Between 2003 and 2024, NASA satellites measured the surface temperature of each of Italy's 7,899 municipalities. Comparing the five years from 2020 to 2024 with the decade from 2003 to 2012, 98.6% of municipalities are hotter during the day, with a national average shift of +0.61 °C. This is not a ranking of one area against another: it measures how much hotter each place has become than it used to be.

How to compare a municipality's climate  

When discussing global warming, the most common habit is to compare different regions: mountains are cooler than plains, and the South is warmer than the North. This exercise primarily measures geography and ends up confirming the obvious. This analysis asks a different question: how much has each municipality warmed compared to the climate that same area experienced at the beginning of the century? Comparing a place with its own history eliminates factors that do not change over time, such as altitude, latitude, or proximity to the sea, because they appear identical in the two periods being compared and cancel each other out in the difference. The starting data is surface temperature – asphalt, roofs, soil – detected by satellites, not the air temperature from weather stations.

Widespread warming, with its own geography  

Map 1 shows a country warming almost everywhere: 98.6% of municipalities show a positive daytime deviation, with a national average of +0.61 °C and peaks exceeding one degree. The usual distinction between a temperate North and a scorching South no longer exists. The most rapid warming is concentrated in the Po Valley, inland and southeastern Sicily, Salento, and along the Western Alps; the most moderate deviations are found in the inland areas of Central Italy and in Sardinia. A useful clarification for correctly interpreting the data: the darker shades do not indicate the hottest places in absolute terms, but those that have deviated the most from their own climatic history. An Alpine municipality can therefore show a higher deviation than a Sicilian one, while remaining much cooler in absolute terms. Areas with a slightly negative deviation (very rare) account for just over one municipality in a hundred.

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Trees mitigate daytime heat  

When municipalities are divided into five homogeneous groups (quintiles) based on their tree cover, the average daytime temperature deviation consistently decreases as greenery increases: from approximately +0.8 °C in the least wooded quintile to about +0.5 °C in the most wooded one. This same pattern is observed when comparing municipalities within the same province, where regional climate and context remain constant. In the Pavia area, Brallo di Pregola, which is nearly 90% forested, warmed by +0.41 °C, compared to +1.84 °C in the nearly barren Semiana, in Lomellina. In the L'Aquila area, Fagnano Alto, with over 70% tree cover, remains at +0.07 °C, essentially at its norm, while Santo Stefano di Sessanio, with 6% tree cover, has risen by +1.31 °C. In the Brindisi area, Ceglie Messapica, which is nearly three-quarters covered, shows +0.41 °C against +1.69 °C in Torchiarolo, which is less than 10% wooded. The gap between the least and most wooded municipalities widens as you move down the Peninsula: about four-tenths of a degree in the Center and South, and less than three-tenths in the North.

Two limitations should be kept in mind. The analysis is descriptive and associative, not causal: it does not isolate other territorial factors, such as land use, irrigation, and morphology, which contribute to the result alongside tree cover. Furthermore, the documented benefit applies only to the daytime: at night, the correlation between trees and temperature deviation changes sign (+0.30), and nights are warming almost twice as much as days, by an average of +1.03 °C, with all municipalities above their norm. This is the most critical dimension and the one least manageable through urban greenery alone.

The risk for the elderly does not align with the heat map

The final part of the analysis identifies the areas where current heat is most dangerous for the elderly population by combining the absolute summer surface temperature from the 2020-2024 five-year period—weighted toward the nighttime component, the primary factor associated with heat-related mortality—with the percentage of residents over 75. The two dimensions combine multiplicatively: a municipality that is scorching but demographically young, or one that is very elderly but cool, does not generate a high level of risk.

At the provincial level, the highest values are recorded where a warm climate and an aging demographic structure overlap: Livorno, Oristano, Terni, Grosseto, and Lecce top the list, while the entire Alpine arc, from Bolzano to Sondrio, Trento, Aosta, and Belluno, ranks at the bottom. At the municipal level, the 77 most extreme cases (1% of the total) are concentrated, with 65 occurring in the South and the Islands: small towns in the Ionian Calabria, Salento, inland Sardinia, and the Sicilian hinterland. This map does not align with tree cover: trees mitigate heat, but the elderly population resides disproportionately in the most wooded rural villages, where depopulation has left behind the most vulnerable residents. The index measures the exposure of the average inhabitant, not the number of people exposed; this is particularly relevant in large cities, where the elderly are numerous in absolute terms but represent a small percentage of the total population.

What can local administrations do?

The data point to two distinct levers. The first concerns tree cover, the only observed variable that an administration can directly influence, and whose benefits for daytime heat are well-documented: increasing it in municipalities with fewer trees produces a measurable effect, even if it cannot be quantified as a direct causal reduction in warming. The second concerns the nighttime component, which is the most critical for health and cannot be mitigated by greenery alone: here, local planning must focus on climate relief facilities, monitoring the elderly population, and organizing services during periods of highest risk. The combination of climatic danger and demographic vulnerability suggests that intervention priorities do not necessarily coincide with the hottest municipalities overall, but rather with those where these two conditions overlap. The availability of these indicators at the municipal level, developed on Civiqa, allows local authorities to anchor their programming and service planning to the actual territorial situation, rather than to regional or national averages.