Sustainable electrification of road transport in cold regions: A decision support framework for optimal energy mix – Insights from Alaska
Bibliographic record
Abstract
• Developed a decision support framework optimizing the electricity mix for EVs in cold regions. • Applied a multi-objective model to identify Pareto-optimal trade-offs between cost and emissions. • Demonstrated in Alaska that coal and natural gas are cost-effective, while renewables reduce emissions. • Showed that a balanced strategy reduces emissions by 15 % and costs by 22 % compared with extremes. • EV charging demand and natural gas prices strongly impact the optimal energy mix. The electrification of road transport presents a significant opportunity to reduce carbon emissions, but its sustainability depends on the composition of electricity generation sources. Cold regions such as Alaska face unique challenges, including heavy reliance on fossil fuels, limited renewable energy capacity, and climatic constraints on renewable energy generation. To address these challenges, this study develops a novel decision support framework that optimizes electricity generation for electric vehicle (EV) integration by balancing economic feasibility and environmental sustainability. The framework combines a multi-objective optimization model with a mixed binary integer programming–based decision model. The optimization model identifies Pareto-optimal generation mixes that minimize costs and emissions. The decision model reflects stakeholder priorities such as economic, environmental, and balanced perspectives. Applied to Alaska, the framework evaluates pathways involving natural gas, coal, hydro, wind, and solar under multiple EV electrification scenarios. Results show that coal and natural gas dominate least-cost solutions, while hydropower, wind, and solar provide the most environmentally sustainable options. A balanced strategy achieves a 15 % emissions reduction compared to the cost-minimizing case, while saving 22 % cost savings relative to the emission-minimizing scenario. Sensitivity analysis highlights that EV charging demand growth and natural gas-based electricity generation costs strongly shape the optimal generation mix. These findings provide actionable insights for policymakers, helping design adaptive, region-specific strategies for sustainable transport electrification in cold, resource-constrained regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".