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Category: Secondary Data

Reassessing the forest impacts of protection: The challenge of nonrandom location and a corrective method

Published January 1, 2010 by Alex Pfaff
Lucas Joppa, Alexander Pfaff
Ann. N.Y. Acad. Sci. 1185 (2010) 135–149

PDF link iconProtected 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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Park Location Affects Forest Protection: Land Characteristics Cause Differences in Park Impacts across Costa Rica

Published January 3, 2009 by Alex Pfaff
Alexander Pfaff, Juan Robalino, G. Arturo Sanchez-Azofeifa, Kwaw S. Andam, Paul J. Ferraro
The B.E. Journal of Economic Analysis & Policy: Vol. 9: Iss. 2 (Contributions), Article 5.

PDF link iconTo 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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High and Far: Biases in the Location of Protected Areas

Published January 2, 2009 by Alex Pfaff
Lucas N. Joppa, Alexander Pfaff
PLoS ONE 2009 4(12): e8273. doi:10.1371/journal.pone.0008273

PDF link iconBackground: 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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Human choices and policies' impacts on ecosystem services: improving evaluations of payment and park effects on conservation and carbon

Published January 1, 2009 by Alex Pfaff
Alexander Pfaff, Juan Robalino
Engel, S. and C. Palmer, editors, “Avoided Deforestation: Prospects for Mitigating Climate Change”, Routledge Explorations in Environmental Economics 2009

PDF link iconThis 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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Measuring the effectiveness of protected-area networks in reducing deforestation

Published January 2, 2008 by Alex Pfaff
Kwaw Andam, Paul Ferraro, Alexander Pfaff, Juan Robalino, G. Arturo Sanchez-Azofeifa
PNAS 105(42):16089-16094

PDF link iconGlobal 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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Effects of Poverty on Deforestation: distinguishing behavior from location

Published January 1, 2008 by Alex Pfaff
Alexander Pfaff, Suzi Kerr, Romina Cavatassi, Benjamin Davis, Leslie Lipper, G. Arturo Sanchez-Azofeifa, J. Timmins
Economics of poverty, environment and natural resource use (chapter 6).

PDF link iconWe review many theoretical predictions that link poverty to deforestation and then examine poverty’s net impact empirically using multiple observations of all of Costa Rica after 1960. Countrywide disaggregate (district-level) data facilitate analysis of both poverty’s location and its impact on forest. If the characteristics of the places the poor live are not controlled for, then poverty’s impact is confounded with differences between poorer and less poor areas and we find no significant effect of poverty. Using our data over space and time to control for effects of locations’ differing characteristics, we find that the poorer are on land whose relative quality discourages forest clearing, such that with these controls the poorer areas are cleared more. The latter result suggests that poverty reduction aids the forest. For the poorest areas, this result is weaker but another effect is found: deforestation responds less to productivity, i.e., the poorest have less ability to expand or to reduce given land quality.

 

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