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  <doi_batch_id>aspg-38-4532-1793997060</doi_batch_id>
  <timestamp>20261106203100</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Sustainable Development and Green Technology</full_title>
    <abbrev_title>JSDGT</abbrev_title>
    <issn media_type="electronic">2836-5399</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <journal_volume>
     <volume>6</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Designing Robust Household Food Waste Targets for Sustainable Consumption Policy</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Miswan</given_name>
      <surname>Gumanti</surname>
      <affiliations>
       <institution>
        <institution_name>Institut Bakti Nusantara, Lampung, Indonesia</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Aa</given_name>
      <surname>Hubur</surname>
      <affiliations>
       <institution>
        <institution_name>Universitas Trisakti, Jakarta, Indonesia</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Decisions must be made both about how food-waste target reductions are allocated and what evidence is used to demonstrate progress. This study develops a bounded policy-accounting framework using the United Nations Environment Programme Food Waste Index 2024. The core sample comprises 38 economies with high-confidence or Eurostat-tagged estimates, representing 82.03 million tonnes of annual household waste. A 50% aggregate reduction is allocated using proportional reductions, equal kilogram reductions, and an explicitly assumed 60% reducibility cap. For the same objective of 41.01 million tonnes, country reduction rates are 50%, 30.43–60%, and 0–60%, respectively. Rankings by per-capita waste and total waste mass share only one economy among their respective top-five lists, and have a Spearman correlation of 0.222. A measured 50% reduction under measurement-error bounds of ±10% corresponds to a true reduction of 38.89–59.09%. Country distinctions also become less clear as error bounds widen. The results support separating rules for allocating physical targets, choosing interventions, and certifying progress. The selected allocation principle should be made public, tested against waste-composition data, and accompanied by comparable measurements before progress toward targets is certified.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <pages>
     <first_page>38</first_page>
     <last_page>52</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">4532</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/JSDGT.060205</doi>
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