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  <titleInfo>
    <title>Assessment of spatio-temporal status and trends of electric vehicle adoption in Thailand</title>
  </titleInfo>
  <name type="personal">
    <namePart>Supawadee Srimongkol</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Pramanik, Malay Kumar</namePart>
    <role>
      <roleTerm type="text">Chairperson</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Tripathi, Nitin Kumar</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Tsusaka, Takuji W.</namePart>
    <role>
      <roleTerm type="text">Examination Committee</roleTerm>
    </role>
  </name>
  <name type="corporate">
    <namePart>Royal Thai Government Fellowship</namePart>
    <role>
      <roleTerm type="text">Scholarship Donor</roleTerm>
    </role>
  </name>
  <typeOfResource>text</typeOfResource>
  <genre authority="marc">series</genre>
  <genre authority="marc">technical report</genre>
  <originInfo>
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      <placeTerm type="code" authority="marccountry">th</placeTerm>
    </place>
    <place>
      <placeTerm type="text">Pathum Thani, Thailand</placeTerm>
    </place>
    <publisher>Asian Institute of Technology</publisher>
    <dateIssued>2025</dateIssued>
    <issuance>continuing</issuance>
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  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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    <extent>87 leaves : ill.+ 1 online resource</extent>
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  <abstract>While electric vehicle (EV) adoption is accelerating globally, research has  disproportionately focused on high-income countries, leaving a critical gap in  understanding how adoption unfolds in emerging economies. In Southeast Asia,  including Thailand, regional disparities, infrastructure limitations, and behavioral  challenges complicate the transition to low-carbon mobility. This study examines the spatio-temporal trends and key factors influencing EV  adoption in Thailand between 2019 and 2024. Using a mixed-methods approach, it  combines GIS-based spatial analysis, a structured driver survey, and binary logistic  regression to analyze regional patterns and determinants of EV ownership. The research  also reviews policy documents to contextualize findings within Thailand{u2019}s evolving  electric mobility landscape.  Results reveal that EV adoption is growing rapidly in urban areas like Bangkok and the  Eastern Economic Corridor (EEC), while rural provinces lag due to limited charging  infrastructure and lower public awareness. Logistic regression shows that age,  occupation, awareness of charging stations, and perceived cost-effectiveness  significantly influence adoption. Contrary to earlier assumptions,respondents with  firsthand EV experience and awareness of government incentives were more likely to  adopt EVs, emphasizing the role of exposure and policy clarity in shaping behavior.  Based on these findings, the study prioritizes five interrelated policy actions: (1) expanding visible and accessible charging infrastructure, (2) enhancing public  awareness campaigns grounded in real user experience, (3) improving the clarity and  delivery of financial incentives, (4) promoting equitable EV access in rural areas, and  (5) fostering local public{u2013}private collaboration. Among these, infrastructure visibility  and affordability emerged as the most influential factors offering clear direction for targeted interventions.This study advances the literature by integrating spatial,  behavioral, and policy dimensions, offering a regionally grounded framework for EV  promotion in developing economies.</abstract>
  <note>A thesis submitted in partial fulfillment of the requirements for the  degree of Master of Science in Development and Sustainability</note>
  <note>Thesis (M. Sc.) - Asian Institute of Technology, 2025</note>
  <subject authority="lcsh">
    <topic>Electric veshicles</topic>
    <geographic>Thailand</geographic>
  </subject>
  <subject authority="lcsh">
    <topic>Electric vehicles</topic>
    <topic>Statistical methods</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Spatial analysis (Statistics)</topic>
  </subject>
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    <titleInfo>
      <title>Thesis ; no. DS-25-01</title>
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      <namePart>Asian Institute of Technology.</namePart>
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  <identifier type="uri">http://203.159.5.9/ait-thesis/detail.php?q=B22595</identifier>
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