Exploring the Impact of Climate Change on Arctic Shipping through the Lenses of Quadruple Bottom Line and Sustainable Development Goals
Bibliographic record
Abstract
Climate change is everywhere, and the Arctic is no exception. The melting sea ice has caused renewed interest in expanding maritime shipping for potentially more accessible ocean routes. Canada emerges as a natural land bridge for trade between Asia, Europe, and the Americas. Plausibly, it is not a choice but an imperative to properly integrate the stakeholders (the environment, countries, remote communities, industrial partners) in opening the Arctic Circle to the global economy while considering the challenges. Keeping sustainability front and center and drawing on the extant literature and government policies, this interdisciplinary study offers a Canadian perspective on Arctic transportation routes over tribal lands and their quadruple bottom line (QBL) impacts on the environment, economy, society, and Indigenous cultures. Unlike the arguable premise that new transport corridors will increase trade traffic and enhance the economy in Northern Canada, the QBL approach enables a more holistic and realistic strategy for the Arctic region’s sustainable development regarding regional economies, rural logistics, supply chain efficiency, and social licensing. Drawing on an integrative literature review as methodology, we highlight the QBL framework and the United Nations Sustainable Development Goals as crucial policy tools. Such a holistic perspective helps stakeholders and decision makers frame better policies in identifying, assessing, adapting, and mitigating risks for transportation infrastructure exposed to climate change. We recap the impacts of Arctic Shipping (ArSh) on QBL pillars in an interaction matrix and emphasize that while ArSh may be complementary to economic development, it poses threats to the viability of the Indigenous cultures.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".