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As road conditions worsen, users experience an increase in fuel consumption and vehicle wear and tear. This increases the costs incurred by the drivers, and also increases the amount of greenhouse gases that vehicles emit. Pavement condition can be improved through rehabilitation activities (resurfacing) to reduce the effects on users, but these activities also have significant cost and greenhouse gas emission impacts. The objective of pavement management is to minimize total societal (user and agency) costs. However, the environmental impacts associated with the cost-minimizing policy are not currently accounted for. Preliminary research has shown that the optimal policies for minimizing total costs and for minimizing total emissions do not coincide. Instead, there exists a range of potentially optimal decisions, known as the Pareto curve, in which it is not possible to decrease total emissions without increasing total costs and vice versa. The first part of the dissertation explores these tradeoffs for a system of pavement segments, taking the approach of an agency who looks to minimize their total costs subject to an emissions constraint. This expands on the existing literature where environmental aspects and costs had only been examined at a single facility level. Other pavement management optimization techniques either did not include costs, or did not examine tradeoffs between costs and environmental aspects. For a case study, a network was created from a subset of California’s highways using available traffic data. The first step was to look at policies that have been used in practice. Caltrans applies a universal trigger roughness policy, which is a policy where all roads in the network are treated the same. Whenever any road reaches the designated trigger roughness value, it will be rehabilitated, independent of road geometry and traffic. The past policy by Caltrans used a trigger roughness value of 3.5 m/km and the current value is 2.7 m/km. Moving from the past policy to current policy was a good decision, resulting in reductions of 14% and 2.5% for GHG emissions and costs, respectively. Further reductions of 2.5% for emissions and 1.5% for costs could be achieved by switching to one of the optimal policies from our model, where different road sections have different trigger roughness values.The slope of the tangent of the Pareto curve gives the societal value of carbon at that point. If there is an accepted societal value of carbon, an agency may choose this as the policy to apply. Alternatively, an agency can use the carbon price as a way to determine feasible emissions reductions targets. If the slope of the curve corresponding to a given emissions constraint was higher than carbon has been valued on the market, the agency may want to reconsider the target. Furthermore, if the emissions target is lower than the emissions minimizing point, the agency can deem the target infeasible without policy change.Policy changes, such as reducing vehicle kilometers traveled (VKT) or improving construction standards, can shift the Pareto curve. For the case of a reduction in VKT, leaving fewer vehicles on the road lowers the total user emissions and also allows the agency to resurface with less frequency. Agency costs and emissions contribute more to the total than user costs and emissions, so larger reductions in the total can be achieved from changes by the agency. Improved construction standards also lead to lower societal costs and emissions. A reduction in the best achievable roughness by 0.25m/km after resurfacing would reduce total costs by 6.5% and total emissions by 9%. Users would now drive on roads in better condition and the agency would not need to resurface as often since roads in better condition deteriorate more slowly.The first part of the dissertation does not account for the case where the available budget to the agency is binding. As an alternative approach, in the second part of the dissertation we look at an agency whose main goal is to reduce its carbon footprint while operating under a constrained budget. Literature considering agency budgets had only considered minimizing total costs or maintaining a certain level of condition in the network. A methodology is applied which selects the optimal timing and optimal action from a set of alternatives for each segment while still retaining the Lagrangian dual formulation. This new formulation quantifies GHG emission savings per additional dollar of agency budget spent, which can be used in a cap-and-trade system or to make budget decisions.We discuss the importance of communication between agencies and their legislature that sets the financial budgets to implement sustainable policies. Using our results, an agency could make a case for needing a certain budget to hit its GHG reduction goals. If it cannot receive any more money for its budget from the legislature, it could sell carbon credits by quantifying the amount of GHG emissions that will be reduced by applying the money they will receive to their rehabilitation budget. We look at the same case study of California roads from the previous section, but now apply this new approach and methodology. We show that it is optimal to apply frequent, thin overlays if the objective is to minimize GHG emissions. This is contrary to the less frequent, thick overlays recommended for minimizing total costs in the literature, but matches what the Washington State Department of Transportation does in practice.This approach confirms that a universal trigger roughness policy is sub optimal. At every possible budget, there is still a range of optimal trigger roughness values. As agency budgets become lower, the range of optimal trigger roughness widens. This is because it becomes more important to spend the little money they have rehabilitating the segments which will result in the largest reductions. Sensitivity analyses were performed with respect to the fuel consumption due to roughness, deterioration rate, and best achievable roughness level, and the solutions were found to be robust with respect to all three parameters.Reducing asphalt emissions by using warm mix asphalt (WMA) were found to only be significant when agency budgets are high. However, at those budget values, the cost of carbon to the agency is upwards of $700 per metric ton (mt) of CO2e, which is significantly higher than carbon has ever been valued on the market. This makes it unlikely that an agency will ever operate at those budget levels, so WMA will not be beneficial for rehabilitation policy. Reductions in asphalt cost see much more significant results, but only when agency budgets are low. A reduction in cost at a low budget effectively gives the agency more money and allows it to rehabilitate more roads where the amount of GHG emissions saved per dollar of agency budget is the highest.

Economically and environmentally informed policy for road resurfacing: tradeoffs between costs and greenhouse gas emissions