectively, postharvest handling and storage, agricultural production, and food consumption were also the primary loss stages for vegetables, cereals, and roots, with an average contribution of 54%, 21%, and 8% to the total loss and waste of the three categories, respectively.

3.2. Nutrient Loss

China’s FLW led to substantial nutrient depletion, with daily per capita losses quantified as 757 kcal energy, 28.4 g protein, 11.9 g dietary fiber, 232 µg vitamin A, 114 mg vitamin C, 11.9 mg vitamin E, 157.7 mg calcium, 247.8 mg magnesium, 9.8 mg iron, and 5.6 mg zinc (Table 1). Relative to Chinese Dietary Reference Intakes (DRIs) for men aged 18–50, these losses represented 29.7% of energy, 43.7% of protein, 39.7% of dietary fiber, 30.1% of vitamin A, 114% of vitamin C, 84.9% of vitamin E, 19.7% of calcium, 75.1% of magnesium, 81.9% of iron, and 46.6% of zinc requirements. Vitamin C and E losses were particularly critical, exceeding daily recommendations by 114% and 84.9%, respectively, while iron (81.9%) and zinc (46.6%) also surpassed half of their DRIs. In contrast, calcium depletion was comparatively lower at 19.7% of its recommended intake.
Wasted Nutrient Days (WNDs) analysis revealed systemic nutritional inefficiencies (Figure 3). For macronutrients, annual FLW-equivalent losses could sustain 108 days (men) or 132 days (women) of caloric needs and 159 days (men) or 188 days (women) of protein requirements. Dietary fiber losses corresponded to 145 days (men) and 174 days (women) of recommended intake. Vitamin losses were most severe, with WND values reaching 416 days for vitamin C and 310 days for vitamin E. Among minerals, magnesium (274 days), iron (299 days for men, 199 days for women), and zinc (170 days for men, 240 days for women) exhibited high WND, while calcium losses (72 days) represented the smallest impact.
Nutrient loss attribution varied significantly across food categories (Figure 4). Cereals dominated macronutrient losses, contributing 55% of calories and 42% of protein, followed by meat (12% calories, 20% protein) and vegetables (11% calories, 17% protein). Dietary fiber losses originated primarily from cereals (50%) and vegetables (37%). Vegetables drove vitamin depletion, accounting for 70% of vitamin A and 86% of vitamin C losses, while roots (62%), vegetables (22%), and cereals (11%) contributed disproportionately to vitamin E losses. Mineral losses were concentrated in vegetables and cereals, responsible for 74% of calcium, 85% of magnesium, 80% of iron, and 74% of zinc depletion. Systemically, vegetables and cereals accounted for 72% of aggregate nutrient losses, underscoring their pivotal role in China’s FLW-driven nutritional inefficiencies.

3.3. GHG Emissions Caused by FLW in China

China’s food loss and waste (FLW) generated substantial greenhouse gas (GHG) emissions, totaling 528 g CO2-eq capita−1 day−1 in 2022 (Table 2). Stage-specific emissions analysis identified postharvest handling and storage, agricultural production, and food consumption as the dominant contributors, collectively responsible for 90.4% of total emissions (Table 2).
Food category contributions exhibited stark contrasts (Table 2). Meat, cereals, and vegetables collectively accounted for 77.8% of total emissions, with per capita daily emissions of 154 g, 131 g, and 126 g CO2-eq, respectively. Notably, meat—representing only 4.3% of total FLW mass—emerged as the largest emission source, driven by its exceptionally high carbon footprint (2.6–18.4 kg CO2-eq kg−1, Figure S1), which reflects methane from enteric fermentation and feed production. Conversely, cereals and vegetables, despite their lower per-unit emissions (0.4–1.2 and 0.3 kg CO2-eq kg−1), contributed significantly due to massive loss/waste volumes (153 g and 436 g capita−1 day−1, respectively). This underscores the dual drivers of FLW-related emissions: carbon-intensive production (meat) and high loss rates (plant-based foods).

3.4. Significantly Reduced FLW and Associated GHG Emissions Under Scenario Modeling

This study evaluated six intervention scenarios: baseline (S1), improved postharvest handling and storage (S2), improved supply chain (S3), optimal diet (S4), optimal diet + improved postharvest handling and storage (S5), and optimal diet + improved supply chain (S6). Under the baseline scenario (S1), FLW equates 780.65 g capita−1 day−1, producing 528.43 g CO2-eq capita−1 day−1 of GHG emissions (Figure 5A,B). Transition to an optimal diet (S4) resulted in a 27.88% reduction in FLW GHG emissions compared to the base scenario (S1). Supply chain upgrades also displayed promising prospects. S2 yielded 28.61% fewer GHG emissions than S1, suggesting that improved postharvest handling and storage is a valid strategy. S3 yielded 52.86% fewer GHG emissions than S1, indicating extended enhancement brought by improvements at other stages along the supply chain. Similar effects were achieved under optimal diet scenarios, where compared to S4, S5, and S6 achieved 27.41% and 47.53% GHG emissions reduction, respectively (Figure 5B). The levels of abatement due to supply chain improvements are consistent under different diet scenarios (GHG emissions S1:S2:S3 = 1:0.71:0.47, S4:S5:S6 = 1:0.73:0.52), suggesting negligible interference between interventions on the supply or demand side.
Nutrition-wise, diet and supply chain schemes both achieved desirable abatement effects. Under the current diet, prioritizing on postharvest handling and storage (S2) decreased the FLW of nutrition elements by 25.95% (vitamin A) to 40.91% (vitamin E) compared to the baseline; thorough betterment of the supply chain (S3) mitigated 45.10% (vitamin A) to 54.19% (protein) of FLW (Figure 5C,D). Similarly, under the optimal diet (S4), FLW nutrition elements were reduced by 29.22% (unweighted mean). In S5, the FLW of nutrition elements was reduced by 22.30% (vitamin A) to 44.20% (vitamin E) compared to S4; in S6, the cutback was 39.92% (vitamin A) to 50.16% (protein) (Figure 5C,D). Overall, the FLW of nutrition elements modelled across scenarios 1–6 decreased at a relatively homogeneous rate. These outcomes demonstrate FLW reduction as a dual-purpose strategy: ensuring dietary nutrient adequacy while advancing low-carbon transitions in food systems.

4. Discussion

4.1. China’s FLW Landscape: Moderate per Capita Rates Mask World-Leading Total Emissions

China’s 415 Mt of annual food loss and waste (FLW) in 2022 accounted for 39.5% of the global total, positioning it as the largest contributor to FLW-related resource and climate impacts worldwide [34]. However, per capita FLW presents a multiplex narrative. At 781 g capita−1 day−1, China’s rates exceed the global average by a noteworthy 29% [3]. Despite being 27% and 85%, respectively, higher than Sub-Saharan Africa and South and Southeast Asia [22], China’s rates remain 36% lower than North America and Oceania and 4% below Europe (Table 3), a duality reflecting divergent development trajectories. Country-wise, China’s per capita FLW is 22%, 5%, 3%, and 17% lower, respectively, compared to figures in developed countries represented by the United States, South Korea, Japan, and Italy [23,26,35,36]. Compared to developing economies like Peru and South Africa, the Chinese rate is 6–45% higher [37,38]. Overall, China’s per capita FLW is at a moderately high level globally, reflecting the nation’s transition from a developing to a developed economy.
This “moderate but massive” landscape of FWL stems from systemic inefficiencies in China’s food supply chain, dominated by postharvest handling and storage (48.9%) and agricultural production (25.4%, primarily driven by low productivity and infrastructural limitations [39] like fragmented cold chains and suboptimal storage infrastructure. These factors disproportionally affect perishables like vegetables (55.9% of total FLW). Compounded by China’s vast population (over 1.4 billion), even moderate per capita losses translate into globally unparalleled totals, highlighting the need for targeted interventions.
Table 3. Food loss and waste in global countries, different regions and countries. The system boundaries are all from agricultural production to dietary consumption.
Table 3. Food loss and waste in global countries, different regions and countries. The system boundaries are all from agricultural production to dietary consumption.
RegionFood Loss and WasteYearReference Documentation
g Capita−1 Day−1Mt Year−1
China7814152022Our research
The United States9521072010Chen & Chen, 2018, Sustainability [40]
Japan807382011Liu et al., 2016, Journal of Cleaner Production [16]
South Korea819152007–2017
average		Adelodun & Choi, 2020, Journal of Cleaner Production [14]
Peru73482012Vázquez-Rowe et al., 2021, Resources, Conservation and Recycling [41]
South Africa540102012Nahman & de Lange, 2013, Waste Management [42]
Poland869122006Bräutigam et al., 2014, Waste Management and Research [43]
Italy913202006
European Union (27 countries)7901432006
World60614822007Gustavsson, J. et al., 2011 [3]
Industrialized Asia7704432011Porter et al., 2016, Science of the Total Environment [5]
Europe8162112011
North America and Oceania12141672011
South Asia and Southeast Asia4223552011
Sub-Saharan Africa6161592011

4.2. FLW Reduction: A Critical Lever for Enhancing Food–Nutrition Security

Food and nutrition security faces intensifying pressures from resource depletion and ecological degradation in China’s agricultural systems [44]. Therefore, it is urgent to strike a balance between “expanding sources” (increasing production and production efficiency) [45,46] and “reducing gross consumption demand” (via minimizing losses and waste in the supply chain). Our analysis reveals that FLW-driven nutrient losses severely undermine security goals, with daily per capita losses of 757 kcal (29.7% of recommended intake) and 28.4 g protein (43.7%) (Table 1). Vegetables (55.9% of FLW mass) and cereals (19.6%) are primary sources for micronutrient depletion, responsible for 85.6% of vitamin C, 86.2% of dietary fiber, and 73.8% of calcium losses (Figure 4). Halving FLW could recover sufficient nutrients to meet adult requirements for 54/80 days (calories/protein) and up to 305 days (Vitamin C), directly combating hidden hunger [21].
Further analysis reveals postharvest handling and storage as the critical control point, accounting for 54% of vegetable losses and consequently 85.6% of wasted vitamin C (Figure 4). Compounding this, dietary imbalances exacerbate nutritional risks. The National Bureau of Statistics of China [47] reveals that consumption shortfalls are for vegetables (−4%), fruits (−25%), eggs (−8%), dairy (−89%), and fish (−5%). Alarmingly, FLW for these deficit categories exceeds consumption by 152% (vegetables), 38% (fruits), 24% (eggs), 21% (dairy), and 32% (fish) (Table S6). (Table S6). Reducing FLW, especially for vegetables, fruits, and fish, could directly bridge these gaps without increasing production.
Additionally, despite past improvements in macronutrient (calorie and protein) intake, China’s persistent “double malnutrition burdens” (hidden hunger and obesity) further underscore FLW’s systemic role [36,38,48]. Average current intakes lag recommendations for dietary fiber (−61%), vitamin A (−41%), vitamin C (−20%), calcium (−54%), and magnesium (−14%) [38]. Aggravating the matter, FLW accounts for the loss of 25% (7.5 g) of daily dietary fiber needs, 9% (73 µg) of vitamin A, 133% (133 mg) of vitamin C, 19% (148 mg) of calcium, and 11% (94 mg) of magnesium (Figure 4). Prioritizing FLW mitigation, especially for nutrient-dense perishables, is therefore an efficient strategy to combat hidden hunger.
Furthermore, while our dietary analyses focused on standard adult requirements, it is important to note that vulnerability varies significantly by demographic group. Women (especially pregnant/lactating), children, and the elderly often have higher nutrient requirements per unit of body weight or face greater absorption challenges [49,50], meaning the actual nutritional risk posed by FLW-driven nutrient losses is likely even more severe for these vulnerable populations.

4.3. A Twofold Potential for Reducing GHG Emissions from FLW by Targeting Supply and Demand

China has been leading the effort towards carbon peaking and carbon neutrality with national-scale leverage. The food system stands as one of the most significant rallying points in China, generating approximately 1.73 Gt CO2-eq year−1 in GHG emissions [35], which accounts for about 31.5% of the food sector’s global total (5.2 Gt CO2-eq year−1) [2], making it the largest contributor worldwide. Strikingly, GHG emissions from FLW (281 Mt CO2-eq year−1) constitute 16.3% of China’s total GHG emissions from the food system. Daily per capita GHG emissions from FLW have reached 528 g CO2-eq, corresponding to the emission of a 5 km drive in a typical gasoline car.
FLW arose from inefficiencies on both the supply and demand sides of the food system. Despite boosts in efficiency from machinery, logistics, and management, a wide gap prevails between the current supply network and optimal conduct. On the demand side, dietary consumption is persistent in cultural norms or subject to economic constraints, deviating from the recommended intakes. Although not designed towards low-carbon objectives, the recommended diet coordinates food items for efficient nutrition supply, thus reducing FLW and the associated GHG emissions.
Scenario analysis confirms the twofold potential to reduce FLW emissions in China’s food system. Switching to the recommended diet can reduce emissions by 78.40 Mt CO2-eq annually, corroborating the potential for a more efficient food demand. Refining postharvest handling and storage practices and the supply chain overall is expected to reduce GHG emissions from FLW under both diet scenarios by around 30% and 50%, respectively. Additionally, results demonstrated negligible interference of improvements on the supply and demand side, suggesting a divide-and-conquer roadmap for policy interventions. Under the current gross dietary consumption structure, the carbon footprint to sustain the daily requirements of individual nutrients ranges from 471.2 g CO2-eq (vitamin C) to 2932.2 g CO2-eq (calcium) (Table S7). This heterogeneity suggests prioritizing agricultural enhancements for nutrients that are carbon-intensive to supply (e.g., calcium) or resistant to other reduction strategies (e.g., vitamin A). Additional carbon reduction capacity may be acquired from adjusted supply and demand facing nutrition-enhanced agricultural products. Overall, prospects from increasing supply and demand efficiencies highlight the strategic value of reducing FLW for carbon neutrality.

4.4. Strategic Priorities for Mitigating FLW Across China’s Food System

Targeted interventions aligned with stage-specific loss patterns are required to address China’s current 415 Mt annual FLW, which undermines food and nutrition security and sustainability. Postharvest handling and storage demand urgent prioritization, accounting for 49% of total FLW and disproportionately affecting plant-based foods (e.g., vegetables at a 54% loss rate) compared to animal-derived foods (e.g., meat and dairy products) due to inadequate cold chains [51]. In addition, suboptimal storage technology and inaccurate demand forecasting lead to long-term stock accumulation [52,53]. Three key measures that may offer high-impact solutions: (1) implementing pre-storage crop sorting to remove mildew-compromised produce; (2) scaling scientific storage infrastructure such as hermetic silos for sealed storage and integrated pest control (IPC); and (3) enhancing policy and financial support for rural cold-chain development and technical training.
Agricultural production losses (25% of FLW) necessitate climate-resilient practices, including (1) deploying high-yield, pest-resistant crop varieties with enhanced storability; (2) advancing early-warning systems for extreme weather; and (3) training farmers in efficient harvesting techniques using modern tools [3]. For consumption-stage waste (17% of FLW), sociocultural and regulatory shifts are imperative. Citizens should embrace frugality traditions supported by systematized food-saving education. Policies like the “Clean Plate Campaign” require legal institutionalization, while catering services must standardize portion sizes/packaging to curb “face-saving” overordering [54,55]. Cross-cutting strategies should adopt best practices from the EU and Japan [56], such as implementing waste-data feedback (e.g., carbon footprint labels) to raise consumer awareness and establishing incentive-penalty systems (e.g., point rewards) to reinforce behavioral change.
Although our analyses are primarily based on national data, it is possible to suggest potential pathways tailored according to regional disparities. Urban centers exhibit higher consumption-stage waste driven by food services and household behaviors [57], while rural/inland areas face elevated production/postharvest losses due to infrastructural gaps (e.g., cold chains and transport). Effective policies thus diverge: Urban strategies should prioritize consumer education and portion control, whereas rural solutions require investments in storage tech and farmer training to maximize resource efficiency.
This proposed integrated strategy framework—spanning infrastructure, technology, policy, and behavior—engages governments, enterprises, and individuals to transform FLW from a systemic liability into a sustainability opportunity, directly supporting China’s food security and carbon neutrality goals.

5. Limitations and Conclusions

Currently, this study comes with limitations. Firstly, FLW estimates for some food groups relied on proxy data (e.g., using FAO coefficients or regional studies where China-specific data was sparse), introducing potential uncertainty. Secondly, GHG emissions calculations excluded contributions from land-use change associated with food production. While this prevented negative carbon footprints calculated from field crops, other nuanced interactions may emit more GHG (such as turning forests into pasture). Furthermore, only cross-sectional data from 2022 were analyzed in this study. Future research should prioritize developing more granular, China-specific FLW factors, incorporating land-use change emissions, and exploring panel data.
Alleviating FLW across the supply and demand network is imperative for transforming towards a sustainable food system, safeguarding national food–nutrition security, and mitigating the GHG emissions of food systems. Using a China-specific material flow model, our results showed that China’s per capita FLW (781 g day−1) ranked in the upper-middle range globally, while the total FLW of 415 Mt year−1 was the highest globally. Concurrently, FLW generated 528 g CO2-eq capita−1 day−1 (281 Mt CO2-eq year−1), representing 28% of global food system emissions. Scenario analysis revealed that under optimal supply and demand-side interventions, there exists the capacity to avert up to 503 g capita−1 day−1 of FLW and 329 g CO2-eq capita−1 day−1 (174.7 Mt CO2-eq year−1) of emissions. To partially realize these prospects, interventions must prioritize the postharvest handling and storage stage, the key fulcrum to leverage a resource-efficient, climate-resilient food system. Potential measures must take both the supply and demand sides into consideration and synergize efforts across sectors and stakeholders.
In essence, practices targeting infrastructure, technology, management, and behavior would advance SDG 12.3 compliance while promoting nutritional security, ecological sustainability, and public health, positioning China as a global leader in sustainable food system innovation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17167341/s1, Figure S1. Carbon footprint of plant-derived food (A) and animal-derived food (B) in China. Medians are denoted by red dashed lines, and means are denoted by blue solid lines. The edges of the boxes correspond to the 25th and 75th percentiles (interquartile range), with the upper/lower whiskers extending to the 10th and 90th percentiles, respectively. Sample sizes are indicated by n. Table S1. China’s Food Balance in 2022 (unit: g capita−1 day −1). Table S2. Proportion of FLW by Category in China’s Supply Chain (unit: %). Table S3. Nutritional content parameters of various foods (unit: 100 g−1). Table S4. Recommended daily intake of various dietary nutrients (unit: capita−1 day−1). Table S5. Projected proportion of loss and waste of various foods in five stages of the supply chain in Scenarios 1–6. Table S6. Food loss and waste, available supply, and recommended intakes of various food categories in China (unit: g capita−1 day−1). The dietary consumption data are from the Ref. [47], and the recommended dietary consumption data are from The Balanced Diet Pagoda of Chinese Residents. Table S7. GHG emissions of various nutrients attributed to food groups and total GHG emissions of recommended diets under the current gross dietary consumption structure (FWL + dietary consumption).

Author Contributions

C.C.: Methodology, Data curation, Writing—original draft, Writing—review and editing. Y.F.: Data curation, Writing—original draft, Writing—review and editing. X.W.: Writing—review and editing, Conceptualization, Supervision, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Innovation Research 2035 Pilot Plan of Southwest University (SWU-XDZD22001).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The author would like to thank the anonymous reviewers for their constructive comments on improving this paper.

Conflicts of Interest

The author declares no conflicts of interest.

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Figure 1. Sankey plot of per capita food flow process in China, 2022. Gross supply consists of available supply, non-food production, and FLW. The FLW flow of 10 food groups is shown in detail. Unit: g capita−1 day−1.
Figure 1. Sankey plot of per capita food flow process in China, 2022. Gross supply consists of available supply, non-food production, and FLW. The FLW flow of 10 food groups is shown in detail. Unit: g capita−1 day−1.
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Figure 2. Percentage of FLW of 10 food groups at each stage in the supply chain. The order of stages along the stacked bars (left to right) indicates the actual sequence along the supply chain. Stages in which FLW is under 5% are not labeled.
Figure 2. Percentage of FLW of 10 food groups at each stage in the supply chain. The order of stages along the stacked bars (left to right) indicates the actual sequence along the supply chain. Stages in which FLW is under 5% are not labeled.
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Figure 3. The Wasted Nutrient Days (WNDs) of respective nutrients caused by FLW across various food groups in 2022. WNDs of the 10 nutrients were calculated based on the recommended dietary intake for Chinese men (A) and women (B) residents aged 18 to 50.
Figure 3. The Wasted Nutrient Days (WNDs) of respective nutrients caused by FLW across various food groups in 2022. WNDs of the 10 nutrients were calculated based on the recommended dietary intake for Chinese men (A) and women (B) residents aged 18 to 50.
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Figure 4. The percentage contribution of 10 food groups to the loss and waste of 10 nutrients. The sum of the contributions from each food group to each nutrient (sum of rows) equals 100%.
Figure 4. The percentage contribution of 10 food groups to the loss and waste of 10 nutrients. The sum of the contributions from each food group to each nutrient (sum of rows) equals 100%.
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Figure 5. FLW (A), GHG emissions (B), and nutritional losses (C,D) under scenarios 1–6. S1: baseline; S2: improved postharvest handling and storage; S3: improved supply chain; S4: optimal diet; S5: optimal diet + improved postharvest handling and storage; and S6: optimal diet + improved supply chain.
Figure 5. FLW (A), GHG emissions (B), and nutritional losses (C,D) under scenarios 1–6. S1: baseline; S2: improved postharvest handling and storage; S3: improved supply chain; S4: optimal diet; S5: optimal diet + improved postharvest handling and storage; and S6: optimal diet + improved supply chain.
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Table 1. The loss of 10 nutrients caused by FLW in China, 2022 (unit: capita−1 day−1).
Table 1. The loss of 10 nutrients caused by FLW in China, 2022 (unit: capita−1 day−1).
Food ItemCalories
Kcal		Protein
g		Dietary Fiber
g		Vitamin A
µg 		Vitamin C
mg		Vitamin E
mg		Calcium
mg		Magnsium
mg 		Iron
mg		Zinc
mg
Cereals420.0 11.8 5.9 0.0 7.4 1.3 26.1 37.3 3.0 2.4
Roots49.5 1.3 0.6 10.9 0.5 7.3 11.2 19.3 0.5 0.3
Pulses and nuts7.1 0.6 0.1 0.5 0.1 0.0 1.1 0.1 0.0 0.1
Vegetable oils44.3 0.0 0.0 0.0 0.0 0.0 0.6 0.2 0.1 0.1
Vegetable82.0 4.8 4.4 161.1 151.7 2.6 90.3 58.0 4.8 1.8
Fruits30.3 0.4 0.9 14.4 7.3 0.3 8.3 6.2 0.2 0.2
Meat90.1 5.5 0.0 6.6 0.0 0.2 2.3 5.8 0.6 0.7
Eggs13.2 1.2 0.2 34.0 0.0 0.0 0.1 1.1 0.3 0.2
Milk4.9 0.2 0.5 3.7 0.0 0.0 0.0 0.8 0.0 0.0
Fish15.5 2.6 0.8 0.5 0.0 0.0 5.8 3.8 0.2 0.9
Total756.8 28.4 13.5 231.7 167.1 11.8 145.8 132.6 9.8 6.7
Table 2. GHG emissions from FLW across supply chain stages and food categories. Unit: g CO2-eq capita−1 day−1.
Table 2. GHG emissions from FLW across supply chain stages and food categories. Unit: g CO2-eq capita−1 day−1.
Food ItemAgricultural ProductionPostharvest Handling and StorageFood Processing and DistributionWaste of ConsumptionTotal
Cereals28.765.64.332.7131.3
Roots3.613.40.41.819.2
Pulses and nuts0.80.40.10.31.6
Vegetable oils1.70.80.10.32.9
Vegetable29.767.510.119.1126.4
Fruits6.23.77.15.122.1
Meat76.930.616.629.4153.5
Eggs9.23.45.0 4.321.9
Milk3.62.20.63.29.6
Fish8.0 15.66.21039.8
Total168.4203.250.5106.2528.3
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Chen, C.; Fang, Y.; Wang, X. Evaluation of Nutrient Loss and Greenhouse Gas Emissions Caused by Food Loss and Waste in China. Sustainability 2025, 17, 7341. https://doi.org/10.3390/su17167341

AMA Style

Chen C, Fang Y, Wang X. Evaluation of Nutrient Loss and Greenhouse Gas Emissions Caused by Food Loss and Waste in China. Sustainability. 2025; 17(16):7341. https://doi.org/10.3390/su17167341

Chicago/Turabian Style

Chen, Chun, Yiman Fang, and Xiaozhong Wang. 2025. "Evaluation of Nutrient Loss and Greenhouse Gas Emissions Caused by Food Loss and Waste in China" Sustainability 17, no. 16: 7341. https://doi.org/10.3390/su17167341

APA Style

Chen, C., Fang, Y., & Wang, X. (2025). Evaluation of Nutrient Loss and Greenhouse Gas Emissions Caused by Food Loss and Waste in China. Sustainability, 17(16), 7341. https://doi.org/10.3390/su17167341

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