Shaping the Landscape: A Framework for Evaluating the Economic Benefits of Fuel Treatments

Climate change has heightened the importance of land management practices that restore ecosystem health and deliver environmental, social, and economic benefits. Fuel treatments have emerged as one such practice, designed to reduce wildfire risk and restore ecosystem function in semiarid and pine-dominated systems that historically experienced low-severity fire. Despite ambitious commitments, such as the U.S. Forest Service’s plan to treat 50 million acres in high-risk landscapes over the next decade, rigorous evidence on their cost-effectiveness remains limited. Evaluating fuel treatments and understanding under which conditions they are cost-effective has been difficult due to data limitations and causal identification challenges. This dissertation addresses this gap by designing empirical frameworks that combine quasi-experimental methods with high-resolution spatial data to evaluate fuel treatment impacts across multiple outcomes, including suppression costs, property loss, smoke emissions, fire spread, and burn severity.Chapter 1 investigates the fundamental question of whether fuel treatments are cost-effective. Focusing on the Pacific Northwest, I leverage variation from Northern Spotted Owl habitat protections—which unintentionally restricted treatment activity—as an instrument to estimate fuel treatments’ causal impact on wildfire suppression costs. I find that each dollar spent on treatments saved an estimated four to seven dollars in suppression expenditures, highlighting the potential for policy reforms that maintain conservation objectives while generating substantial economic savings.Building on this foundation, Chapter 2 asks which types of treatments are most effective in reducing damages from wildfires, such as property loss and smoke emissions. Using high-resolution spatial data from 2017–2023 across the Western United States and a spatial difference-in-differences design, I estimate that treatments avoided $2.7 billion in damages, yielding a benefit–cost ratio of$3.42. Large-scale treatments and prescribed burns provide the greatest returns, underscoring how both treatment type and scale influence overall cost-effectiveness.Chapter 3 returns to the Pacific Northwest to assess the impact of environmental regulatory reforms on treatment activity and their subsequent impacts on suppression costs and wildfire damages. Specifically, I evaluate the 2011 Revised Recovery Plan for the Northern Spotted Owl, which relaxed restrictions on fuel treatments in protected habitat. I find that the policy increased treatment activity by roughly 91,000 acres and generated over $1 billion in economic benefits, largely through avoided suppression costs. Ecological gains, however, were modest, with limited reductions in burn severity within Northern Spotted Owl habitat and old-growth forest.Taken together, this dissertation demonstrates that fuel treatments are a cost-effective tool for mitigating wildfire risk, with effectiveness varying by treatment type, size, region, and regulatory context. The empirical frameworks developed here can be applied in future evaluations to strengthen policy and improve project design. More broadly, the findings highlight persistent regulatory and capacity constraints within U.S. land agencies, underscoring both the promise and the limitations of policy reforms aimed at promoting proactive forest management that advances wildfire mitigation and species conservation goals

Density functional theory based screening of ternary alkali-transition metal borohydrides: A computational material design project

The dissociation of molecules, even the most simple hydrogen molecule, cannot be described accurately within density functional theory because none of the currently available functionals accounts for strong on-site correlation. This problem led to a discussion of properties that the local Kohn-Sham potential has to satisfy in order to correctly describe strongly correlated systems. We derive an analytic expression for the nontrivial form of the Kohn-Sham potential in between the two fragments for the dissociation of a single bond. We show that the numerical calculations for a one-dimensional two-electron model system indeed approach and reach this limit. It is shown that the functional form of the potential is universal, i.e., independent of the details of the two fragments.We acknowledge funding by the Spanish MEC (Grant No. FIS2007-65702-C02-01), “Grupos Consolidados UPV/EHU del Gobierno Vasco” (Grant No. IT-319-07), and the European Community through e-I3 ETSF project (Grant Agreement No. 211956).Peer reviewe

Genetic algorithms for computational materials discovery accelerated by machine learning

Abstract Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets. Where datasets are lacking, unbiased data generation can be achieved with genetic algorithms. Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. The approach is used to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materials discovery, e.g., nanoalloy catalysts. The machine learning accelerated approach, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations