Eurostat Roundwood and Wood Products Statistics (FOR_BASIC)

Europe's official answer to the oldest question in the timber trade: how much wood came out of the forest, and where did it go next? Eurostat compiles the table through the Joint Forest Sector Questionnaire, the survey instrument operated jointly with UNECE, FAO and ITTO and validated by the Inter-Secretariat Working Group on Forest Sector Statistics. That lineage matters in practice: the same questionnaire drives global forestry collections, so a European production figure here reconciles with its international counterpart instead of sitting next to it at a discrepancy.

Three things make the table hard to substitute. First, shape: one annual cube along seven dimensions holding 144,153 observations - compact enough to reason about whole, deep enough to span reference years 1988 through 2024. Second, the axis set: twelve basic product codes from roundwood in the rough to wood pellets, four species groupings down to non-coniferous tropical species, five flows including the extra-EU-only import and export variants, four units running from thousand cubic metres to thousand euro. Third, method: removals measured in cubic metres over or under bark, products classified against the Harmonised System and Combined Nomenclature alongside the FAO forest-product classification, so joins onto customs and trade tables stay defensible.

Inside the forest-products shelf this is the pan-European baseline: national statistical banks dig deeper into single countries, global collections reach further but flatten the detail. Browse the forest products data hub for the rest of the slice, or get a sample of this dataset cut to your countries and products.

One row per country x product x species group x flow x unit x reference year, flat enough to enter a model without reshaping. Four verified observations:

geo      : FI   (Finland)
prod_wd  : PEL   (wood pellets)
stk_flow : PRD   (production)
unit     : THS_T (thousand tonnes)
time     : 2018    value: 385
time     : 2019    value: 362.53
time     : 2020    value: 322

geo      : AT   (Austria)
prod_wd  : AGG   (other agglomerates)
stk_flow : EXP   (exports)
unit     : THS_EUR (thousand euro)
time     : 2015    value: 7376

The first block is a bioenergy story in three lines: Finnish pellet output slid from 385 to 362.53 to 322 thousand tonnes across 2018-2020, a 16 percent retreat that a single headline year would hide. The Austrian row shows the other half of the cube - the same grid carries euro-valued trade, so 7,376 thousand euro of agglomerate exports sits one filter away from somebody else's production volumes. Volume and value, domestic and cross-border, all keyed identically. Request a sample and rows arrive in exactly this shape, extended across whichever countries, products and years you name.

Nine documented fields define every observation, and each is a coded dimension rather than a prose label - which is why a 1995 figure and a 2024 figure join on identical keys. Every definition below was verified against the delivered data structure during research, not inferred from documentation.

Derived reshapes - year-over-year deltas, apparent-consumption and self-sufficiency ratios, custom country groupings, extracts aligned to companion forestry tables - depend on the cut you specify, so they fold under additional fields on request and are confirmed with your sample rather than guessed at here.

Geography - 32 codes in one keyed dimension: the EU27_2020 aggregate, every EU member state, plus Iceland, Liechtenstein, Norway and Switzerland. Aggregates ship beside the individual countries, so a bloc-level chart and a single-country cut come off the same query with no rollup step of your own.

Temporal - reference years 1988 through 2024, 37 consecutive years. Roundwood removals run the full span; the metadata notes most wood-product series begin in 1992 and tropical wood imports in 1999, which turns early-year gaps into documented start dates rather than holes. Thirty-seven years hold several complete housing and bioenergy cycles, which is the difference between fitting a trend and extrapolating one.

Granularity - country x product x species group x flow x unit, one annual observation per combination. Twelve products, four species groupings, five flows, four units. There is no regional, mill or company detail: this is the country-level instrument, and it pairs naturally with asset-level sources when the mills behind the tonnage matter.

Set against the wider catalog - average quality score 7.81 across all 1,744 datasets - this slice scores 9/10, carried by fully verified field definitions and a documented, methodology-stable collection framework.

API, files, or your warehouse. Daily, weekly, or hourly.

Pick the channel and the cadence; the rows arrive identical either way - decoded from the source cube into one fully keyed observation per row, typed against the field dictionary above.

• Bioenergy and pellet-market analysis - the PEL and PEL_AGG codes tracked across production and extra-EU trade size European wood-pellet capacity against demand; the workflow pattern sits on our demand forecasting page.
• Fiber-security and raw-material planning - chips, particles, residues and sawdust flows reveal where mill-residue markets tighten before they show up in delivered prices; sizing work like this is sketched on the market sizing use-case page.
• Trade-flow and trade-policy work - IMP_XEU and EXP_XEU strip intra-EU churn out of the totals, leaving trade with genuine non-EU partners, which is the series customs analysts actually want.
• Quant and macro factor construction - thirty-seven annual reference years of country-by-product panels make clean supply-side factors for timber, panel and paper exposures, as covered on the quant backtesting page.
• Citation-grade research and journalism - every figure names its compiler and collection framework, which is why academic work on European forest economics cites it directly; patterns on our citation-grade research page.

Market researchers and consultants get the fastest route to a defensible European wood-market sizing - one harmonized frame, ready-made aggregates, no stitching of national releases. Investors and quant researchers get a thirty-seven-year supply-side backdrop for timber, panel and bioenergy positions. Data scientists and ML engineers get a fully keyed, schema-stable panel whose codes survive revisions intact, long enough to train on without augmentation. Developers building data products get typed rows that load straight into a warehouse and diff cleanly across vintages. Journalists, academics and students get citable official statistics whose caveats are documented dimensions rather than buried footnotes. Persona workflows: market researchers x forest products, investors & quants x forest products, data scientists x forest products, developers & builders x forest products, journalists & academics x forest products.

Its nearest neighbour answers a different question about the same trees. FAOSTAT Forestry - Forest Production and Trade spans roughly 285 areas and 104 product items back to 1961 - global breadth collected through the very same questionnaire - while FOR_BASIC stays inside Europe and pays for it with depth: extra-EU trade splits, four species groupings and euro-valued flows the global file flattens away. The head-to-head continues on our FOR_BASIC vs FAOSTAT Forestry page.

National sources then go deeper than either. Natural Resources Institute Finland (Luke) - Statistical Database stays inside one country and digs deeper there - regional stumpage prices and a trade history reaching back to 1949. Statistics Canada - Lumber Production, Shipments and Stocks tracks Canadian lumber monthly. Neither covers Europe; this cube does.

Layered together they read as zoom levels: global context, European structure, national texture. The full ranked slate sits on our best forest products datasets list.

Three things worth knowing upfront.

First, these are the basic products and nothing else. Sawnwood, veneers, panels, pulp, paper and recovered paper belong to the wider forest-sector collection built on the same questionnaire - they are adjacent tables, not rows you are missing. If your model needs the processed chain, say so in the sample request and we will scope the companion tables alongside.

Second, the grid is honest about its gaps. Series start dates differ by product - most wood-product series from 1992, tropical wood imports from 1999 - and cells suppressed below the statistical confidentiality threshold arrive absent rather than zeroed. Treat absence as a reporting fact, not noise.

Third, the cube carries volumes and values only. There are no geolocation, species-composition or legality attributes, so EUDR-style sourcing-risk screening needs complementary datasets layered on top; we will suggest candidates with your sample.

Because the useful test is a join, not a glance. In the raw artifact the observations sit behind a flattened index across seven dimensions - decoding it correctly is a chore nobody should repeat per project. Datadory decodes once, types every column, and ships rows cut to the countries, products, flows and years you nominate, with keys held stable across releases so a re-pull diffs cleanly against last quarter's extract.

Start small: get a sample of this dataset scoped to the slice you will actually use, or browse the forest products data hub, the best forest products datasets ranking, or the full catalog.