Sustainable municipal solid waste management: A comparative analysis of enablers and barriers to advance governance in the Arctic
Bibliographic record
Abstract
Despite the proliferation of studies on waste governance over the past decades, comparative research on advancing sustainable municipal solid waste management (MSWM) in medium-sized cities across various political regimes and remote geographies has been overlooked. Comprehending factors for governance advancement is critical for Arctic cities, as they face unique challenges due to their geographical remoteness, population size, economic constraints, and severe weather conditions. This qualitative study, drawn from the cases of Anchorage (USA), Murmansk (Russia), and Tromsø (Norway), uses Evolutionary Governance Theory (EGT) to examine how dependencies between actors and institutions enable and create barriers to the advancement of MSWM. Our analysis indicates that path dependencies enable innovations or create barriers to local waste policies, depending on municipal authority and capacity. Further, interdependencies enable the advancement of MSWM when there is commitment from local leadership. Goal dependencies create barriers to advancing MSWM when there is a lack of overarching alignment with actor and institution goals. Our research contributes to EGT by showing how geography and population size influence MSWM. This study highlights the importance of understanding local capacity, leadership commitment, adapting global and national regulations to local contexts, and securing public support to integrate waste policies into everyday practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".