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 |
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| Online Access: | https://neptjournal.com/upload-images/(3)D-1250.pdf |
| Summary: | 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. |
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| ISSN: | 0972-6268 2395-3454 |