An analytical framework to quantify municipal solid waste disposal service inequality using three simple socioeconomic indicators
Bibliographic record
Abstract
The ability of residents in an industrialized nation to afford waste management services is linked to equitable distribution of resources across diverse populations. Landfilling is the ultimate endpoint of all waste management systems, and landfill disposal fees have a significant impact on the financial sustainability and accessibility of waste management systems. This study introduces three indicators: Disposable Income Affordability (DIA), Household Burden Index (HBI), and Labor Cost Index (LCI) to assess the economic burden of disposal fees on individuals, households, and labor in four selected U.S. states. A Borda Count-based method was then used to compute a composite affordability score. Considerable disparities were observed from 2016 to 2023. Florida had the highest mean DIA (961 US tons/person), indicating strong individual affordability. Conversely, Maine recorded the highest mean HBI and LCI values of 1.25 × 10 −3 and 6.70 × 10 −4 ton −1 , respectively, suggesting a higher household financial strain and possible underinvestment in labor relative to tipping revenue. Lower-income states are experiencing up to 40 % higher household affordability burdens. California ranked first with 12 points, reflecting favorable economic and policy conditions. Maine ranked lowest with 3 points, highlighting ongoing affordability challenges. The results underscore the importance of multidimensional affordability metrics that encompass economic, social, and labor factors. The proposed framework offers a replicable, policy-relevant tool for evaluating the distributional effects of tipping fees. The results support a waste management system that is more equitable and transparent across diverse regional and economic contexts. • Three indicators were used to assess landfill disposal affordability in 4 US states. • Tipping fees varied from $43 to $108 per ton across the study areas. • Lower-income states experienced up to 40 % higher household affordability burdens. • Tipping fees grew at widely varying rates, with CAGR between −2.3 % and 8.5 %. • Equitable landfill pricing is recommended for a sustainable waste management system.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".