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.
Review of Environmental Economics and Policy, volume 7, issue 1, winter 2013, pp. 114–135 doi:10.1093/reep/res023
This article, which is part of a symposium on the economics of REDD, identifies three common settings for forest loss involving different types of decision-making agents that operate under different markets and institutions. That suggests using different theoretical frameworks for these three settings, which in turn generates different predictions concerning policies’ impacts. The first model, “producer profit maximization given market integration,” has been applied to many private decisions about the best locations for profitable land uses, such as agriculture and forest. Its predictions have been widely studied empirically, beginning no later than von Thunen (1826). The second model, “rural household optimization given incomplete markets and household heterogeneity,” has been applied to more isolated settings featuring high transactions costs that yield incomplete integration of households in input and output markets. Its policy impact predictions have been tested with surveys at household and village levels. In the third model, “public optimization given production and corruption responses by private firms,” a public agency determines public forest access by balancing public goods, public revenue needs, and private rents to award concessions. There is potential for corruption, and the decisions may be affected by decentralization. This model’s predictions can be tested using observed policies. We find that past policies rarely addressed the incentives driving forest loss effectively. This helps to explain the limited impact of past policies on deforestation and forest degradation. It also suggests directions for the design of future policies. In sum, the theory and the evidence suggest that REDD success requires an understanding of all the incentives that drive forest loss, so that domestic policy can be tailored to specific settings (i.e., relevant agents and institutions).
Encyclopedia of Energy, Natural Resource, and Environmental Economics, (2013), vol. 2, pp. 144-149
Encyclopedia entry: reviews impacts of policies relevant for deforestation
Report from the Nicholas Institute for Environmental Policy Solutions, Duke University (with the support of the Packard Foundation)
National and international efforts within the last few decades to reduce forest loss, while having some impact, have failed to substantially slow the loss of the world’s forests. Forest loss, i.e., deforestation and forest degradation, is widespread and accounts for 12%–17% of the world’s greenhouse gas (GHG) emissions. Global concern about climate change and the realization that reduced emissions from deforestation and degradation (REDD) can play a role in climate change mitigation make it critical to learn from our past experiences with policies to reduce forest loss. Within the UN Framework Convention on Climate Change (UNFCCC), negotiators are actively considering ways to include incentives for REDD and other forest carbon activities in any post-2012 treaty. In parallel, the U.S. Congress is developing proposals for a long-term climate policy that includes incentives for REDD, and possibly other international forest carbon activities. Such policies may mobilize new funds for forest conservation, including for addressing drivers of deforestation and forest degradation in developing countries. Climate-related incentives for REDD are likely to be performance-based, i.e., to emphasize the measurement, reporting, and verification of all results. The implementation of this emphasis, alongside the introduction of new financial incentives, could increase such policies’ impacts on forest loss relative to the past. Policy effectiveness, efficiency, and equity can increase if we learn lessons from the past about what drives and what inhibits deforestation and degradation. It is in the interest of any REDD program to understand what has worked in reducing deforestation and degradation and what has not, as well as the reasons for observed differences in outcomes. Investments and policies can then more effectively embrace and extend success while reducing risks of further failures. This report aims to provide lessons to inform U.S. and international policymakers by analyzing dominant influences on deforestation and degradation. We study not only forest-focused policies, but also other policies that directly or indirectly influence forest loss, all in light of relevant nonpolicy factors such as trends in commodity prices. We provide examples of previous policies to draw lessons from successes and failures, then link those observations about the past to the decisions current policymakers must soon make within ongoing climate policy deliberations.
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.