Accounting for carbon stocks in models of land-use change: an application to Southern Yucatan

To assess the impact of land-use change on carbon stocks, we apply a new methodology, linking ecological and economic modeling, to southern Yucatan, Mexico. A spatial econometric multinomial logit model of ten land-cover classes is estimated (four primary forest categories, three secondary growth categories, an invasive species, and two agricultural land-cover categories), using satellite data on land cover, linked with census socioeconomic data and other biophysical spatial data from 2000. The analysis is novel in that it is the first attempt to link detailed satellite data on land use, with on-the-ground estimates of carbon stocks in a spatial econometric model of land use. The estimated multinomial logit model is then used with two scenarios of future economic growth (“low growth” and “high growth” changes in population, agricultural land use, market access, and education levels) in the region to predict land-cover changes resulting from the economic growth. The per hectare carbon (C) stocks in each land-cover class are derived from previously published estimates of biomass from field sampling across the study region. We consider aboveground-only, aboveground plus soil, transient and non-transient pools of carbon. These estimates are scaled up to the total area in each class according to the predictions of the model baseline and the two development scenarios. Subsequently, the changes in carbon stocks resulting from the predicted land-cover changes are calculated. Under the low growth scenario, carbon stocks declined by 5%; under the high growth scenario, losses were 12%. Including soil C, the proportional losses were lower, but the absolute amount lost was more than double (to 6 Tg C under the low and almost 15 Tg C under the high-growth scenario). This methodology could be further developed for applications in global change policy, such as payments for environmental services (PES) or reduction in emissions from deforestation and degradation (REDD).