首页|工业降碳潜力测算与情景预测——以陕西省为例

工业降碳潜力测算与情景预测——以陕西省为例

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推动工业降碳是实现"双碳"目标的必然选择.文章基于陕西省2011-2020 年工业能源消耗数据,利用IPCC测算法计算陕西省工业碳排放量.基于STIRPAT模型从人口、经济和技术三个方面构建陕西省工业碳排放预测模型,通过设定 3 种情景模式,预测2021-2035年工业碳排放量及实现碳达峰碳中和的时间.结果显示,基准情景下陕西省工业无法实现碳达峰,在低碳情景和强化情景模式下分别在 2030 年和 2025 年实现碳达峰,碳达峰预测值分别为 20911 万吨和 18836 万吨,相较于 2020 年实际工业碳排放量分别增长1911万吨和-164万吨.为此提出建议:加强宣传教育力度,提高民众节能减排参与度;促进工业绿色转型,发展绿色经济;积极研发节能减排技术,加快实现工业碳减排;推进清洁能源的开发和利用,加大能源结构调整力度碳减排,以推动陕西省工业碳减排迈向新高度.
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 re-sults show that the industry in Shaanxi Province cannot achieve a carbon peak under the baseline scenario,alt-hough 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 educa-tion 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.

industrial carbon reductionpotential measurementSTIRPAT modelscenario prediction

王文军、应鑫如、寇晨露

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陕西师范大学国际商学院,西安 710119

双碳 工业降碳 碳排放测算 STIRPAT模型 情景预测

2024

资源与生态学报(英文版)
中国科学院地理科学与资源研究所

资源与生态学报(英文版)

CSTPCD
影响因子:0.388
ISSN:1674-764X
年,卷(期):2024.15(4)