Concept Note · Agricultural and Forestry Exposure in the PPA Allocation Structure
Domain D-1 · D-3 · June 2026
Continues: CN-024 · SM-013
Relates: SM-012 · SM-007
CN-024's allocation analysis produced a working three-layer model of electricity market exposure in Finland:
This three-layer model is a useful first approximation. It is incomplete.
Layer 3 is the agricultural and forestry expression of SM-013's SurplusCitizen condition: too large for consumer protection, too small for industrial instruments, and with income irregularity that breaks the assumptions of both frameworks. The system identifies this category as commercial — and therefore capable of self-protection — while providing none of the instruments that would make self-protection viable at the relevant scale.
Agricultural and forestry operators do not fit cleanly into any layer. They have some features of Layer 2 (commercial entities with some contractual flexibility) and some features of Layer 3 (no effective cost pass-through, no PPA access). In critical dimensions — particularly regional concentration and income structure — they are more exposed than either.
Finnish farms face a structural double constraint that neither households nor industrial SMEs share in the same combination.
Constraint 1 — Cost absorption without pass-through. Electricity costs in Finnish agriculture are embedded in production costs (drying, heating, refrigeration, machinery). When spot prices spike, the farm absorbs the cost. Unlike an industrial SME, a farm cannot reprice its output dynamically — grain price is set at the time of sale, often months after the electricity cost was incurred. The cost asymmetry is structural, not a consequence of poor risk management.
Constraint 2 — Income irregularity against stable energy costs. Farm income concentrates in harvest periods. Electricity costs distribute across the year. This mismatch means a farm cannot time its market exposure the way a continuous industrial process can. A spike in January affects cash flow in a way that a spike in September — closer to the income event — does not. Risk management instruments that assume regular cash flow (fixed-term contracts, FPA-type instruments) are structurally ill-fitted to agricultural income profiles.
The regulatory binary blind spot: Energy market regulation operates on a strict binary — households versus commercial entities. The implicit assumption is that any entity with a business registration possesses corporate treasury functions, data-driven trading capability, and structural capacity to manage wholesale market risk. A family farm or forestry contractor has the same effective market leverage as an individual household (zero), but carries multiple times the volumetric exposure. They absorb corporate-level risk without corporate-level defensive tools.
Observed market behaviour: Many Finnish farmers have migrated to spot-indexed contracts in the 2022–2025 period, attracted by the average price advantage over fixed retail contracts. This leaves them directly exposed to the spike amplification mechanism described in CN-024 §02 and TN-019 §03: PPA-driven inelastic supply extends negative price duration and amplifies positive spikes, with the spike burden falling on uncontracted actors.
A farm that moved to spot pricing to reduce average costs has inadvertently become a residual absorber of the volatility created by large-scale PPA contracting. It has no instrument to exit this position without accepting a substantial risk premium in the retail market.
Forestry-sector electricity exposure operates differently from agriculture but shares the core structural feature: no protective instrument exists at the relevant scale.
Small-scale forestry operations — timber harvesting contractors, small sawmills, drying facilities, wood chip processors — use electricity intermittently and in quantities too small for PPA access (typically below 1 MW). Fixed-term retail contracts provide some protection but are priced at a significant premium to PPA rates. The premium reflects the risk that the retailer absorbs; the retailer ultimately prices it based on the same spot market exposure that the forestry operator is trying to avoid.
A compounding feature: forestry income, like agricultural income, is irregular. Timber sales generate lump-sum income when markets are favourable. Operating costs — including electricity for drying and processing — accumulate independently. This income structure makes multi-year forward contracts (which would provide genuine protection) impractical for most forestry operators.
The result is structural invisibility: forestry operators appear in aggregate statistics as "commercial" consumers and are therefore assumed to have access to commercial hedging instruments. In practice, their scale, income structure, and operational profile disqualify them from the instruments available to larger commercial users.
Agricultural and forestry operators are geographically concentrated in regions where the grid load from large industrial users is — or will be — highest: Kainuu, Pohjois-Pohjanmaa, Lappi, and parts of Etelä-Pohjanmaa.
These are the same regions targeted by:
The regional concentration creates a spatial extraction loop. Layer 3 operators host the physical footprint of wind farms and major transmission corridors — they are the landowners, the communities, the regional tax base. But the stable, low-cost electricity enabled by that infrastructure flows to Layer 1 under long-term PPA contracts. The residual volatility and the socialised tariff costs of grid reinforcement (necessitated by Layer 1 connection volumes) fall back on the rural consumers who hosted the infrastructure in the first place.
This generates a falsifiable empirical question: what fraction of the grid reinforcement costs triggered by large industrial connections in northern and western Finland is socialised through regional network tariffs rather than borne directly by the connecting party? If the socialised fraction is substantial, Layer 3 operators are effectively subsidising the infrastructure of Layer 1 actors whose PPA-driven spot compression simultaneously increases Layer 3's own exposure. This is measurable from Fingrid's published cost allocation methodology — and currently unmeasured.
The regional concentration creates a compounding exposure beyond this loop. The same geographic areas that attract high-consumption industrial users are the areas where agricultural and forestry operators are most densely present. When industrial load growth tightens local grid capacity and raises connection costs, the cost socialisation through grid tariffs falls disproportionately on the rural consumers in those areas — who are also the least able to absorb it.
This is the regional expression of SM-012's fiscal retention argument: not only does the industrial allocation reduce national fiscal retention, it concentrates the infrastructure cost burden on the rural regions that host the physical infrastructure.
The agricultural and forestry electricity exposure sits in a gap between three ministries.
| Institution | Mandate | Gap |
|---|---|---|
| TEM (Ministry of Economic Affairs) | Energy market design, large industrial users | Rural small-scale exposure not primary mandate |
| MMM (Ministry of Agriculture and Forestry) | Agricultural support, forestry policy | Energy exposure not within MMM's analytical toolkit |
| Energiavirasto (Energy Authority) | Market regulation, consumer protection | Agricultural operators classified as commercial — standard consumer protections do not apply |
No institution holds the complete picture. TEM analyses large industrial users. MMM tracks agricultural income and subsidy flows. Energiavirasto monitors consumer markets. The intersection — commercial-scale rural operators structurally exposed to PPA-compression effects — is unmonitored.
This is the same institutional gap that CN-024 identified for household PPA exposure (§05), replicated one layer up the commercial scale. The mechanism is identical: the cumulative effect of individually rational decisions (large-scale PPA contracting) creates a distributed cost burden on actors that no institution has been tasked with protecting.
The three-layer model from CN-024 should be extended to four layers for analytical completeness:
Layer 3 is the missing analytical object. Public debate addresses Layer 1 (industrial allocation) and Layer 4 (household bills). Layer 3 is neither visible in industrial policy nor in consumer protection. Its exposure is real, geographically concentrated, and structurally created by the same PPA mechanisms that protect Layer 1.
Ruotsin hallitus (27.5.2026) esitti maatalouskoneissa käytettävän dieselin CO2- ja energiaveronalennuksen jatkamista joulukuuhun 2027. Suomen vastaava järjestelmä perustuu yleiseen CO2-veroalennukseen täydennettynä palautusjärjestelmällä joka vaatii aktiivista hakemista.
DA-007:n kolme ehtoa vertailussa: Automaattisuus — Ruotsi ✓, Suomi ✗ (vaatii hakemuksen). Ennakointi — Ruotsi ✓ (olemassaolevan jatkaminen), Suomi ✗ (reaktiivinen). Pakkoaseman tunnistaminen — Ruotsi ✓ (maatalous erityissektori), Suomi ✗ (yleinen kilpailukykykysymys).
Rakenteellinen havainto: Suomen politiikka toimii samanaikaisesti molempiin suuntiin — datakeskusten sähkötuet alentavat Layer 1:n kustannuksia samalla kun ne nostavat Layer 3A:n sähkön hintaa. Tämä kaksijakoinen tulos on koordinaatiovajeen suora seuraus, ei tarkoituksellinen valinta. Lähde: ATL/HS 27.5.2026.
This memo does not propose a subsidy programme. The exposure is structural and requires structural responses.
Three observations, not yet proposals:
O-1 — Collective procurement. Agricultural and forestry operators individually fall below PPA thresholds. Collectively, a regional cooperative of grain dryers, greenhouse operators, timber processors, and sawmills represents a substantial, highly predictable demand block — in some rural regions, comparable in annual volume to a mid-sized industrial user. The aggregator model described in TN-017 and TN-018 (AAP) was designed for exactly this scale: entities too small for direct PPA access, large enough collectively to support aggregated procurement. A further advantage: agricultural and forestry operations have inherent thermal and process flexibility — non-perishable drying schedules, biomass chipping, refrigeration setpoints — that could be monetised as demand response, partially offsetting their own spot exposure without new capital investment. Whether the AAP framework could extend to rural commercial cooperatives warrants examination as a near-zero-cost structural response.
O-2 — Regional grid tariff design. If grid tariff structures socialise the infrastructure costs of large industrial users across all regional consumers, the burden falls disproportionately on Layer 3 operators who are geographically co-located with the large users but lack their contractual protections. Fingrid's tariff design currently does not distinguish between Layer 2 and Layer 3 commercial users in this regard.
O-3 — Monitoring mandate. The minimum intervention is measurement. MMM or Energiavirasto should be mandated to track the electricity cost exposure of agricultural and small forestry operators separately from general commercial consumer statistics — the same transparency logic as CN-024's CM-1 (mandatory liquidity reporting), applied one layer down. There is a prior problem, however: the monitoring mandate cannot be assigned without first assigning institutional ownership of the problem. MMM lacks energy analysis capacity; Energiavirasto lacks an agricultural sector mandate; TEM's mandate centres on large industrial users. The monitoring gap is a consequence of the institutional gap in §05 — assigning the measurement task requires first resolving which institution owns the intersection.
CN-025 should be substantially revised if any of the following are confirmed by data:
FC-1 — Agricultural electricity cost volatility (standard deviation of monthly bills, normalised by consumption) does not exceed household volatility over the 2024–2027 period when controlling for contract type. If Layer 3A exposure is comparable to household exposure, the "structurally unprotected commercial classification" claim weakens significantly.
FC-2 — The hedging instruments available to farms and forestry operators are demonstrably as effective, per MWh consumed, as those available to large industrial users. If a Layer 3A cooperative can access AAP-type aggregated procurement on comparable terms to a Layer 1 PPA, the instrument gap closes without institutional intervention.
FC-3 — MMM, TEM, or Energiavirasto publishes systematic, publicly available tracking of primary sector electricity cost exposure, disaggregated from general commercial consumer statistics. The existence of such monitoring would falsify the "institutionaalinen kuilu" (institutional gap) claim in §05 — the gap would be named and owned.
FC-4 — Grid reinforcement costs attributable to large industrial connections in northern and western Finland are shown to be borne substantially by the connecting parties rather than socialised through regional network tariffs. If the tariff socialisation fraction is below 20%, the spatial extraction loop argument in §04 weakens to a distributional concern rather than a structural design failure.