Data Mining in the EU-27 - Methodology



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Data Mining in the EU-27 - Methodology Alina Rejman-Burzyńska, Anna Śliwińska, Eugeniusz Jędrysik Final Workshop, Copenhagen, 25 November 29 Główny Instytut Górnictwa-Central Mining Institute, Katowice, POLAND

Methodology of data mining Supply and Use Tables (SUTs) - input data for model Construction of SUTs data sources and tools SUTs MASTERs for EU-23 Gaps Conclusions

FORWAST - WP4 1. Data collection and compilation from different sources Data collection guidelines and forms, scope of data and level of details were provided by 2.-LCA(WP1) and RMA (WP2) Data mining and review: national sector statistics, European Statistics, UN Statistics, other databases and information systems, existing reports and studies for sector economy

FORWAST WP4 2. Analysis, validation and identification of gaps Data were analysed and validated Gaps were identified Missing data were estimated 3. Construction of SUTs Matrices were filling in Inconsistencies in SUTs Master were corrected

Supply and Use Tables MASTER INPUT DATA for MODEL 8 Residuals Supply - W 7 Residuals Distribution - J 6 5 Emissions Distribution Gc, Gr Emissions B 4 Resources R 3 2 1 Prices Pv, Pu Physical SUTs Import/Export intra EU-27, extra EU27 Monetary SUTs Import/Export intra EU-27, extra EU27

Supply and Use Tables MASTER INPUT DATA for MODEL DATA TOOLS

Data mining Data sources National sectoral Statistics: Agriculture, Forestry, Industrial Production, etc. EUROSTAT, UN Database, FAOSTAT, UNCE Timber Database Energy Statistics: IEA Statistics, IMUNDI, etc. Minerals: USGS, BGS, PAN Metals: IISICES (Steel Yearbook), EAMI, EUROFER, Emissions: UNFCCC (National Inventory-CRF) Waste: EUROSTAT, EIONET, UN, CEPI, National Waste Management Programs Import/Export intra EU-27 and extra EU-27: EUROSTAT, External Trade, ComExt

1. Construction of Monetary Supply and Use Tables EUROSTAT National statistics MSUTs (59X59) Basic price converter release MSUTs (59X59) Monetary Data Production, Import, Export Monetary Disaggregation Coefficients Matrix Expander tool FORWAST MSUTs 117 x 117

2. Construction of Physical Supply and Use Tables National statistics (other sources) Mass Flows: Production, Use, Import, Export (in EU, ex EU MSUTs (59X59) Data Processing and Estimations PST (59X59) PUT (59X59) via prices Physical Disaggregation Coefficients Matrix Expander tool FORWAST PSUTs 117 x117

PSUTs Construction of PSUTs cases PSUTs Lithuania: Agriculture products PSUTs Finland: Coke, petroleum refined products

4. Matrix Resources -R Material No. 1 Material (Dry Mass) Aluminium Agriculture products Gg Agriculture products Forestry products 2 3 Fibre carbon Food carbon, (including tobacco) 2384,9 14238 Fishing products Coal, lignite and peat 4 5 Coal carbon Crude oil and natural gas carbon Crude petroleum and gas Metal ores 6 7 8 Carbonate carbon Copper Iron Other mining and quarrying products 9 Metals, n.e.c. 1 11 Minerals, n.e.c. (including nitrogen) Oxygen (only in products, but not in H2O) 655,3 131 Emission Machine 2.-LCA 12 Clay and soil 13 Sand, gravel and stone 14 Total material (T) 36273

5-6. Matrices: Emissions B and G 1 2 3 4 Emissions Ammonia Arsenic Cadmium Carbon dioxide, fibre carbon Agriculture Gg 1,79 > UNFCCC- Data CRF: Fuels, Industry, Solvents, Agriculture, Waste > Respiratory emissions 5 6 7 Carbon dioxide, food carbon Carbon dioxide, coal carbon Carbon dioxide, crude oil and natural gas carbon 16741 225,51 1434 Emissions Machine 2.-LCA (DM and carbon Balance) 8 Carbon dioxide, carbonate 9 1 11 12 13 14 15 16 Carbon monoxide Chromium Copper Dinitrogen monoxide Hydrogen chloride Hydrogen fluoride Lead Mercury 2,437 37,881 3 steps: 1. crude data for emissions 2. data were fitted to the FORWAST activities 3. matrices B and G were generated 17 Methane 234,34

7. Matrix Residuals Distribution - J Waste Treatment Activities: Recycling Incineration of waste Manure treatment Biogasification of waste Composting of waste Wastewater treatment Landfill of waste Land application Export Import Estimations / Assumptions on the basis of available data and information or expert knowledge

Correcting inconsistency in SUTs MASTER DATA Balance checks: > Monetary SUT products > Monetary SUT activities > Physical SUT products > Physical SUT activities > Physical SUT negative waste > D matrix = (,1)

Supply and Use Tables MASTER for EU-23 FORWAST - WP4 SUTs (117 x 117) GREECE AUT IRELAND - UST NETHERLANDS -BRGM SUTs (Quasi 117 x 117) CZECH R.-SLOVAKIA- HUNGARY - GIG ESTONIA-LITHUANIA- LATVIA - GIG FINLAND - GIG (SUTs 57 x 57) BELGIUM - 2.- LCA BULGARIA - BRGM CYPRUS - AUT POLAND - GIG SLOVENIA - RMA ITALY - AUT LUXEMBURG - AUT SPAIN - TUV SWEDEN- 2.- LCA UNITED KINGDOM -UST MALTA - AUT PORTUGAL - AUT ROMANIA - BRGM

Identification of gaps -1 FORWAST - WP4 MSUTs (23) for some countries are not available from EUROSTAT, in some cases not consistence MSUTs: for confidentiality reasons some products and industries have been merged and added up, the sum was shown for one of the products No available physical data for USE for most of the product / activities categories Mass data for heterogeneous streams of products were difficult for estimation, different units from mass units National classifications of products/activities are not exactly harmonized with CPA/NACE

Identification of gaps - 2 FORWAST - WP4 Lack of data and clear information on mass flows of waste directed to different activities of waste treatment For many recycled flows corresponding to FORWAST categories of products statistical data are not available (except of basic metals)

Conclusions -1 FORWAST - WP4 Within FORWAST (WP4) SUTs Masters for EU-23 countries have been developed The availability and quality of physical data as well as data processing related to supply, use, import and export, varied significantly between categories of products/industries. The availability of statistical data differed from one matrix to another.

Conclusions - 2 FORWAST - WP4 Two areas related to waste statistics emerged during data mining, which we consider to be of particular importance, it means there is a need for good quality data and consistent information on mass flows of waste directed to different activities of waste treatment as well as quantity of recycled materials defined in FORWAST