Adaptation is the process of adjusting to ongoing climate impacts, yet most available adaptation strategies are designed to deal with a limited amount of change. Adaptation will need to address not only some amount of change, but also ongoing rates of change. We provide a conceptual framework for adaptation strategies, grounding our discussion with an example of optimal coastal investment in the face of ongoing sea level rise.
We present a quantitative model that illustrates the interplay among various important factors governing coastal development decisions. Resources could be devoted to defending the coast. Buffer zones could be created where development is restricted, in anticipation of a future higher sea level. Or investment in new infrastructure can shift inland in anticipation of continued sea level rise. Optimal investment strategies must take into account future rates of sea-level rise, as well as social and political constraints. 

The cost and climate tradeoffs of electric power generation choices are key to decisions in both the electricity sector and in climate policy globally. Here we contrast the extremes in electricity generation choices by integrating climate change and power generation expansion models: making choices on new generation facilities based on generation cost only, versus making choices based on climate impacts only. Incorporating the expected drop in cost as experience grows, on a pure cost-minimization basis, renewable technologies could displace coal and natural gas within two decades. This is the natural gas as a bridge fuel scenario, and technology advancement to bring down the cost of renewables requires some commitment to renewables generation in the near term. Adopting the objective of minimizing climate damage, essentially moving immediately to low greenhouse gas generation technologies, results in faster cost reduction of new technologies and may result in different technologies becoming dominant in global electricity generation. This illustrates that today’s choices for new electricity generation by individual countries and utilities have implications not only for their direct costs and the global climate, but also for the future costs and availability of emerging electricity generation options.

We study the problem of decision making under uncertainty in finite horizon and with continuous state space. We develop a method for value function approximation in approximate dynamic programming that combines offline calculation with online rolling horizon methods. This method consists of using an H-step-ahead approximation for estimating the value function and finding the optimal action online, and a value iteration algorithm to update the parameters of the approximated value function offline. Conditions on the step size that guarantee the convergence of the value function approximation are derived. We illustrate the approach for an integrated model of climate and the world economy. We analyze the impact of discount factor on the choice of approximation and provide insight into the robustness of approximation.

Integrated assessment models are powerful tools for providing insight into the interaction between the economy and climate change over a long time horizon. However, knowledge of climate parameters and their behavior under extreme circumstances of global warming is still an active area of research. In this thesis we incorporated the uncertainty in one of the key parameters of climate change, climate sensitivity, into an integrated assessment model and showed how this affects the choice of optimal policies and actions. We constructed a new, multi-step-ahead approximate dynamic programing (ADP) algorithm to study the effects of the stochastic nature of climate parameters. We considered the effect of stochastic extreme events in climate change (tipping points) with large economic loss. The risk of an extreme event drives tougher GHG reduction actions in the near term. On the other hand, the optimal policies in post-tipping point stages are similar to or below the deterministic optimal policies. Once the tipping point occurs, the ensuing optimal actions tend toward more moderate policies. Previous studies have shown the impacts of economic and climate shocks on the optimal abatement policies but did not address the correlation among uncertain parameters. With uncertain climate sensitivity, the risk of extreme events is linked to the variations in climate sensitivity distribution. We developed a novel Bayesian framework to endogenously interrelate the two stochastic parameters. The results in this case are clustered around the pre-tipping point optimal policies of the deterministic climate sensitivity model. Tougher actions are more frequent as there is more uncertainty in likelihood of extreme events in the near future. This affects the optimal policies in post-tipping point states as well, as they tend to utilize more conservative actions. As we proceed in time toward the future, the (binary) status of the climate will be observed and the prior distribution of the climate sensitivity parameter will be updated.

In 2010, there were approximately six million vehicle crashes leading to 32,788 traffic deaths, or approximately 15 fatalities per 100,000 people. The leading cause of death for Americans of all ages, coronary heart disease, ranks only 19th for Americans between the ages of 4–34 while vehicle crashes are the leading killer for Americans in the same age group. Of all traffic accidents, 93% are attributable to human error. Investigations into vehicular collision human factors cover scenarios such as driving under the influence or falling asleep at the wheel and include factors such as driver behavior, visual and auditory acuity, decision-making ability, and reaction speed. Many of the behaviors and human elements that contribute to accidents can be mitigated by replacing human drivers with automated functions, which can assess and react much faster to many road conditions. The safety improvements promised by the widespread adoption of autonomous vehicles (AVs), in addition to other benefits to be discussed, motivate this study.

Biofuel can play a crucial role in achieving energy independence goals. The success story of the Brazil's ethanol program started in 1976 and during the past four decades it has gained remarkable achievements in terms of economic development and energy independence. The aim of this study is to show how Brazil has achieved its goal of energy independence by implementing and updating its renewable fuel policies over the past 40 years. We adopt an evolutionary policy framework to study the emergence of ethanol as an alternative fuel in Brazil. We propose a dynamic framework for technology diffusion policies that can explain why Brazil chose ethanol as an alternative fuel and how this policy affected the country’s energy market. We argue that the success of the Brazilian ethanol program is due to its flexibility and adaptability over the past four decades. In addition to this, we note that other domestic and international forces such as oil price and domestic politics have been largely responsible for the success or failure of these policies. Lastly, we discuss the key lessons from Brazil which can help the policy makers in other countries in devising a sustainable energy policy in the future.