Sameer Rege

Application of three independent consequential LCA approaches to the agricultural sector in Luxembourg

PurposeConsequential Life Cycle Assessment (C-LCA) is a “system modelling approach in which activities in a product system are linked so that activities are included in the product system to the extent that they are expected to change as a consequence of a change in demand”. Hence, C-LCA focuses on micro-economic actions linked to macro-economic consequences, by identifying the (marginal) suppliers and technologies prone to be affected by variable scale changes in the demand of a product. Detecting the direct and indirect environmental effects due to changes in the production system is not an easy task. Hence, researchers have combined the consequential perspective with different econometric models. Therefore, the aim of this study is to assess an increase in biocrops cultivation in Luxembourg using three different consequential modelling approaches to understand the benefits, drawbacks and assumptions linked to each approach as applied to the case study selected.MethodsFirstly, a partial equilibrium (PE) model is used to detect changes in land cultivation based on the farmers’ revenue maximisation. Secondly, another PE model is proposed, which considers a different perspective aiming at minimising a total adaptation cost (so-called opportunity cost) to satisfy a given new demand of domestically produced biofuel. Finally, the consequential system delimitation for agricultural LCA approach, as proposed by Schmidt (Int J Life Cycle Assess 13:350–364, 2008), is applied.Results and discussionThe two PE models present complex shifts in crop rotation land use changes (LUCs), linked to the optimisation that is performed, while the remaining approach has limited consequential impact on changes in crop patterns since the expert opinion decision tree constitutes a simplification of the ongoing LUCs. However, environmental consequences in the latter were considerably higher due to intercontinental trade assumptions recommended by the experts that were not accounted for in the economic models. Environmental variations between the different scenarios due to LUCs vary based on the different expert- or computational-based assumptions. Finally, environmental consequences as compared with the current state-of-the-art are lame due to the limited impact of the shock within the global trade market.ConclusionsThe use of several consequential modelling approaches within the same study may help widen the interpretation of the advantages or risks of applying a specific change to a production system. In fact, different models may not only be good alternatives in terms of comparability of scenarios and assumptions, but there may also be room for complementing these within a unique framework to reduce uncertainties in an integrated way.

Cost versus life cycle assessment-based environmental impact optimization of drinking water production plants.

Empowering decision makers with cost-effective solutions for reducing industrial processes environmental burden, at both design and operation stages, is nowadays a major worldwide concern. The paper addresses this issue for the sector of drinking water production plants (DWPPs), seeking for optimal solutions trading-off operation cost and life cycle assessment (LCA)-based environmental impact while satisfying outlet water quality criteria. This leads to a challenging bi-objective constrained optimization problem, which relies on a computationally expensive intricate process-modelling simulator of the DWPP and has to be solved with limited computational budget. Since mathematical programming methods are unusable in this case, the paper examines the performances in tackling these challenges of six off-the-shelf state-of-the-art global meta-heuristic optimization algorithms, suitable for such simulation-based optimization, namely Strength Pareto Evolutionary Algorithm (SPEA2), Non-dominated Sorting Genetic Algorithm (NSGA-II), Indicator-based Evolutionary Algorithm (IBEA), Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results of optimization reveal that good reduction in both operating cost and environmental impact of the DWPP can be obtained. Furthermore, NSGA-II outperforms the other competing algorithms while MOEA/D and DE perform unexpectedly poorly.

Integrated environmental assessment of future energy scenarios based on economic equilibrium models

The future evolution of energy supply technologies strongly depends on (and affects) the economic and environmental systems, due to the high dependency of this sector on the availability and cost of fossil fuels, especially on the small regional scale. This paper aims at presenting the modeling system and preliminary results of a research project conducted on the scale of Luxembourg to assess the environmental impact of future energy scenarios for the country, integrating outputs from partial and computable general equilibrium models within hybrid Life Cycle Assessment (LCA) frameworks. The general equilibrium model for Luxembourg, LUXGEM, is used to evaluate the economic impacts of policy decisions and other economic shocks over the time horizon 2006-2030. A techno-economic (partial equilibrium) model for Luxembourg, ETEM, is used instead to compute operation levels of various technologies to meet the demand for energy services at the least cost along the same timeline. The future energy demand and supply are made consistent by coupling ETEM with LUXGEM so as to have the same macro-economic variables and energy shares driving both models. The coupling results are then implemented within a set of Environmentally-Extended Input-Output (EE-IO) models in historical time series to test the feasibility of the integrated framework and then to assess the environmental impacts of the country. Accordingly, a disaggregated energy sector was built with the different ETEM technologies in the EE-IO to allow hybridization with Life Cycle Inventory (LCI) and enrich the process detail. The results show that the environmental impact slightly decreased overall from 2006 to 2009. Most of the impacts come from some imported commodities (natural gas, used to produce electricity, and metalliferous ores and metal scrap). The main energy production technology is the combined-cycle gas turbine plant “Twinerg�?, representing almost 80% of the domestic electricity production in Luxembourg. In the hybrid EE-IO model, this technology contributes to around 7% of the total impact of the country’s net consumption. The causes of divergence between ETEM and LUXGEM are also thoroughly investigated to outline possible strategies of modeling improvements for future assessment of environmental impacts using EE-IO. Further analyses focus first on the completion of the models’ coupling and its application to the defined scenarios. Once the coupling is consistently accomplished, LUXGEM can compute the IO flows from 2010 to 2030, while the LCI processes in the hybrid system are harmonized with ETEM to represent the future domestic and imported energy technologies.

Introducing LCA Results to ABM for Assessing the Influence of Sustainable Behaviours

The paper is focused on the application of ABM to simulate the evolution of the agricultural system of the Grand Duchy of Luxembourg under the external shock of fostering the production of maize to be used for bio-methane generation. The modeling exercise is part of a larger scale effort, which aims at the evaluation of the potential environmental impacts arising from policy implementation, following the methodology known as Consequential Life Cycle Assessment (CLCA). The application of ABM brings from one side an additional level of complexity to the assessment, but it also allows a finer scale bottom-up modeling, allowing the simulation of aspects (such as behavioral components in farmers’ choices or adaptive social processes such as imitation) that was not possible to grasp from other (e.g. purely economically oriented) modeling perspectives. The ABM model implemented has as many agents as the inventoried farmers in Luxembourg are. Although the paper only presents preliminary results, it already allows exploring the influence of farmers’ environmental awareness on the environmental impacts linked to farming activities. This is possible thanks to the attribution to the agents’ profiles of one specific feature which simulates their “green consciousness level”.

Sustainable Farming Behaviours: An Agent Based Modelling and LCA Perspective

The paper is focused on the application of ABM (Agent Based Models) to simulate the evolution of the agricultural system of the Grand Duchy of Luxembourg, which aims at the evaluation of the potential environmental impacts arising from policy implementation, following the methodology known as Consequential Life Cycle Assessment (CLCA). The novelty of our approach is on the multi-modeling consideration of the problem of how to evaluate potential environmental impact of farmer’s behaviours. We consider the coupling of a computational model (ABM) and a matrix-based LCA model. The paper only presents preliminary results, exploring the influence of farmers’ environmental awareness on the environmental impacts linked to farming activities. This is possible thanks to the attribution to the agents’ profiles of one specific feature which simulates their “green consciousness level”.