A return on experience from the application of agent-based simulations coupled with life cycle assessment to model agricultural processes

Marvuglia, Antonino; Rege, Sameer; Gutiérrez, Tomás Navarrete; Vanni, Laureen; Stilmant, Didier; Benetto, Enrico

doi:10.1016/j.jclepro.2016.11.150

Cited by 45 publications

(23 citation statements)

References 31 publications

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“…The review indicates that the coupling of dynamic model and LCA is applied for one sector. This coincides with Beaussier et al's study, which clarified that ABM-LCA was frequently used for one (or several) product(s) of one sector [43], with one product in [74,75,78,79] and four products in [76,77].…”

Section: Dynamic Modellingsupporting

confidence: 88%

A Conceptual Review on Using Consequential Life Cycle Assessment Methodology for the Energy Sector

et al. 2020

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Energy is engaged in the supply chain of many economic sectors; therefore, the environmental impacts of the energy sector are indirectly linked to those of other sectors. Consequential life cycle assessment (CLCA) is an appropriate methodology to examine the direct and indirect environmental impacts of a product due to technological, economic or social changes. To date, different methodological approaches are proposed, combining economic and environmental models. This paper reviews the basic concept of CLCA and the coupling of economic and environmental models for performing CLCA in the energy sector during the period 2006–2020, with the aim to provide a description of the different tools, highlighting their strengths and limitations. From the review, it emerges that economic modelling tools are frequently used in combination with environmental data for CLCA in the energy sector, including equilibrium, input-output, and dynamic models. Out of these, the equilibrium model is the most widely used, showing some strengths in availability of data and energy system modelling tools. The input-output model allows for describing both direct and indirect effects due to changes in the energy sector, by using publicly available data. The dynamic model is less frequently applied due to its limitation in availability of data and modelling tools, but has recently attracted more attention due to the ability in modelling quantitative and qualitative indicators of sustainability.

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Section: Dynamic Modellingsupporting

confidence: 88%

A Conceptual Review on Using Consequential Life Cycle Assessment Methodology for the Energy Sector

et al. 2020

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“…The hybrid approach requires both models to be linked so that the exchanged parameters can flow from one model to another. However, as highlighted by (Marvuglia et al, 2016), ABM suffers from the difficulties that are linked to its implementation. LCA current tools have limited functionalities to support interaction with other software, thus resulting in difficulties in directly calling these latter from a simulator.…”

Section: Limitations and Perspectives Of The Coupling Methodologymentioning

confidence: 99%

“…Marvuglia et al (2018) -Several case studies are covered by two or more papers. The paper by (Navarrete Gutierrez et al, 2015a) was also covered by (Marvuglia et al, 2016), who made a return on experience. In the same way, the framework of Davis et al was developed in two papers, but only the peer-reviewed one (Davis et al 2009) was studied,…”

Section: Selection Of Articlesmentioning

confidence: 99%

“…We defined three degrees of coupling as described in Figure 1: hard, tight and softcoupling. The term "hard-coupling" was first used by (Marvuglia et al, 2017) to define the degree of coupling they are using, which is why the choice has been made to say "soft" as opposed to "hard", and "tight" is used to express an intermediate interdependence of both models.…”

Section: Type Of Couplingmentioning

confidence: 99%

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To what extent can agent-based modelling enhance a life cycle assessment? Answers based on a literature review

Micolier

Loubet

Taillandier

et al. 2019

Journal of Cleaner Production

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Life cycle assessment (LCA) has proven its worth in modelling the entire value chain associated with the production of goods and services. However, modelling the consumption system, such as the use phase of a product, remains challenging due to uncertainties in the socioeconomic context. Agent-based models (ABMs) can reduce these uncertainties by improving the consumption system modelling in LCA. So far, no systematic study is available on how ABM can contribute towards a behavior-driven modelling in LCA. This paper aims at filing this gap by reviewing all papers coupling both tools. A focus is carried out on 18 case studies which are analysed according to criteria derived from the four phases of LCA international standards. Criteria specific to agent-based models and the coupling of both tools, such as the type and degree of coupling, have also been selected. The results show that ABMs have been coupled to LCA in order to model foreground systems with too many uncertainties arising from a behaviour-driven use phase, local variabilities, emerging technologies, to explore scenarios and to support consequential modelling. Foreground inventory data have been mainly collected from ABM at the use phase. From this review, we identified the potential benefits from ABM at each LCA phase: (i) scenario exploration, (ii) foreground inventory data collection, (iii) temporal and/or spatial dynamics simulation, and (iv) data interpretation and communication. Besides, methodological guidance is provided on how to choose the type and degree of coupling during the goal and scope phase. Finally, challenging LCA areas of research that could benefit from the agent-based approach to include behaviour-driven dynamics at the inventory and impact assessment phase have been identified.

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“…The results of the simulation showed that physical parameters have a non‐negligible contribution to the uncertainty of the insulation materials. Moreover, in Ref , an agent‐based simulation is combined with LCA for simulating future crop patterns in the Grand‐Duchy of Luxembourg. The findings demonstrate that a ‘greenness’ criterion aimed only at reducing greenhouse gases emissions does not constitute a suboptimal choice since it generates burden shifts to other impact categories.…”

Section: Interaction Of Building Simulation With Other Thermal‐energymentioning

confidence: 99%

Uses of dynamic simulation to predict thermal‐energy performance of buildings and districts: a review

Castaldo

Pisello

2017

WIREs Energy & Environment

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The design of sustainable constructions also resilient to climate change has become a challenging issue given the increasing greenhouse emissions rate imputable to the built environment in urban areas. In this context, dynamic simulation models represent a suitable tool to support the design from very preliminary phases, since they allow an accurate prediction of the constructions requirements, their environmental performance, and indoor comfort conditions for their occupants. Therefore, starting from specific inputs, that is, weather conditions, construction technologies, materials, energy systems, operation settings, occupancy, and so forth, it is possible to estimate the realistic building energy performance. Moreover, the calibration procedures allow making the model even more representative of the field conditions of a construction. Given the massive progress carried out by the scientific community during the last decades, this paper presents a comprehensive review of the different building dynamic simulation approaches and available tools. While previous review studies focused on single separated aspects of dynamic simulations approaches, that is, calibration methods, software, simulation of single building energy systems, the aim of the present review is to propose a more holistic approach by investigating the recent scientific progress in simulating realistic dense urban environments. In this view, the review focus bridges the gap between the simulation at single-building level and simulation at the increasingly important neighborhood scale by showing the multiple benefits deriving from using dynamic simulation tools at district level, for a more reliable investigation of building performance in their urban context, where more than 50% of the global population worldwide currently lives.

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A return on experience from the application of agent-based simulations coupled with life cycle assessment to model agricultural processes

Cited by 45 publications

References 31 publications

A Conceptual Review on Using Consequential Life Cycle Assessment Methodology for the Energy Sector

A Conceptual Review on Using Consequential Life Cycle Assessment Methodology for the Energy Sector

To what extent can agent-based modelling enhance a life cycle assessment? Answers based on a literature review

Uses of dynamic simulation to predict thermal‐energy performance of buildings and districts: a review

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