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Assessing Sustainable Development Goal Alignment in Local Food Systems: Insights from an Automated Text Analysis of the Organizational Literature

Author

Listed:
  • Coralie Gaudreau

    (Faculty of Nursing Sciences, Public/Community Health Programs, Université Laval, Quebec City, QC G1V 0A6, Canada)

  • Arbi Chouikh

    (Faculty of Administrative Sciences, Université Laval, Quebec City, QC G1V 0A6, Canada)

  • Laurence Guillaumie

    (Faculty of Nursing Sciences, Public/Community Health Programs, Université Laval, Quebec City, QC G1V 0A6, Canada
    Centre de Recherche du CHU de Québec, Université Laval, Quebec City, QC G1V 4G2, Canada
    Centre NUTRISS—INAF, Université Laval, Quebec City, QC G1V 0A6, Canada)

  • Daniel Forget

    (Vice-Rectorate of International Affairs and Sustainable Development, Université Laval, Quebec City, QC G1V 0A6, Canada)

  • Stéphane Roche

    (Faculty of Forestry, Geography and Geomatics, Université Laval, Quebec City, QC G1V 0A6, Canada
    Institute Environment, Development and Society (EDS), Université Laval, Quebec City, QC G1V 0A6, Canada)

Abstract
There is growing interest in assessing local food systems to guide efforts toward sustainability and aligning these assessments with the United Nations’ 17 Sustainable Development Goals (SDGs). However, the complexity of portraying local food systems poses numerous challenges for local communities, and automated text analysis and artificial intelligence (AI) offer promising solutions. This study tested the use of an automated textual analysis to assess the alignment of the Mauricie region’s food system in Quebec, Canada, with the SDGs. The analysis examined 35 organizational documents from the region using an automated text analysis based on a list of keywords for each SDG. Initially, the analysis revealed that several initiatives in the Mauricie region covered specific SDGs quite well, such as eliminating hunger (SDG 2). Areas such as health and well-being (SDG 3) received moderate attention, while SDGs such as life below water and on land (SDGs 14 and 15) were less emphasized. When these results were presented to regional stakeholders, these stakeholders reported that the findings did not closely reflect their perceptions of the food system. This study confirms the potential of automated textual analysis and AI in assessing local food systems and underscores the parameters and challenges of accurately portraying sustainability in local food systems.

Suggested Citation

  • Coralie Gaudreau & Arbi Chouikh & Laurence Guillaumie & Daniel Forget & Stéphane Roche, 2024. "Assessing Sustainable Development Goal Alignment in Local Food Systems: Insights from an Automated Text Analysis of the Organizational Literature," Social Sciences, MDPI, vol. 13(11), pages 1-13, October.
    • Handle: RePEc:gam:jscscx:v:13:y:2024:i:11:p:582-:d:1508361
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    References listed on IDEAS

    as
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    4. Cristiana Peano & Nadia Tecco & Egidio Dansero & Vincenzo Girgenti & Francesco Sottile, 2015. "Evaluating the Sustainability in Complex Agri-Food Systems: The SAEMETH Framework," Sustainability, MDPI, vol. 7(6), pages 1-21, May.
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