Understanding the Role of Transportation in Meeting California’s Greenhouse Gas Emissions Reduction Target: A Focus on Technology Forcing Policies, Interactions with the Electric Sector and Mitigation Costs

The CA-TIMES is an energy economic optimization model that is being developed at UC Davis to study how California can meet its stringent greenhouse gas (GHG) emissions target in 2050. The focus of the CA-TIMES is on identifying optimal and feasible pathways that can potentially be used to achieve the GHG reduction target by 2050.

The overarching goal of this dissertation is to learn about the future options and policies for a low carbon energy system and transportation sector. It consists of three chapters that build upon and extend the CA-TIMES framework, and address three key issues in energy/economic modeling. Cost-effectiveness is an imperative performance measure in the policy world to evaluate any potential technology/policy measures considered for adoption to meet the long-term reduction target. Currently, insufficient attention has been paid to individual technology options or policy measures and their relative cost-effectiveness in the results of CA-TIMES, e.g., how cost-effective are electric vehicles under different scenarios/policies. In the first chapter, the author addresses the shortcomings of conventional cost-effectiveness curves, which are widely used in the policy world. The obtained curves in this chapter show cost-effectiveness of various technology and policy options considering system-wide behavioral, technological, and intertemporal interactions.

The underlying assumption of a technology-forcing policy, where a regulator specifies a standard that cannot be met with the existing technology without switching to more expensive options, is that technology costs will eventually come down with more investments in research and development and/or deployment of technologies due to so-called "learning by doing". The current version of the CA-TIMES model does not consider endogenous technological learning and the effects of technology-forcing policies on technological learning. In the second chapter of this dissertation, the author incorporates endogenous learning into the CA-TIMES model where future values of technology costs are no longer a function of time alone, but they depend on the cumulative investment decisions taken by the model. The results show the zero-emission vehicle (ZEV) mandate plays a critical role in promoting ZEVs in the absence of carbon cap as well as in the medium-term. However, carbon cap is a single important policy in promoting zero emissions vehicles in the long-term.

In the third chapter, the author expands the existing California TIMES (CA-TIMES) model and includes the Western Electricity Coordinating Council (WECC) electricity region into this model. He explores the impact of California's policies on the WECC grid. His analysis shows that a climate target on California only and not on the other states could contribute to the greening of power plants in the Western States, driven by the possibility to export electricity to California. When a carbon target is extended to the grid of all Western States, as well as the entire energy system of California, there cannot be zero emissions without adopting carbon capture and storage.

Together this dissertation presents novel methodological improvements to the CA-TIMES energy/economic framework, enabling a more nuanced view of California's transition to a low carbon transportation energy system, under different policy and technology assumptions.


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