Abstract
Most data-based studies require significant amounts of data to support their decision-making process. Apart from increasing data quantity, scientists tend to be aware of the quality of data that influences the robustness of the results. A Pedigree matrix method is presented to characterize the data quality aspects and quantify the quality rating. Five quality aspects (reliability, completeness, temporal, geographical and technological representativeness) are defined as the characteristics to describe how well the reference data is fit for the underlying study. Reference rules are made subjectively for allocating the quality rating, which enable the computer to select appropriate data effectively from among different data sources. The overall data quality rating is calculated reflecting the quality level and converted to the four-parameter Beta probability distribution for uncertainty quantification. This is complemented by the Monte Carlo simulation that identifies uncertainty hotspots, to further improve the quality of identified data. This study provides an effective way to identify the data of good quality through the definition of reference rules. Making such rules can help the users to effectively capture the descriptive information regarding the data quality, further assess the quality levels consistently. The four-parameter Beta distribution is used for quantitative transformation, since it is appropriate to represent expert judgement. Therefore, the definition of distribution parameters is flexible depending on the expert understanding of uncertainty. This strength extends the application of the method to different data systems. Further research can focus on the development of reference rules for different quality aspects, as well the integration of the Pedigree matrix in various data systems.
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References
Funtowicz, S.O., Ravetz, J.R.: Science for policy: Uncertainty and quality. In: Uncertainty and quality in science for policy. Springer, Berlin (1990)
Weidema, B., Wesnaes, M.S.: Data quality management for life cycle inventories-an example of using data quality indicators. J. Clean. Prod. 4(3–4), 167–174 (1996)
van der Sluijs, J., Kloprogge, P., Risbey, J., Ravetz, J.: Towards a synthesis of qualitative and quantitative uncertainty assessment: applications of the numeral, unit, spread, assessment, pedigree (NUSAP) system. In: International Workshop on Uncertainty, Sensitivity and Parameter Estimation 2003, Rockville, USA (2003)
May, J.R., Brennan, D.J.: Application of data quality assessment methods to an LCA of electricity generation. Int. J. Life Cycle Assess. 8(4), 215–225 (2003)
Ewertowska, A., Pozo, C., Gavalda, J., Jimenez, L., Guillen-Gosalbez, G.: Combined use of life cycle assessment, data envelopment analysis and Monte Carlo simulation for quantifying environmental efficiencies under uncertainty. J. Clean. Prod. 166, 771–783 (2017)
Miah, J.H., Griffiths, A., McNeill, R., Halvorson, S., Schenker, U., Espinoza-Orias, N., Morse, S., Yang, A.D., Sadhukhan, J.: A framework for increasing the availability of life cycle inventory data based on the role of multinational companies. Int. J. Life Cycle Assess. 23(9), 1744–1760 (2018)
Weidema, B.P., Bauer, C., Hischier, R., Mutel, C., Nemecek, T., Reinhard, J., Vadenbo, C., Wernet, G.: Overview and Methodology: Data Quality Guideline for the Ecoinvent Database Version 3. Swiss Centre for Life Cycle Inventories (2013)
Muller, S., Lesage, P., Ciroth, A., Mutel, C., Weidema, B.P., Samson, R.: The application of the pedigree approach to the distributions foreseen in ecoinvent v3. Int. J. Life Cycle Assess. 21(9), 1327–1337 (2016)
Ciroth, A., Muller, S., Weidema, B., Lesage, P.: Empirically based uncertainty factors for the pedigree matrix in ecoinvent. Int. J. Life Cycle Assess. 21(9), 1338–1348 (2016)
Coulon, R., Camobreco, V., Teulon, H., Besnainou, J.: Data quality and uncertainty in LCI. Int. J. Life Cycle Assess. 2(3), 178 (1997)
Kennedy, D.J., Montgomery, D.C., Rollier, D.A., Keats, J.B.: Data Quality. Int. J. Life Cycle Assess. 2(4), 229–239 (1997)
Edelen, A., Ingwersen, W.W.: The creation, management, and use of data quality information for life cycle assessment. Int. J. Life Cycle Assess. 23(4), 759–772 (2018)
JRC: ILCD handbook. General guide for life cycle assessment—Detailed guidance. European Commission, Joint Research Centre—Institute for Environment and Sustainability, Luxembourg (2010)
Johnson, N.L., Kotz, S., Balakrishnan, N.: Chapter 21: Beta Distributions. In: Continuous Univariate Distributions, 2nd edn. Houghton Mifflin Boston (1970)
Kennedy, D.J., Montgomery, D.C., Quay, B.H.: Data Quality. Stochastic environmental life cycle assessment modeling. Int. J. Life Cycle Assess. 1(4), 199–207 (1996)
ISO: ISO 9000:2015 Quality management systems—Fundamentals and vocabulary. International Organization for Standardization, Geneve, Switzerland (2015)
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This work has been conducted as part of the PLEIADES project that has received funding from the European Union’s Seventh Programmes for research, technological development and demonstration under grant agreement No. 603843.
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Chen, X., Lee, J. (2021). The Identification and Selection of Good Quality Data Using Pedigree Matrix. In: Scholz, S.G., Howlett, R.J., Setchi, R. (eds) Sustainable Design and Manufacturing 2020. Smart Innovation, Systems and Technologies, vol 200. Springer, Singapore. https://doi.org/10.1007/978-981-15-8131-1_2
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DOI: https://doi.org/10.1007/978-981-15-8131-1_2
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