Geert Woltjer

What can and can't we say about indirect land-use change in Brazil using an integrated economic - land-use change model?

It is commonly recognized that large uncertainties exist in modelled biofuel‐induced indirect land‐use change, but until now, spatially explicit quantification of such uncertainties by means of error propagation modelling has never been performed. In this study, we demonstrate a general methodology to stochastically calculate direct and indirect land‐use change (dLUC and iLUC) caused by an increasing demand for biofuels, with an integrated economic – land‐use change model. We use the global Computable General Equilibrium model MAGNET, connected to the spatially explicit land‐use change model PLUC. We quantify important uncertainties in the modelling chain. Next, dLUC and iLUC projections for Brazil up to 2030 at different spatial scales and the uncertainty herein are assessed. Our results show that cell‐based (5 × 5 km2) probabilities of dLUC range from 0 to 0.77, and of iLUC from 0 to 0.43, indicating that it is difficult to project exactly where dLUC and iLUC will occur, with more difficulties for iLUC than for dLUC. At country level, dLUC area can be projected with high certainty, having a coefficient of variation (cv) of only 0.02, while iLUC area is still uncertain, having a cv of 0.72. The latter means that, considering the 95% confidence interval, the iLUC area in Brazil might be 2.4 times as high or as low as the projected mean. Because this confidence interval is so wide that it is likely to straddle any legislation threshold, our opinion is that threshold evaluation for iLUC indicators should not be implemented in legislation. For future studies, we emphasize the need for provision of quantitative uncertainty estimates together with the calculated LUC indicators, to allow users to evaluate the reliability of these indicators and the effects of their uncertainty on the impacts of land‐use change, such as greenhouse gas emissions.

The Effects of Bioenergy Production on Food Security

The purpose of this paper is to deliver a framework and initial application of a model-based assessment of the food security impacts of changes in bioenergy production and relevant policies on food security. In an economic framework, four pathways are established by which biofuel production potentially affects the dimensions of food security: (1) food availability in connection with the competition for arable land; (2) the contribution of biofuel use of feedstock to food price volatility; (3) biofuel markets as a source of income opportunities for farmers; (4) sector-wide contributions to macroeconomic performance and living standards. Computable general equilibrium (CGE) modeling is proposed as methodology for an encompassing empirical examination of these pathways, although the limitations of the tool and data warrant the use of complementary qualitative and quantitative analyses.

Mapping land use changes resulting from biofuel production and the effect of mitigation measures

Many of the sustainability concerns of bioenergy are related to direct or indirect land use change (LUC) resulting from bioenergy feedstock production. The environmental and socio‐economic impacts of LUC highly depend on the site‐specific biophysical and socio‐economic conditions. The objective of this study is to spatiotemporally assess the potential LUC dynamics resulting from an increased biofuel demand, the related greenhouse gas (GHG) emissions, and the potential effect of LUC mitigation measures. This assessment is demonstrated for LUC dynamics in Brazil towards 2030, considering an increase in the global demand for bioethanol as well as other agricultural commodities. The potential effects of three LUC mitigation measures (increased agricultural productivity, shift to second‐generation ethanol, and strict conservation policies) are evaluated by using a scenario approach. The novel modelling framework developed consists of the global Computable General Equilibrium model MAGNET, the spatiotemporal land use allocation model PLUC, and a GIS‐based carbon module. The modelling simulations illustrate where LUC as a result of an increased global ethanol demand (+26 × 109 L ethanol production in Brazil) is likely to occur. When no measures are taken, sugar cane production is projected to expand mostly at the expense of agricultural land which subsequently leads to the loss of natural vegetation (natural forest and grass and shrubland) in the Cerrado and Amazon. The related losses of above and below ground biomass and soil organic carbon result in the average emission of 26 g CO2‐eq/MJ bioethanol. All LUC mitigation measures show potential to reduce the loss of natural vegetation (18%–96%) as well as the LUC‐related GHG emissions (7%–60%). Although there are several uncertainties regarding the exact location and magnitude of LUC and related GHG emissions, this study shows that the implementation of LUC mitigation measures could have a substantial contribution to the reduction of LUC‐related emissions of bioethanol. However, an integrated approach targeting all land uses is required to obtain substantial and sustained LUC‐related GHG emission reductions in general.