Sustainable Strategies to Current Conditions and Climate Change at U.S. Military Bases and Other Nations in the Arctic Region: A 20-Year Comparative Review
Bibliographic record
Abstract
Amidst the backdrop of growing great power competition, heightened United States presence via military bases has manifested in the Arctic. However, the then design and implementation have hampered the resilience of these bases in a region warming at nearly four times the rate of the rest of the globe. Two-thirds of the United States’ 79 military bases in the Arctic remain underprepared against permafrost thaw and rising sea levels despite rampant calls for sustainable strategies. Damages emanating from climate-related failures will continue to cost the U.S. billions of dollars and render crucial infrastructure unusable. The objective of this study is to present a comprehensive literature review of the extent of Arctic warming and its significance for U.S. bases, the negative implications of military infrastructure deterioration, and methods to adapt both existing and forthcoming bases to a rapidly warming atmosphere. Eighty published papers that directly or indirectly referenced U.S. military bases or climate-oriented engineering in the aforementioned contexts were identified and analyzed over a 20-year period from 2004 to 2024. The literature review concludes that warming concerns were often not taken into much account by civil engineers during initial base construction, an oversight that now jeopardizes runways, docks, and highways. Other nations that have a sizeable footprint in the Arctic Circle, such as Canada and Russia, have demonstrated progress by utilizing pile-driven substructures, thawing permafrost before construction, and ventilated crawlspaces. Alternative solutions, such as cooling permafrost via thermosiphons or refrigeration systems, employing spatially oriented foundations composed of specific materials, and preventative measures such as floodwalls and revetments, have also shown considerable promise in simulations and practice. A table illustrating a holistic literature summary of sustainable strategies to current conditions and climate change at U.S. Military Bases in the Arctic region is also developed. Modeling successful engineering concepts and incorporating existing innovations into military infrastructure should be at the forefront of the United States’ sustainable policy.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".