Assessing the impact of supply-side policies on oil extraction, health, and employment in California

California's ambitious goal to slash GHG emissions by 90% by 2045 marks a significant shift towards sustainability. Supply-side policies, such as Senate Bill 1137, which bans new oil and gas wells within 3,200 feet of sensitive areas, signal a commitment to environmental and public health protection. To gauge SB 1137's impact accurately, the existing model must be adapted to incorporate this setback distance. This capstone project aims to bridge this gap by updating the model and creating accessible educational materials for Californians. Objectives include updating the model, predicting well locations and oil extraction using machine learning, and developing a public online app with R Shiny. The MEDS capstone group will investigate the effects of the 3,200-foot setback distance on emissions, employment, and health, contributing to the evidence supporting SB 1137.

加州提出的到2045年将温室气体（GHG, Greenhouse Gas）排放量削减90%的宏伟目标，标志着其向可持续发展方向的重大转变。以《参议院第1137号法案》（Senate Bill 1137）为例，该法案禁止在敏感区域3200英尺范围内新增油气井，这类供给侧政策彰显了加州对环境保护与公众健康保护的坚定承诺。为精准评估《参议院第1137号法案》的实施影响，现有相关模型需进行适配调整，以纳入该避让退距要求。本顶石项目旨在通过更新模型、为加州民众打造普惠性教育材料，填补这一研究空白。项目目标具体包括：更新现有模型、利用机器学习预测油气井点位与石油开采量，以及基于R Shiny框架开发一款面向公众的在线应用程序。本MEDS顶石项目小组将探究3200英尺避让退距要求对碳排放、就业与公众健康的影响，为支撑《参议院第1137号法案》的落地实施提供实证依据。