We aim to offer guideposts for forming expectations about when SSIs are “effective” (defined below). We suggest some factors that affect SSIs’ effects on both narrow and broader goals. With improved understanding of how those factors affect behaviors and outcomes, actors developing and improving SSIs can better predict which efforts will improve sustainability and can better organize empirical SSI studies.
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.
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).
We examine theoretically the emergence of participatory comanagement agreements that share between state and user the management of resources and the benefits from use. Going beyond user-user interactions, our state-user model addresses a critical question—when will comanagement arise?— in order to consider the right baseline for evaluating comanagement’s forest and welfare impacts. We then compare our model’s hypotheses concerning de facto rights, negotiated agreements, and transfers (all endogenous) with community-level data including observed agreements in a protected Indonesian forest. These unique data could refute the model, despite being limited, but instead offer support.
Farmers have to make key decisions, such as which crops to plant or whether to prepare the soil, before knowing how much water they will get.They face losses if they make costly decisions but do not receive water, and they may forego profits if they receive water without being prepared.We consider the coordination of farmers’ decisions, such as which crops to plant or whether to prepare the soil when farmers must divide an uncertain water supply. We compare ex-ante queues (pre-decision) to an ex-post spot market (post-decision & post-rain) in experiments in rural Brazil and a university in England. Queues have greater coordination success than does the spot market.
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).
