{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T16:32:22Z","timestamp":1772037142348,"version":"3.50.1"},"reference-count":88,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences.","award":["KF-2022"],"award-info":[{"award-number":["KF-2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Urbanization has led to rapid growth in energy consumption and CO2 emissions in the building sector. Building operation emissions (BCEs) are a major part of emissions in the building life cycle. Existing studies have attempted to estimate fine-scale BCEs using remote sensing data. However, there is still a lack of research on estimating long-term BCEs by integrating multi-source remote sensing data and applications in different regions. We selected the Beijing\u2013Tianjin\u2013Hebei (BTH) urban agglomeration and the National Capital Region of Japan (NCRJ) as research areas for this study. We also built multiple linear regression (MLR) models between prefecture-level BCEs and multi-source remote sensing data. The prefecture-level BCEs were downscaled to grid scale at a 1 km2 resolution. The estimation results verify the method\u2019s difference and accuracy at different development stages. The multi-scale BCEs showed a continuous growth trend in the BTH urban agglomeration and a significant downward trend in the NCRJ. The decrease in energy intensity and population density were the main factors contributing to the negative growth of BCEs, whereas GDP per capita and urban expansion significantly promoted it. Through our methods and analyses, we contribute to the study of estimating greenhouse gas emissions with remote sensing and exploring the environmental impact of urban growth.<\/jats:p>","DOI":"10.3390\/rs15082204","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T07:51:17Z","timestamp":1682063477000},"page":"2204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Mapping and Influencing the Mechanism of CO2 Emissions from Building Operations Integrated Multi-Source Remote Sensing Data"],"prefix":"10.3390","volume":"15","author":[{"given":"You","family":"Zhao","sequence":"first","affiliation":[{"name":"Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5284-8113","authenticated-orcid":false,"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"College of Resource and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenchen","family":"Jiang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographic Information System, School of Earth and Space Sciences, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinnan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Oceanography, Shanghai Jiao Tong University, Shanghai 200030, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2019). 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