资源环境科技发展态势分析平台

Projected increases in population, income and consumption rates are expected to lead to rising pressure on the land system. Ambitions to limit global warming to 2 degrees C or even 1.5 degrees C could also lead to additional pressures from land-based mitigation measures such as bioenergy production and afforestation. To investigate these dynamics, this paper describes five elaborations of the Shared Socio-economic Pathways (SSP) using the IMAGE 3.0 integrated assessment model framework to produce regional and gridded scenarios up to the year 2100. Additionally, land-based climate change mitigation is modelled aiming for long-term mitigation targets including 1.5 degrees C. Results show diverging global trends in agricultural land in the baseline scenarios ranging from an expansion of nearly 826 Mha in SSP3 to a decrease of more than 305 Mha in SSP1 for the period 2010-2050. Key drivers are population growth, changes in food consumption, and agricultural efficiency. The largest changes take place in Sub-Saharan Africa in SSP3 and SSP4, predominantly due to high population growth. With low increases in agricultural efficiency this leads to expansion of agricultural land and reduced food security. Land use also plays a crucial role in ambitious mitigation scenarios. First, agricultural emissions could form a substantial component of emissions that cannot be fully mitigated. Second, bioenergy and reforestation are crucial to create net negative emissions reducing emissions in SSP2 in 2050 by 8.7 Gt CO2/yr and 1.9 Gt CO2/yr, respectively (1.5 degrees C scenario compared to baseline). This is achieved by expansion of bioenergy area (516 Mha in 2050) and reforestation. Expansion of agriculture for food production is reduced due to REDD policy (290 Mha in 2050) affecting food security especially in Sub-Saharan Africa indicating an important trade-off of land-based mitigation. This set of SSP land-use scenarios provides a comprehensive quantification of interacting trends in the land system, both socio-economic and biophysical. By providing high resolution data, the scenario output can improve interactions between climate research and impact studies.