Conservation programs have increased significantly, as has the evaluation of their impacts. However,the evaluation of their potential impacts beyond program borders has been scarce.Such spillovers can significantly reduce or increase net impacts. In this review, we discuss how conservation programs might affect outcomes beyond their borders and present some evidence of when they have or have not. We focus on five major channels by which spillovers can arise:(1) input reallocation; (2) market prices; (3) learning; (4) nonpecuniary motivations; and (5) ecological-physical links. We highlight evidence for each channel and emphasize that estimates often may reflect multiple channels. Future research could test for spillovers within different contexts and could separate the effects of different channels.
Lisa Mandle, Benjamin P. Bryant, Mary Ruckelshaus, Davide Geneletti, Joseph M. Kiesecker, Alexander Pfaff
Conservation Letters 2015 (online 9/28, doi 10.1111/conl.12201)
New infrastructure is needed globally to support economic development and improve human well-being. Investments that do not consider ecosystem services (ES) can eliminate these important societal benefits from nature, undermining the development benefits infrastructure is intended to provide. Such tradeoffs are acknowledged conceptually but in practice have rarely been considered in infrastructure planning. Taking road investments as one important case, here we examine where and what forms of ES information have the potential to meaningfully influence decisions by multilateral development banks (MDBs). Across the stages of a typical road development process, we identify where and how ES information could be integrated, likely barriers to the use of available ES information, and key opportunities to shift incentives and thereby practice. We believe inclusion of ES information is likely to provide the greatest development benefit in early stages of infrastructure decisions. Those strategic planning stages are typically guided by in-country processes, with MDBs playing a supporting role, making it critical to express the ES consequences of infrastructure development using metrics relevant to government decision makers. This approach requires additional evidence of the in-country benefits of cross-sector strategic planning and more tools to lower barriers to quantifying these benefits and facilitating ES inclusion.
Alexander Pfaff, Gregory S. Amacher, Erin O. Sills
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).
Oxford Review of Economic Policy, Volume 28, Number 1, 2012, pp. 164–179
Policies must balance forest conservation’s local costs with its benefits—local to global—in terms of biodiversity, the mitigation of climate change, and other eco-services such as water quality. The trade-offs with development vary across forest locations. We argue that considering location in three ways helps to predict policy impact and improve policy choice: (i) policy impacts vary by location because baseline deforestation varies with characteristics (market distances, slopes, soils, etc.) of locations in a landscape; (ii) different mixes of political-economic pressures drive the location of different policies; and (iii) policies can trigger ‘second-order’ or ‘spillover’ effects likely to differ by location. We provide empirical evidence that suggests the importance of all three considerations, by reviewing highquality evaluations of the impact of conservation and development on forest. Impacts of well-enforced conservation rise with private clearing pressure, supporting (i). Protection types (e.g. federal/state) differ in locations and thus in impacts, supporting (ii). Differences in development process explain different signs for spillovers, supporting (iii).
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)
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.
