A small but growing number of watershed investment programs in the western United States focus on wildfire risk reduction to municipal water supplies. This paper used return on investment (ROI) analysis to quantify how the amounts and placement of fuel treatment interventions would reduce sediment loading to the Strontia Springs Reservoir in the Upper South Platte River watershed southwest of Denver, Colorado following an extreme fire event. We simulated various extents of fuel mitigation activities under two placement strategies: (a) a strategic treatment prioritization map and (b) accessibility. Potential fire behavior was modeled under each extent and scenario to determine the impact on fire severity, and this was used to estimate expected change in post-fire erosion due to treatments. We found a positive ROI after large storm events when fire mitigation treatments were placed in priority areas with diminishing marginal returns after treating >50-80% of the forested area. While our ROI results should not be used prescriptively they do show that, conditional on severe fire occurrence and precipitation, investments in the Upper South Platte could feasibly lead to positive financial returns based on the reduced costs of dredging sediment from the reservoir. While our analysis showed positive ROI focusing only on post-fire erosion mitigation, it is important to consider multiple benefits in future ROI calculations and increase monitoring and evaluation of these benefits of wildfire fuel reduction investments for different site conditions and climates.

SUMMARYForest conservation incentives are a popular approach to combatting tropical deforestation. Here we consider a case where direct economic incentives for forest conservation were offered to newly titled smallholders in a buffer zone of a protected area in the northeastern Ecuadorian Amazon. We used quasi-experimental impact evaluation methods to estimate changes in forest cover for 63 smallholders enrolled in Ecuador's Socio Bosque program compared to similar households that did not enroll. Focus group interviews in 15 communities provided insight into why landowners enrolled in the program and how land use is changing. The conservation incentives program reduced average annual deforestation by 0.4–0.5% between 2011 and 2013 for those enrolled, representing as much as a 70% reduction in deforestation attributable to Socio Bosque. Focus group interviews suggested that some landowners chose to ‘invest’ in conservation because the agricultural capacity of their land was limited and economic incentives provided an alternative livelihood strategy. Interviews, however, indicated limits to increasing enrollment rates under current conditions, due to lack of trust and liquidity constraints. Overall, a hybrid public–private governance approach can lead to larger conservation outcomes than restrictions alone.

Concerns over wildfire impacts to water supplies have motivated efforts to mitigate risk by reducing forest fuels. Methods to assess fuel treatment effects and prioritise their placement are needed to guide risk mitigation efforts. We present a fuel treatment optimisation model to minimise risk to multiple water supplies based on constraints for treatment feasibility and cost. Risk is quantified as the expected sediment impact costs to water supplies by combining measures of fire likelihood and behaviour, erosion, sediment transport and water supply vulnerability. We demonstrate the model’s utility for prioritising fuel treatments in two large watersheds in Colorado, USA, that are critical for municipal water supply. Our results indicate that wildfire risk to water supplies can be substantially reduced by treating a small portion of the watersheds that have dense, fire-prone forests on steep slopes that drain to water supply infrastructure. Our results also show that the cost of fuel treatments outweighs the expected cost savings from reduced sediment inputs owing to the low probability of fuel treatments encountering wildfire and the high cost of thinning forests. This highlights the need to expand use of more cost-effective treatments, like prescribed fire, and to identify fuel treatment projects that benefit multiple resources.

Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented—from protected areas to payments for ecosystem services (PES)—to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing ‘matching’ to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods—an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators—due to the presence of unobservable bias—that lead to differences in conclusions about effectiveness. The Ecuador case illustrates that if time-invariant unobservables are not present, matching combined with differences in means or cross-sectional regression leads to similar estimates of program effectiveness as matching combined with fixed effects panel regression. These results highlight the importance of considering observable and unobservable forms of bias and the methodological assumptions across estimators when designing an impact evaluation of conservation programs.

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