Waste Score: Trade Analytics Risk Measure

• Waste Score is a model-based risk measure using logistic regression to distinguish scrap-like trade signatures from finished goods.
• It leverages features such as low unit price, high volume, and declining price trends to rank products for customs inspection.
• The score is context-bound and complements other domain-specific waste indices in manufacturing, food, and hazardous waste trade.

Searching arXiv for papers explicitly defining or closely using a “Waste Score,” plus adjacent waste-assessment literature mentioned in the provided data. “Waste Score” most explicitly denotes a model-based risk measure for illicit e-waste misclassification in trade data, introduced as a way to rank products by how strongly their observed trade behavior resembles known scrap rather than legitimate finished goods (Ramli, 24 Sep 2025). In that formulation, the score is tied to a “waste signature” defined by low unit price, high traded volume, and often declining price over time, with a particularly important inverse price-volume pattern. More broadly, related literature uses cognate constructs for distinct waste domains: calibrated attenuation-based estimates of food-heavy bin contents (Koohmarey et al., 2019), reel-selection indices that quantify marginal long-run material waste in additive manufacturing (Celenk et al., 24 Mar 2026), congestion-based country risk indicators in transboundary hazardous-waste trade (Martínez et al., 2021), and contamination-aware routing probabilities in household waste classification pipelines (Kumar, 14 Jun 2026). The concept therefore spans several methodological families, but the clearest formal “Waste Score” in the supplied literature is the logistic-regression probability of scrap-like trade behavior developed for customs-risk prioritization (Ramli, 24 Sep 2025).

1. Definition and conceptual scope

In the trade-analytics setting, the Waste Score is the output of a Logistic Regression model trained to distinguish known scrap materials from high-value finished commodities, with the score interpreted as the probability that a product’s trade signature is statistically similar to scrap (Ramli, 24 Sep 2025). The paper’s motivating premise is that suspicious products behave economically more like scrap than like normal finished goods. The central signal is a “waste signature” defined by low unit price, high traded volume, and often declining price over time, with the inverse price-volume relation treated as the signature’s most characteristic manifestation (Ramli, 24 Sep 2025).

This notion differs from other waste-related scoring constructs in the supplied literature. In industrial additive manufacturing, the closest analogue is a reel-level index derived from a single-reel bias function and interpreted as the marginal increase in long-run discarded filament caused by assigning the current job to a reel (Celenk et al., 24 Mar 2026). In food-waste sensing, the relevant quantity is not a named score in the paper itself but an estimate of food-heavy waste derived from RSSI attenuation and an installation-specific calibration model (Koohmarey et al., 2019). In country-level waste-trade analysis, the core risk variable is waste congestion risk, combined with environmental underperformance to identify countries at high risk of improper handling and disposal (Martínez et al., 2021). These variants suggest a family resemblance: each score compresses a complex waste-related state into an operationally useful scalar or ranking variable. A plausible implication is that “Waste Score” functions less as a universal metric than as a domain-specific decision quantity.

2. Trade-data Waste Score methodology

The trade-data formulation uses public UN Comtrade records for six-digit HS codes over 2020–2024, compares Malaysian and global datasets, standardizes trade volumes to kilograms and monetary values to USD, computes unit price as trade value divided by trade volume, interpolates short gaps, excludes HS codes with more than 20% of annual records missing, adjusts monetary figures for annual inflation, caps extreme values at the 1st and 99th percentiles, and standardizes engineered features using z-score normalization (Ramli, 24 Sep 2025). The engineered variables listed are average annual traded volume in kilograms, average annual unit price in USD/kg, standard deviation of annual unit price, slope of a linear regression on trade volume over time, and slope of a linear regression on unit price over time (Ramli, 24 Sep 2025).

The paper first performs Outlier-Aware Segmentation. An initial K-Means pass isolates extreme outliers, the remaining products are re-clustered, and DBSCAN is used for “dual confirmation” of outliers so that extreme products do not collapse the remaining market into a single broad cluster (Ramli, 24 Sep 2025). The resulting structure is a consistent four-tier hierarchy in both Malaysian and global datasets, described as an outlier tier, a high-value niche tier, and a dominant super-core (Ramli, 24 Sep 2025). Random Forest validation reproduces this segmentation with 100% accuracy, which the paper interprets as evidence that the discovered clusters are coherent rather than arbitrary (Ramli, 24 Sep 2025).

The Waste Score itself is then built with Logistic Regression on labeled examples of known scrap materials, such as HS 7204, versus high-value finished commodities, such as HS 8542 (Ramli, 24 Sep 2025). The paper does not print the regression coefficients, but the implied model is the standard logistic probability

$$\text{Waste Score}(x)=\frac{1}{1+\exp\left(-\left(\beta_0+\sum_{j=1}^{p}\beta_j x_j\right)\right)}.$$

In this setup, a higher score means that the product’s trade signature is more scrap-like. SHAP analysis is used to interpret the model, and the paper states that high trade volume and low unit price are the strongest drivers of a high Waste Score, with price trend, kg trend, price volatility, average price, and average volume also appearing in the explanatory summary (Ramli, 24 Sep 2025).

3. Interpreting high and low scores

A high Waste Score means that a product’s observed trade behavior is more similar to the signature of known scrap: high volume, low unit price, and often a declining price trend (Ramli, 24 Sep 2025). A low Waste Score means the product looks less like scrap and more like ordinary finished-goods trade (Ramli, 24 Sep 2025). The paper is careful that the score is not proof of illegality. It is presented as a screening signal for risk-based customs inspection, route targeting, and review of suspicious low-tariff HS codes rather than as direct evidence of e-waste fraud (Ramli, 24 Sep 2025).

The paper’s most prominent example is HS 8502, electric generating sets. It is described as a finished good that nevertheless falls in the high-volume, low-value quadrant and receives a Waste Score of 0.52, which is higher than HS 7204 ferrous waste and scrap at 0.50 and HS 8542 integrated circuits at 0.49 (Ramli, 24 Sep 2025). The interpretation is that HS 8502 is suspicious because its trade behavior resembles scrap despite its finished-good identity. The authors connect this to tariff asymmetry in Malaysia, noting a 5% tariff for HS 7204 and a 0% import rate for HS 8502 (Ramli, 24 Sep 2025). This suggests that the Waste Score is most informative when paired with semantic mismatch: a finished good whose score places it near scrap-like behavior is more concerning than a code already expected to behave like waste.

A methodological caveat is that the absolute score spread shown in the examples is narrow. The paper’s practical emphasis is therefore on ranking, quadrant position, and economic interpretation rather than on a universal threshold (Ramli, 24 Sep 2025). It does not provide a recommended decision rule such as “flag all products above 0.7.” This suggests that the score is intended for prioritization workflows rather than automatic classification.

4. Related score-like constructs in other waste domains

Other papers in the supplied literature define quantities that are not called “Waste Score” but play analogous roles. In “Minimizing Material Waste in Additive Manufacturing through Online Reel Assignment” (Celenk et al., 24 Mar 2026), the closest construct is the reel index

$$\text{Score}_n(w_n,x)=c(w_n,x)+h_1(e(w_n,x))-h_1(w_n),$$

which combines immediate waste, expected future waste from the post-assignment state, and the opportunity cost of consuming a reel in its current state (Celenk et al., 24 Mar 2026). The paper explicitly interprets this as the marginal cost of assigning the current job to reel $n$, and proves that the resulting index policy is a one-step policy improvement over random allocation (Celenk et al., 24 Mar 2026). This is structurally similar to the trade-data Waste Score in that it ranks options by future waste resemblance, but it is grounded in an average-cost MDP rather than statistical classification.

In “Food Waste Estimation Using Received Signal Strength Indicator” (Koohmarey et al., 2019), the score concept is attenuation-based. The system uses two Arduino Uno boards with RFM22B-S2 transceivers and spring antennas designed for 915 MHz, places one transceiver at the top of the trash bin and one at the bottom, and uses baseline empty-bin RSSI plus repeated packet measurements to estimate food-heavy waste (Koohmarey et al., 2019). The paper’s held-out test predicts a 10.6 lb grocery-waste bag as 7.2 lb, corresponding to 32% error, using the cubic model

$$\text{RSSI}(x) = -0.000161541x^3 + 0.0202049x^2 - 0.82621x - 22.$$

The paper itself does not define a formal Waste Score, but the supplied details explicitly note that a calibrated attenuation-based index or coarse food-mass estimate would be the most defensible translation (Koohmarey et al., 2019). Because multipath, environment, and installation geometry materially affect readings, any such score is installation-specific rather than universal.

In “The world-wide waste web” (Martínez et al., 2021), the closest score is country-level congestion risk in global waste trade. Waste trade is modeled as a directed weighted network over 108 Basel Convention waste categories grouped into seven waste types across 2001–2019, and the paper defines

$R_i = 1 - \hat t_i / \max_j \hat t_j,$

where $\hat t_i$ is the minimum of arrival-side and departure-side half-congestion times (Martínez et al., 2021). This is paired with environmental underperformance

$U_i = 1 - \frac{EPI(i)}{100}$

to identify countries at high risk of improper handling and disposal (Martínez et al., 2021). The recommended single-number score proposed in the supplied synthesis is $R_i \cdot U_i$, though the paper itself presents PEIWC tolerance-zone logic rather than a branded scalar (Martínez et al., 2021). This suggests a broader pattern: waste scores often become most meaningful when burden is adjusted by handling capacity.

5. Computer-vision and contamination-aware extensions

Household and industrial waste-vision papers in the supplied set support further score interpretations, though often only indirectly. “EcoBin: A Two-Stage Deep Convolutional Neural Network for Contamination-Aware Waste Classification” (Kumar, 14 Jun 2026) classifies household waste into 30 categories mapped to four disposal pathways, then invokes a second-stage contamination classifier for recycling-bound items and overrides the decision to garbage if contamination probability reaches 0.90 (Kumar, 14 Jun 2026). The contamination probability is defined operationally as

$$P(\text{contaminated}\mid x)=1-P(\text{clean}\mid x).$$

Stage A achieves 87.42% test accuracy and 96.13% pathway-adjusted accuracy, while Stage B reaches 0.99 ROC-AUC; on a real contaminated-recyclables test set, the full pipeline routes 24 of 25 items correctly versus 1 of 25 for the base classifier alone (Kumar, 14 Jun 2026). Although the paper does not define a Waste Score, its outputs naturally support a contamination-risk score or recyclability-suitability score.

Industrial sorting papers offer analogous quality or contamination measures. “WS$^2$: Weakly Supervised Segmentation using Before-After Supervision in Waste Sorting” (Marelli et al., 8 Sep 2025) segments unwanted items on a conveyor belt using before/after human-removal supervision and reports best performance with POF-CAM at 42.58 mIoU on before images and 19.78 mIoU on after images (Marelli et al., 8 Sep 2025). The supplied details explicitly suggest contamination ratio, residual unwanted burden, or before-to-after removal efficiency as natural score constructions based on the predicted unwanted-object masks (Marelli et al., 8 Sep 2025). “StreetView-Waste: A Multi-Task Dataset for Urban Waste Management” (Paulo et al., 20 Nov 2025) similarly supports route-level or container-level urban waste severity scoring through container detection, tracking, and overflow segmentation, even though it does not name a single score (Paulo et al., 20 Nov 2025).

These papers suggest that, outside trade analytics, a Waste Score is often best treated as a composite over burden, contamination, persistence, and uncertainty rather than a single direct observable.

6. Limitations and interpretive cautions

The trade-data Waste Score has several explicit limitations. It depends on official trade statistics, which may contain reporting lags, asymmetries, incomplete quantity information, and the very miscoding it seeks to detect (Ramli, 24 Sep 2025). The paper does not report the logistic model’s train/test split, cross-validation, regularization, classification threshold, ROC-AUC, confusion matrix, or coefficient table (Ramli, 24 Sep 2025). It also does not disclose whether one global model or separate Malaysia/global models were used. These omissions limit strict statistical reproducibility and mean that the score is presently more transparent conceptually than inferentially.

Another limitation is semantic ambiguity. Scrap-like trade behavior is informative, but a high score does not prove that the product is e-waste. Legitimate second-hand, low-grade, damaged, or bulk low-value goods may also appear scrap-like (Ramli, 24 Sep 2025). Conversely, illicit flows can remain hidden inside apparently legitimate price-volume patterns. This suggests that the Waste Score is strongest as a triage variable to be combined with tariff structure, route information, and product semantics.

Comparable cautions apply in other domains. RSSI-based food-waste estimation is highly sensitive to multipath reflections, bin geometry, and environmental context (Koohmarey et al., 2019). Conveyor-belt contamination scores depend on pseudo-label quality, background bias mitigation, and the difficulty of after-image residual detection (Marelli et al., 8 Sep 2025). Household contamination scores depend on synthetic contamination training data and locality-specific disposal rules (Kumar, 14 Jun 2026). A general implication is that no waste score should be interpreted outside the sensing and decision context in which it was calibrated.

6. Significance and broader implications

The primary significance of the trade-data Waste Score is operational. It converts a diffuse illicit-trade detection problem into a ranked list of products whose trade behavior resembles scrap, thereby supporting risk-based customs inspection and environmental enforcement (Ramli, 24 Sep 2025). Its conceptual contribution is to define waste not merely by declared category but by observable market behavior. That perspective aligns with other score-like constructs in the literature, where waste is inferred from attenuation patterns, long-run marginal discard, congestion dynamics, or contamination overrides rather than from taxonomy alone (Koohmarey et al., 2019, Celenk et al., 24 Mar 2026, Martínez et al., 2021, Kumar, 14 Jun 2026).

A plausible synthesis is that Waste Score methodologies tend to share three properties. First, they are comparative rather than absolute: they ask whether an object, product, country, or site behaves more like a waste-related reference class than a non-waste class. Second, they are operational: they are designed to prioritize action, not just describe systems. Third, they are context-bound: each score depends on the sensing modality, annotation regime, and downstream decision problem. In that sense, the logistic-regression Waste Score for illicit e-waste trade is not a universal waste metric, but it is a clear exemplar of a broader methodological pattern in contemporary waste analytics (Ramli, 24 Sep 2025).