{'fraction_non_alphanumeric': 0.02190580503833516, 'fraction_numerical': 0.0, 'mean_word_length': 5.571942446043166, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Quantification of Agricultural Land Use Changes in Consequential Life Cycle Assessment Using a Partial Equilibrium Model

Conventional Life Cycle Inventories (LCIs) are static models not considering any mechanism of revenue maximization and price equilibrium under external constraints. The underlying assumption is that an additional demand of a given commodity, irrelevant the amount, can always be supplied by the average supplier (fully elastic market). This constitutes a recognized limitation for the application of Life Cycle Assessment (LCA) to the evaluation of complex systems, like agro-systems. In the so called Consequential LCI, the relationships between the activities and processes of a life-cycle are no longer seen as essentially technical connections, based on average data; instead the relevant socio-economic mechanisms are considered via market information and economic models (partial or computable general equilibrium). The paper presents two partial equilibrium (PE) models of the agricultural sector of the Grand Duchy of Luxembourg, which allows the calculation of the LCI and the Indirect Land Use Change (ILUC) effects related to the introduction of a given demand for biomass (in particular maize) for biogas generation. The model, which is market-based and has farmers’ revenue maximization as the driver, represents the first step of a Consequential Life Cycle Assessment (CLCA) for biogas production in Luxembourg. The paper aims at presenting the main rational behind the choice of this type of model and the main challenges encountered during the modelling exercise with respect to the main decision variables, thus providing a useful guidance for other researchers embarking on the complex problem of economic modelling for CLCA studies applied to other (also larger) geographical contexts.