Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf
<p>China’s total carbon emissions account for one-third of the world’s total. How to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060 is an important policy orientation at present. Therefore, it is of great significance to analyze the characteristics and driving...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
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إضافة وسم
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| _version_ | 1852021948538683392 |
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| author | Sanglin Zhao (20915732) |
| author2 | Zhetong Li (20915735) Hao Deng (409186) Xing You (11352431) Jiaang Tong (20915738) Bingkun Yuan (20915741) Zihao Zeng (14076333) |
| author2_role | author author author author author author |
| author_facet | Sanglin Zhao (20915732) Zhetong Li (20915735) Hao Deng (409186) Xing You (11352431) Jiaang Tong (20915738) Bingkun Yuan (20915741) Zihao Zeng (14076333) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sanglin Zhao (20915732) Zhetong Li (20915735) Hao Deng (409186) Xing You (11352431) Jiaang Tong (20915738) Bingkun Yuan (20915741) Zihao Zeng (14076333) |
| dc.date.none.fl_str_mv | 2025-03-21T11:22:36Z |
| dc.identifier.none.fl_str_mv | 10.3389/fenvs.2024.1497941.s001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Supplementary_file_1_Spatial-temporal_evolution_characteristics_and_driving_factors_of_carbon_emission_prediction_in_China-research_on_ARIMA-BP_neural_network_algorithm_pdf/28639070 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Environmental Science carbon emissions ARIMA-BP model LMDI decomposition temporal and spatial evolution standard elliptic difference |
| dc.title.none.fl_str_mv | Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>China’s total carbon emissions account for one-third of the world’s total. How to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060 is an important policy orientation at present. Therefore, it is of great significance to analyze the characteristics and driving factors of temporal and spatial evolution on the basis of effective calculation and prediction of carbon emissions in various provinces for promoting high-quality economic development and realizing carbon emission reduction. Based on the energy consumption data of 30 provinces in China from 2000 to 2021, this paper calculates and predicts the total carbon emissions of 30 provinces in China from 2000 to 2035 based on ARIMA model and BP neural network model, and uses ArcGIS and standard elliptic difference to visually analyze the spatial and temporal evolution characteristics, and further uses LMDI model to decompose the driving factors affecting carbon emissions. The results show that: (1) From 2000 to 2035, China’s total carbon emissions increased year by year, but the growth rate of carbon emissions gradually decreased; The carbon emission structure is “secondary industry > residents’ life > tertiary industry > primary industry”, and the growth rate of carbon in secondary industry and residents’ life is faster, while the change trend of primary industry and tertiary industry is smaller; (2) The spatial distribution of carbon emissions in China’s provinces presents a typical pattern of “eastern > central > western” and “northern > southern”, and the carbon emission centers tend to move to the northwest; (3) The regions with high level of digital economy, advanced industrial structure and new quality productivity have relatively less carbon emissions, which has significant group difference effect; (4) The intensity effect of energy consumption is the main factor driving the continuous growth of carbon emissions, while the per capita GDP and the structure effect of energy consumption are the main factors restraining carbon emissions, while the effects of industrial structure and population size are relatively small. Based on the research conclusion, this paper puts forward some policy suggestions from energy structure, industrial structure, new quality productivity and digital economy.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_e3ed157fc0eb777dbf269fe8e3408c48 |
| identifier_str_mv | 10.3389/fenvs.2024.1497941.s001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28639070 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdfSanglin Zhao (20915732)Zhetong Li (20915735)Hao Deng (409186)Xing You (11352431)Jiaang Tong (20915738)Bingkun Yuan (20915741)Zihao Zeng (14076333)Environmental Sciencecarbon emissionsARIMA-BP modelLMDI decompositiontemporal and spatial evolutionstandard elliptic difference<p>China’s total carbon emissions account for one-third of the world’s total. How to reach the peak of carbon emissions by 2030 and achieve carbon neutrality by 2060 is an important policy orientation at present. Therefore, it is of great significance to analyze the characteristics and driving factors of temporal and spatial evolution on the basis of effective calculation and prediction of carbon emissions in various provinces for promoting high-quality economic development and realizing carbon emission reduction. Based on the energy consumption data of 30 provinces in China from 2000 to 2021, this paper calculates and predicts the total carbon emissions of 30 provinces in China from 2000 to 2035 based on ARIMA model and BP neural network model, and uses ArcGIS and standard elliptic difference to visually analyze the spatial and temporal evolution characteristics, and further uses LMDI model to decompose the driving factors affecting carbon emissions. The results show that: (1) From 2000 to 2035, China’s total carbon emissions increased year by year, but the growth rate of carbon emissions gradually decreased; The carbon emission structure is “secondary industry > residents’ life > tertiary industry > primary industry”, and the growth rate of carbon in secondary industry and residents’ life is faster, while the change trend of primary industry and tertiary industry is smaller; (2) The spatial distribution of carbon emissions in China’s provinces presents a typical pattern of “eastern > central > western” and “northern > southern”, and the carbon emission centers tend to move to the northwest; (3) The regions with high level of digital economy, advanced industrial structure and new quality productivity have relatively less carbon emissions, which has significant group difference effect; (4) The intensity effect of energy consumption is the main factor driving the continuous growth of carbon emissions, while the per capita GDP and the structure effect of energy consumption are the main factors restraining carbon emissions, while the effects of industrial structure and population size are relatively small. Based on the research conclusion, this paper puts forward some policy suggestions from energy structure, industrial structure, new quality productivity and digital economy.</p>2025-03-21T11:22:36ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.3389/fenvs.2024.1497941.s001https://figshare.com/articles/dataset/Supplementary_file_1_Spatial-temporal_evolution_characteristics_and_driving_factors_of_carbon_emission_prediction_in_China-research_on_ARIMA-BP_neural_network_algorithm_pdf/28639070CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/286390702025-03-21T11:22:36Z |
| spellingShingle | Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf Sanglin Zhao (20915732) Environmental Science carbon emissions ARIMA-BP model LMDI decomposition temporal and spatial evolution standard elliptic difference |
| status_str | publishedVersion |
| title | Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf |
| title_full | Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf |
| title_fullStr | Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf |
| title_full_unstemmed | Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf |
| title_short | Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf |
| title_sort | Supplementary file 1_Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm.pdf |
| topic | Environmental Science carbon emissions ARIMA-BP model LMDI decomposition temporal and spatial evolution standard elliptic difference |