| 基于LMDI和系统动力学的陕西省CO2减排潜力估算 |
| 摘要点击 285 全文点击 45 投稿时间：2024-07-10 修订日期：2024-09-24 |
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| 中文关键词 陕西省碳排放 对数平均迪氏指数（LMDI）模型 系统动力学 情景分析法 减排政策 |
| 英文关键词 Shaanxi carbon emissions extended log-mean Dee's index （LMDI） model system dynamics scenario analysis emission reduction policy |
| DOI 10.13227/j.hjkx.202407100 |
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| 中文摘要 |
| 采用扩展的对数平均迪氏指数（LMDI）模型和系统动力学（SD）模型,探究2007~2021年陕西省CO2排放变化的影响因素,并预测陕西省2022~2035年的减排潜力. 结果表明：①陕西省CO2排放量从2007年的103.14 Mt增长到2021年的336.87 Mt,平均年增长率为7.14%,各部门碳强度总体呈下降趋势. ②在LMDI分解结果中,人均GDP增长对CO2排放的促进作用最大,其次是能源结构和民用车辆拥有量. 能源强度对CO2排放的抑制作用最大,其次是产业结构、居民能源强度和平均车辆产值. ③基准情景下,陕西省CO2排放量将持续增长,直至2035年,其排放量将是2021年的1.75倍. ④在综合减排措施下,该区域可在2030年实现碳排放达峰,基准情景下,2030年陕西省碳强度比2007年减低71.67%. ⑤在单项减排政策中,经济规模情景最为有效,其次是结构优化情景,此外,能源强度优化、新能源车情景的潜力较小. 本研究为陕西省制定低碳政策,促进碳减排,早日实现碳排放达峰提供了有效的数据支撑. |
| 英文摘要 |
| The extended logarithmic mean Dee's index （LMDI） model and system dynamics （SD） model were used to explore the influencing factors of CO2 emissions in Shaanxi Province from 2007 to 2021 and to predict the emission reduction potential of Shaanxi Province from 2022 to 2035. The results showed that： ① The CO2 emissions in Shaanxi Province increased from 103.14 Mt in 2007 to 336.87 Mt in 2021, with an average annual growth rate of 7.14%, and the carbon intensity of various sectors generally showed a downward trend. ② In the LMDI decomposition results, the growth of per capita GDP had the greatest promoting effect on CO2 emissions, followed by that of energy structure and civilian vehicle ownership. Energy intensity had the greatest inhibitory effect on CO2 emissions, followed by that of industrial structure, residents' energy intensity, and average vehicle output value. ③ Under the baseline scenario, CO2 emissions in Shaanxi Province will continue to grow until 2035, and its emissions will be 1.75 times that of 2021. ④ Under comprehensive emission reduction measures, the region could achieve carbon emission peak in 2030. Under the baseline scenario, the carbon intensity of Shaanxi Province in 2030 will be reduced by 71.67% compared with that in 2007. ⑤ Among the single emission reduction policies, the economic scale scenario was the most effective, followed by the structural optimization scenario. In addition, the energy intensity optimization and new energy vehicle scenarios have less potential. This study provides effective data support for Shaanxi Province to formulate low-carbon policies, promote carbon emission reduction, and achieve carbon emission peak as soon as possible. |
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