{
  "abstract": "Neighborhood-scale energy planning is often constrained by limited and inconsistent ground-truth data. Although community-led data collection has been effective in other sectors, its application to building energy modeling and retrofit decision-making remains limited. This study presents a community-led data collection and modeling workflow that couples resident-gathered measurements with physics-based analysis and a transparent retrofit decision framework. In Georgetown, PE, Canada, a research team and trained volunteers collected thermographic images, energy-systems data, occupancy, building characteristics, etc., for 157 residential buildings using research ethics-approved protocols and standardized kits. Infrared images, fused with local weather data, drive an exterior-surface heat-balance model to estimate building envelope thermal resistances. Validations against in-situ heat flux meters (walls of 8 buildings) are performed along with a comparative analysis with energy audit data for 24 buildings. A composite envelope performance score (EPS) ranked retrofit tiers and identified systematic shortfalls guiding targeted retrofit strategies. Demonstrated in a real-world case study, the community-led protocol, processing pipeline, and decision method are portable and scalable to other communities with different housing stocks and envelope measurements. The originality of this study lies in its first-time designation of a standardized community-led protocol that integrates physics-based modeling and retrofit decision rules at the neighborhood scale. This integration transforms resident-gathered observations into simulation-ready evidence and actionable retrofit pathways in energy planning.",
  "abstract_shown": true,
  "affiliations": [
    "Future Urban and Energy Lab for Sustainability (FUEL-S), Faculty of Sustainable Design Engineering (FSDE), University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada",
    "Institute of Island Studies, University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada",
    "Canadian Center for Climate Change and Adaptation, University of Prince Edward Island, St Peter’s C0A 2A0, Canada",
    "Future Urban and Energy Lab for Sustainability (FUEL-S), Faculty of Sustainable Design Engineering (FSDE), University of Prince Edward Island, Charlottetown, PE C1A 4P3, Canada; Corresponding author."
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Sudipta Debnath"
    },
    {
      "affs": [
        0
      ],
      "name": "Noushad A.C. Mohammed"
    },
    {
      "affs": [
        0
      ],
      "name": "Misbaudeen A. Adesanya"
    },
    {
      "affs": [
        1
      ],
      "name": "Andrew Halliday"
    },
    {
      "affs": [
        2
      ],
      "name": "Aitazaz A. Farooque"
    },
    {
      "affs": [
        3
      ],
      "name": "Kuljeet S. Grewal"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3e02ns0qqvjss.bib",
  "cited_by": 0,
  "doi": "10.1016/j.enbenv.2026.02.009",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S2666123326000231",
  "id": "3e02ns0qqvjss",
  "issns": [
    "2666-1233"
  ],
  "landing_url": "https://doi.org/10.1016/j.enbenv.2026.02.009",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "TD1-1066"
    ],
    [
      "lcc",
      "TH1-9745"
    ],
    [
      "openalex",
      "2215|Building Energy and Comfort Optimization"
    ]
  ],
  "openalex": "W7131677102",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "KeAi Communications Co., Ltd.",
  "ris": "https://research.draconitic.com/paper/3e02ns0qqvjss.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:31Z",
      "native_id": "5970960da30746e5b82f3333aa531d2c",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.enbenv.2026.02.009"
    }
  ],
  "subjects": [
    {
      "field": "earth",
      "origin": "lcc:TD1-1066",
      "subfield": "environmental-science"
    }
  ],
  "title": "Community-led data collection approach leading to energy labeling and retrofit decision framework with case application",
  "title_text": "Community-led data collection approach leading to energy labeling and retrofit decision framework with case application",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:31Z",
  "url": "https://research.draconitic.com/paper/3e02ns0qqvjss",
  "venue": "Energy and Built Environment",
  "versions": []
}