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Estimating the Impacts of Local Policy Innovation: the synthetic control method applied to tropical deforestation

Erin O. Sills, Diego Herrera, A. Justin Kirkpatrick, Amintas Brandao Jr., Rebecca Dickson, Simon Hall, Subhrendu Pattanayak, David Shoch, Mariana Vedoveto, Luisa Young, Alexander Pfaff
PLOS ONE 2015 (forthcoming) DOI:10.1371/journal.pone.0132590

PDF link iconQuasi-experimental methods increasingly are used to evaluate the impacts of conservation interventions by generating credible estimates of counterfactual baselines. These methods generally require large samples for statistical comparisons, presenting a challenge for evaluating innovative policies implemented within a few pioneering jurisdictions. Single jurisdictions often are studied using comparative methods, which rely on analysts’ selection of best case comparisons. The synthetic control method (SCM) offers one systematic and transparent way to select cases for comparison, from a sizeable pool, by focusing upon similarity in outcomes before the intervention. We explain SCM, then apply it to one local initiative to limit deforestation in the Brazilian Amazon. The municipality of Paragominas launched a multi-pronged local initiative in 2008 to maintain low deforestation while restoring economic production. This was a response to having been placed, due to high deforestation, on a federal “blacklist” that increased enforcement of forest regulations and restricted access to credit and output markets. The local initiative included mapping and monitoring of rural land plus promotion of economic alternatives compatible with low deforestation. The key motivation for the program may have been to reduce the costs of blacklisting. However its stated purpose was to limit deforestation, and thus we apply SCM to estimate what deforestation would have been in a (counterfactual) scenario of no local initiative. We obtain a plausible estimate, in that deforestation patterns before the intervention were similar in Paragominas and the synthetic control, which suggests that after several years, the initiative did lower deforestation (significantly below the synthetic control in 2012). This demonstrates that SCM can yield  helpful land-use counterfactuals for single units, with opportunities to integrate local and expert knowledge and to test innovations and permutations on policies that are implemented in just a few locations.

 

Policy Impacts on Deforestation: Lessons Learned from Past Experiences to Inform New Initiatives

Alexander Pfaff, Erin O. Sills, Gregory S. Amacher, Michael J. Coren, Kathleen Lawlor, Charlotte Streck
Report from the Nicholas Institute for Environmental Policy Solutions, Duke University (with the support of the Packard Foundation)

PDF link iconNational 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.