Carbon emission forecasting in Zhejiang Province based on LASSO algorithm and grey model
Under the green and low-carbon development goal of achieving "carbon peaking and carbon neutrality" in China, cyclical analysis and accurate prediction of carbon emissions are of great importance. This paper investigates carbon emissions in Zhejiang Province. First, the variable mode decom...
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| Language: | zho |
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Editorial Office of Energy Environmental Protection
2024-06-01
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| Series: | 能源环境保护 |
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| Online Access: | https://eep1987.com/en/article/4960 |
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| author | HONG Jingke DU Wei SHAO Jin* LAO Huimin |
| author_facet | HONG Jingke DU Wei SHAO Jin* LAO Huimin |
| author_sort | HONG Jingke |
| collection | DOAJ |
| description | Under the green and low-carbon development goal of achieving "carbon peaking and carbon neutrality" in China, cyclical analysis and accurate prediction of carbon emissions are of great importance. This paper investigates carbon emissions in Zhejiang Province. First, the variable mode decomposition method is used to decompose the historical data of carbon emissions in Zhejiang Province, enabling an analysis of its cyclicality fluctuations. Second, the LASSO algorithm is employed to identify the key influencing factors of carbon emissions. Finally, considering the 14th Five-Year Plan and the province′s development trajectory, three development scenarios (normal, low-carbon, and inertia) are assumed, and the GM (1, N) model is used to predict the carbon emissions in Zhejiang Province from 2020 to 2030. The analysis reveals that the dominant factors affecting carbon emissions in Zhejiang Province are the proportion of the third industry in GDP, the number of private cars, the total fixed asset investment in the province, the total electricity consumption, R&D intensity, and technology market turnover. Under the low-carbon scenario, carbon emissions are projected to peak at 400.28 Mt in 2030. In contrast, under the normal scenario, carbon emissions are estimated to reach 474.23 Mt, while the inertia development scenario predicts carbon emissions of 568.77 Mt. Furthermore, carbon emissions are expected to continue rising beyond 2030 in the normal and inertia development scenarios. In light of these findings, It is recommended that Zhejiang Province should focus on optimizing its industrial structure, improving energy efficiency, increasing investment in low-carbon research and development, and steadily advancing the goal of "carbon peaking" . |
| format | Article |
| id | doaj-art-e4932c05af824bce914ee14b26d0d17f |
| institution | DOAJ |
| issn | 2097-4183 |
| language | zho |
| publishDate | 2024-06-01 |
| publisher | Editorial Office of Energy Environmental Protection |
| record_format | Article |
| series | 能源环境保护 |
| spelling | doaj-art-e4932c05af824bce914ee14b26d0d17f2025-08-20T03:09:24ZzhoEditorial Office of Energy Environmental Protection能源环境保护2097-41832024-06-0138315216110.20078/j.eep.20240101Carbon emission forecasting in Zhejiang Province based on LASSO algorithm and grey modelHONG Jingke0DU Wei1SHAO Jin* 2LAO Huimin3School of Management Science and Real Estate, Chongqing UniversitySchool of Management Science and Real Estate, Chongqing UniversitySchool of Management Science and Real Estate, Chongqing UniversityInstitute of Science and Technology Information of Zhejiang ProvinceUnder the green and low-carbon development goal of achieving "carbon peaking and carbon neutrality" in China, cyclical analysis and accurate prediction of carbon emissions are of great importance. This paper investigates carbon emissions in Zhejiang Province. First, the variable mode decomposition method is used to decompose the historical data of carbon emissions in Zhejiang Province, enabling an analysis of its cyclicality fluctuations. Second, the LASSO algorithm is employed to identify the key influencing factors of carbon emissions. Finally, considering the 14th Five-Year Plan and the province′s development trajectory, three development scenarios (normal, low-carbon, and inertia) are assumed, and the GM (1, N) model is used to predict the carbon emissions in Zhejiang Province from 2020 to 2030. The analysis reveals that the dominant factors affecting carbon emissions in Zhejiang Province are the proportion of the third industry in GDP, the number of private cars, the total fixed asset investment in the province, the total electricity consumption, R&D intensity, and technology market turnover. Under the low-carbon scenario, carbon emissions are projected to peak at 400.28 Mt in 2030. In contrast, under the normal scenario, carbon emissions are estimated to reach 474.23 Mt, while the inertia development scenario predicts carbon emissions of 568.77 Mt. Furthermore, carbon emissions are expected to continue rising beyond 2030 in the normal and inertia development scenarios. In light of these findings, It is recommended that Zhejiang Province should focus on optimizing its industrial structure, improving energy efficiency, increasing investment in low-carbon research and development, and steadily advancing the goal of "carbon peaking" .https://eep1987.com/en/article/4960carbon emissionslasso algorithmgm (1，n)forecasting |
| spellingShingle | HONG Jingke DU Wei SHAO Jin* LAO Huimin Carbon emission forecasting in Zhejiang Province based on LASSO algorithm and grey model 能源环境保护 carbon emissions lasso algorithm gm (1，n) forecasting |
| title | Carbon emission forecasting in Zhejiang Province based on LASSO algorithm and grey model |
| title_full | Carbon emission forecasting in Zhejiang Province based on LASSO algorithm and grey model |
| title_fullStr | Carbon emission forecasting in Zhejiang Province based on LASSO algorithm and grey model |
| title_full_unstemmed | Carbon emission forecasting in Zhejiang Province based on LASSO algorithm and grey model |
| title_short | Carbon emission forecasting in Zhejiang Province based on LASSO algorithm and grey model |
| title_sort | carbon emission forecasting in zhejiang province based on lasso algorithm and grey model |
| topic | carbon emissions lasso algorithm gm (1，n) forecasting |
| url | https://eep1987.com/en/article/4960 |
| work_keys_str_mv | AT hongjingke carbonemissionforecastinginzhejiangprovincebasedonlassoalgorithmandgreymodel AT duwei carbonemissionforecastinginzhejiangprovincebasedonlassoalgorithmandgreymodel AT shaojin carbonemissionforecastinginzhejiangprovincebasedonlassoalgorithmandgreymodel AT laohuimin carbonemissionforecastinginzhejiangprovincebasedonlassoalgorithmandgreymodel |