LACEEP Working Paper Series WP78
Robalino-et-al-Park-Spillovers-LACEEP-WP78.Spillovers can significantly reduce or enhance the effects of land-use policies, yet there exists little rigorous evidence concerning their magnitudes. We examine how national parks within Costa Rica affect the clearing of forest nearby. We find that average deforestation spillover impacts are not significant within 0-5km and 5-10km rings around parks. However, we argue that this average blends multiple spillover effects, each of which is likely to vary in magnitude across the landscape, yielding varied net effects. We distinguish these effects using distances to roads and park entrances, given the importance of transport costs and, for Costa Rica, tourism. We find large and statistically significant leakage close to roads in areas without tourism, i.e., far from the park entrances. In contrast, no leakage is found far from roads or close to park entrances. In sum, the combination of low transport costs and low returns to forest is conducive to deforestation leakage around the parks.
Philosophical Transactions B 2015 volume 370 (online http://dx.doi.org/10.1098/rstb.2014.0273)
The leading policy to conserve forest is protected areas (PAs). Yet, they are not a single tool: land users and uses vary by PA type; and public PA strategies vary in the extent of each type, as well as in the determinants of impact for each type, i.e. siting and internal deforestation. Further, across regions and time, strategies respond to pressures (deforestation and political).We estimate deforestation impacts of PA types for a critical frontier, the Brazilian Amazon. We separate regions and time periods that differ in their deforestation and political pressures and document considerable variation in PA strategies across regions, time periods and types. The siting of PAs varies across regions. For example, all else being equal, PAs in the arc of deforestation are relatively far from non-forest, while in other states they are relatively near. Internal deforestation varies across time periods, e.g. it is more similar across the PAtypes for PAs after 2000. By contrast, after 2000, PA extent is less similar across PA types with little non-indigenous area created inside the arc. PA strategies generate a range of impacts for PA types—always far higher within the arc—but not a consistent ranking of PA types by impact.
PLOS ONE 2015 (forthcoming) DOI:10.1371/journal.pone.0129460
Protected areas are the leading forest conservation policy for species and ecoservices goals and they may feature in climate policy if countries with tropical forest rely on familiar tools. For Brazil’s Legal Amazon, we estimate the average impact of protection upon deforestation and show how protected areas’ forest impacts vary significantly with development pressure.We use matching, i.e., comparisons that are apples-to-apples in observed land characteristics, to address the fact that protected areas (PAs) tend to be located on lands facing less pressure. Correcting for that location bias lowers our estimates of PAs’ forest impacts by roughly half. Further, it reveals significant variation in PA impacts along development-related dimensions: for example, the PAs that are closer to roads and the PAs closer to cities have higher impact. Planners have multiple conservation and development goals, and are constrained by cost, yet still conservation planning should reflect what our results imply about future impacts of PAs.
PLoS ONE 10(4):e0124910 (2015) doi:10.1371/journal.pone.0124910
We estimate the effects on deforestation that have resulted from policy interactions between parks and payments and between park buffers and payments in Costa Rica between 2000 and 2005. We show that the characteristics of the areas where protected and unprotected lands are located differ significantly. Additionally, we find that land characteristics of each of the policies and of the places where they interact also differ significantly. To adequately estimate the effects of the policies and their interactions, we use matching methods. Matching is implemented not only to define adequate control groups, as in previous research, but also to define those groups of locations under the influence of policies that are comparable to each other. We find that it is more effective to locate parks and payments away from each other, rather than in the same location or near each other. The high levels of enforcement inside both parks and lands with payments, and the presence of conservation spillovers that reduce deforestation near parks, significantly reduce the potential impact of combining these two policies.
World Development 2014 volume 55, pp. 7–20
For Acre, in the Brazilian Amazon, we find that protection types with differences in governance, including different constraints on local economic development, also differ in their locations. Taking this into account, we estimate the deforestation impacts of these protection types that feature different levels of restrictions. To avoid bias, we compare these protected locations with unprotected locations that are similar in their characteristics relevant for deforestation. We find that sustainable use protection, whose governance permits some local deforestation, is found on sites with high clearing threat. That allows more avoided deforestation than from integral protection, which bans clearing but seems feasible only further from deforestation threats. Based on our results, it seems that the political economy involved in siting such restrictions on production is likely to affect the ability of protected areas to reduce emissions from deforestation and degradation.
Proceedings of the Royal Society B 2010 doi:10.1098/rspb.2010.1713
Protected areas (PAs) dominate conservation efforts. They will probably play a role in future climate policies too, as global payments may reward local reductions of loss of natural land cover. We estimate the impact of PAs on natural land cover within each of 147 countries by comparing outcomes inside PAs with outcomes outside. We use ‘matching’ (or ‘apples to apples’) for land characteristics to control for the fact that PAs very often are non-randomly distributed across their national landscapes. Protection tends towards land that, if unprotected, is less likely than average to be cleared. For 75 per cent of countries, we find protection does reduce conversion of natural land cover. However, for approximately 80 per cent of countries, our global results also confirm (following smaller-scale studies) that controlling for land characteristics reduces estimated impact by half or more. This shows the importance of controlling for at least a few key land characteristics. Further, we show that impacts vary considerably within a country (i.e. across a landscape): protection achieves less on lands far from roads, far from cities and on steeper slopes. Thus, while planners are, of course, constrained by other conservation priorities and costs, they could target higher impacts to earn more global payments for reduced deforestation.
Ann. N.Y. Acad. Sci. 1185 (2010) 135–149
Protected areas are leading tools in efforts to slow global species loss and appear also to have a role in climate change policy. Understanding their impacts on deforestation informs environmental policies. We review several approaches to evaluating protection’s impact on deforestation, given three hurdles to empirical evaluation, and note that “matching” techniques fromeconomic impact evaluation address those hurdles. The central hurdle derives from the fact that protected areas are distributed nonrandomly across landscapes.Nonrandom location can be intentional, and for good reasons, including biological and political ones. Yet even so, when protected areas are biased in their locations toward less-threatened areas, many methods for impact evaluationwill overestimate protection’s effect. The use ofmatching techniques allows one to control for known landscape biases when inferring the impact of protection. Applications of matching have revealed considerably lower impact estimates of forest protection than produced by other methods. A reduction in the estimated impact from existing parks does not suggest, however, that protection is unable to lower clearing. Rather, it indicates the importance of variation across locations in how much impact protection could possibly have on rates of deforestation.Matching, then, bundles improved estimates of the average impact of protection with guidance on where new parks’ impacts will be highest.While many factors will determine where new protected areas will be sited in the future, we claim that the variation across space in protection’s impact on deforestation rates should inform site choice.
Global efforts to reduce tropical deforestation rely heavily on the establishment of protected areas. Measuring the effectiveness of these areas is difficult because the amount of deforestation that would have occurred in the absence of legal protection cannot be directly observed. Conventional methods of evaluating the effectiveness of protected areas can be biased because protection is not randomly assigned and because protection can induce deforestation spillovers (displacement) to neighboring forests. We demonstrate that estimates of effectiveness can be substantially improved by controlling for biases along dimensions that are observable, measuring spatial spillovers, and testing the sensitivity of estimates to potential hidden biases. We apply matching methods to evaluate the impact on deforestation of Costa Rica’s renowned protected-area system between 1960 and 1997. We find that protection reduced deforestation: approximately 10% of the protected forests would have been deforested had they not been protected. Conventional approaches to evaluating conservation impact, which fail to control for observable covariates correlated with both protection and deforestation, substantially overestimate avoided deforestation (by over 65%, based on our estimates). We also find that deforestation spillovers from protected to unprotected forests are negligible. Our conclusions are robust to potential hidden bias, as well as to changes in modeling assumptions. Our results show that, with appropriate empirical methods, conservation scientists and policy makers can better understand the relationships between human and natural systems and can use this to guide their attempts to protect critical ecosystem services.