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

Bernardino D'Amico; Achille Fonzone; Emma Hart

cs.LGApr 10, 2026

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

Authors: Bernardino D'Amico, Achille Fonzone, Emma Hart

Organizations: Digital Built Environment Group (DiBEG), UK · Centre For Artificial Intelligence and Robotic, UK · School of Engineering and the Built Environment, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK · Transport Research Institute, UK

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.

Figures & tables

Figure 1: Causal graphical structure of the parking provision and EV ownership toy example. The presence of household wealth ( $Z$ ) creates an undirected back-door path ( $X\leftarrow Z\rightarrow Y$ ), inducing a spurious correlation between dwelling type ( $X$ ) and EV ownership ( $Y$ ) that biases the true causal effect of interest, $X\rightarrow Y$ .

Household income band, $v_{1}$	Marginal probability $P(v_{1})$	Cumulative sample total
1: < £6000	0.0202	0.020
2: £6001-10,000	0.0364	0.057
3: £10,001-15,000	0.0786	0.135
4: £15,001-20,000	0.1358	0.271
5: £20,001-25,000	0.1079	0.379
6: £25,001-30,000	0.1064	0.485

Table 1: Marginal probabilities and cumulative sample distribution for the eight classification bands of household income (

V_{1}

Figure 2: Causal discovery workflow. (a) Initial, fully connected skeleton. (b) Maximal Ancestral Graph discovered via the MIIC algorithm, featuring directed ( $\rightarrow$ ), bi-directed ( $\leftrightarrow$ ) and undirected ( $-$ ) edges. The isolated variable subset $\{V_{6},V_{10},V_{11},V_{14}\}$ is statistically independent of the main network. (c) Final refined DAG: fully specified model after omitting the disconnected subset and incorporating post-hoc a latent confounder between $V_{8}$ and $Y$ .

Figure 3: Final DAG ( $\mathcal{G}$ ) 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 ( $U_{i}$ ). Directed solid edges indicate causal effects, dashed edges denote unobserved common causes. The DAG structure provides the formal basis for identifying sufficient adjustment sets $\mathbf{Z}$ required to estimate post-treatment distributions from observational data via the back-door criterion (section 2.4 ).

Figure 4: Mutilated graphs used for the identification of causal effects via the back-door criterion. (a) Sub-graph $\mathcal{G}_{\underline{V_{7}}}$ used to identify the effect of parking provision ( $V_{7}$ ) on EV intentions, $Y$ . (b) Sub-graph $\mathcal{G}_{\underline{V_{1}}}$ used to identify the effect of household income ( $V_{1}$ ) on $Y$ .

Figure 5: Probability distributions of EV adoption status and intentions ( $Y$ ) as a result of intervening or observing parking provision ( $V_{7}$ ). (a) Post-intervention probability distributions comparing households with and without off-street parking. (b) Raw observational probability distributions. (c) Causal treatment effects ( $\Delta_{TE}$ ), illustrating the redistribution of probability mass from future intent toward immediate intent and ownership. (d) Observational shifts ( $\Delta_{obs.}$ ), highlighting the selection bias in the “Not considering to buy one” category (-2.2 pp) which is absent in the causal estimation.

Figure 6: Total causal effect of household income ( $V_{1}$ ) on EV adoption ( $Y$ ). (a) Post-intervention distributions comparing high vs. low income. (b) Total treatment effect ( $\Delta_{TE,Inc.}$ ) accounting for both direct affordability ( $V_{1}\rightarrow Y$ ) and indirect mediation ( $V_{1}\dots\rightarrow V_{7}\rightarrow Y$ ). The -23.1 pp drop in non-participation and +13.3 pp rise in future intent indicate income as the primary driver for joining the EV adoption process.

	$\delta_{\text{placebo}}$	$\delta_{\text{placebo}}^{\prime}$	$\delta_{\text{sub.}}$	$\delta_{\text{bootsr.}}$	$\delta_{TE}$ (baseline)	$\delta_{TE}^{\prime}$ (baseline)
Mean	$-0.021$ pp	$+0.008$ pp	$+2.292$ pp	$+2.586$ pp	$+2.3$ pp	$+0.5$ pp
Median	$-0.020$ pp	$-0.011$ pp	$+2.303$ pp	$+2.597$ pp
Perc. (1st; 99th)	( $-2.48$ ; $+2.34$ )	( $-4.98$ ; $+5.48$ )	( $+1.20$ ; $+3.41$ )	( $+0.26$ ; $+4.77$ )
$p$ -value	0.024	0.817	0.983	—
Sample size $n$	1000	1000	1000	1000

Table 2: Robustness checks for the estimated treatment effect of parking provision on the probability lift of owning an electric car or van (

\delta_{TE}

) and the lift in probability of not considering to buy one (

\delta_{TE}^{\prime}

). The placebo test (columns 2–3) evaluates whether a treatment effect persists when the treatment values are randomly reassigned. The subsample test (column 4) assesses the stability of the estimate across random subsamples (

\approx

80% of the full training dataset). Column 5 (

\delta_{\text{bootsr.}}

) provides the empirical bootstrap distribution used to quantify sampling uncertainty under data sparsity. Columns 6–7 report the baseline estimates of the true causal effect.

Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

9-fold classification	2-fold classification
Integral/attached garage	Off-street
Garage on plot	Off-street
Space on plot	Off-street
Space/garage elsewhere	Off-street
Adequate, on-street	On-street
Inadequate on-street	On-street

Appendix

Table A1: Mapping between the 9-fold classification for the Parking Provision variable,

V_{7}

, (as reported in the SHCS dataset) and the corresponding binary classification

\{\text{Off-street};\text{On-street}\}

Variable label	Variable symbol	Value states	Probability distribution $P(\cdot)$
EV ownership status & intention	$Y$	Already own electric car/van; Thinking to buy one soon; Thinking to buy one in the future; Not considering to buy one;	0.0485 0.0462 0.4149 0.4904
Household income	$V_{1}$	< £6000; £6001-10,000; £10,001-15,000; £15,001-20,000; £20,001-25,000; £25,001-30,000; £30,001-40,000; > £40,000;	0.0202 0.0364 0.0786 0.1358 0.1079 0.1064 0.1602 0.3546
No. of vehicles	$V_{2}$	No car; One car; Two cars or more;	0.0719 0.6157 0.3124
No. of adults in household	$V_{3}$	1; 2; 3; 4; $\geq$ 5;	0.3668 0.5400 0.0705 0.0211 0.0017
No. of children in household	$V_{4}$	0; 1; 2; 3; $\geq$ 4;	0.7764 0.1149 0.0871 0.0161 0.0055
Household composition	$V_{5}$	Single adult; Small - multiple adults; Single parent; Small - family; Large - family; Large - multiple adults; Small - old adults; Single pensioner;	0.1759 0.2149 0.0901 0.1029 0.0289 0.0615 0.2030 0.1227

Appendix

Table A2: Set

\textbf{V}=\{Y,V_{1},...,V_{14}\}

of observed variables used to discover and then train the model parameters of the causal Bayesian Network.

Figure 1: Information-theoretic signatures of causality in a three-node skeleton. (a) Initial skeleton: the MIIC algorithm starts with an undirected graph where $I(X;Y)$ represents the total association between $X$ and $Y$ . (b) Interaction information $I(X;Y;Z)>0$ is a signature of a causal chain or common cause. In these patterns, $Z$ explains part of the mutual information, reducing the residual conditional mutual information between $X$ and $Y$ . (c) Interaction information $I(X;Y;Z)<0$ is a signature of a collider (v-structure). Conditioning on $Z$ increases the conditional mutual information between $X$ and $Y$ , leading to its rejection from the conditioning set $\{Z_{i}\}$ during skeleton pruning.

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