1. National examples of data validation
– Austria

2. Overview of the presentation
Overview of the methods used for the compilation of waste statistics in Austria
Examples of data validation
Use of economic data in the validation
Analysis of time series
Quality assurance of data collected by a survey study
Concept for a comprehensive process for data evaluation
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

3. Overview of the methods used for the compilation of waste statistics in Austria
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

4. Generation of hazardous waste Reference years 2004, 2006 and 2008
Use of administrative data sources (EDM)
Evaluation of consigment notes and notifications of transboundary shipments of waste
Remaining data gaps were filled by using statistical estimations or other administrative data sources
Study: “Wastes Generated in Agriculture, Forestry and Fishing”
Waste electrical and electronic equipment
End-of life vehicles
Hazardous waste generated by households
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

5. Generation of non-hazardous waste Reference years 2004, 2006 and 2008 (1)
Total quantities per waste category were based on data collected for the Austrian Federal Waste Management Plan. These data are based inter alia on:
Administrative data bases
Studies
Data collected by provincial governments
Data obtained from waste management associations (e.g. Association for the recycling of C&D waste)
Data obtained from waste treatment facilities
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

6. Generation of non-hazardous waste Reference years 2004, 2006 and 2008 (2)
The breakdown of waste quantities to economic sectors was based on two survey studies
Random sample of generators from the production sector
The first survey was carried out in 2002 (reference year 2001)
The second survey was carried out in 2008 (reference year 2006)
Data on wastes generated in agriculture, forestry and fishing were based on the pilot study carried out in 2004
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

7. Data basis and methods as of reference year 2010
The main data source will be the annual waste balance sheets (according to Waste Balance Sheet Ordinance 2008):
Waste collectors and waste processors have to report annual waste balance sheets electronically to the Provincial Governor not later than on 15 March of each year.
Covering pick-ups of waste from other legal entities, deliveries of waste to other legal entities, in-house waste movements and storage level information
Wastes received from initial waste producers shall be reported as total value per type of waste, broken down by the federal province of origin of the waste and by the economic sector of waste producer.
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

8. Examples of data validation: Use of economic data in the validation
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

9. Studies on NAMEA-waste
interdependencies (parallel increase / decrease) between the quantities of waste generated and gross value added
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

10. Studies on NAMEA-waste
Effects of changes in waste legislation and policy
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

11. Conclusions of NAMEA-studies
Correlations of waste quantities seem to be stronger with gross value added than with the number of persons employed
Some waste types/categories correlate with gross value added while others do not
For example waste oils and combustion wastes often seem to correlate
Occasionally occuring waste types e.g. contaminated soils or fire debris do not correlate with economic data
The quantities of waste generated might develop in advance or with a delay in relation to the values of gross value added
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

12. Analysis of time series
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

13. Workflow Visualisation of time series of aggregated results
Identification of peak values / especially low values
Analysis of the reasons for peak values by means of checking the source data:
Which specific waste types caused the peak values?
Which companies were responsible for the peak values?
Interpretation and evaluation
Can the changes be explained by e.g. changes in legislation or changes in the industry/economy?
Could the source data be inplausible?  further research & enquiries
(Correction of data, if necessary)
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

14. Example on Hazardous wastes
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ESTP: Waste Statistics Wien ■ April 2012

15. Example: Distribution of a waste category to economic sectors
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ESTP: Waste Statistics Wien ■ April 2012

16. Example: Distribution by waste categories in an economic sector
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

17. Quality assurance of data collected by a survey study
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

18. Supporting measures aiming at a better quality of replies in the survey study
Webpage with instructions, inter alia:
Classification of waste types
Conversion of waste volumes (m3 or l) to waste quantities in tonnes)
Hotline
Enterprises could contact the project team and ask questions
Telephone Support:
The objective was to increase the return rate
Enterprises were actively contacted
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

19. Data base design aiming at better quality of the data
In the questionnaire waste quantities per waste type and the total quantity of waste generated were asked.
The data base compares the sum of quantities per waste type with the total quantity
 inconsistent replies were labelled
The persons entering data in the database could write notes
On the whole questionnaire
On individual entries on the level of waste types
“Signal light” – green, yellow, red
The persons entering data in the database could mark such questionnaires which should be once more controlled
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

20. Umweltbundesamt www.umweltbundesamt.at
ESTP: Waste Statistics Wien ■ April 2012

21. Umweltbundesamt www.umweltbundesamt.at
ESTP: Waste Statistics Wien ■ April 2012

22. Quality control of the data entered in the database
Questionnaires marked with „red“ were once more controlled and the notes and commets were taken into account
Plausibility checks:
Analysis of waste quantities per number of employees (data from the Business Register)
Plausibility check of especially high quantities
Comparison of companies within a specific economic sector
Comparison of the replies of individual companies in 2008 and 2002
 Inquiries by telephone in case of obviously incorrect data
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

23. Concept for a comprehensive process for data evaluation
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

24. Electronic Data Management (EDM) in the field of environment …
is an integrated EGovernment IT-application in the environmental field
replaces conventional – step by step - paper-based records and reports (including applications submitted to the authorities) through efficient electronic data management
Objectives:
Reduction of the administrative burden
Prevention of data redundancy
To provide easy-to-handle data queries
To allow advanced evaluation of data
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

25. Electronic Data-Management in the Environmental Field
Emission-Trading
Fluorinated Hydrocarbons
eWater Emissions
Register
Radiation Sources
European Pollutant
& Transfer Register
EDM Waste-Management
eIncineration
eWEEE
eShipment
eBatteries & Accumulators
ePackaging
eEoL-Vehicles
eWaybill
eWaste-Balance
eLandfill
eLicence
eCertificate
eCompost
Central
register of
master
data
eRAS
EDM-Environment
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

26. eRAS
The core of the IT system is the central Electronic Register of Personal and Industrial Plant Data (eRAS).
All the applications draw on that register and therefore share the same information base.
Any changes made to that data are available immediately to all the applications simultaneously

27. Procedures of data-evaluation
Hard-coded queries, implemented within the EDM- application following strictly the process model, which meet well-defined requirements of the enforcement authority and other institutions/persons entitled to make queries
Data warehouse queries: Data are extracted from EDM, transformed (summarized into aggregates) and loaded into a data repository. Data from the data repository are read and processed for reports
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

28. Why different procedures of data-evaluation?
The hard-coded queries are in general quite simple and straight-forward so as not to slow the performance of the operational system. Advantage: Up-to date information on original data
In the data warehouse, data is stored in a structure designed to facilitate reporting. The data warehouse is updated from data in the operational system on a regular basis. Reporting does not impact the operational system's performance, thus complex data-evaluation procedures can be made.
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012

29. Umweltbundesamt www.umweltbundesamt.at
ESTP: Waste Statistics Wien ■ April 2012

30. Umweltbundesamt www.umweltbundesamt.at
ESTP: Waste Statistics Wien ■ April 2012

31. Umweltbundesamt www.umweltbundesamt.at
ESTP: Waste Statistics Wien ■ April 2012

32. Umweltbundesamt www.umweltbundesamt.at
ESTP: Waste Statistics Wien ■ April 2012

33. Umweltbundesamt www.umweltbundesamt.at
ESTP: Waste Statistics Wien ■ April 2012

35. Milla Neubauer milla.neubauer@umweltbundesamt.at
Contact & Information
Milla Neubauer
Umweltbundesamt
ESTP: Waste Statistics Wien ■ April 2012