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  <doi_batch_id>aspg-38-4315-1792228740</doi_batch_id>
  <timestamp>20261017091900</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Sustainable Development and Green Technology</full_title>
    <abbrev_title>JSDGT</abbrev_title>
    <issn media_type="electronic">2836-5399</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <journal_volume>
     <volume>6</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Price-Aware and Explainable Analytics for Urban Electric Vehicle Charging Networks: Forecasting Utilization Regimes for Sustainable Charging Operations</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Heba</given_name>
      <surname>Moselhy</surname>
      <affiliations>
       <institution>
        <institution_name>Business Administration Department, Delta Higher Institute for Management and Accounting Information Systems, Egypt</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Noura</given_name>
      <surname>Metawa</surname>
      <affiliations>
       <institution>
        <institution_name>College of Business Administration, University of Sharjah, United Arab Emirates</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>The efficient functioning of the electric-vehicle charging systems that are publicly operated has become focused on reliable short-horizon forecasting. The paper establishes an explainable and price-conscious analytical model to predict short-term charging usage and demonstrate the utility of tariff signals in an urban charging system. The analysis is based on UrbanEV benchmark, a new six months hourly panel of Shenzhen public charging infrastructure, which integrates occupancy, charging time, charging volume, electricity tariffs, service charges, weather and spatial descriptors. The concept of charging occupancy is considered an operation state variable with connection to queue exposure, reliability of service, and tactical intervention. A succinct mathematical formulation is created to use it in one-step-ahead utilization forecasting and in interpreting low-, medium-, and high-utilization regime. The empirical analysis is pegged to benchmark evidence reported to UrbanEV, where transformer-based forecasting had the best node-level performance and TimeXer had the best RMSE values of 0.07 in occupancy, 2.73 in charging duration, and 43.66 in charging volume. Further discussion indicates that occupancy prediction is accurate enough to justify regime based intervention and strongest additional gains are obtained through the joint effect of pricing variables and temperature-price interactions as opposed to single covariates. The results justify the justifiable, price-conscious forecasting as an operational decision tool to alleviate congestion, design tariffs and specific capacity planning in sustainable charging networks.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2026</year>
    </publication_date>
    <pages>
     <first_page>25</first_page>
     <last_page>30</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">4315</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/JSDGT.060104</doi>
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