PNNL

Abstract

Forestry-based measures such as afforestation/reforestation (A/R) and reduced deforestation (RDF) are considered promising options to mitigate climate change, yet their mitigation potentials are limited by biophysical and economic factors that are largely uncertain. The range of potential estimates from integrated assessment models (IAMs) raises concerns about the capacity of the land systems to provide cost-effective and permanent land-based mitigation. We use a widely used IAM model, GCAM, to quantify the economic mitigation potential of forestry mitigation measures by simulating a climate policy limiting global CO2 emissions from energy and industrial processes in addition to implementing a GHG tax on AFOLU emissions. The modelled scenarios assess the impacts of increased land competition from different uses of the AFOLU sector. The implications of constraints on the rate of forest expansion, fallow land availability and global bioenergy demand are assessed in terms of GHG emissions, land use change effects and impacts on agriculture and trade flows. Results show that the forestry-based mitigation potential ranges between 1335 and 305 MtCO2.yr-1 from 2020 to 2050, with constraints affecting differently the regional potentials of A/R and RDF. Achieving high A/R potentials requires converting agricultural land to forests at fast rates which can increase agricultural prices. Increasing bioenergy demand poses trade-offs with forestry-based mitigation, as it contributes to deforestation in regions such as Indonesia despite high land carbon prices. Implementing both bioenergy and forestry mitigation would imply fast conversion of fallow land, with direct agronomic consequences. While the global forestry sector bears the potential to mitigate climate change and contribute to countries’ NDCs towards the Paris Agreement, our results suggest that additional policies must be put in place to minimize land competition between AFOLU sectors and avoid that forestry-based mitigation translates into negative sustainability outcomes.