Impact Evaluation of Forest Conservation Programs: Benefit-Cost Analysis, Without the Economics


Economists are increasingly using impact evaluation methods to measure the effectiveness of forest conservation programs. Theoretical analysis of two complementary economic models demonstrates that the average treatment effect on the treated (ATT) typically reported by these studies can be related to an economic measure of program performance only under very restrictive conditions. This is because the ATT is usually expressed in purely physical terms (e.g., avoided deforestation) and ignores heterogeneity in the costs and benefits of conservation programs. For the same reasons, clinical trials are a misleading analogy for the evaluation of conservation programs. To be more useful for economic analyses of conservation programs, impact evaluations should work toward developing measures of program outcomes that are economically more relevant, data that would enable the evaluation of impacts on forest degradation (not just deforestation) and primary forests (not forests in general), better estimates of spatially disaggregated treatment effects (not program-wide averages), and better information on the accuracy of estimated treatment effects as predictors of future risks.

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  1. 1.

    Experimental methods have also been used to evaluate conservation programs, but not as commonly. For an early example, see The and Ngoc (2006).

  2. 2.

    Several impact evaluations of conservation programs have used poverty alleviation as an outcome measure (Sims 2010; Andam et al. 2010; Ferraro et al. 2011; Ferraro and Hanauer 2014).

  3. 3.

    One could instead employ a continuous outcome measure, such as the proportion of a spatial unit that was deforested.

  4. 4.

    The assumption that the evaluator has information on site-specific values is a big one, of course, easier to make in a conceptual setting than in practice. Improvements in spatial datasets and ecosystem service models are making the estimation of conservation benefits at large spatial scales increasingly possible, however. For examples, see Naidoo and Ricketts (2006) and Bateman et al. (2013).

  5. 5.

    It is well-known that maximizing a benefit-cost ratio does not necessarily lead to the same ranking of alternative investment projects as maximizing NPV or NFV. Inspection of (7) indicates that the conservation program that maximized the benefit-cost ratio would be the same as the one that maximized NPV or NFV if the two programs were constrained to have the same aggregate cost. Otherwise, the programs could differ. In fact, in the simple context of (7), maximizing the benefit-cost ratio would imply protecting just the single site with the highest deforestation probability, which is hardly a reasonable conservation policy recommendation.

  6. 6.

    I note in passing that the existence of a feasible program with an average avoided deforestation rate higher than the agency’s program does not necessarily imply that the agency behaved irrationally by selecting the latter program. The agency’s program might have been the rational choice given the ex ante information on expected deforestation rates that was available to the agency when it selected sites for the program.

  7. 7.

    See Syrbe and Walz (2012) for a general discussion of spatial heterogeneity in the values of services provided by forests and other ecosystems. The most careful empirical analysis of the relationship between land cover, including forest cover, and the spatial distribution of multiple ecosystem services is probably a study by Eigenbrod et al. (2010) for England, which found that land cover (such as area forested) was a poor proxy for the value of services provided.

  8. 8.

    Costello and Polasky (2004, Table 1) provide a simple example that nicely illustrates this point. Their example includes three sites, which offer different benefits (numbers of species present) and face different conversion probabilities. Protection costs are uniform across the sites. The authors demonstrate that the dynamically optimal decision is to select the site that has the intermediate conversion probability, not the highest probability.

  9. 9.

    A more realistic model would include harvesting of roundwood (fuelwood or timber) in the country’s forests, as an economic activity that generates a valuable extractive resource but degrades the forests’ conservation value. The model in this section ignores degradation resulting from wood harvesting because nearly all impact evaluations of forest conservation programs have also ignored it. I return to this point in the final section of the paper.

  10. 10.

    The implicit assumption here is that a donor country randomly selects the area to be protected in a forested country, which is the only possible selection procedure in this aspatial model.

  11. 11.

    Because the donor country selects a single area for protection, unlike in Sect. 2 the deforestation rate is not averaged across multiple protected areas. Put another way, the deforestation rate given by (9) is the average across the landscape within which the donor country randomly selects a single area to protect.

  12. 12.

    The current-value Hamiltonian for land conversion in the country is \(\pi \left( {K(t)} \right) -C\left( {\dot{K}(t)} \right) +\lambda \left( t \right) \dot{K}(t)\), where \(\lambda (t)\) is the costate variable. The costate variable gives the present value of current and future agricultural profits from a marginal unit of agricultural land. The first-order condition with respect to the control variable \(\dot{K}(t)\) is \({\lambda (t)=\partial C\left( {\dot{K}(t)} \right) }/{\partial \dot{K}(t)}\). Hence, the marginal value of agricultural land equals the marginal cost of conversion: forest is converted to agriculture up to the point where the long-run marginal benefit of conversion equals the long-run marginal cost.

  13. 13.

    The marginal benefit described here is a current value, received only in the period when the donor country makes its protection decision. In contrast, the marginal cost of protection described in the previous paragraph is a long-run value that is equivalent to the present value of current and future marginal agricultural profits. An apples-to-apples comparison requires expressing the marginal benefit to the donor as a present value, too. All that needs to be shown here, however, is that the ranking of long-run marginal benefits is the same as the ranking of current marginal benefits, and doing this does not require formal analysis. The present value of future benefits from a marginal forest area is higher in the forest-poor country than in the forest-rich country because economic conditions in the forest-poor country at time \(t\) are equivalent to those in the forest-rich country at a later time \(t+x\). So, period-by-period into the future, the current marginal benefit of forest protection cannot be lower in the forest-poor country than in the forest-rich country, and so neither can the long-run marginal benefit calculated at a common point (i.e., the same time period) in the two countries.

  14. 14.

    Similarly, I might have obtained different results if I had developed a more complicated model than the simple one in this section, but that would just reinforce this point: the relationship between the ATT and economically rational conservation decisions is context-dependent. The ATT does not necessarily provide a useful proxy for the latter.

  15. 15.

    A study on community-based forest management by Somanathan et al. (2009) provides a rare example of combining impact evaluation results with information on conservation costs.

  16. 16.

    Similarly, spatial data on the ranges of threatened species from IUCN ( or NatureServe ( could be used to develop outcome measures related to reduced species loss. Species loss correlates less closely with biodiversity-related benefits than reduced carbon emissions correlate with climate-related benefits, however, because different species or assemblages of species do not necessarily have identical values.

  17. 17.

    I am grateful to an anonymous reviewer for very helpful suggestions regarding the issues discussed in this paragraph.

  18. 18.

    Ferraro, Hanauer, Miteva et al. (2013, p. 2) state, however, that “most [impact evaluations] treat ‘protection’ as if it were homogeneous.”

  19. 19.

    It follows that the ATTs estimated by these studies will necessarily be small in most cases: annual deforestation rates are only a few percentage points even in countries with “rapid” deforestation, and so nearly all of the land that was forested at the start of the interval will still be forested at the end, regardless of protection status. It also follows that a conservation program with an ATT \(=\) 0 during such a short interval could nevertheless reflect smart economic decisions: protection could generate a positive net return in the long run, after accounting for benefits and costs that occur beyond the interval evaluated.

  20. 20.

    For example, see the University of Texas at Austin’s Perry-Castañeda Library Map Collection (


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Correspondence to Jeffrey R. Vincent.



Differentiating (9) with respect to time yields:

$$\begin{aligned} \frac{\ddot{K}\left( t \right) \left( {L-K(t)} \right) +\dot{K}(t)^{2}}{\left( {L-K(t)} \right) ^{2}} \end{aligned}$$

Chiang (1992) demonstrates that the following relationship holds along the optimal path,

$$\begin{aligned} K\left( t \right) =\overline{{K}}-\left( {\overline{{K}}-K(0)} \right) e^{r_2 t}, \end{aligned}$$

where \(r_{2}\) is a negative constant, \(\frac{1}{2}\left( {\rho -\sqrt{\rho ^{2}+\frac{4\beta }{a}}} \right) \). This simplifies to \(K(t)=\overline{{K}}\left( {1-e^{r_2 t}} \right) \) if \(K\)(0) \(=\) 0 (all land is initially in forest). It follows that \(\dot{K}(t)=-r_2 e^{r_2 t}\overline{{K}}\) and \(\ddot{K}\left( t \right) =-r_2 ^{2}e^{r_2 t}\overline{{K}}\).

Substituting these expressions into (10) and simplifying, we obtain

$$\begin{aligned} \frac{r_2 \dot{K}(t)\left( {L-\overline{{K}}} \right) }{\left( {L-K(t)} \right) ^{2}}. \end{aligned}$$

This expression is negative because \(r_{2}\) is negative and all the other terms are positive. Hence, the deforestation rate declines over time.

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Vincent, J.R. Impact Evaluation of Forest Conservation Programs: Benefit-Cost Analysis, Without the Economics. Environ Resource Econ 63, 395–408 (2016).

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  • Conservation economics
  • Deforestation
  • Degradation
  • Impact evaluation
  • Primary forest
  • Protected area