Supplementary Files
Keywords
Colombia
counterfactuals
deforestation
impact evaluation
How to Cite
Downloads
Societal impact
Abstract
Rigorous impact evaluation of conservation strategies is essential for making informed conservation decisions, improving intervention effectiveness, and allocating resources efficiently. However, in practice, this type of evaluation remains limited, particularly regarding the use of counterfactual methods that enable causal inference. In this article, we discuss current implementation gaps and present CONTRAFACTUAL, a practical and accessible tool developed to support the evaluation of area-based conservation strategies, such as protected areas and OECMs, in reducing deforestation in Colombia. This tool is based on a counterfactual analysis approach that uses statistical matching through propensity scores to compare treated spatial units with equivalent control units, correcting for biases resulting from the non-random assignment of interventions. We applied the tool to analyze 23 national parks in Colombia in the year 2024, revealing wide variability in the effectiveness of protected areas in preventing forest loss (effect range: –54% to 100%), with mainly positive effects observed (X̄ =73,6%; SD=28,9; n=16). The results highlight the importance of contextualizing metrics of forest loss and gain and demonstrate that counterfactual analyses offer a robust approach to understanding and monitoring the true impact of area-based conservation interventions. This initiative seeks to bridge the gap between conservation science and practice, providing a useful resource for land managers, environmental authorities, and policymakers aiming to evaluate and enhance the performance of conservation areas in Colombia.
References
González-González, A., Villegas, J. C., Clerici, N., Salazar, J. F. (2021). Spatial-temporal dynamics of deforestation and its drivers indicate need for locally-adapted environmental governance in Colombia. Ecological Indicators, 126. https://doi.org/10.1016/j.ecolind.2021.107695
Ho, D. E., Imai, K., King, G., Stuart, E. A. (2011). MatchIt: Nonparametric Preprocessing for Parametric Causal Inference. In JSS Journal of Statistical Software, 42. http://www.jstatsoft.org/
IDEAM (Instituto de Hidrología, Meteorología y Estudios Ambientales). (2025). Resultados Monitoreo de Bosques. Sistema de Monitoreo de Bosques y Carbono, Subdirección de Ecosistemas e Información Ambiental, IDEAM, Colombia. Recuperado el 15 de noviembre de 2025 de https://www.ideam.gov.co/transparencia/datos-abiertos/seccion-de-datos-abiertos/resultados-monitoreo-de-bosques
Imbens, G. W., Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5-86. https://doi.org/10.1257/jel.47.1.5
Johnsson, T. (1992). A procedure for stepwise regression analysis. Statistical Papers, 33, 21-29.
Joppa, L. N., Loarie, S. R., Pimm, S. L. (2008). On the protection of “protected areas.” Proceedings of the National Academy of Sciences of the United States of America, 105(18), 6673-6678. https://doi.org/10.1073/pnas.0802471105
Joppa, L. N., Pfaff, A. (2009). High and far: Biases in the location of protected areas. PLoS ONE, 4(12). https://doi.org/10.1371/journal.pone.0008273
Joppa, L. N., Pfaff, A. (2011). Global protected area impacts. Proceedings of the Royal Society B: Biological Sciences, 278(1712), 1633-1638. https://doi.org/10.1098/rspb.2010.1713
Joppa, L., Pfaff, A. (2010). Reassessing the forest impacts of protection: The challenge of nonrandom location and a corrective method. Annals of the New York Academy of Sciences, 1185(January 2010), 135-149. https://doi.org/10.1111/j.1749-6632.2009.05162.x
Langhammer, P. F., Bull, J. W., Bicknell, J. E., Oakley, J. L., Brown, M. H., Bruford, M. W., Butchart, S. H. M., Carr, J. A., Church, D., Cooney, R., Cutajar, S., Foden, W., Foster, M. N., Gascon, C., Geldmann, J., Genovesi, P., Hoffmann, M., Howard-McCombe, J., Lewis, T., … Brooks, T. M. (2024). The positive impact of conservation action. Science, 384(6694), 453-458. https://doi.org/10.1126/science.adj6598
Negret, P. J., Di-Marco, M., Sonter, L. J., Rhodes, J., Possingham, H. P., Maron, M. (2020). Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness. Conservation Biology. https://doi.org/10.1111/cobi.13522
Neugarten, R., Rodewald, A., Eklund, J., O’Garra, T. (2025). An introduction to impact evaluation for conservation. In Conservation Science and Practice, 7(11). John Wiley and Sons Inc. https://doi.org/10.1111/csp2.70169
Olmos, A., Govindasamy, P. (2015). Propensity Scores: A Practical Introduction Using R. In Journal of MultiDisciplinary Evaluation, 11. http://www.jmde.com
Ribas, L. G. dos S., Pressey, R. L., Loyola, R., Bini, L. M. (2020). A global comparative analysis of impact evaluation methods in estimating the effectiveness of protected areas. In Biological Conservation, 246. Elsevier Ltd. https://doi.org/10.1016/j.biocon.2020.108595
Rosenbaum, P. R., Rubin, D. B. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70(1).
Schleicher, J., Eklund, J., Barnes, M. D., Geldmann, J., Oldekop, J. A., Jones, J. P. G. (2020). Statistical matching for conservation science. Conservation Biology, 34(3), 538-549. https://doi.org/10.1111/cobi.13448
Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. Statistical Science, 25(1), 1-21. https://doi.org/10.1214/09-STS313
Stuart, E. A., Rubin, D. B. (2008). Matching with multiple control groups with adjustment for group differences. Journal of Educational and Behavioral Statistics, 33(3), 279-306. https://doi.org/10.3102/1076998607306078
VanderWeele, T. J. (2019). Principles of confounder selection. European Journal of Epidemiology, 34(3), 211-219. https://doi.org/10.1007/s10654-019-00494-6
Vélez, M. A., Robalino, J., Cardenas, J. C., Paz, A., Pacay, E. (2020). Is collective titling enough to protect forests? Evidence from Afro-descendant communities in the Colombian Pacific region. World Development, 128. https://doi.org/10.1016/j.worlddev.2019.104837
Wolf, C., Levi, T., Ripple, W. J., Zárrate-Charry, D. A., Betts, M. G. (2021). A forest loss report card for the world’s protected areas. Nature Ecology & Evolution, 5(4), 520-529. https://doi.org/10.1038/s41559-021-01389-0
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2026 Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales