the climate models. Future increase in atmospheric CO2 is also taken into account and increases both light‐use and water‐use efficiency. Such a representation of CO2 effects generates estimates that lie in the upper range of the wide span of results found in the literature. However, since the strength of the final CO2 fertilization effect is still debated (Tubiello _et al._ , 2007), we consider two different levels of CO2 fertilization responses – full and none – which frame the space of the uncertainty in this respect. EPIC already includes in its modeling some adjustments in crop response to climate (field scale autonomous adjustments). This includes in particular changes in fertilizer and irrigation water use, as well as shifts in annual planting and harvesting dates. Other larger scale adjustments mechanisms are handled directly in GLOBIOM.

Competition between agricultural and forestry uses of land is at the core of the climate mitigation challenge, due to the large needs for biomass resource to substitute fossil energy. Biomass supply for energy production can be sourced in GLOBIOM from dedicated forest plantations or from managed forests. Dedicated plantations potential are determined on the basis of suitability maps and net primary productivity (NPP) estimates as documented in Havlík _et al._ (2011). The available woody biomass resources from managed forests are inputs to GLOBIOM from the G4M model for each forest area unit determined by mean annual increments, which are based on NPP maps from (Cramer _et al._ , 1999) and from different downscaling techniques as described in (Kindermann _et al._ , 2008b). The main forest management options considered by G4M are variation of thinning levels and choice of rotation length. The rotation length can be individually selected or optimally estimated to maximize increment, stocking biomass or harvestable biomass.

The model set‐up described above has been applied successfully to various scientific and policy applications (see section 1) but has not yet been used to look at climate impact and climate mitigation simultaneously. For this purpose, a consistent scenario framework is necessary, as outlined in the following section.

# **3. Baseline and scenario description**

We apply in this paper the new IPCC scenario framework which adopts a matrix approach to look at climate change impacts and mitigation strategies. This framework is articulated around two orthogonal dimensions of scenario drivers: socioeconomic drivers with the Shared Socioeconomic Pathways (SSPs) and level of radiative forcing with the Representative Concentration Pathways (RCPs) for the climate change component (van Vuuren _et al._ , 2011).

# **3.1. Socioeconomic scenarios and drivers**

Five different SSPs have been developed in the framework of climate change research (O’Neill _et al._ , 2014). These correspond to five different visions of the future structured around two axes: level of challenge to adaptation and level of challenge to mitigation. In this paper we focus on two particularly contrasted scenarios for the developing world: SSP4 with pronounced inequality between developed and developing countries, and SSP5, where developing countries are catching up with the developed ones.

SSP4, commonly labelled “Inequality”, is characterized by a world where emissions can be kept low through development of new technologies but least advanced countries experience very limited growth and a significant rise of inequalities increases the vulnerability to climate change of the poorest 9