Forecasting carbon dioxide emissions in Chongming: a novel hybrid forecasting model coupling gray correlation analysis and deep learning method.
Carbon dioxide emission forecasting
Carbon neutrality
Dual-channel convolutional neural network
Gated recurrent unit
Grey correlation analysis
Journal
Environmental monitoring and assessment
ISSN: 1573-2959
Titre abrégé: Environ Monit Assess
Pays: Netherlands
ID NLM: 8508350
Informations de publication
Date de publication:
17 Sep 2024
17 Sep 2024
Historique:
received:
27
04
2024
accepted:
06
09
2024
medline:
18
9
2024
pubmed:
17
9
2024
entrez:
17
9
2024
Statut:
epublish
Résumé
Predicting regional carbon dioxide (CO2) emissions is essential for advancing toward global carbon neutrality. This study introduces a novel CO2 emissions prediction model tailored to the unique environmental, economic, and energy consumption of Shanghai Chongming. Utilizing an innovative hybrid approach, the study first applies grey relational analysis to evaluate the influence of economic activity, natural conditions, and energy consumption on CO2 emissions. This is followed by the implementation of a dual-channel pooled convolutional neural network (DCNN) that captures both local and global features of the data, enhanced through feature stacking. Gated recurrent unit (GRU) network then assesses the temporal aspects of these features, culminating in precise CO2 emission predictions for the region. The results indicate: (1) The proposed hybrid model achieves accurate predictions based on accounting data, with high precision, low error, and good stability. (2) The study found an overall increase in Chongming's carbon emissions from 2000 to 2022, with the prediction results being generally consistent with existing research findings. (3) The proposed method, based on Chongming's CO2 emission predictions, addresses issues such as the scarcity of effective accounting data and inaccuracies in traditional calculation methods. The results can provide effective technical support for local government policies on carbon reduction and promote sustainable development.
Identifiants
pubmed: 39287717
doi: 10.1007/s10661-024-13092-1
pii: 10.1007/s10661-024-13092-1
doi:
Substances chimiques
Carbon Dioxide
142M471B3J
Air Pollutants
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
941Subventions
Organisme : Science and Technology Innovation Plan of Shanghai Science and Technology Commission
ID : 22DZ1209500
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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