Operations research models of climate change mitigation and adaptation strategies at diverse scales
The impacts of climate change are beyond the observable effects on the environment and it requires extensive efforts in all aspects of society to take actions and ensure a sustainable future. Therefore, it is crucial for policymakers to understand the associated risks, plan for the adverse impacts brought by climate change and make informed decisions. To assist this, through a variety of operations research models and case studies from three projects, this dissertation examines climate change mitigation and adaptation strategies and explores relevant policy implications at three different scales: the urban city level, the individual project level, and the national level. Chapter 2 focuses on vehicle fuel efficiency policies at the urban scale. Given the large and rapidly increasing share of global energy consumption taking place in cities, it becomes crucial to understand the interactions between urban form and energy consumption and avoid unintended policy consequences. By extending the classic monocentric city model to incorporate endogenous investment in vehicle fuel efficiency by households, we investigate the systemic impacts of vehicle fuel efficiency on urban form and urban form feedbacks for energy consumption in cities. We prove analytically that raising vehicle fuel efficiency induces a more compact urban form if households are underinvesting in efficiency, but a more sprawling urban form if efficiency is increased beyond the rational household optimum. In the former case, the urban form adjustment further reduces energy use in the transportation and residential sectors. In the latter case, the urban form adjustment increases vehicle travel, which offsets at least some of the direct reduction in transportation energy use stemming from the efficiency improvement (direct rebound). Additionally, its larger homes demand more energy in the residential sector (indirect rebound). Numerical illustrations are included to confirm the analytical findings for a stylized city. Chapter 3 addresses decision-making under uncertainty on infrastructure resilience upgrade programs. As climate change threatens to cause increasingly frequent and severe natural disasters, decision-makers must consider costly investments to enhance the resilience of critical infrastructures. Evaluating these potential resilience improvements using traditional cost-benefit analysis (CBA) approaches is often problematic because disasters are stochastic and can destroy even hardened infrastructure, meaning that the lifetimes of investments are themselves uncertain. In this work, we develop a novel Markov decision process (MDP) model for CBA of infrastructure resilience upgrades that offer prevention (reduce the probability of a disaster) and/or protection (mitigate the cost of a disaster) benefits. Stochastic features of the model include disaster occurrences and whether or not a disaster terminates the effective life of an earlier resilience upgrade. From our MDP model, we derive analytical expressions for the decision-maker’s willingness to pay (WTP) to enhance infrastructure resilience and conduct a comparative static analysis to investigate how the WTP varies with the fundamental parameters of the problem. The applicability of this MDP framework is demonstrated by two case studies of electric utility infrastructure hardening programs. The first case study considers elevating a flood-prone substation and the second evaluates upgrading transmission structures to withstand high winds. Results from these two case studies show that assumptions about the value of lost load during power outages and the distribution of customer types significantly influence the WTP for the resilience upgrades and are material to the decisions of whether or not to implement them. In Chapter 4, we use the US-TIMES model to explore the decarbonization pathways in the U.S. by modeling portfolios of granular sectoral mitigation policies in the electricity, transportation and buildings sectors. By combining the complementary dimensions of techno-economic and political-organizational feasibilities, we design three different policy scenarios based on portfolios of sectoral mitigation policies that would be politically feasible under Low Alignment, Medium Alignment and High Alignment scenarios of the federal government’s commitment to reducing GHG emissions. We compare these three scenarios with the business-as-usual (BAU) scenario and a stylized 80% system-wide decarbonization scenario. This work contributes to the literature by incorporating granular sectoral climate policies in an energy system optimization to enhance policy realism. Our findings highlight the significance of decarbonizing the electricity sector and electrifying the other sectors such as transportation to cost-effectively reduce GHG emissions. Given the complexity of the energy system, the effectiveness of sectoral policies should be evaluated from a systems level. Our findings also reveal that the GHG emissions reduction relative to 2010’s emissions in Low Alignment, Medium Alignment and High Alignment scenarios are 24.4%, 36.5%, and 44.3%, respectively. The average abatement costs of employing these portfolios of policies are $16.2, $15.8 and $12.8/tCO2e, which are 4.1, 2.1, 1.6 times as high as the average abatement costs obtained through the optimal (least system-wide cost) decarbonization pathways. The AACs that we obtain in this study is lower than mainstream estimates of Social Cost of Carbon (Interagency Working Group on Social Cost of Greenhouse Gases, 2015; Nordhaus, 2017). This suggests that even if political considerations mean that we do not necessarily follow the most cost-effective approach to decarbonize the economy, there are still likely to be net benefits. Each of these three projects, on its scale, captures the underlying characteristics of climate change mitigation or adaptation strategies. While the focus of each project varies, they serve the common goal of providing system-wide and high-level insights on decision-making for cost-effectively reducing GHG emissions or adapting to the impacts of climate change.