Native vegetation and wood for paper and pulp across São Paulo’s municipalities

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Plate No. 113, the third of five in the collection Bioenergy, crossed; the previous one is plate 112; the next, plate 114.

The collection crosses São Paulo’s bioenergy with what filling stations sell, with the countryside and with town halls, across the state’s 645 municipalities. The pairs came from a correlation screen between CP2B biomethane potential, fuel sales in the SEMIL Yearbook, plantation forestry, the cattle herd, the Agricultural Census, the vehicle fleet and MUNIC. Each pair went through raw and partial rank correlation (net of urbanisation, density and income), an effective sample corrected for spatial autocorrelation, Benjamini–Hochberg q values and bivariate LISA. The last plate is descriptive, with no test, and the text says so.

The map’s callouts point to Capão Bonito, with 39% of its area under native vegetation and 1.70 million m³ of pulpwood in 2024 (top third of both), and Agudos, in the centre-west, with 21% and 845 thousand m³.

What the files contain

Layer (GPKG)	Geometry	Content
municipios	polygon	645 municipalities with cane and ethanol, biomethane and diesel, the fleet, native cover and pulpwood, cattle and organic fertiliser, distances to the pipeline, piped gas and the MUNIC answers on waste; classes b111, b113, b114, arranjo115 and clusters lisa111, lisa113, lisa114
gasodutos	line	transmission pipelines and distribution network (tipo)
regioes_intermediarias, limite_estadual	polygon	reference

The GPKG is the same for all five plates and carries a QGIS style for each. The CSV has the municipal table, without geometry. Empty fields are missing data, not zero. LISA clusters come as AA (high-high), BB (low-low), AB, BA and ns (not significant); the first letter is the municipality’s variable, the second its neighbours’.

Method

1. pct_area_nativa: native vegetation, MapBiomas natural formations (collection 10, 2024), over municipal area, in %.
2. madeira_papel_m3 and madeira_papel_m3_km2: plantation roundwood for paper and pulp in 2024, in m³ and per km² of municipal area (IBGE, PEVS, SIDRA 291).
3. Thirds of native cover: up to 9.2%, 9.2% to 18.1% and over 18.1%. Wood: none; up to 165.8 m³/km² (median of the 127 producers); more than that. b113: bivariate class, tens = third of native cover (1 to 3), units = third of wood (1 to 3). From bottom to top and left to right, the nine cells hold 113, 73, 53; 8, 29, 26; 1, 20, 43 municipalities.
4. PEVS publishes 366 São Paulo municipalities for this variable; the other 279 have no data, not zero, and the map hatches them. Native cover × pulpwood per km² (log): Spearman rank correlation 0.42 (366 municipalities), partial 0.47, effective sample 167, p < 0.001, q < 0.001.
5. lisa113: bivariate LISA clusters (native cover in the municipality × wood in its neighbours): 65 high-high, 0 low-low, 0 high-low and 13 low-high; bivariate Moran’s I 0.37. High-high by intermediate region: Sorocaba (23), Bauru (18), São José dos Campos (8) and Marília (7).
6. Partial correlation: the correlation between the residuals of each variable’s ranks regressed on the ranks of the controls, which are urbanisation (residents in urban areas), log of population density and log of income per head (2022 Census). Effective sample under spatial autocorrelation: n·(1 − Iₓ·Iᵧ)/(1 + Iₓ·Iᵧ), where Iₓ and Iᵧ are the Moran’s I of the residuals over a 6-nearest-neighbour neighbourhood; Benjamini–Hochberg q values across all pairs in the screen. Bivariate LISA: the municipality’s value against the mean of its 6 neighbours on the other variable, 999 permutations, seed 20261019, p < 0.05.

Limitations

• PEVS covers 366 of the 645 municipalities and does not give the species: eucalyptus dominates São Paulo’s plantations, but the south-west also grows pine. Pulpwood is not all plantation wood: firewood and sawlogs are left out.
• Rugged relief and soils that keep cane and machinery away may explain both; the map does not say that plantations protect the forest, nor that they threaten it.
• Ecological reading: the map compares municipalities, not people or farms. A correlation between municipalities does not say that the same farms or drivers have both traits, nor that one causes the other.