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Sörme, Louise; Hedbrant, Johan

doi:10.1023/a:1017591718463

Cited by 108 publications

(14 citation statements)

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“…Research works related to the range endpoints of the input parameter have begun, as Finnveden and Lindfors [50] carried out research on the range endpoints of the input parameter as minimum (x/factor) and maximum (x × factor) and the factor varied from 2 to 100 based on the "rule of thumb". In addition, Hedbrant and Sorme [51] proposed the ranges of the five data quality levels from x/1 to x × 10 related to the data collection in the heavy metal industry. Here, X is the amount of input parameter in the life cycle inventory and the factor was varied by the authors from 2 to 100.…”

Section: Discussionmentioning

confidence: 99%

“…In theory, the site-specific approach [50][51][52][53] is required for the uncertainty analysis of the IDEA; however, the collection of data, measurements, and specific statistical data of each parameter is not practical. From the comparative study of the uncertainty results with the stochastic modeling approach, alternative approach B was developed in this research.…”

Section: Discussionmentioning

confidence: 99%

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Parameter Uncertainty Analysis of the Life Cycle Inventory Database: Application to Greenhouse Gas Emissions from Brown Rice Production in IDEA

2018

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Abstract:The objective of this paper is to develop a simple method for analyzing the parameter uncertainty of the Japanese life cycle inventory database (LCI DB), termed the inventory database for environmental analysis (IDEA). The IDEA has a weakness of poor data quality because over 60% of datasets in IDEA were compiled based on secondary data (non-site-specific data sources). Three different approaches were used to estimate the uncertainty of the brown rice production dataset, including the stochastic modeling approach, the semi-quantitative DQI (Data Quality Indicator) approach, and a modification of the semi-quantitative DQI approach (including two alternative approaches for modification). The stochastic modeling approach provided the best estimate of the true mean of the sample space and its results were used as the reference for comparison with the other approaches. A simple method for the parameter uncertainty analysis of the agriculture industry DB was proposed by modifying the beta distribution parameters (endpoint range, shape parameter) in the semi-quantitative DQI approach using the results from the stochastic modeling approach. The effect of changing the beta distribution parameters in the semi-quantitative DQI approach indicated that the proposed method is an efficient method for the quantitative parameter uncertainty analysis of the brown rice production dataset in the IDEA.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Parameter Uncertainty Analysis of the Life Cycle Inventory Database: Application to Greenhouse Gas Emissions from Brown Rice Production in IDEA

2018

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“…(2) Parameter tables that include the nutrient content factors were formed from databases and literature [36][37][38][39]. (3) An uncertainty analysis was done for the individual SITC classes and the lower and upper bounds of nutrient content were formed [40,41]. (4) Confidence intervals for quantified nutrients were formed for the time series.…”

Section: Data and Calculation Methodsmentioning

confidence: 99%

“…The uncertainty for each basic heading was assessed based on the quality of each input data source. The data quality levels were formed as described by Hedbrant (2001) [40]. Each source value has uncertainties based on the source quality of the data, which were obtained by an individual assessment of values.…”

Section: Uncertainty Analysismentioning

confidence: 99%

“…Levels 1-4 are given to each value of each the SITC heading level depending on the quality of the source data. For each class from these assessed uncertainties, multiplication was done by Taylor series expansion for a calculation of the variance of multiplied variables [40]. In this approach, all trade nutrient values have source value-based uncertainty that can be estimated, and the assessor bias is controlled by using clear levels.…”

Section: Uncertainty Analysismentioning

confidence: 99%

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Trade Imports Increasingly Contribute to Plant Nutrient Inputs: Case of the Finnish Food System 1996–2014

Parviainen

Helenius

2020

Sustainability

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In Finland, while total agricultural production has remained relatively constant, nutrient input from industrial mineral fertilizers has declined over the past 20 years, which has been the target of environmental policies due to eutrophication risks. From 1996 to 2014, the use of nitrogen (N) declined by 18%, phosphorus (P) by 49%, and potassium (K) by 49%. However, at the same time, the international agricultural products trade has increased dramatically by mass (58%), and Finland has increased imports of food and feed products, such as, protein feeds, vegetables, and fruits. We analyzed the nutrient contents of foreign trade from 1996 to 2014 by using a substance flow analysis. We discovered that, when comparing nutrients contained in trade to the use of fertilizers, the trade of food and feed accounts for more than one-third (40%) of the fertilizer input to the Finnish food system. In 2014, 53 Gg of N, 8 Gg of P, and 15 Gg of K were imported due to trade, equating to 35%, 70%, and 45%, respectively, compared to the use of fertilizers in the food system. Declines in fertilizer inputs to crop production are partially offset by flows of plant nutrients from feed imports. In formulating agri-environmental policies targeting nutrient loading, more attention should be paid to national imports–export balances and, especially, to the spatial distribution of flows in feed trade.

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Material Flow Analysis

Laner

Rechberger

2016

LCA Compendium – The Complete World of Life Cycle Assessment

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Cited by 108 publications

References 1 publication

Parameter Uncertainty Analysis of the Life Cycle Inventory Database: Application to Greenhouse Gas Emissions from Brown Rice Production in IDEA

Parameter Uncertainty Analysis of the Life Cycle Inventory Database: Application to Greenhouse Gas Emissions from Brown Rice Production in IDEA

Trade Imports Increasingly Contribute to Plant Nutrient Inputs: Case of the Finnish Food System 1996–2014

Material Flow Analysis

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