Applying agent-based modelling for Consequential Life Cycle Assessment (CLCA) of agro-systems: challenges, strategies and assets

Transcription

1 Applying agent-based modelling for Consequential Life Cycle Assessment (CLCA) of agro-systems: challenges, strategies and assets Antonino Marvuglia, Sameer Rege, Ian Vázquez-Rowe, Enrico Benetto Public Research Centre Henri Tudor (CRPHT) Resource Centre for Environmental Technologies (CRTE) Esch-sur-Alzette Luxembourg

2 Introduction and Research Question CLCA aims at evaluating the (direct and indirect) environmental consequences of a strategic decision For example, in the specific case of agro-systems, the land use changes and related consequences following a bioenergy policy Economic modelling approaches are increasingly applied; however, behavioural criteria and adaptive social processes (e.g. imitation) are barely considered. Can Multi Agent Simulation (MAS) be a pertinent technique to catch the complexity of a system made up of several interacting components in CLCA? 1/13/2014 2

3 Agro-systems as complex systems A complex system is a system in which large networks of components with no central control and simple rules of operation give rise to a complex collective behavior, sophisticated information processing, and adaptation via learning or evolution [M. Mitchell, 2009]. t t+n Mitchell, M Complexity: A Guided Tour. Oxford: Oxford University Press. 1/13/2014 3

4 Multi agent systems (MAS) MUSA MUlti agent Simulation for consequential Life Cycle Assessment of Agro-systems 1/13/2014 4

5 A simplified model in Netlogo Simulation of 4 years crop-rotation Random initial distribution of Nitrogen NN μμ, σσ Basic social imitation component 15 patches Basic economic driver 5

6 Basic rules of simplified model C R O P R O T A T I O N 1. Review crop history. Remove crop from fields permanently if bad weeds/disease (patch-crop-disease-loss > 0.65 or patch-crop-weed-loss > 0.80) and build a list of possible crops to plant. 2. If there are no possible crops to plant, let the field lay fallow. 3. Fallow fields for weed or pest control (if patch-previous-crop-yield < lowyield and patch-previous-crop fallow). 4. Never grow a crop consecutively (4a if no available crop let the field lay fallow; 4b first available crop). 5. Plant a crop only if it is already present in less than 20% of the total land (5a if no available crop let the field lay fallow; 5b first available crop). 6. Allow some random fallow periods (with probability of occurrence of 2%). 7. Allow some random cover (grass, clover) periods (with probability of occurrence of 5%). 8. Social imitation rule. 9. Economic rule. Only plant a crop when the expected gain is above the minimum revenue wanted (depending on max_margin). 10. Random selection among the available crops left in the crops vector. 6

7 Results: NO Social effect (1/2) Total plantings Wheat Humans Wheat Animals Spelt Rye Barley Oats Triticale Forage_Crops Maize Beans Potatoes Rapeseed Cover Fallow 0 σ µ

8 Results: NO Social effect (2/2) µ= 50 σ= 3 µ= 300 σ= 3 8

9 Results: WITH Social effect (1/3) µ = Wheat Humans Wheat Animals Spelt Total plantings Rye Barley Oats Triticale Forage_Crops Maize Beans Potatoes Rapeseed Cover Fallow 0 Max marg Imit. infl σ

10 Results: WITH Social effect (2/3) µ = # of times the rule is executed Rule_2 Rule_3 Rule_4a Rule_4b Rule_5a Rule_5b Rule_6 Rule_7 Rule_8 Rule_9 Rule_10 0 Max marg Imit. infl σ

11 Results: WITH Social effect (3/3) µ= 300 σ= 3 Imit infl= 30% Max margin= 3 µ= 300 σ= 3 Imit infl= 60% Max margin= 3 µ= 300 σ= 3 Imit infl= 90% Max margin= 3 11

12 Conclusions Challenges Poor level of detail of the agents different profiles Static nature of the data used (crops market prices, yields, fertilizers costs, ) Absence of a real (geographically coherent) spatial component Strategy More realistic agents profiles will be defined using the result of a survey More complex and consistent rules of interaction will be elaborated A new piece of code will be developed in Java to better manage a complex model. Possibly also switching from the reactive approach to the cognitive approach. Assets Even with such a simplified model it was possible to catch the effect of the social interaction component, therefore ABM looks like a promising approach. ABM will allow the derivation of a more realistic LCI compared to a simple (parametric) scenario-based approach and will assist the development of a fully fledged CLCA. 12

13 Acknowledgements The project MUSA (MUlti agent Simulation for consequential Life Cycle Assessment of Agrosystems) is supported by the National Research Fund, Luxembourg. Florent Querini (CRP Henri Tudor/CRTE) and Prof. Andrew Croocks (George Mason University, USA) are gratefully acknowledged for their support in the initial steps of implementation of the Netlogo model. Background literature Marvuglia A., Benetto E., Rege S., Jury C. Modelling approaches for Consequential Life Cycle Assessment (C-LCA) of bioenergy: critical review and proposed framework for biogas production. Renewable & Sustainable Energy Reviews. Vol. 25, 2013, p Vázquez-Rowe I., Rege S., Marvuglia A., Thénie J., Haurie A., Benetto E. Application of three independent consequential LCA approaches to the agricultural sector in Luxembourg. Accepted by International Journal of Life Cycle Assessment. DOI: /s Vázquez-Rowe I., Marvuglia A., Rege S., Benetto E. Applying C-LCA to support energy policy in Luxembourg: land use change effects of bioenergy production. Submitted to Science of the Total Environment. Rege S., Arenz M., Marvuglia A., Vazquez-Rowe I., Koster D., Benetto E. Quantification of agricultural land use changes in consequential Life Cycle Assessment using a partial equilibrium model. Submitted to Journal of Environmental Informatics Vázquez-Rowe I., Marvuglia A., Flammang K., Braun C., Leopold U., Benetto E. The use of temporal dynamics for the automatic calculation of land use impacts in LCA using the R programming environment. A case study for Luxembourg. Submitted to International Journal of Life Cycle Assessment. 13

14 Thank you for your attention Contacts: Antonino Marvuglia Ph: Fax: