The enablers, opportunities and challenges of electric vehicle adoption in Qatar: A systematic review of the literature and assessment of progress toward transportation transformation targets
Bibliographic record
Abstract
Governments around the world are working to reduce greenhouse gas emissions, and the transportation system is focal to the transition toward more renewable energy sources. The State of Qatar has transitioned buses in its public transportation system to be fully electric and has set a 2030 target for 10% of all new sales of vehicles to be electric vehicles (EVs). Although constrained by data limitations, this paper synthesizes and assesses the evidence and makes recommendations to support the transportation transition. OBJECTIVE: This paper assesses the available evidence on EV transitions in Qatar, identifying enablers and barriers through the use of a systematic literature review and data obtained from government sources within Qatar. METHODS: The systematic literature review was conducted in March of 2023 using two academic databases (Scopus and Web of Science). Only English language peer-reviewed articles, books, and conference proceedings pertaining to Qatar and EVs or EV charging stations were included. No resources were identified on an Arabic language database. RESULTS: The systematic review process identified 26 relevant publications, which is synthesized and critically assessed into the following thematic clusters: (a) assessments related to the electrical grid and diversifying the energy mix, (b) the planning and distribution of charging stations, and (c) knowledge, attitudes, and behaviors as it relates to the socio-cultural dimensions of EV adoption. DISCUSSION: The authors conclude that to meet the 2030 target, the State of Qatar must improve data collection for monitoring, rapidly expand charging station infrastructure, enable private sector engagement, and raise awareness regarding EVs to change consumer perception and choices. They explore the specific policy interventions that these domains require for the country to meet its transportation transition objectives. OTHER: This review received no funding, and the authors have no registration name or number to declare.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".