{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T15:10:08Z","timestamp":1733411408359,"version":"3.30.1"},"reference-count":28,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AIS"],"published-print":{"date-parts":[[2023,12,5]]},"abstract":"Detecting carbon emissions is the key to carbon peaking and carbon neutrality goals. Existing research has focused on utilizing data-driven method to study carbon emissions off a single object. This study proposes a regional carbon emissions prediction method. The area objects are divided into dynamic objects for vehicles and static objects for buildings. For the dynamic object, carbon emissions is modeled using the vehicle location provided by the BeiDou satellite navigation system (BDS). For the static object, the neural network R3det (rotation remote sensing target detection) is used to identify the buildings in remote sensing images, and then the trained ARIMA time series model is used to predict the carbon emissions. The model is tested in an industrial park in Tangshan, Hebei Province in China. The result of the regional three-dimensional emission map shows that the method provided a novel and feasible idea for carbon emissions prediction.<\/jats:p>","DOI":"10.3233\/ais-220163","type":"journal-article","created":{"date-parts":[[2023,1,27]],"date-time":"2023-01-27T16:42:59Z","timestamp":1674837779000},"page":"401-417","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of regional carbon emissions using deep learning and mathematical\u2013statistical model"],"prefix":"10.1177","volume":"15","author":[{"given":"Yutao","family":"Mu","sequence":"first","affiliation":[{"name":"International College of Engineering, Changsha University of Science and Technology, Changsha, China"}]},{"given":"Kai","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, China"},{"name":"Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha, China"}]},{"given":"Ronghua","family":"Du","sequence":"additional","affiliation":[{"name":"College of Automotive and Mechanical Engineering, Changsha University of Science and Technology, Changsha, China"},{"name":"Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha, China"}]}],"member":"179","reference":[{"key":"10.3233\/AIS-220163_ref1","first-page":"4","article-title":"Study on the relationship between driving speed and carbon emissions","volume":"3","author":"Cai","year":"2015","journal-title":"Roads and Air Transport"},{"key":"10.3233\/AIS-220163_ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2021.102477"},{"key":"10.3233\/AIS-220163_ref3","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.renene.2021.09.072","article-title":"A novel method for carbon emission forecasting based on Gompertz\u2019s law and fractional grey model: Evidence from American industrial sector","volume":"181","author":"Gao","year":"2022","journal-title":"Renewable Energy"},{"key":"10.3233\/AIS-220163_ref4","doi-asserted-by":"publisher","first-page":"7238","DOI":"10.1016\/j.egyr.2021.10.075","article-title":"Forecasting carbon dioxide emissions in BRICS countries by exponential cumulative grey model","volume":"7","author":"Guo","year":"2021","journal-title":"Energy Reports"},{"key":"10.3233\/AIS-220163_ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2021.114153"},{"key":"10.3233\/AIS-220163_ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2021.102576"},{"issue":"3","key":"10.3233\/AIS-220163_ref7","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.gloenvcha.2004.04.009","article-title":"Another reason for concern: Regional and global impacts on ecosystems for different levels of climate change","volume":"14","author":"Leemans","year":"2004","journal-title":"Global environmental change"},{"key":"10.3233\/AIS-220163_ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnucene.2021.103856"},{"key":"10.3233\/AIS-220163_ref9","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.isprsjprs.2020.04.008","article-title":"Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China","volume":"164","author":"Liu","year":"2020","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"10.3233\/AIS-220163_ref10","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2019.119642"},{"key":"10.3233\/AIS-220163_ref11","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS journal of photogrammetry and remote sensing"},{"key":"10.3233\/AIS-220163_ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.122942"},{"key":"10.3233\/AIS-220163_ref13","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.jclepro.2019.05.153","article-title":"A two-stage methodology based on ensemble adaptive neuro-fuzzy inference system to predict carbon dioxide emissions","volume":"231","author":"Mardani","year":"2019","journal-title":"Journal of cleaner production"},{"key":"10.3233\/AIS-220163_ref14","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1016\/j.rser.2018.05.060","article-title":"Estimation of renewable energy and built environment-related variables using neural networks\u00a0\u2013 A review","volume":"94","author":"Rodrigues","year":"2018","journal-title":"Renewable and Sustainable Energy Reviews"},{"issue":"3","key":"10.3233\/AIS-220163_ref15","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1016\/j.jestch.2019.10.002","article-title":"Multi-response optimization of carbon fiber reinforced polymer (CFRP) drilling using back propagation neural network-particle swarm optimization (BPNN-PSO)","volume":"23","author":"Soepangkat","year":"2020","journal-title":"Engineering Science and Technology, an International Journal"},{"issue":"1","key":"10.3233\/AIS-220163_ref16","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.apr.2018.07.001","article-title":"Applying machine learning methods in managing urban concentrations of traffic-related particulate matter (PM10 and PM2.5)","volume":"10","author":"Suleiman","year":"2019","journal-title":"Atmospheric Pollution Research"},{"key":"10.3233\/AIS-220163_ref17","doi-asserted-by":"publisher","first-page":"2605","DOI":"10.1016\/j.egyr.2021.04.061","article-title":"The impact of energy consumption structure on China\u2019s carbon emissions: Taking the Shannon\u2013Wiener index as a new indicator","volume":"7","author":"Sun","year":"2021","journal-title":"Energy Reports"},{"key":"10.3233\/AIS-220163_ref18","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.ecolind.2014.10.004","article-title":"Quantifying the relationship between urban development intensity and carbon dioxide emissions using a panel data analysis","volume":"49","author":"Wang","year":"2015","journal-title":"Ecological Indicators"},{"key":"10.3233\/AIS-220163_ref19","doi-asserted-by":"crossref","unstructured":"X.Q.\u00a0Wang, C.W.\u00a0Su, O.R.\u00a0Lobon\u0163, H.\u00a0Li and M.\u00a0Nicoleta-Claudia, Is China\u2019s carbon trading market efficient? Evidence from emissions trading scheme pilots, Energy 245 (2022), 123240.","DOI":"10.1016\/j.energy.2022.123240"},{"key":"10.3233\/AIS-220163_ref20","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.petlm.2021.11.001","article-title":"The prospect of natural gas hydrate (NGH) under the vision of peak carbon dioxide emissions in China","volume":"7","author":"Wei","year":"2021","journal-title":"Petroleum"},{"key":"10.3233\/AIS-220163_ref22","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.enpol.2017.05.021","article-title":"Microsimulation of low carbon urban transport policies in Beijing","volume":"107","author":"Yang","year":"2017","journal-title":"Energy Policy"},{"key":"10.3233\/AIS-220163_ref23","doi-asserted-by":"crossref","unstructured":"L.\u00a0Ye, D.\u00a0Yang, Y.\u00a0Dang and J.\u00a0Wang, An enhanced multivariable dynamic time-delay discrete grey forecasting model for predicting China\u2019s carbon emissions, Energy 249 (2022), 123681.","DOI":"10.1016\/j.energy.2022.123681"},{"key":"10.3233\/AIS-220163_ref24","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.procs.2021.05.037","article-title":"Deep learning and remote sensing: Detection of dumping waste using UAV","volume":"185","author":"Youme","year":"2021","journal-title":"Procedia Computer Science"},{"key":"10.3233\/AIS-220163_ref25","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote sensing of environment"},{"key":"10.3233\/AIS-220163_ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2020.102733"},{"key":"10.3233\/AIS-220163_ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102875"},{"key":"10.3233\/AIS-220163_ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110968"},{"key":"10.3233\/AIS-220163_ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2020.102701"}],"container-title":["Journal of Ambient Intelligence and Smart Environments"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/AIS-220163","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T14:39:07Z","timestamp":1733409547000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/AIS-220163"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,5]]},"references-count":28,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/ais-220163","relation":{},"ISSN":["1876-1372","1876-1364"],"issn-type":[{"type":"electronic","value":"1876-1372"},{"type":"print","value":"1876-1364"}],"subject":[],"published":{"date-parts":[[2023,12,5]]}}}