We estimate a two-sector DSGE model for Brazil (2005–2019) that links agricultural land accumulation, deforestation, and \(CO_2\) emissions to non-agricultural productivity through an emissions–damage channel. Enforcement is modeled as a policy shock that slows land expansion. Using Bayesian estimation on joint macroeconomic and environmental data, we obtain three results. First, stronger enforcement persistently lowers \(CO_2\) while aggregate output responses are small and centered around zero: the contraction in agriculture is largely offset by a transitory productivity gain outside agriculture. Second, variance decompositions show that GDP fluctuations are driven mainly by non-agricultural productivity shocks, whereas enforcement contributes little to GDP volatility but is pivotal for deforestation dynamics. Third, the findings are robust in structured sensitivity exercises that vary (i) the emissions–land elasticity \(\theta\) , (ii) the damage elasticity \(\gamma\) , and (iii) an enforcement semi-elasticity \(\zeta\) governing how enforcement curbs effective land expansion and associated emissions. Environmental gains and near-zero aggregate costs persist across wide parameter ranges. Overall, the evidence indicates that tightening enforcement can deliver sizable reductions in land-use emissions at limited macroeconomic cost, with sectoral reallocation cushioning aggregate output.
