Project level assessment of indirect land use changes arising from biofuel production

Abstract

The use of land resources has a strong influence on the sustainability of biofuel production. An assessment of both direct and indirect changes in land use is necessary if an accurate assessment of sustainability is to be made. An increasing number of studies have developed approaches to estimate the Indirect Land Use Change (ILUC) impacts of biofuels at global, national or regional level, but assessing ILUC remains a challenging task and estimates vary widely. In this study, we suggest that a socially motivated, project level approach can provide robust insight into the conditions affecting land use change dynamics. We developed a causal‐descriptive approach named ILUC Project ASsessment Tool (ILUC PAST) for project level assessment of ILUC. It uses a tiered multitool analysis—from local to global—combined with extensive stakeholder engagement. A real‐world project for the production of cellulosic ethanol in Sardinia (Italy) was used to evaluate the tool and benchmark the results against two alternatives for project level assessment: the ‘Low Indirect Impact Biofuel’ methodology and the ‘iLUC Club’ method. The results of the case study of advanced biofuels suggest that the quantitative estimates of ILUC combined with the in‐depth understanding of the cause‐and‐effect dynamics provided by ILUC PAST are sufficiently credible, salient and legitimate to support project level and local decision‐making.

Bioenergy for climate change mitigation: Scale and sustainability

“…Attribution is more challenging for iLUC because, by definition, it is not directly connected to a biomass producer, and there are many interacting drivers of land use change (Efroymson et al, 2016; Egeskog et al, 2016). Instead iLUC effects need to be quantified using modelling approaches, such as general equilibrium models, that consider second order factors such as prices, government policy, regulations, trade relationships and market expectations (Chen et al, 2021; Di Lucia et al, 2012, 2019; Hudiburg et al, 2016; Khanna & Crago, 2012; Khanna et al, 2017; Malins et al, 2014; Wicke et al, 2012). Global IAM modelling frameworks capture the land use/land cover and GHG impacts of iLUC, but only at a highly aggregate regional level.…”

“…Estimates of iLUC effects are also non‐linear and will change with the level of biofuel demand and land conversion (Melillo et al, 2009). A single biofuel project, for instance, may have negligible iLUC emissions when assessed in isolation (Di Lucia et al, 2019), and in cases where bioenergy policies induce the conversion of pasture or marginal land to forestland carbon stocks may be increased (Dale et al, 2017; Duden et al, 2017).…”

Cost Benefit and Risk Analysis of Low iLUC Bioenergy Production in Europe Using Monte Carlo Simulation

“…Tables 1 and 2 describe the crops and bioenergy pathways considered for this study, whilst Figure 1 shows a map of the potential bioenergy case study sites; total hectares considered for the investments are presented in Table 3. As shown in Table 1, in Italy, the case study (CS) site was the contaminated land in Carbonia in the Sulcis area, where the land has been affected by chemical contamination, and therefore, all agricultural activities are banned by both local and National Government (although some illegal agriculture and pasture use occurs) [17,18]. Irrigated Giant reed was considered for the production of 2G ethanol and electricity as co-product with an average yield of 25 tons ha −1 yr −1 of dry matter (DM).…”

Combining Environmental and Economic Performance for Bioprocess Optimization

“…While the monetized results for each TEA and LCA indicator help to identify the predominantly contributing processes (hotspots), combining all results into a single score helps to identify trade-offs across environmental and economic indicators. Both require consideration of a comprehensive set of TEA and LCA indicators, including, for example, impacts from indirect land-use change relevant for different feedstocks [7,64]. Our overall set of proposed indicators is presented in Figure 2.…”