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    <identifier>10.57760/sciencedb.19316</identifier>
    <datestamp>2025-01-03T10:35:57Z</datestamp>
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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:date>2025-01-03</dc:date>
  <dc:title>Carbon emissions forecast for each province from 2000 to 2035 (ARIMA-BP model)</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.19316</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>This data uses raw carbon emission data from statistical yearbooks, calculates carbon emissions using the carbon emission factor method, and predicts future carbon emission data for various provinces in China using the ARIMA-BP model. (Due to missing data, excluding data from Hong Kong, Macau, and Taiwan), the data is the process data from the SCI paper &amp;quot;Spatial temporal evolution characteristics and driving factors of carbon emission prediction in China search on ARIMA-BP neural network algorithm&amp;quot;&amp;nbsp;</dc:description>
  <dc:subject> carbon; carbon emission; LMDI;  prediction</dc:subject>
  <dc:creator>Zhao Sanglin</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.3389/fenvs.2024.1497941</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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