Forest clearing and degradation account for roughly 15% of global greenhouse gas emissions, more than all the cars, trains, planes, ships, and trucks on earth. This is simply too big a piece of the problem to ignore; fail to reduce it and we will fail to stabilize our climate. Although the recent climate summit in Copenhagen failed to produce a legally binding treaty, the importance of forest conservation in mitigating climate change was a rare point of agreement between developed and developing countries and is emphasized in the resulting Copenhagen Accord. Language from the meeting calls for developing countries to reduce emissions from deforestation and degradation (nicknamed REDD), and for wealthy nations to compensate them for doing so. For REDD to succeed, forest nations must develop policies and institutions to reduce and eventually eliminate forest clearing and degradation. One of the most straightforward components of such a program is also one of the oldest and most reliable tricks in the conservation book: protected areas. Indigenous lands and other protected areas (hereafter ILPAs)— created to safeguard land rights, indigenous livelihoods, biodiversity, and other values— contain more than 312 billion tons of carbon (GtC). Crucially, and paradoxically, this ‘‘protected carbon’’ is not entirely protected. While ILPAs typically reduce rates of deforestation compared to surrounding areas, deforestation (with resulting greenhouse gas [GHG] emissions) often continues within them, especially inside those that lack sufficient funding, management capacity, or political backing. These facts suggest an attractive but overlooked opportunity to reduce GHG emissions: creating new ILPAs and strengthening existing ones. Here, we evaluate the case for this potential REDD strategy. We focus on the Amazon basin given its importance for global biodiversity, its enormous carbon stocks, and its advanced network of indigenous lands and other protected areas.
Category: Secondary Data
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.
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To support conservation planning, we ask whether a park’s impact on deforestation rates varies with observable land characteristics that planners could use to prioritize sites. Using matching methods to address bias from non-random location, we find deforestation impacts vary greatly due to park lands’ characteristics. Avoided deforestation is greater if parks are closer to the capital city, in sites closer to national roads, and on lower slopes. In allocating scarce conservation resources, policy makers may consider many factors such as the ecosystem services provided by a site and the costs of acquiring the site. Pfaff and Sanchez 2004 claim impact can rise with a focus upon threatened land, all else equal. We provide empirical support in the context of Costa Rica’s renowned park system. This insight, alongside information on eco-services and land costs, should guide investments.
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Background: About an eighth of the earth’s land surface is in protected areas (hereafter ‘‘PAs’’), most created during the 20th century. Natural landscapes are critical for species persistence and PAs can play a major role in conservation and in climate policy. Such contributions may be harder than expected to implement if new PAs are constrained to the same kinds of locations that PAs currently occupy.
Methodology/Principal Findings: Quantitatively extending the perception that PAs occupy ‘‘rock and ice’’, we show that across 147 nations PA networks are biased towards places that are unlikely to face land conversion pressures even in the absence of protection. We test each country’s PA network for bias in elevation, slope, distances to roads and cities, and suitability for agriculture. Further, within each country’s set of PAs, we also ask if the level of protection is biased in these ways. We find that the significant majority of national PA networks are biased to higher elevations, steeper slopes and greater distances to roads and cities. Also, within a country, PAs with higher protection status are more biased than are the PAs with lower protection statuses.
Conclusions/Significance: In sum, PAs are biased towards where they can least prevent land conversion (even if they offer perfect protection). These globally comprehensive results extend findings from nation-level analyses. They imply that siting rules such as the Convention on Biological Diversity’s 2010 Target [to protect 10% of all ecoregions] might raise PA impacts if applied at the country level. In light of the potential for global carbon-based payments for avoided deforestation or REDD, these results suggest that attention to threat could improve outcomes from the creation and management of PAs.
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This chapter conveys why human choices complicate correct evaluations of impacts. Unobservable land choices, choices affecting policy location and interactions among choices complicate both ex post impact evaluation and ex ante policy planning. Based on application of proper methods to Costa Rica, we then suggest how these hurdles can best be addressed. We provide examples of: how a best practice deforestation baseline rightly conveys the constraints on the impact the pioneering Costa Rican eco-payments programme could have; why it may be critical to have different baselines for different locations to correctly infer the impacts of Costa Rican protected areas; and how choices by conservation agencies and landowners can determine the bias within heretofore typical approaches to impact evaluation.
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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.
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