{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:14:18Z","timestamp":1760145258206,"version":"build-2065373602"},"reference-count":71,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T00:00:00Z","timestamp":1719446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2023YFB3907500","131211KYSB20180002"],"award-info":[{"award-number":["2023YFB3907500","131211KYSB20180002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"International Partnership Program of Chinese Academy of Science","award":["2023YFB3907500","131211KYSB20180002"],"award-info":[{"award-number":["2023YFB3907500","131211KYSB20180002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimating city\u2013scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city\u2013scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global\u2013regional\u2013city scales. Our findings reveal the following: (1) Kalman fusion post\u2013reconstruction reduces estimate uncertainties for 2000\u20132014 and 2015\u20132021 to \u00b19.77% and \u00b111.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long\u2013term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a \u22120.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a \u201ccore\u2013subcore\u2013periphery\u201d emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second\u2013largest emitter, and HK as the seventh\u2013highest emitter. Factors influencing the center\u2013shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision\u2013making accuracy for policymakers.<\/jats:p>","DOI":"10.3390\/rs16132354","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T08:57:50Z","timestamp":1719478670000},"page":"2354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Advancing Regional\u2013Scale Spatio\u2013Temporal Dynamics of FFCO2 Emissions in Great Bay Area"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7838-9832","authenticated-orcid":false,"given":"Jing","family":"Zhao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"National Earth Observation Data Center, Beijing 100094, China"}]},{"given":"Qunqun","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1710-8301","authenticated-orcid":false,"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Guoqing","family":"Li","sequence":"additional","affiliation":[{"name":"National Earth Observation Data Center, Beijing 100094, China"},{"name":"Satellite Data Technology Research, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Tuo","family":"Wang","sequence":"additional","affiliation":[{"name":"National Engineering Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Naixia","family":"Mou","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Tengfei","family":"Yang","sequence":"additional","affiliation":[{"name":"National Earth Observation Data Center, Beijing 100094, China"},{"name":"Satellite Data Technology Research, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"key":"ref_1","unstructured":"IPCC (2014). 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