Industrial Carbon Reduction Potential Measurement and Scenario Prediction in Shaanxi Province

Promoting industrial carbon reduction is an inevitable step for achieving the Chinese carbon peak and neutrality targets. Based on the industrial energy consumption data of Shaanxi Province from 2011 to 2020, this study uses the IPCC calculation method to calculate the industrial carbon emissions in Shaanxi Province. The prediction model for industrial carbon emissions in Shaanxi Province was constructed based on the STIRPAT model from three aspects: population, economy, and technology. By setting three scenario models, the industrial carbon emissions from 2021 to 2035 and the time to achieve peak carbon neutrality were then predicted. The results show that the industry in Shaanxi Province cannot achieve a carbon peak under the baseline scenario, although it can achieve carbon peaking in 2030 under a low-carbon scenario or in 2025 under an enhanced low-carbon scenario. The predicted carbon peak values are 209.11 million t and 188.36 million t, respectively. Based on the results of this study, four policy recommendations are proposed: (1) strengthen publicity and education efforts to increase public participation in energy conservation and emission reduction; (2) promote the green transformation of industry and develop a green economy, including the active development of energy-saving and emission reduction technologies; (3) accelerate the implementation of industrial carbon reduction; and (4) promote the development and utilization of clean energy and increase efforts to adjust the energy structure.

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Publication Details

Journal
Journal of Resources and Ecology
Published
2024-08-12
DOI
https://doi.org/10.5814/j.issn.1674-764x.2024.04.007
Citations
2
Primary Topic
Regional Development and Environment
Type
article
Field-Weighted Citation Impact
2.26
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article

Industrial Carbon Reduction Potential Measurement and Scenario Prediction in Shaanxi Province

Wenjun Wang, Kou Chenlu, Ying Xinru
2 citations
Journal of Resources and Ecology
Regional Development and Environment
2.26
article

Industrial Carbon Reduction Potential Measurement and Scenario Prediction in Shaanxi Province

Wenjun Wang, Kou Chenlu, Ying Xinru
article en
2 citations

Abstract

Promoting industrial carbon reduction is an inevitable step for achieving the Chinese carbon peak and neutrality targets. Based on the industrial energy consumption data of Shaanxi Province from 2011 to 2020, this study uses the IPCC calculation method to calculate the industrial carbon emissions in Shaanxi Province. The prediction model for industrial carbon emissions in Shaanxi Province was constructed based on the STIRPAT model from three aspects: population, economy, and technology. By setting three scenario models, the industrial carbon emissions from 2021 to 2035 and the time to achieve peak carbon neutrality were then predicted. The results show that the industry in Shaanxi Province cannot achieve a carbon peak under the baseline scenario, although it can achieve carbon peaking in 2030 under a low-carbon scenario or in 2025 under an enhanced low-carbon scenario. The predicted carbon peak values are 209.11 million t and 188.36 million t, respectively. Based on the results of this study, four policy recommendations are proposed: (1) strengthen publicity and education efforts to increase public participation in energy conservation and emission reduction; (2) promote the green transformation of industry and develop a green economy, including the active development of energy-saving and emission reduction technologies; (3) accelerate the implementation of industrial carbon reduction; and (4) promote the development and utilization of clean energy and increase efforts to adjust the energy structure.

Journal of Resources and EcologyVol. 15(4)
Shaanxi Normal University (CN)
Openalex Percentile: Top 10%
Regional Development and Environment
2.26
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Industrial Carbon Reduction Potential Measurement and Scenario Prediction in Shaanxi Province — Wenjun Wang, Kou Chenlu, et al. · Journal of Resources and Ecology (2024) | TGRS Research Map | TGRS