Globally, on-road transportation accounts for 15% of greenhouse gas (GHG) emissions and an estimated 385,000 premature deaths from PM2.5. Cities play a critical role in meeting IPCC targets, generating 75% of global energy-related GHG emissions. In Houston, Texas, on-road transportation represents 48% of baseline emissions in the Climate Action Plan (CAP). To reach net-zero by 2050, the CAP targets a 70% emissions reduction from a 2014 baseline, offset by 30% renewable energy. This goal is challenging because Houston is low-density and auto-dependent, with 89% of on-road emissions from cars and small trucks and limited public transit usage. Socio-economic disparities further constrain Zero Emissions Vehicle (ZEV) adoption. Strategies focus on expanding ZEV access and reducing Vehicle Miles Traveled (VMT) by 20% through transit improvements and city design. This paper presents methods for establishing an on-road emissions baseline and evaluating policies that leverage socio-economic indicators and Intelligent Transportation Systems (ITS) to accelerate ZEV adoption and reduce VMT. Smart parking, transit incentives, secure data systems, and ZEV fleet management support improvements in modal split and system reliability. Policy options are analyzed and potential actions identified. To support evaluation, a simulation environment was developed in Unity 3D, enabling dynamic modeling of urban mobility and visualization of policy scenarios. Auto-dependent cities aiming for 2050 emission targets can benefit from the indicators, metrics, and technologies discussed.
翻译:全球范围内,道路交通贡献了15%的温室气体排放,并导致约38.5万人因PM2.5暴露而过早死亡。城市在实现政府间气候变化专门委员会目标中具有关键作用,其能源相关温室气体排放占全球总量的75%。在德克萨斯州休斯顿市,道路交通占《气候行动计划》基准排放量的48%。为实现2050年净零排放目标,该计划要求以2014年为基准减排70%,并通过30%可再生能源抵消剩余排放。这一目标极具挑战性,因为休斯顿呈现低密度、汽车依赖型城市特征:89%的道路排放来自轿车和小型卡车,公共交通使用率有限。社会经济差异进一步制约了零排放汽车的普及。应对策略聚焦于扩大零排放汽车覆盖范围,并通过交通系统优化与城市设计将车辆行驶里程降低20%。本文提出了建立道路排放基准的方法,并评估了利用社会经济指标与智能交通系统加速零排放汽车普及、减少车辆行驶里程的政策路径。智能停车系统、公交激励措施、安全数据平台及零排放车队管理等技术手段,可有效提升交通方式分担率与系统可靠性。研究对政策选项进行了分析并识别了潜在行动方案。为支持评估,本研究基于Unity 3D开发了仿真环境,实现了城市交通动态建模与政策情景可视化。致力于实现2050年减排目标的汽车依赖型城市,可借鉴本文讨论的指标体系、评估方法与技术方案。