Analysis of Carbon Emission and Its Temporal and Spatial Distribution in County-Level: A Case Study of Henan Province, China
Estimating carbon emissions and assessing their contribution are critical steps toward China’s objective of reaching a “carbon peak” in 2030 and “carbon neutrality” in 2060. This paper selects relevant statistical data on carbon emissions from 2000 to 2018, combines the emission coefficient method a...
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| Format: | Article |
| Language: | English |
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Technoscience Publications
2022-06-01
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| Series: | Nature Environment and Pollution Technology |
| Subjects: | |
| Online Access: | https://neptjournal.com/upload-images/(3)D-1250.pdf |
| _version_ | 1811240462370996224 |
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| author | Sen Li, Yanwen Lan and Lijun Guo |
| author_facet | Sen Li, Yanwen Lan and Lijun Guo |
| author_sort | Sen Li, Yanwen Lan and Lijun Guo |
| collection | DOAJ |
| description | Estimating carbon emissions and assessing their contribution are critical steps toward China’s
objective of reaching a “carbon peak” in 2030 and “carbon neutrality” in 2060. This paper selects
relevant statistical data on carbon emissions from 2000 to 2018, combines the emission coefficient
method and the Logarithmic Mean Divisia Index model (LMDI) to calculate carbon emissions, and
analyses the driving force of carbon emission growth using Henan Province as a case study. Based on
the partial least squares regression analysis model (PLS), the contributions of inter-provincial factors
of carbon emission are analyzed. Finally, a county-level downscaling estimation model of carbon
emission is further formulated to analyze the temporal and spatial distribution of carbon emissions and
their evolution. The research results show that: 1) The effect of energy intensity is responsible for 82
percent of the increase in carbon emissions, whereas the effect of industrial structure is responsible
for -8 percent of the increase in carbon emissions. 2) The proportion of secondary industry and energy
intensity, which are 1.64 and 0.82, respectively, have the most evident explanatory effect on total carbon
emissions; 3). Carbon emissions vary widely among counties, with high emissions in the central and
northern regions and low emissions in the southern. However, their carbon emissions have constantly
decreased over time. 4) The number of high-emission counties, their carbon emissions, and the degree
of their discrepancies are gradually reduced. The findings serve as a foundation for relevant agencies
to gain a macro-level understanding of the industrial landscape and to investigate the feasibility of
carbon emission reduction programs. |
| first_indexed | 2024-04-12T13:19:45Z |
| format | Article |
| id | doaj.art-8341f078c764466689c00ca10d8eb900 |
| institution | Directory Open Access Journal |
| issn | 0972-6268 2395-3454 |
| language | English |
| last_indexed | 2024-04-12T13:19:45Z |
| publishDate | 2022-06-01 |
| publisher | Technoscience Publications |
| record_format | Article |
| series | Nature Environment and Pollution Technology |
| spelling | doaj.art-8341f078c764466689c00ca10d8eb9002022-12-22T03:31:31ZengTechnoscience PublicationsNature Environment and Pollution Technology0972-62682395-34542022-06-0121244745610.46488/NEPT.2022.v21i02.003Analysis of Carbon Emission and Its Temporal and Spatial Distribution in County-Level: A Case Study of Henan Province, ChinaSen Li, Yanwen Lan and Lijun GuoEstimating carbon emissions and assessing their contribution are critical steps toward China’s objective of reaching a “carbon peak” in 2030 and “carbon neutrality” in 2060. This paper selects relevant statistical data on carbon emissions from 2000 to 2018, combines the emission coefficient method and the Logarithmic Mean Divisia Index model (LMDI) to calculate carbon emissions, and analyses the driving force of carbon emission growth using Henan Province as a case study. Based on the partial least squares regression analysis model (PLS), the contributions of inter-provincial factors of carbon emission are analyzed. Finally, a county-level downscaling estimation model of carbon emission is further formulated to analyze the temporal and spatial distribution of carbon emissions and their evolution. The research results show that: 1) The effect of energy intensity is responsible for 82 percent of the increase in carbon emissions, whereas the effect of industrial structure is responsible for -8 percent of the increase in carbon emissions. 2) The proportion of secondary industry and energy intensity, which are 1.64 and 0.82, respectively, have the most evident explanatory effect on total carbon emissions; 3). Carbon emissions vary widely among counties, with high emissions in the central and northern regions and low emissions in the southern. However, their carbon emissions have constantly decreased over time. 4) The number of high-emission counties, their carbon emissions, and the degree of their discrepancies are gradually reduced. The findings serve as a foundation for relevant agencies to gain a macro-level understanding of the industrial landscape and to investigate the feasibility of carbon emission reduction programs.https://neptjournal.com/upload-images/(3)D-1250.pdfcarbon emission, driving force analysis, down-scaling temporal and spatial, distribution |
| spellingShingle | Sen Li, Yanwen Lan and Lijun Guo Analysis of Carbon Emission and Its Temporal and Spatial Distribution in County-Level: A Case Study of Henan Province, China Nature Environment and Pollution Technology carbon emission, driving force analysis, down-scaling temporal and spatial, distribution |
| title | Analysis of Carbon Emission and Its Temporal and Spatial Distribution in County-Level: A Case Study of Henan Province, China |
| title_full | Analysis of Carbon Emission and Its Temporal and Spatial Distribution in County-Level: A Case Study of Henan Province, China |
| title_fullStr | Analysis of Carbon Emission and Its Temporal and Spatial Distribution in County-Level: A Case Study of Henan Province, China |
| title_full_unstemmed | Analysis of Carbon Emission and Its Temporal and Spatial Distribution in County-Level: A Case Study of Henan Province, China |
| title_short | Analysis of Carbon Emission and Its Temporal and Spatial Distribution in County-Level: A Case Study of Henan Province, China |
| title_sort | analysis of carbon emission and its temporal and spatial distribution in county level a case study of henan province china |
| topic | carbon emission, driving force analysis, down-scaling temporal and spatial, distribution |
| url | https://neptjournal.com/upload-images/(3)D-1250.pdf |
| work_keys_str_mv | AT senliyanwenlanandlijunguo analysisofcarbonemissionanditstemporalandspatialdistributionincountylevelacasestudyofhenanprovincechina |