CAREER: Advanced demand estimators for energy-efficiency in personal transportation

1253475(达齐亚诺)。PI的长期职业目标是为决策分析方面的研究和教育做出重大贡献，以更好地了解消费者对节能工程技术的行为反应。为了追求这一目标，职业研究的目标是利用微观计量经济学的离散选择理论来更好地为低排放车辆的工程提供信息。着眼于培养工程师创造社会愿意采用的技术上合理的解决方案的长期目标，职业教育的目标是为未来的工程师提供多学科的工程决策愿景，了解消费者的需求。该提案的研究计划旨在：1)生成具有不同消费者和节能汽车技术决策规则的大规模联立式离散选择系统的结构参数的新需求估计；2)推导考虑行为不确定性的支付意愿和消费者剩余指标的非参数贝叶斯估计；3)对低排放汽车部署的综合反事实场景进行系统的贝叶斯成本效益分析，为政策、技术、工程和基础设施规划决策提供信息。这三项研究任务将使用不同来源的消费者采用低排放车辆的数据进行验证和测试。教育计划以计划中的研究为基础，并将有助于计划的研究，包括四个步骤：1)与康奈尔大学可持续发展研究中心合作，为具有不同背景但对能源可持续发展有共同兴趣的大学生培养多学科研究和学习经验；2)广泛推广可持续旅行行为，教育公众，并激励社会多样化的子孙后代追求工程事业；3)通过实施并不断改进三门旨在将需求侧动态融入工程学的课程，加强高年级本科生和研究生的课程；以及4)指导博士和研究生。主要的研究成果将是解决基于随机效用最大化的复杂结构方程系统的联合估计问题，该问题可用于建立能源效率的需求模型。将解决尚未解决的计量经济学挑战，为已确定的简化参数推导灵活的贝叶斯参数和非参数模拟辅助推理。联立方程需求模型将考虑消费者反应与经济、环境、能源和交通系统的相互作用。需求模型还将纳入社会象征价值，如支持环境的偏好、能源安全担忧，以及消费者对新兴可持续技术的认识及其采用这些技术的准备情况。为了代表在不断发展的技术之间的选择，需求系统还将考虑能源多样化、选择动态和质量属性的衡量。通过与福特汽车公司、加州大学伯克利分校、欧洲经济研究中心和罗马大学的合作，将促进数据的获取。技术成果将有助于在不确定情况下做出决策的领域。根据需求估计器得出的决策分析工具将不仅用于评估先进的节能推进技术和基础设施的定价和投资战略，而且还将用于评估公共政策和激励措施，以最好地促进行业转向低排放汽车和消费者对低排放汽车的接受。这一结果不仅对美国政策制定者和交通规划者具有重要意义，还将为汽车制造商提供信息，帮助他们改进行业工程师如何设计车辆。将通过康奈尔大学内外的多学科合作，包括国际伙伴关系，实现知识转让。教育举措将侧重于传播消费者的反应与成功的工程解决方案的相关性。康奈尔工程学院的卓越教学学院将使用个人反应系统协助创建创新的主动选择实验。公开讨论放映关于电动汽车的纪录片，以及每年参加纽约展会，将支持针对年轻人和大学新生的可持续出行行为的外联计划，特别关注代表性不足的群体。

1253475 (Daziano). The long-term career goal of the PI is to contribute significantly to both research and education in decision-making analysis to better understand consumer behavioral response to energy-efficient engineered technologies. In pursuit of this goal, the CAREER research objective is to exploit microeconometric discrete choice theory to better inform engineering of low emission vehicles. Aiming at the long-term goal of preparing engineers to create technically sound solutions that society is willing to adopt, the CAREER educational objective is to provide future engineers with a multidisciplinary vision of engineering decision-making informed by consumer demand. The research plan of this proposal seeks to: 1) generate new demand estimators of the structural parameters of a large-scale simultaneous-equations discrete choice system with heterogeneous consumers and decision rules for energy-efficient automobile technologies, 2) derive nonparametric Bayesian estimators of willingness-to-pay and consumer-surplus measures that account for behavioral uncertainties, 3) formulate a systematic Bayesian cost-benefit analysis of integrative counterfactual scenarios of low emission vehicle deployment for informing policy, technology, engineering, and infrastructure planning decisions. The three research tasks will be validated and tested using data on consumer adoption of low-emission vehicles from different sources. The education plan builds on and will contribute to the planned research, and comprises four steps: 1) collaboration with sustainability research centers at Cornell to foster multidisciplinary research and learning experiences for college students with different backgrounds but with common interests in energy sustainability, 2) extensive outreach on sustainable travel behavior to educate the public and to motivate socially-diverse future generations to pursue engineering careers, 3) enhancement of the curriculum at the senior undergraduate and graduate levels through implementing and continuously improving three courses aimed at integrating demand-side dynamics into engineering, and 4) mentoring graduate students at the PhD and MEng levels. The main research outcome will be a solution for the joint estimation problem of a complex system of structural equations based on random utility maximization that can be applied to formulate demand models for energy efficiency. Unsolved econometric challenges will be addressed for deriving flexible Bayesian parametric and nonparametric simulation-aided inference for identified reduced parameters. The simultaneous equations demand model will account for interactions of consumer response with the economic, environmental, energy, and transportation systems. The demand model will also incorporate social symbolic values such as pro-environmental preferences, energy security concerns, as well as consumers' awareness of emerging sustainable technologies and their readiness to adopt them. To represent choice among continuously evolving technologies, the demand system will also account for energy diversification, choice dynamics, and measurement of qualitative attributes. Access to data will be facilitated through collaborations with Ford Motors, UC Berkeley, the Centre for European Economic Research, and the University of Rome 3. The technical results will contribute to fields where decision-making under uncertainty is needed. Decision-making analysis tools derived from the demand estimators will serve to evaluate not only pricing and investment strategies for advanced energy-efficient propulsion technologies and infrastructure, but also public policies and incentives to best promote industry conversion to and consumer acceptance of low-emission vehicles. The results will be significant not only for US policymakers and transportation planners, but also for informing auto manufacturers to improve how industry engineers vehicles. Knowledge transfer will be achieved through multidisciplinary collaboration within and beyond Cornell, including international partnerships. Educational initiatives will be focused on disseminating the relevance of consumer response for successful engineering solutions. Cornell Engineering's Teaching Excellence Institute will assist in creating innovative active choice experiments using personal-response systems. Publicly discussed screenings of documentaries about electric vehicles and yearly participation at the NYS fair will support outreach plans on sustainable travel behavior to youth and college freshmen, with a special focus on underrepresented groups.