How Predictable Is Electric Vehicle Adoption? Exploring the Broader Role of Renewables in Transportation Using a Data-Driven Approach
This study harnesses data from a questionnaire carried out in May 2022 from the Pew Research Center to explore energy sources, climate change issues and factors influencing the adoption of Electric Vehicles (EVs). It was administered to a sample of 10,282 U.S. adults from a total of 11,674, achievin...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11121188/ |
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| Summary: | This study harnesses data from a questionnaire carried out in May 2022 from the Pew Research Center to explore energy sources, climate change issues and factors influencing the adoption of Electric Vehicles (EVs). It was administered to a sample of 10,282 U.S. adults from a total of 11,674, achieving an 88% response rate. The survey was conducted to gauge public opinion on topics related to climate change. Cross-tabulation between the six questions related to EVs and four questions on demographics is performed. The interpretation of the results is essential to support EV adoption. Equally important is the capacity to predict the likelihood of EV adoption. A classification model is proposed, embedding several cutting-edge classifiers. As the neutral segment of population is significant (over 22%), the 1st mapping included this segment associated to the “Likely” (to adopt) class, whereas the 2nd mapping included this segment associated to the “Unlikely” class. Several classifiers are tested as baseline (Logistic Regression) or as cutting-edge algorithms (Random Forest-RF, eXtreme Gradient Boost-XGB, Light Gradient Boosting Machine-LGBM, Support Vector Classifier). For the 1st mapping, the RF model shows the best performance AUC=0.83. For the 2nd mapping, RF again performs well with the highest accuracy and AUC (0.86). XGB and LGBM have higher precision, but significantly lower recall compared to RF, which reduces their F1 scores. The most influential features for EV adoption are identified with feature importance: 1) Favor or oppose phasing out new gasoline cars and trucks by 2035; 2) Favor or oppose providing incentives to increase use of hybrid and EV; 3) More important priority for addressing America’s energy supply; (4) Family income. |
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| ISSN: | 2169-3536 |