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<p>Local food systems are crucial in addressing contemporary issues of food security, environmental sustainability, and community resilience. We assess the sustainability of local food systems by aggregating evidence from 52 random studies using a meta-analytic mode analysis in JASP 0.95.4, employing the maximum likelihood method (MLM). The study examines the environmental, economic, and social impacts of local production, distribution, consumption, and waste management. The MLM outcomes show positive, statistically significant model coefficients, indicating that local food systems as a whole have a meaningful impact on sustainability indicators. Model fit statistics, residual heterogeneity tests, and funnel plot diagnostics support the robustness of findings with no firm indication of publication bias. The findings indicate that environmental gains extend beyond reduced food miles; economic benefits are offset by changes in local markets, and social benefits are influenced by increased consumption of fresh foods and community participation. These insights highlight the need for an interconnected, place-based approach to food. The results provide relevant policy and entrepreneur recommendations for enhancing the design of sustainable food systems, resilient local economies, and inclusive, community-centred food initiatives.</p>

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Assessing sustainable local food systems using a maximum likelihood–based meta-analysis: a global synthesis for policymakers and entrepreneurs

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Jiangang Feng,
  • Usman Akbar,
  • Sheena Kumari,
  • Sayed Fayaz Ahmad,
  • Wajahat Ali

摘要

Local food systems are crucial in addressing contemporary issues of food security, environmental sustainability, and community resilience. We assess the sustainability of local food systems by aggregating evidence from 52 random studies using a meta-analytic mode analysis in JASP 0.95.4, employing the maximum likelihood method (MLM). The study examines the environmental, economic, and social impacts of local production, distribution, consumption, and waste management. The MLM outcomes show positive, statistically significant model coefficients, indicating that local food systems as a whole have a meaningful impact on sustainability indicators. Model fit statistics, residual heterogeneity tests, and funnel plot diagnostics support the robustness of findings with no firm indication of publication bias. The findings indicate that environmental gains extend beyond reduced food miles; economic benefits are offset by changes in local markets, and social benefits are influenced by increased consumption of fresh foods and community participation. These insights highlight the need for an interconnected, place-based approach to food. The results provide relevant policy and entrepreneur recommendations for enhancing the design of sustainable food systems, resilient local economies, and inclusive, community-centred food initiatives.