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Land demand is driven by an increasing population and changing consumption patterns. When land is required Land Use Changes (LUC) are triggered, causing several environmental and social impacts. Particularly topical is the assessment of indirect LUC effects. Several methodological approaches have been proposed for carrying out the assessment. In this paper we classified LUC models for Life Cycle Assessment (LCA) applications into three main categories: Economic, Causal–Descriptive and Normative models. Six models were selected as representative of these three categories and compared according to fifteen criteria covering: modeling framework, impact categories assessed and model transparency. The results show that, progresses have been made in the Economic General Equilibrium Models and the Causal–Descriptive Models compared. Causal–Descriptive models appear more suitable for long-term assessments in the LCA context while the compared economic models are more suitable for short/medium-term assessments of LUC consequences. As LUC dynamics involve interdisciplinary knowledge, a combination of economic, biophysical and statistical data is however required to achieve a robust assessment of complex LUC dynamics. There is still considerable scope for improving current LUC models. In particular, there is room for improving precision of data, identification of marginal land and inclusion of a broader range of impact categories. Current models mainly focus on GHG emission-related impacts and rarely on other environmental impacts such as nutrient leaching, biodiversity impacts and water resource depletion. Socio-economic analyses of LUC patterns are currently excluded from LCA analysis, preventing a holistic assessment of land occupation impacts. [ABSTRACT FROM AUTHOR]

Abstract: Combining the economics and the environmental and social aspects of bioenergy chain implementation is a difficult task, particularly for wood fuel production. Multiple aspects, such as financial analysis, technology development, natural resource exploitation and avoided environmental pollution, must be considered simultaneously. Thus, decision support models seem to be the proper tools for the overall definition of strategies in the wood energy sector. The objective of this study has been to develop a partial equilibrium model able to quantify the socio-economic and environmental effects of policy, technology and best biomass allocation scenarios on the forest residue chain. The model, based on multi-objective linear programming and spatial analysis, considers the financial benefits/losses and the potential trends of three compartments: sawmills, forest enterprises and energy plants. In addition, the model computes avoided emissions for bioheat and bioelectricity production and introduces an impact indicator for the road transport of biomass. Model outputs are defined using a multi criteria approach. The main results stress the importance of both environmental parameters and the implementation of organic Rankine cycle technology for the optimization of the entire bioenergy chain. The model was tested in an Italian Alpine region (province of Trento). [Copyright &y& Elsevier]