Water quality amelioration is one of the key ecosystem services provided by forests in the catchment areas of water supply systems. In this study, we applied random effect models and the least absolute shrinkage and selection regression method of machine learning to South African panel data to estimate the causal effect of natural forest cover on municipalities’ water treatment cost. We controlled for a range of confounding covariates including other land cover variables including wetlands, plantation forests, grassland, woodland etc. The Lasso based instrumental variable (IV) method allowed us to simultaneously account for model uncertainty surrounding variable selection and endogeneity bias. We found significant and robust evidence that natural forestland cover reduces water treatment costs at the intensive margin. Estimates from our preferred models indicated that the marginal benefit of increasing forest cover is R310.63 /ha/year. We also found that the elasticity response of water treatment cost to natural forest area is 0.02%. Our estimate of the marginal value of the water purification service is small compared to the producer’s surplus from alternative land uses. However, protection of natural forest land use might be defended if other ecosystem goods and services provided by natural forests are taken into account.
Gelo, & Turpie, J. (2022). The effect of forest land use on the cost of drinking water supply: machine learning evidence from South African data. Journal of Environmental Economics and Policy, 1–14. https://doi.org/10.1080/21606544.2021.2024094