Predicting the Location of Deforestation: The Role of Roads and Protected Areas in North Thailand

Cropper, Maureen; Puri, Jyotsna; Griffiths, Charles; Barbier, Edward B.; Burgess, Joanne C.

doi:10.2307/3147088

Cited by 216 publications

(111 citation statements)

References 3 publications

Supporting

Mentioning

106

Contrasting

Unclassified

Order By: Relevance

“…Protected forests thus had a below-average probability of being deforested in the absence of protection. This pattern of protection is common globally (3,9,(15)(16)(17)(18)(19)(20)22). Thus, although further empirical confirmation is needed, our analysis suggests that much of what is being described as protection's impacts may result from protected-area location rather than protection itself.…”

Section: Discussionmentioning

confidence: 77%

“…A few assessments have formally controlled for such differences (19,(21)(22)(23)(24), but they either use a small set of covariates, which can exacerbate bias when other relevant covariates are not included, or highly parametric, regression-based methods. Moreover, no published analysis has tested the sensitivity of results to hidden bias that may not have been removed by conditioning on covariates.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Measuring the effectiveness of protected area networks in reducing deforestation

Andam

Ferraro²,

Pfaff³

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

837

830

View full text Add to dashboard Cite

Global 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.avoided deforestation ͉ conservation policy ͉ empirical evaluation ͉ spatial spillovers

show abstract

Section: Discussionmentioning

confidence: 77%

mentioning

confidence: 99%

Measuring the effectiveness of protected area networks in reducing deforestation

Andam

Ferraro²,

Pfaff³

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

837

830

View full text Add to dashboard Cite

show abstract

“…Examples in the domestic literature often use the census tract [Collins, 2004] or county [Miller and Plantinga, 1999]. Other examples in the economic literature on land use in developing countries include work at the municipal level [Pfaff, 1999], census tract [Chomitz and Thomas, 2003] or at the pixel level [Cropper et al, 2001;Munroe et al, 2002]. One benefit of using section level data in this analysis is that section boundaries are exogenously determined, in comparison to an analysis at the field level.…”

Section: Datamentioning

confidence: 99%

Price elasticity reconsidered: Panel estimation of an agricultural water demand function

Schoengold

Sunding

Moreno

2006

Water Resources Research

121

View full text Add to dashboard Cite

[1] Using panel data from a period of water rate reform, this paper estimates the price elasticity of irrigation water demand. Price elasticity is decomposed into the direct effect of water management and the indirect effect of water price on choice of output and irrigation technology. The model is estimated using an instrumental variables strategy to account for the endogeneity of technology and output choices in the water demand equation. Estimation results indicate that the price elasticity of agricultural water demand is À0.79, which is greater than that found in previous studies.

show abstract

“…( found a positive impact of PAs on deforestation while controlling for the location bias (Cropper et al 2001;Joppa & Pfaff 2009). For instance (Andam et al 2008;Pfaff et al 2009;Robalino et al 2015) focused on the well-known protected-area system in Costa Rica.…”

Section: Introductionmentioning

confidence: 99%

Addressing Contextual and Location Biases in the Assessment of Protected Areas Effectiveness on Deforestation in the Brazilian Amazônia

Kéré¹,

Choumert

Motel

et al. 2017

Ecological Economics

View full text Add to dashboard Cite

Using a remotely sensed pixel data set, we develop a multilevel model and propensity score weighting with multilevel data to assess the impact of protected areas on deforestation in the Brazilian Amazon. These techniques allow taking into account location bias, contextual bias and the dependence of spatial units. The results suggest that protected areas have slowed down deforestation between 2005 and 2009, whatever the type of governance. The results also evidence that protected and unprotected areas do not share the same location characteristics. In addition, the effectiveness of protected areas differs according to socioeconomic and environmental variables measured at municipal level.

show abstract

Predicting the Location of Deforestation: The Role of Roads and Protected Areas in North Thailand

Cited by 216 publications

References 3 publications

Measuring the effectiveness of protected area networks in reducing deforestation

Measuring the effectiveness of protected area networks in reducing deforestation

Price elasticity reconsidered: Panel estimation of an agricultural water demand function

Addressing Contextual and Location Biases in the Assessment of Protected Areas Effectiveness on Deforestation in the Brazilian Amazônia

Contact Info

Product

Resources

About