Measuring progress towards sustainable development requires appropriate frameworks and databases. The System of Environmental-Economic Accounts (SEEA) is undergoing continuous refinement with these objectives in mind. In SEEA, there is a need for databases to encompass the global dimension of societal metabolism. In this paper, we focus on the latest effort to construct a global multi-regional input−output database (EXIOBASE) with a focus on environmentally relevant activities. The database and its broader analytical framework allows for the as yet most detailed insight into the production-related impacts and “footprints” of our consumption. We explore the methods used to arrive at the database, and some key relationships extracted from the database.
This deliverable described the compilation of CREEA based waste accounts for the Netherlands. A comparison is also made with the Dutch waste accounts that are regularly compiled as part of the Dutch environmental accounting program.
This draft report contains the description of Task 4.3: Compilation of standardized waste tables. Based on the framework in Task 4.1 and the data collection and calculations in Task 4.2, standardized waste tables for the Netherlands are developed in this Task 4.3. These tables show the supply and use (industries according to the NACE classification, households, imports, exports and flows to the environment) of all solid waste flows including dry matter
in waste water of an economy.
In this task waste tables will be developed following the procedure that is followed as part of the PSUT (see task 4.2). Also, Statistics Netherlands has filled the tables with data available from National statistics, based on registrations, in order to be able to compare and assess the results obtained in Task 4.2.
This may provide on the one hand a quality assurance of the CREEA results, but on the other hand may also be used to further improve the Dutch waste accounts.
.....
Land use and total (or gross) land use change data were analysed with focus on UNFCCC national inventory data and CORINE land cover data, and in comparison with FAO data and specific national data. For the European countries for which CORINE data are available this data base represents in most cases the preferable one with regard to land use change. The data quality and comprehensiveness however differs a lot between the individual countries. Due to restricted or unclear data availability other global or international data bases could not be used to derive data for gross land use change. Data from FAO and in particular the Global Forest Resources Assessment could be used to indirectly estimate net land use changes such as cropland expansion as a result of deforestation. This was not further elaborated here but specific studies on the issue exist. Allocation of land use (occupation) to industry sectors in SUTs can be done following the common approach for environmental extension data (see report to WP 7: Provision of data on land use by country and category).
Alignment of land use / land cover accounts with SEEA 2012 is currently hampered due to different classification systems. Further, available data do not fit with proposed SEEA categories which are characterised as being provisional.
With regard to land use change, our proposal is to keep it in a separate account by country/region which might be aggregated to a global account once comprehensive, harmonised data sets were available. The effects on land use change could then be included by linking the land occupation (as above) to the global market for land (the global LUC matrix).
......
Ecoinvent applies a method for estimation of default standard deviations for flow data from characteristics of these flows and the respective processes that are turned into uncertainty factors in a pedigree matrix, starting from qualitative assessments. The uncertainty factors are aggregated to the standard deviation in a formula that is valid for lognormally distributed data only.
This report covers phase 0 of a three-phased project that will in detail refine this “pedigree approach” in ecoinvent, aiming to put it on an empirically better founded basis.
The phase 0 will work entirely with the existing approach and will
(1) derive empirically based, reasonable values for the uncertainty factors used in this
approach, and
(2) provide practical considerations on how to apply the approach to other distributions
Ecoinvent applies a method for estimation of default standard deviations for flow data from characteristics of these flows and the respective processes that are turned into uncertainty factors in a pedigree matrix, starting from qualitative assessments. The uncertainty factors are aggregated to the standard deviation. This approach allows calculating uncertainties for all flows in the ecoinvent database. In ecoinvent 2 the uncertainty factors were provided based on expert judgment, without (documented) empirical foundation. This paper presents (1) a procedure to obtain an empirical foundation for the uncertainty factors that are used in the pedigree approach and (2) a proposal for new uncertainty factors, received by applying the developed procedure. Both the factors and the procedure are a result of a first phase of an ecoinvent project to refine the pedigree matrix approach. A separate paper in the same edition, also the result of the aforementioned project, deals with extending the developed approach to other probability distributions than lognormal (Muller et al.).
Uncertainty is defined here simply as geometric standard deviation (GSD) of intermediate and elementary exchanges at the unit process level. This fits to the lognormal probability distribution that is assumed as default in ecoinvent 2, and helps to overcome scaling effects in the analysed data. In order to provide the required empirical basis, a broad portfolio of data sources is analysed; it is especially important to consider sources outside of the ecoinvent database to avoid circular reasoning. The ecoinvent pedigree matrix from version 2 is taken as a starting point, skipping the indicator “sample size” since it will not be used in ecoinvent 3. This leads to a pedigree matrix with five data quality indicators, each having five score values. The analysis is conducted as follows: for each matrix indicator and for each data source, indicator scores are set in relation to data sets, building groups of data sets that represent the different data quality indicator scores in the pedigree matrix. The uncertainty in each of the groups is calculated. The uncertainty obtained for the group with the ideal indicator score is set as a reference, and uncertainties for the other groups are set in relation to this reference uncertainty. The obtained ratio will be different from 1, it represents the unexplained uncertainty, additional uncertainty due to a lower data quality, and can be directly used as uncertainty factor candidates.
The developed approach was able to derive empirically based uncertainty factor candidates for the pedigree matrix in ecoinvent. Uncertainty factors were obtained for all data quality indicators and for almost all indicator scores in the matrix. The factors are the result of the first analysis of several data sources, further analyses and discussions should be used to strengthen their empirical basis. As a consequence, the provided uncertainty factors can change in future. Finally, a few of the qualitative score descriptions in the pedigree matrix left room for interpretation, making their application not ambiguous.
An empirical foundation for the uncertainty factors in the pedigree matrix overcomes one main argument against their use, which in turn strengthens the whole pedigree approach for quantitative uncertainty assessment in ecoinvent. This paper provides an approach to obtain an empirical basis for the uncertainty factors, and it provides also empirically based uncertainty factors, for indicator scores in the pedigree matrix. Basic uncertainty factors are not provided, it is recommended to use the factors from ecoinvent 2 for the time being. In the developed procedure, using GSD as the uncertainty measure is essential to overcome scaling effects; it should therefore also be used if the analysed data do not follow a lognormal distribution. As a consequence, uncertainty factors obtained as GSD ratios need to be translated to range estimators relevant for these other distributions. Formulas for this step are provided in a separate paper (Muller et al.). The work presented in this paper could be the starting point for a much broader study to provide a better basis for input uncertainty in LCA, not only in ecoinvent.
ShareIt link: http://rdcu.be/mT28
Data used in life cycle inventories are uncertain (Ciroth et al. Int J Life Cycle Assess 9(4):216–226). The ecoinvent LCI database considers uncertainty on exchange values. The default approach applied to quantify uncertainty in ecoinvent is a semi-quantitative approach based on the use of a pedigree matrix; it considers two types of uncertainties: the basic uncertainty (the epistemic error) and the additional uncertainty (the uncertainty due to using imperfect data). This approach as implemented in ecoinvent v2 has several weaknesses or limitations, one being that uncertainty is always considered as following a lognormal distribution. The aim of this paper is to show how ecoinvent v3 will apply this approach to all types of distributions allowed by the ecoSpold v2 data format.
A new methodology was developed to apply the semi-quantitative approach to distributions other than the lognormal. This methodology and the consequent formulas were based on (1) how the basic and the additional uncertainties are combined for the lognormal distribution and on (2) the links between the lognormal and the normal distributions. These two points are summarized in four principles. In order to test the robustness of the proposed approach, the resulting parameters for all probability density functions (PDFs) are tested with those obtained through a Monte Carlo simulation. This comparison will validate the proposed approach.
In order to combine the basic and the additional uncertainties for the considered distributions, the coefficient of variation (CV) is used as a relative measure of dispersion. Formulas to express the definition parameters for each distribution modeling a flow with its total uncertainty are given. The obtained results are illustrated with default values; they agree with the results obtained through the Monte Carlo simulation. Some limitations of the proposed approach are cited.
Providing formulas to apply the semi-quantitative pedigree approach to distributions other than the lognormal will allow the life cycle assessment (LCA) practitioner to select the appropriate distribution to model a datum with its total uncertainty. These data variability definition technique can be applied on all flow exchanges and also on parameters which play an important role in ecoinvent v3.
ShareIt: http://rdcu.be/mT3l
Input–output tables (IOTs) are widely used in several types of analyses. Although born in an economic context, IOTs are increasingly used for the environmental impact assessment of product systems, e.g. in environmental policy analysis, and for several others such as the accounting of greenhouse gases.
However, the use in these contexts does not ensure the validity of the IOT as a consistent and robust multidisciplinary modeling tool in itself. It is in respect to certain basic requirements that IOTs should find their legitimacy. In this paper, we study their validity with respect to a well-established scientific law: the mass balance. Compliance with this basic balance is an important check for data consistency.
Following such a track, we focus specifically on monetary input–output tables and we reach the conclusion that IOTs can fail in respecting the basic balance laws whenever prices differ per purchaser. Therefore caution is needed because the estimations in terms of environmental pressures can be biased. The drawback lays in the use of homogeneous prices, which determines a discrepancy in physical units between what is used and what is asked for, within and between activities.
A formal procedure for data quality management in life cycle inventory is described. The procedure is applied to the example of an energy inventory for 1 kg rye bread. Five independent data quality indicators are suggested as necessary and sufficient to describe those aspects of data quality which influence the reliability of the result. Listing these data quality indicators for all data gives an improved understanding of the typical data quality problems of a particular study. This may subsequently be used for improving the data collection strategy during a life cycle study. To give an assessment of the reliability of the overall result of a life cycle inventory, the data quality indicators are transformed into estimates of the additional uncertainty due to the insufficient data quality. It is shown how a low data quality can both increase the uncertainty and change the mean value. After assigning additional uncertainties to all data in the study, a calculation of the uncertainty of the overall result is made by the use of simulations. The use of default estimates of additional uncertainties is suggested as a way to both simplify and improve the procedure.
