LCA Food 2012 in Saint Malo, France! - Manifestations et colloques ...

PARALLEL SESSION 2C: QUANTIFICATION AND REDUCTION OF UNCERTAINTY 8 th Int. Conference on LCA in the
Agri-Food Sector, 1-4 Oct 2012
204
Influence of scenario uncertainty in agricultural inputs on LCA
results for agricultural production systems
Kiyotada Hayashi 1,* , Naoki Makino 2 , Koichi Shobatake 2 , Shingo Hokazono 1
1 National Agriculture and Food Research Organisation
2 TCO2 Co. Ltd.
Corresponding author. E-mail: hayashi@affrc.go.jp
ABSTRACT
Practical applications of life cycle assessment (LCA) to agricultural production systems necessitate articulating uncertainties caused
by scenario indeterminacy, because practitioners do not have sufficient knowledge about agricultural input production processes.
However, current understanding about scenario uncertainties is still limited on account of insufficient knowledge. Here, we propose a
method to quantify scenario uncertainty in agricultural inputs and to assess the uncertainty in comparative LCA of agricultural production
systems. We formulate mathematical expressions about uncertainty intervals due to scenario indeterminacy and derive uncertainty
intervals for conventional, environmentally friendly, and organic rice production systems in Japan. Scenario uncertainty in
chemical fertiliser production is analysed as an example. The results indicate that uncertainty intervals are useful in understanding the
stability of results. The methodology proposed in this study can be further developed as a technique to deal with uncertainty and
instability in LCA of agricultural production systems.
Keywords: scenario uncertainty, agricultural inputs, adaptation, comparative LCA, inventory analysis
1. Introduction
Uncertainty analysis using the pedigree matrix for data quality together with Monte Carlo simulations is a
common practice in life cycle assessment (LCA), and several LCA software products provide simulation
functions. Because of certain special characteristics of agriculture, uncertainties in parameters such as crop
yield and direct field emissions are integrated into the LCA for agriculture (Basset-Mens et al., 2006). In
addition, uncertainties attributed to a wide variety of management practices and uncertainties in the relationship
between management practices and environmental impacts have been estimated using statistical resampling
(nonparametric bootstrapping) (Hayashi, 2011).
Although these studies mainly clarified uncertainties in models and parameters used to conduct LCA,
practical applications of LCA to agricultural production systems necessitate articulating uncertainties caused
by scenario indeterminacy, because practitioners do not have sufficient knowledge of the details in agricultural
input production processes (background processes of agricultural production). For example, farmers in
general do not know what kind of technologies are used for making chemical fertilisers and where fertiliser
factories are located. In other words, decision makers or analysts face decision problems under insufficient
knowledge.
However, current understanding about scenario uncertainties is still limited in LCA of agricultural production
systems as a result of insufficient knowledge. Therefore, this study establishes a method to quantify
scenario uncertainty in agricultural inputs and assesses the influence of this uncertainty on a comparative
LCA of agricultural production systems.
2. Methods
2.1. Classification of uncertainties
We classify uncertainty using a tri-partition for the uncertainty typology, namely parameter, model, and
scenario uncertainty. Parameter uncertainty reflects our incomplete knowledge about the true value of a parameter
(Huijbregts et al., 2003) and is related to inventory data and characterisation and weighting factors.
Common sources of parameter uncertainty are imprecise measurements, incomplete or outdated measurements,
and no measurements (lack of data) (Huijbregts, 1998). Model uncertainty concerns assumptions and
simplifications that lead to uncertainty about the validity of the model’s predictions for a real world situation
(Huijbregts et al., 2003). An important example of model uncertainty is the loss of spatial and temporal characteristics
in inventory analysis. Scenario uncertainty was originally termed ‘uncertainty due to choices’
(Huijbregts, 1998) and ‘decision rule uncertainty’ (Hertwich et al., 2000), because it refers to uncertainty
caused by normative choices on functional units and system boundaries in goal and scope definition, allocation
in inventory analysis, and the number of impact categories and definitions in impact assessment
(Huijbregts et al., 2003). We extend the scope of scenario construction to scenario indeterminacy, which we
encounter in practical situations.