This research employs spatial econometric techniques to assess the sufficiency of electric vehicle (EV) charging infrastructure, adopting Thailand as a case study. As electric vehicle adoption accelerates, it is essential to identify infrastructure deficiencies and determine where further investment will have the greatest impact on effective transportation and energy policy. This study presents a systematic approach for identifying underserved provinces and assessing the influence of chargers on electric vehicle adoption, utilising Gap Analysis, the Spatial Durbin Model (SDM), and Multiscale Geographically Weighted Regression (MGWR).

The Gap Analysis commences by evaluating the adequacy of charging infrastructure with a Charger-to-EV Ratio benchmark of 0.1, as frequently used in global best practices. The results indicate that 59 out of 77 provinces (76.6%) do not meet the baseline, signifying extensive deficiencies. The mean ratio is 0.1102, with rural areas like Sakon Nakhon and Yasothon exhibiting the most significant undersupply. Even high-density provinces such as Bangkok and Lop Buri remain slightly below the threshold, pointing to infrastructure pressure in both rural and urban contexts.

Subsequently, the SDM assesses the comprehensive elasticity of electric vehicle adoption concerning charger availability. A 1% increase in local chargers is associated with a 1.368% rise in EV registrations, underlining infrastructure as a crucial factor in adoption. A notable adverse spillover impact (-0.834%) from neighboring provinces suggests that well-equipped urban centers may attract EV users from nearby rural areas, exacerbating regional disparities.

To capture province-specific dynamics, the MGWR is employed. The findings indicate that all provinces have a substantial positive correlation between charger availability and electric vehicle adoption, with elasticities varying from 0.59 to 1.26. Rural provinces like Chiang Rai and Uttaradit exhibit significant response, suggesting that additional chargers in these regions may yield higher marginal benefits compared to urban centers such as Bangkok and Nonthaburi.

Through the integration of these three approaches, the study not only pinpoints areas where infrastructure is currently lacking but also indicates where future expenditures are most likely to make a significant impact. Two principal policy recommendations are suggested based on the findings: (1) prioritise the expansion of charging infrastructure in rural provinces characterised by low charger-to-EV ratios and high responsiveness (e.g., Lop Buri, Ubon Ratchathani); (2) prevent excessive concentration in urban areas to mitigate adverse spatial spillovers and regional crowding-out effects; and (3) establish regionally interconnected charging networks that connect rural and urban areas, thereby minimising spatial competition and promoting balanced EV adoption nationwide.