Part I — Situation overview

The European Union’s climate monitoring service, Copernicus, announced on 10 August 2026 that in Western Europe the combined June–July average temperature was 21.62 degrees Celsius, breaking the record set in 2022. According to the service this reflects “the exceptional persistence of the heat” — four heatwaves have passed over the region since May. Soil moisture was significantly lower than during the severe drought of 2022, and the July dryness affected Hungary as well as France, Spain, Austria and the United Kingdom. The flow of the Seine, the Rhine and the Danube is unusually low; because of the low water level of the Danube, output has already been limited at the Paks Nuclear Power Plant, while Romania has diverted water to its last operating reactor by blasting rock. For Europe as a whole July was only the eleventh warmest (20.49 degrees Celsius) — the eastern half of the continent and Scandinavia got cooler weather.

In the same week the fresh analysis appeared that also calculated the summer in money. According to the estimate of Triodos Bank the heat will reduce the output of the EU economy this year by roughly 180 billion euros — that is about 1 per cent of gross domestic product (GDP), the economy’s output for one year. In the analysis’s own words: “At first glance this may seem modest, yet it is precisely the amount of economic growth expected from the EU this year.” The largest item is not drought damage but work: according to the estimate, falling labour productivity in the heat alone takes roughly 0.6 per cent off EU GDP, while agricultural output may fall by 3–7 per cent. France could lose 1.4 percentage points (pp) of its growth, which would turn its economy into a contraction of 0.6 per cent; in the case of the Netherlands a deduction of 0.8 percentage points practically consumes this year’s expansion. In Austria, according to the estimate of the local hail insurer, the drought caused around 1 billion euros of agricultural damage.

On MIAK’s reading, the real content of this news is not the temperature record but its operational part: European analysts are already quantifying the heatwave not as an environmental event but as a growth and budgetary item. One finding of the analysis deserves separate attention, because it runs counter to everyday expectations: “The result is not simply that the hottest countries lose the most” — Spain and Italy have the greatest physical exposure, but because of decades of adaptation the marginal effect of any one hot day is relatively small there. The loss is therefore produced not by temperature in itself but by unpreparedness. In Hungary this number does not exist today: the heat effect appears as a separate risk line neither on the revenue nor on the expenditure side, even though in the summer of 2026 it demonstrably affected industrial production, agricultural yields and the burden on the health system. This is not a scientific but a planning gap — and like every planning gap, it can be filled cheaply and quickly.

Part II — Literature foundation

Pricing the heat effect involves two separate questions: how to measure it, and why we do not measure it. The answer to the first is given by Warming the World by William Nordhaus (American economist, founder of the economic modelling of climate change, awarded the Nobel Memorial Prize in Economics in 2018): the integrated assessment models he developed treat climate damage not as a single global number but build it up sector by sector — agriculture, health, sea level, ecosystems — and separate effects that appear on the market from those that do not. Measurement therefore does not begin with building the whole model but with the sectoral breakdown. The second question is answered by the World Bank’s World Development Report 2015: the attention of planners is limited, and people — decision-makers included — typically process the information that is salient, that is, which has been given a separate name and place in the decision frame. A risk that has no line is not denied by people; they simply do not see it. The two ideas together give the structure of MIAK’s proposal: first let there be a line, then a sectoral estimate behind it. The detailed treatment of the literature — author by author, with quotations — can be found in the 6.4 Literature in detail section.

📖 Source: William Nordhaus: Warming the World; World Bank: World Development Report 2015 — Mind, Society, and Behavior

Part III — MIAK’s concrete proposal

MIAK proposes three measurable measures, none of which requires a new institution or a legislative amendment — all three can be carried out within the existing budgetary planning and statistical system.

3.1 A separate heat-effect risk line in the next budget bill (by submission)

The risk chapter of the budget bill today names exchange rate, interest rate and energy price risk; the heat effect does not feature in it. MIAK proposes that the Ministry of Finance insert a separate, quantified heat-effect item into next year’s bill, and that the Fiscal Council address it separately in its opinion. The content of the line is modest but defined: how much revenue shortfall and how much additional expenditure a summer similar to this year’s could cause, and with what probability. The item is valuable even if the estimate gives a wide range — the quality of planning is improved not by precision but by existence. The G1 data-driven budget programme point already sets out this requirement in general terms; the present proposal is a concrete, immediately achievable application of it. Nordhaus’s sectoral approach (see 6.4.1) provides a direct model here: behind the line there should be not one number but four or five sectoral sub-estimates.

3.2 A simplified heat-effect estimation module, with public methodology (within 90 days)

This summer’s data are already sufficient for a first domestic estimate. MIAK proposes that the Ministry of Finance and the Hungarian Central Statistical Office (KSH) jointly put together a simplified calculation module with four inputs: the number of heat-alert days, monthly industrial production data, agricultural yield data and the time series of the burden on health care. The module is the first stage of the domestic integrated climate-economic model — programme point K9 — not its substitute: what has so far figured as a multi-year research programme becomes with this an autumn planning task. Publishing the methodology is not a formality but the essence of the proposal: if the course of the calculation is public, then the estimate can be disputed, improved and compared from year to year. If it is not, the number remains a political claim. The K1 measurable climate targets programme point fixes the same principle on the emissions side.

3.3 A working-time exposure indicator by sector (from the first quarter of 2027)

The largest single item in the Triodos estimate is the fall in labour productivity — yet in Hungary we do not know today how many working hours fall on heat-alert days, and in which sectors. MIAK proposes that the KSH, by linking the existing labour force survey with the heat-alert calendar of the National Meteorological Service, publish a simple exposure indicator quarterly: how many hours worked fell on first, second or third degree heat-alert days by sector, broken down into outdoor and indoor work. This indicator is not a damage value but an exposure figure — and precisely for that reason it complements rather than repeats the earlier proposal on a sectoral drought damage report. From the point of view of the K5 just transition programme point, it is this datum that decides which groups of workers actually bear the burden: construction, agriculture and logistics workers are affected differently from office employees.

The three proposals are held together by a single principle: the heat enters policy when it becomes a number. According to Nordhaus’s modelling logic climate damage is manageable because it can be broken down by sector; according to the World Bank’s report, however, the decision-maker sees what has a separate place in the decision frame. The risk line, the public estimate and the exposure indicator together create precisely these two conditions — measurability and visibility — without taking a single competence away from anybody.

Part IV — Expected effects and risks

Dimension Expected effect Risk
Economy The budgetary forecast becomes more realistic: the revenue plan builds not on an average but on an observed summer; heat load appears as a cost item in investment decisions The wide estimation range gives a political target (“the government is scaremongering with a made-up number”) if the methodology is not public; poor calibration can lead to over-planning and unnecessary reserves
Society Heat load becomes measurable in the most exposed sectors, which provides a basis for targeted occupational safety and compensation decisions If publication of the exposure indicator is not followed by action, the measurement in itself creates distrust; the data can be distorted into an employer sanction if the focus falls on punishment rather than protection
Public administration The data link between the Ministry of Finance, the KSH and the meteorological service becomes regular; climate adaptation steps out of the sectoral frame and becomes a planning task A new data collection burden if the module does not build on existing time series; the distribution of responsibility can generate disputes over which body “owns” the estimate

The main question to weigh in the package is whether an imprecise number is better than the absence of a number. MIAK’s position is that it is — but only if the uncertainty of the estimate is itself published. A range estimate whose method and inputs are known can be improved from year to year; a missing item, by contrast, never improves, because there is nothing to improve. The proposal can tip to the risk side if the heat-effect line becomes a political instrument: if the government of the day invokes it as an excuse for its own performance, the credibility of the indicator is lost. Only one thing prevents this — that the methodology of the estimate is fixed not by the government but by a professionally verifiable, public procedure, and that the Fiscal Council attaches an independent opinion to it. The other condition for the proposal to work is that the indicators be built not as one-off announcements but as a continuous time series: no planning conclusion can be drawn from a single year’s data, but from three or four years’ worth it can.

Part V — Measurability and summary

5.1 What is worth tracking? (proposed KPIs)

The performance indicators (KPIs) below will show in 6, 12 and 24 months whether the proposal has been fulfilled. All of them are proposals, not government decisions — MIAK recommends them for tracking.

  • 6 months: whether the risk chapter of the next budget bill contains a separate, quantified heat-effect item (yes/no), and whether the Fiscal Council’s opinion addresses it.
  • 12 months: whether the methodological description of the heat-effect estimate is public, including the sources of the four input time series; it is worth tracking whether the estimate is a range or a point figure.
  • 12 months: whether at least two quarterly working-time exposure reports have appeared with a sectoral breakdown.
  • 24 months: the difference between the first and the second year’s estimate — that is, how much the calibration of the module improved in the second year; this shows whether we are dealing with a learning system or a one-off gesture.

5.2 Summary

MIAK’s request can be summed up in a single sentence: let the next budget bill contain a line on the effect of the heat, with a sectoral estimate of public methodology behind it. MIAK is not asking for a turn in climate policy, nor for a new institution — it asks for a planning item that is missing today, even though the data from which it could be produced are already available. From the decision-maker this requires work that can be done by the autumn; from the public it requires only that the uncertainty of the estimate be read not as an error but as an inherent part of honest planning.

The proposal engages two MIAK foundational values. Data-drivenness is concerned because this is precisely the situation to which that value responds: the phenomenon is measurable, the data exist, yet it does not enter the decision frame — the gap is not one of knowledge but of procedure. Transparency is engaged not because of the number but because of the calculation: a published heat-effect estimate whose method is secret is worse than nothing, because it makes a disputable claim indisputable. This is why MIAK proposes both at once — the item and the methodology — because separately neither works.

Part VI — Justifications and further sources

6.1 The press framing by spectrum

The topic appeared in the international press, and the framing diverged sharply according to the type of source. The economic-policy band (Politico Europe) alone carried the financial reading through: the headline “Europe’s scorching summer is erasing its economic growth, says report” and the structure of the article treat the heat from the outset as a growth item, with the temperature data serving only as background. This band brought the most concrete figures — the 180 billion euro estimate, the growth effect by country and the labour productivity breakdown — and it was the only place where the finding appeared that it is not the hottest countries that lose the most.

The EU specialist portal and public service band (Euractiv, Deutsche Welle, BBC) by contrast carried the measurement story: the Copernicus record data, soil moisture, the ocean temperature record and the El Niño effect. This framing is scientifically accurate but formulates no policy conclusion — the reader learns that there was a record, not what the price of it is. The news agency band (AP News) highlighted the operational side of preparing for the next heatwave, while EUobserver approached through the destruction of marine ecosystems, that is, moved towards non-market damages. The globally oriented band (Al Jazeera) emphasised the serial character of the heatwaves.

What is striking: the economic reading of the topic did not appear in the domestic press on this day. Hungarian papers discussed the heat in an energy supply and water management frame — which is understandable, because the output limitation at Paks is an immediate, visible event — but with that the budgetary dimension of the heat dropped out of domestic public discourse. This is not an editorial failure but precisely the attention pattern discussed in the literature section of Part VI: the immediate and visible crowds out the slow and quantified risk.

6.2 Facts and data

Indicator Value Source
Western Europe’s June–July average temperature 21.62 °C (record, above the 2022 peak) Copernicus, 10 August 2026
July average temperature for Europe as a whole 20.49 °C (11th warmest) Copernicus, 10 August 2026
Global average July surface temperature relative to the pre-industrial level +1.47 °C (1850–1900 baseline) Copernicus, 10 August 2026
Global average sea surface temperature in July 20.96 °C (record) Copernicus, 10 August 2026
Estimated EU economic cost of the heat in 2026 ~180 billion euros (~1% of GDP) Triodos Bank analysis, Politico Europe, August 2026
Of which the fall in labour productivity ~0.6% of EU GDP Triodos Bank analysis
Estimated fall in agricultural output 3–7% Triodos Bank analysis
France’s growth loss 1.4 pp (may turn into a contraction of 0.6%) Triodos Bank analysis
The Netherlands’ growth loss 0.8 pp Triodos Bank analysis
Austria’s agricultural drought damage ~1 billion euros Austrian hail insurer, August 2026
Estimated excess mortality in the six most affected European countries (mid-June – early July) at least 14,000 people Politico Europe estimate

The table contains two groups of data of different natures: the Copernicus rows are observed measurements, the Triodos rows are model estimates. Confusing the two is the most common error in analyses of this kind — measurement and estimate have different reliability, and it is precisely for this reason that MIAK proposes that the domestic risk line mark both separately.

6.3 Policy dimensions

  • Environment and climate (programme points) — the programme points on a domestic integrated climate-economic model and on measurable climate targets provide the frame for the estimation module; climate adaptation appears here not as an emissions but as a planning question;
  • Economy (programme points) — a direct application of the data-driven budget programme point: extending the substance of the risk chapter with an item that is measurable and comparable from year to year;
  • Employment policy (background material) — the regulatory questions of heat load and outdoor work; the exposure indicator is the data basis for occupational safety decisions;
  • Healthcare (background material) — the additional burden caused by the heat on the care system, which is one of the input time series of the estimation module.

6.4 Literature in detail

6.4.1 William Nordhaus: Warming the World

The chapter of Nordhaus’s volume dealing with the effects of climate change is more instructive methodologically than in its results. The author records that his approach — like the first generation of analyses — examines effects on a sectoral basis, and departs from earlier practice at three points: it covers every region, not only the United States; it places greater weight on effects that do not appear on the market; and it values damages through willingness to pay for prevention. At the same time the volume honestly signals the limits as well: “On the basis of a review of current research it is clear that the results are highly conditional, and it remains difficult to give a reliable estimate of the impacts of climate change.” The author adds that among the sectors only agriculture and sea level rise have reached detailed, regional-level estimation, while for ecosystems and human health the difficulties are particularly great.

Translated to the Hungarian situation this means two things. First: the estimate does not begin by building the whole model — it begins by breaking the effect down into those sectors where the data already exist. Here that means agriculture, industrial production, transport and health care. Second: uncertainty is not an excuse for abandoning the estimate but part of the estimate. Even in the most thoroughly worked-out sector Nordhaus signals that only scattered estimates are available on the shape of the damage function — and yet he built the model, because even an uncertain number makes decision alternatives comparable. The simplified module proposed in point 3.2 follows exactly this logic: few inputs, a wide range, public methodology.

📖 Source: William Nordhaus: Warming the World

6.4.2 World Bank: World Development Report 2015 — Mind, Society, and Behavior

The World Bank’s report is about the fact that decision-makers — from private individuals to the state apparatus — are not computers working with unlimited attention but people of limited attention whose decisions are shaped by the construction of the decision situation. One basic proposition of the report applies directly to budgetary planning: “People often process only the information that is most salient to them, which can lead them to miss key information and disregard critical consequences.” The report adds that it is not only the totality of available information that matters but also its order and psychological salience — how easily a fact comes to mind at the moment of decision.

Applied to Hungarian budgetary planning this explains the paradox of the situation: the heat effect is missing from planning not because planners deny its existence but because there is no heading that would bring it to mind at the moment of decision. Exchange rate risk has a line, therefore everyone deals with it; the heat has none, therefore nobody does. It follows that the step proposed by MIAK in point 3.1 — the separate risk line — is not an administrative formality but the substantive intervention itself: creating the line is what makes the risk visible, and visibility brings the estimate with it, not the other way round.

📖 Source: World Bank: World Development Report 2015 — Mind, Society, and Behavior

6.5 International comparison

There is no settled European practice for the budgetary treatment of the heat effect — which is precisely what makes the present moment interesting. The financial pricing of climate risks has so far advanced along two channels: on the central bank and financial supervisory side (climate stress tests for the banking system) and on the disaster insurance side (flood and storm risk). Heat as a productivity and revenue risk has so far been given no separate place in fiscal planning anywhere, even though the international methodology — the relationship between lost working hours and temperature — has long been available from the occupational health literature.

The analysis quoted here offers two lessons that are usable at home as well. The first is that physical exposure and economic loss are not the same: Mediterranean countries are hotter, yet they lose less on any given hot day, because the building stock, the organisation of work and the cooling infrastructure have adapted. Hungary is in an intermediate position — the summers are warming, but adaptation has not yet happened — and therefore the marginal effect here is relatively large. The second lesson lies in the closing sentence of the analysis: “Every year in which we only adapt but do not cut emissions is a year borrowed from a warmer starting point.” MIAK reads this not as a mobilising slogan but as a planning assumption: if the starting point is higher year after year, then the heat-effect estimate too has to be built not as a one-off exercise but as a rolling time series.

Environment and climate

  • K9 — Domestic integrated climate-economic model (DICE-HUNGARY)
  • K1 — Measurable climate targets
  • K5 — Just transition programme
  • K7 — Energy market shock resilience

Economy

  • G1 — Data-driven budget
  • G25 — Energy price shock preparedness plan

Proposed new programme point: A heat-effect risk item in budgetary planning — for the Economy area, with a methodology shared with the Environment and climate area.

6.7 List of sources

Press sources (MIAK foreign press monitor, 11 August 2026 — topic 6):

Knowledge-base references (books):

  • 📖 William Nordhaus: Warming the World
  • 📖 World Bank: World Development Report 2015 — Mind, Society, and Behavior

Note: the local file path of the books does not appear in the visible text of the blog — only the author and the title. The file path is an internal matter of the generation process, not the reader’s.

MIAK internal materials:

  • MIAK policy area: Environment and climate (programme points; programme point ID: K1, K5, K7, K9)
  • MIAK policy area: Economy (programme points; programme point ID: G1, G25)
  • MIAK policy area: Employment policy (background material)
  • MIAK foreign press monitor, 11 August 2026 — topic 6, score: 90/100

Additional public data sources (where used):

  • Copernicus Climate Change Service (C3S) — monthly climate bulletins
  • Eurostat — heat-related excess mortality time series
  • KSH — monthly industrial production and labour force survey data
  • National Meteorological Service — heat-alert calendar

Generation metadata