Aggregate Fuel Indicators and Spatial Autocorrelation as Pitfalls in the Distributional Analysis of Zero Emission Zones: A Cautionary Study Using Dutch CBS and RDW Open Data

Abstract

Zero Emission Zones (ZEZ) are spreading rapidly across the Netherlands, and their distributional consequences are increasingly assessed at the municipal scale with open administrative data. This study shows, with a worked Dutch example, that two methodological traps can invert or fabricate such conclusions, and identifies the data level at which the genuine signal survives. Two public sources are used: the CBS StatLine Kerncijfers wijken en buurten 2017 table (325 municipalities) and RDW open vehicle-registration micro-data. First, the widely available CBS "non-petrol vehicle share" (other_fuel_share) pools electric vehicles with diesel and LPG; because diesel and LPG concentrate in lower-income rural-agricultural municipalities (Friesland, Flevoland, Overijssel and Groningen), the indicator produces a counter-theoretical negative income association (Pearson r = −0.15, p = 0.006) that does not measure EV adoption at all. Second, the corresponding OLS income coefficient (β = −0.0021, p = 0.014) is largely a spatial artefact: the residuals are strongly autocorrelated (Moran's I = 0.34, P<0.001), and under Spatial Error (SEM) and Spatial Lag (SLM) models the coefficient loses significance (p = 0.62 and p = 0.37), with both spatial models decisively outperforming OLS on AIC. A ZEZ dummy is the only municipal predictor that survives spatial correction (β = 0.052, P<0.001), reflecting that ZEZ adoption is concentrated in larger, higher-income cities rather than any income gradient in EV uptake. The constructive corollary is that the genuine distributional signal must be sought at the vehicle level: in a same-year (2022) registration cohort the BEV median catalogue price (€46,274) exceeds the pooled-ICE median (€37,340) by about 24% (Mann–Whitney U, P<0.000001; rank-biserial |r| = 0.37). We conclude that municipal-scale distributional claims about ZEZ built on composite fuel indicators and non-spatial regressions are unreliable; the apparent income gradient is geographically structured, not a household-level income effect, and individual-level micro-data are required to recover the real barrier — the BEV purchase-price premium.

Keywords

• Zero emission zone
• Spatial autocorrelation
• Ecological fallacy
• Composite indicator bias
• Electric vehicle
• Open administrative data

Ethical Statement

Ethics committee approval was not required for this study because there was no study on animals or humans. The study uses only publicly accessible, aggregated open data and does not process directly identifiable personal data.

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