| 西安市道路交通减污降碳策略协同效应评价 |
| 摘要点击 730 全文点击 10 投稿时间：2025-07-08 修订日期：2025-09-26 |
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| 中文关键词 道路交通 LEAP平台 控制措施 减污降碳 协同效应 |
| 英文关键词 road traffic LEAP system control measure pollution and carbon reduction synergistic effect |
| DOI 10.13227/j.hjkx.202507075 |
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| 中文摘要 |
| 基于长期能源可替代规划系统（LEAP）构建了同时考虑直接和间接排放的西安市2022~2050年道路交通碳污排放模型，测算了不同情景措施下的碳污减排量，并采用减排弹性系数（ELS）法评估了各情景措施的协同减排效应. 情景设置包括7个单一措施情景和1个综合措施情景（SA），针对新能源车管控还设置了1个组合措施情景（CS）. 结果表明，单一措施均有正向协同控制效应，其中CO2e-PM2.5协同效益最显著，其次是CO2e-CO，但CO2e减排强度普遍高于NOx和VOC；燃油车技术提升情景（S6）减污降碳能力优异，低碳出行情景（S1）降碳效果突出，公转铁情景（S2）削减NOx排放明显，车辆低饱和情景（S7）则对VOC、PM2.5和CO减排效果显著. 新能源车单一措施的减污降碳效果有限，需实施综合管控进一步释放其带来的减排效益；CS情景CO2e-PM2.5和CO2e-CO协同效益显著，对NOx减排弱于CO2e，VOC减排则优于CO2e. SA情景展现出最为卓越的减排潜力，CO2e排放年均降低1.22%，NOx最终减排近50%，VOC、PM2.5和CO减排超70%；其ELS值基本在1附近波动，表明情景设置合理，实现了碳污协同控制. 此外，SA情景下NOx排放后期出现反弹，凸显现有措施对柴油货车管控力度不足，未来需通过精细化监管、后处理技术提升以及清洁能源转型等多种途径深度减排. |
| 英文摘要 |
| Based on the long-range energy alternative planning system （LEAP）， a carbon and pollutant emission model for Xi'an's road traffic from 2022 to 2050 was constructed， which incorporates both direct and indirect emissions. The model estimated the carbon and pollutant emission reductions under different scenario measures， and the synergistic emission reduction effects of each scenario were evaluated using the emission reduction elasticity coefficient （ELS） method. The scenario settings include seven single-measure scenarios， one comprehensive measure scenario （SA）， and an additional combined measure scenario （CS） specifically designed for the regulation of new energy vehicles. The results show that all single measures exhibited positive synergistic control effects. Among them， the co-benefit of CO2e-PM2.5 was the most significant， followed by that of CO2e-CO. However， the intensity of CO2e reduction was generally higher than that of NOx and VOC. Specifically， the scenario of technology improvement for fuel vehicles （S6） had excellent pollution and carbon reduction capabilities； the scenario of low-carbon travel （S1） stood out in carbon reduction； the scenario of road-to-rail shift for freight transport （S2） significantly cut NOx emissions； and the scenario of low vehicle saturation （S7） notably reduced VOC， PM2.5， and CO emissions. Single measures for new energy vehicles have limited pollution and carbon reduction effects， and comprehensive management measures are needed to further unleash their emission reduction benefits. The CS scenario showed significant co-benefits for CO2e-PM2.5 and CO2e-CO； its NOx reduction effect was weaker than that of CO2e， while its VOC reduction was stronger. The SA scenario demonstrated the most remarkable emission reduction potential， with annual CO2e emissions decreasing by 1.22%； NOx emissions ultimately reduced by nearly 50%； and VOC， PM2.5， and CO emissions reduced by over 70%. Its ELS values generally fluctuated around 1， indicating that the scenario setting was reasonable， and synergistic control of carbon and pollution had been achieved. Additionally， the rebound of NOx emissions in the later stage of the SA scenario highlights insufficient control over diesel trucks by existing measures. Future efforts should focus on in-depth emission reductions through multiple approaches like refined supervision， post-treatment technology improvement， and clean energy transition. |
