The causal relation between off-street parking and electric vehicle adoption in Scotland

Achille Fonzone; Bernardino D'Amico; Emma Hart

arxiv: 2604.09271 · v1 · submitted 2026-04-10 · 💻 cs.LG

The causal relation between off-street parking and electric vehicle adoption in Scotland

Bernardino D'Amico , Achille Fonzone , Emma Hart This is my paper

Pith reviewed 2026-05-10 17:45 UTC · model grok-4.3

classification 💻 cs.LG

keywords electric vehicle adoptionoff-street parkingcausal inferencehome chargingincome barriersselection biasScotland households

0 comments

The pith

Access to private off-street parking raises the probability of electric vehicle ownership from 3.3% to 5.6%.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper uses a probabilistic causal model on a nationally representative set of Scottish households to test whether off-street parking truly enables more electric vehicle purchases or simply tracks existing economic advantages. It shows that parking access functions as a conversion catalyst, lifting ownership odds by 2.3 percentage points, yet this boost applies mainly to households already able to afford the vehicles. Income emerges as the larger constraint, cutting non-participation rates by 23.1 points when comparing income groups. Conventional observational approaches overstate parking's standalone role because richer households are more likely to have both parking and the funds for an EV. The work therefore recommends separate policy tracks for affordability and for home-charging access.

Core claim

Private off-street parking functions as a conversion catalyst: enabling access to home-charging increases the probability of EV ownership from 3.3% to 5.6% (a 70% relative, 2.3 percentage point absolute increase). However, this effect primarily accelerates households already economically positioned to purchase an EV rather than recruiting new entrants. By contrast, household income operates as the fundamental affordability ceiling. A causal contrast between lower- and higher-income strata shows a reduction in market non-participation by 23.1 percentage points. The analysis demonstrates that standard observational models overstate the isolated effect of off-street parking infrastructure due 5

What carries the argument

Probabilistic causal framework that neutralizes confounding socio-economic factors to estimate the isolated effect of off-street parking access on EV ownership.

If this is right

Financial instruments that lower the affordability ceiling can recruit new entrants into the EV market beyond what parking access alone achieves.
Home-charging infrastructure policies should target the latent-intent cohort in high-density urban settings where off-street parking is scarce.
Dual-track strategies are required: affordability support for non-participants paired with charging access for those ready to buy.
Observational studies of infrastructure effects on adoption must apply causal adjustment to avoid overstating benefits.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same causal separation of infrastructure access from income could be applied to public charging networks to test substitution effects.
Longitudinal data tracking households before and after parking changes would confirm whether the conversion catalyst effect holds over time.
The identified selection bias implies that correlational estimates in related mobility or energy-adoption domains may also require causal re-examination.

Load-bearing premise

The causal model successfully removes all relevant confounding influences and the household survey data contain no selection bias or measurement error that would alter the estimated effects.

What would settle it

A controlled comparison of EV purchase rates in households that gain or lose off-street parking while income and other measured traits stay fixed would show whether the 2.3-point lift persists or disappears.

Figures

Figures reproduced from arXiv: 2604.09271 by Achille Fonzone, Bernardino D'Amico, Emma Hart.

**Figure 2.** Figure 2: Causal discovery workflow. (a) Initial, fully connected skeleton. (b) Maximal Ancestral Graph [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗

**Figure 3.** Figure 3: Final DAG (G) representing the causal mechanisms of EV ownership status and intentions (Y ). The model integrates automated causal discovery results with post-hoc manual refinements to account for latent confounding (Ui). Directed solid edges indicate causal effects, dashed edges denote unobserved common causes. The DAG structure provides the formal basis for identifying sufficient adjustment sets Z requir… view at source ↗

**Figure 4.** Figure 4: Mutilated graphs used for the identification of causal effects via the back-door criterion. (a) Sub [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗

**Figure 5.** Figure 5: Probability distributions of EV adoption status and intentions ( [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗

**Figure 6.** Figure 6: Total causal effect of household income ( [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗

read the original abstract

The transition to electric mobility hinges on maximising aggregate adoption while also facilitating equitable access. This study examines whether the 'charging divide' between households with and without off-street parking reflects a genuine infrastructure constraint or a by-product of socio-economic disparity. Moving beyond conventional predictive models, we apply a probabilistic causal framework to a nationally representative dataset of Scottish households, enabling estimation of policy interventions while explicitly neutralising the confounding effect of other causal factors. The results reveal a structural hierarchy in the EV adoption process. Private off-street parking functions as a conversion catalyst: enabling access to home-charging increases the probability of EV ownership from 3.3% to 5.6% (a 70% relative, 2.3 percentage point absolute increase). However, this effect primarily accelerates households already economically positioned to purchase an EV rather than recruiting new entrants. By contrast, household income operates as the fundamental affordability ceiling. A causal contrast between lower- and higher-income strata, shows a reduction in market non-participation by 23.1 percentage points, identifying financial capacity as the principal gatekeeper to entering the EV transition funnel. Crucially, the analysis demonstrates that standard observational models overstate the isolated effect of off-street parking infrastructure. The apparent effect emerges from selection bias: higher-income households are disproportionately likely to possess both private parking and the means to purchase EVs. These findings support a dual-track policy strategy: lowering the affordability ceiling for non-participants through financial instruments, while addressing EV home-charging access for the 'latent intent' cohort in high-density urban contexts.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper supplies specific numbers on parking's role in Scottish EV uptake but the causal separation from income rests on an unverified claim of full confounding control.

read the letter

The headline result is that private off-street parking raises EV ownership probability from 3.3% to 5.6% in the Scottish data, a 2.3 point lift, while income cuts non-participation by 23 points between strata. The authors say ordinary regressions overstate the parking effect because of selection, and they position parking as a catalyst mainly for households already able to buy rather than a broad recruiter. They recommend a dual policy track: subsidies first, then targeted home-charging access in dense areas. That hierarchy and the concrete percentages are the clearest new pieces here. The work applies an existing probabilistic causal setup to a national household dataset and produces policy-facing contrasts that prior observational studies on EV adoption have not quantified this way. The reminder that selection bias inflates infrastructure effects is also useful for the field. The central weakness is identification. The entire 2.3 point claim and the income-over-parking ranking depend on the framework having blocked every relevant confounder—urban form, preferences, education, and anything else that jointly affects parking access and EV purchase. The abstract asserts this neutralization happens, but the letter does not show the exact conditioning set, the back-door criterion used, or any sensitivity or placebo checks. Without those, it is impossible to tell whether the reported contrast is clean or still carries residual bias. The paper is for transport economists and policy analysts who need quantitative input on infrastructure versus affordability trade-offs in EV rollout. A reader looking for Scotland-specific effect sizes and a clear policy ordering will find usable material even if the methods section requires heavy revision. It should go to peer review because the dataset is national, the question is timely, and the observational-bias point is worth testing properly.

Referee Report

2 major / 2 minor

Summary. The manuscript applies a probabilistic causal framework to a nationally representative Scottish household dataset to estimate the causal effect of off-street parking access on electric vehicle (EV) ownership. It reports that private parking raises EV ownership probability from 3.3% to 5.6% (2.3 pp absolute increase), primarily accelerating adoption among higher-income households already positioned to buy, while household income acts as the fundamental barrier (23.1 pp reduction in non-participation for higher-income strata). The analysis claims standard observational models overstate the parking effect due to selection bias from socio-economic confounders.

Significance. If the causal identification holds, the results support a dual-track EV policy: financial instruments to address affordability for non-participants and targeted home-charging infrastructure for the latent-intent cohort in dense urban areas. The explicit contrast with observational models and use of causal methods to isolate infrastructure from disparity effects represent a methodological strength for policy-relevant inference in sustainable mobility.

major comments (2)

[Methods (probabilistic causal framework)] The headline causal contrast (3.3% to 5.6%) and the claim that the effect mainly accelerates affluent households rest on the probabilistic framework having blocked all back-door paths from unmeasured confounders (income, education, urban density, preferences). The manuscript must specify the exact conditioning set, causal graph, and identification strategy (e.g., which variables are observed and conditioned on) so that readers can verify completeness; without this, the reported effect sizes and the superiority claim over observational models cannot be assessed.
[Results (income strata and effect sizes)] Table or figure reporting the income-stratified contrasts and the 23.1 pp reduction in market non-participation should include standard errors, confidence intervals, and robustness checks to alternative specifications or sensitivity to unmeasured confounding; the current presentation leaves the precision and stability of these policy-relevant quantities unclear.

minor comments (2)

[Abstract] The abstract and introduction could more explicitly label the 3.3%/5.6% figures as model-based counterfactual probabilities rather than raw sample proportions to avoid misinterpretation.
[Methods] Notation for the causal quantities (e.g., potential outcomes or intervention probabilities) should be introduced consistently in the methods section and reused in results to improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive feedback on our manuscript. We have carefully considered each major comment and provide point-by-point responses below, along with our plans for revision.

read point-by-point responses

Referee: [Methods (probabilistic causal framework)] The headline causal contrast (3.3% to 5.6%) and the claim that the effect mainly accelerates affluent households rest on the probabilistic framework having blocked all back-door paths from unmeasured confounders (income, education, urban density, preferences). The manuscript must specify the exact conditioning set, causal graph, and identification strategy (e.g., which variables are observed and conditioned on) so that readers can verify completeness; without this, the reported effect sizes and the superiority claim over observational models cannot be assessed.

Authors: We agree that a more explicit description of the causal identification strategy is necessary to allow readers to evaluate the assumptions. In the revised version, we will add a new subsection in the Methods detailing the causal graph (including a figure of the DAG), the full list of observed variables used for conditioning (such as household income, education level, urban/rural density, household size, and other socio-demographic factors available in the Scottish Household Survey dataset), and the specific identification assumptions (e.g., no unmeasured confounding after conditioning on these variables, and how the probabilistic framework implements the do-operator or equivalent). This will directly address the completeness of blocking back-door paths and substantiate the comparison to observational models. Note that income is an observed variable that is explicitly conditioned on and used for stratification. revision: yes
Referee: [Results (income strata and effect sizes)] Table or figure reporting the income-stratified contrasts and the 23.1 pp reduction in market non-participation should include standard errors, confidence intervals, and robustness checks to alternative specifications or sensitivity to unmeasured confounding; the current presentation leaves the precision and stability of these policy-relevant quantities unclear.

Authors: We acknowledge the need for greater transparency regarding the statistical precision of our estimates. In the revision, we will update the relevant table and/or figure to include standard errors and 95% confidence intervals for the income-stratified contrasts and the 23.1 percentage point reduction. Furthermore, we will add a robustness section that includes checks against alternative specifications (e.g., different model parameterizations within the probabilistic framework) and sensitivity analyses for potential unmeasured confounding, such as bounding the effect under varying degrees of unobserved bias. These additions will provide readers with a clearer understanding of the stability and reliability of the policy-relevant quantities. revision: yes

Circularity Check

0 steps flagged

No circularity: causal estimates derived from data fitting, not by construction

full rationale

The paper applies a probabilistic causal framework to a nationally representative Scottish household dataset to estimate intervention effects. The headline results (EV ownership rising from 3.3% to 5.6% with off-street parking access, and income as the primary gatekeeper) are outputs of model-based estimation that conditions on observed confounders. No equations or steps reduce these quantities to fitted parameters by definition, nor do they rely on self-citations whose content is itself unverified or tautological. The contrast with 'standard observational models' is presented as an empirical finding rather than a definitional necessity. The derivation chain is therefore self-contained against external data and standard causal identification assumptions.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claims rest on standard causal inference assumptions and parameters estimated from the Scottish household dataset. No invented entities are introduced.

free parameters (2)

causal effect of parking
The 2.3 percentage point increase is estimated from the probabilistic model applied to the data.
income strata effects
The 23.1 percentage point reduction in non-participation is fitted from contrasts in the dataset.

axioms (2)

domain assumption No unmeasured confounding between parking, income, and EV adoption
Invoked by the probabilistic causal framework to isolate the parking effect.
domain assumption Positivity and consistency assumptions hold for the interventions considered
Required for valid causal contrasts in the model.

pith-pipeline@v0.9.0 · 5585 in / 1490 out tokens · 59557 ms · 2026-05-10T17:45:33.007640+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

we apply a probabilistic causal framework... enabling estimation of policy interventions while explicitly neutralising the confounding effect of other causal factors... back-door criterion... P(Y|do(V7)) = sum P(Y|V7,V8,V9)P(V8,V9)
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

The results reveal a structural hierarchy... parking... conversion catalyst... income... affordability ceiling

What do these tags mean?

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
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contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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