Governance models based on subject participation and their effectiveness regarding anti-food waste in China

Purpose This study aims to classify anti-food waste governance models based on participation from stakeholders, evaluate and predict the dynamic waste reduction effectiveness of different models, and select the optimal governance scheme to reduce food waste, contributing to the SDGs. Design/methodology/approach Based on 489 policy texts in China from 1955 to 2023, this study identifies governance subjects, classifies governance models, evaluates their effectiveness in curbing typical categories of food waste and further predicts the main amounts of food waste under different governance-model-oriented scenarios for 2024–2030 to identify the optimal scheme. Findings In model classification, the primary models are government-led, government-enterprise cooperation and social organization-facilitated. In effectiveness evaluation, these models significantly reduce waste of vegetables, milk, soybeans, fruits, nuts and eggs. Different models vary in effectiveness in restraining different types of food waste. Classification governance can be implemented in the future. Anti-food waste governance exhibits a lag, and the simple superposition of governance models cannot effectively inhibit food waste. In scenario prediction, the amount of food waste in China from 2024 to 2030 shows an overall upward trend and will not peak in the short term. However, the study combines the effectiveness of different models to identify the optimal governance scheme. Originality/value Classifying governance models based on participation enables precise identification of weak governance links and facilitates targeted incentives. Dynamic effectiveness evaluation and prediction provide an empirical basis for evidence-based policymaking.

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Publication Details

Journal
British Food Journal
Published
2025-10-28
DOI
https://doi.org/10.1108/bfj-04-2025-0520
Primary Topic
Food Waste Reduction and Sustainability
Type
article
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article

Governance models based on subject participation and their effectiveness regarding anti-food waste in China

Jing Hou, Shiyan Jiang, Feiyu Chen, Guangyu Liu et al.
British Food Journal
Food Waste Reduction and Sustainability
article

Governance models based on subject participation and their effectiveness regarding anti-food waste in China

Jing Hou, Shiyan Jiang, Feiyu Chen, Guangyu Liu, Fan-Min Ran
article en

Abstract

Purpose This study aims to classify anti-food waste governance models based on participation from stakeholders, evaluate and predict the dynamic waste reduction effectiveness of different models, and select the optimal governance scheme to reduce food waste, contributing to the SDGs. Design/methodology/approach Based on 489 policy texts in China from 1955 to 2023, this study identifies governance subjects, classifies governance models, evaluates their effectiveness in curbing typical categories of food waste and further predicts the main amounts of food waste under different governance-model-oriented scenarios for 2024–2030 to identify the optimal scheme. Findings In model classification, the primary models are government-led, government-enterprise cooperation and social organization-facilitated. In effectiveness evaluation, these models significantly reduce waste of vegetables, milk, soybeans, fruits, nuts and eggs. Different models vary in effectiveness in restraining different types of food waste. Classification governance can be implemented in the future. Anti-food waste governance exhibits a lag, and the simple superposition of governance models cannot effectively inhibit food waste. In scenario prediction, the amount of food waste in China from 2024 to 2030 shows an overall upward trend and will not peak in the short term. However, the study combines the effectiveness of different models to identify the optimal governance scheme. Originality/value Classifying governance models based on participation enables precise identification of weak governance links and facilitates targeted incentives. Dynamic effectiveness evaluation and prediction provide an empirical basis for evidence-based policymaking.

British Food JournalVol. 128(2)
Jiangsu Normal University (CN), Nanjing Normal University (CN), China University of Mining and Technology (CN)
Openalex Percentile: Top 63%
Food Waste Reduction and Sustainability
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