Abstract
The study objective was to develop a method for an uncertainty analysis of the greenhouse gas (GHG) emission model output based on consecutive use of an analytical and a stochastic approach. The contribution to variance (CTV) analysis followed by the data quality analysis are the main feature of the procedure. When a set of data points of a certain input variable has a high CTV, but its data quality indicator (DQI) is good, then there is no need to iterate data collection of this input variable. This is because the DQI of this data set indicates that there is no room for the reduction of its variance, and the high variance must be its inherent attribute. Through the CTV analysis and data quality analysis, the identified input variables were selected as the input variables for the data from the iteration of data collection. The statistical parameters of the GHG emissions of the model were calculated using the Monte Carlo simulation (MCS). In the case study of a cattle dairy farm, the relative reduction in the CV value was 47.6%. In this study, a procedure was developed for the selection of the input variables for iteration of data collection to reduce their variance and subsequently reduce the uncertainty in the model output. The dairy cow case study showed that the uncertainty in the model output was decreased by the iteration of data collection, indicating that CTV analysis can be used to identify the input variables, contributing considerably to the uncertainty in the model output.
Highlights
It is an international consensus that human production and consumption activities cause climate change [1]
The reduction in the interval length from that of the initial dataset and the recollected dataset showed that the relative reduction of the interval length was 51.8%, whereas the relative reduction in the coefficient of variation (CV) value was 47.6%. These results clearly indicate that the uncertainty of the model output was reduced significantly by the iteration of data collection of the problematic input vaSruisatabinleasbi.liTtya2b0l1e9,811s,hxoFwOsRtPhEeERMRCESVIrEeWsults for the total greenhouse gas (GHG) emission for 1 kg of dairy cow FPCM1.7 of 22
A stochastic approach can prevent the risk of incorrect estimation of the uncertainty of the model output via probability density function (PDF) estimation and Monte Carlo simulation
Summary
It is an international consensus that human production and consumption activities cause climate change [1]. Life cycle assessment (LCA) studies have been extended to food production and cooking appliances [2,3] This is a phenomenon that confirms that interest in the environmental impacts that occur throughout human life in the LCA field has increased. In Europe, an effort is being made to manage and control GHG emissions from the industrial products sectors and from the dairy industry sector through the product environmental footprint (PEF). This includes the development of the quantification method of GHG emissions from the dairy sector [8,9]. Any carbon certification or trading requires that the credibility of the GHG emission results, such as the quantification in the uncertainty of the GHG emission resulting from the industry sectors or product, is necessary and must be ensured [10]
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