Why Sustainability Isn't Black and White
Decoding the Science Behind Environmental Claims in a Complex World
We live in an era of eco-labels: products boast "clean" formulas, companies pledge "green" operations, and technologies are hailed as "sustainable." Yet a critical question lingers: What do these terms truly mean? Decades ago, an academic posed a simple but revolutionary challenge: Why do we say "clean" when we really mean "cleaner"? This linguistic nuance reveals a fundamental flaw in environmental discourse—our tendency to treat sustainability as a binary state rather than a spectrum of improvement 1 3 .
Consider this startling finding: When TerraChoice examined 4,744 products making environmental claims, only 4.5% were "sin-free"—meaning their claims were fully justified. The rest relied on misleading or unsubstantiated assertions 1 .
This "fuzziness" isn't just semantic; it reflects a scientific measurement crisis with real consequences for our planet.
"Clean" vs. "Cleaner": No process is entirely free of environmental impact. The term "clean" implies absoluteness, while "cleaner" acknowledges comparative improvement. Regulatory frameworks historically defined cleanliness through waste reduction thresholds (e.g., "How clean is clean?" in 1980s waste management). However, voluntary sustainability claims lack such standardization 1 3 .
Single-Metric Fallacy: Companies often tout narrow metrics like carbon footprints while ignoring other impacts like water use or toxicity. This is akin to the parable of blind men describing an elephant by touching only one body part—partially right but wholly misleading 1 .
Traditional yes/no logic fails when evaluating sustainability, where criteria are often subjective, incomplete, or contradictory. Fuzzy mathematics—pioneered by Lotfi Zadeh—introduces degrees of truth (e.g., 0 to 1) to handle ambiguity 2 6 . For example:
Intuitionistic Fuzzy Sets (IFS): Developed by Atanassov, IFS assigns membership (how "green" a product is), non-membership (how "non-green" it is), and hesitancy (uncertainty) scores. This captures the complexity of real-world trade-offs 2 5 .
| Claim | Reality | Example |
|---|---|---|
| "Zero Carbon" | Ignores water/land impacts | Biofuel production causing deforestation |
| "100% Recyclable" | Overlooks energy-intensive recycling processes | Plastic downcycling |
| "Non-Toxic" | Neglects supply chain contamination | "Clean" batteries using mined lithium |
Table 1: Why Absolute "Green" Claims Fail
Interactive: Hover to see how different products score across sustainability dimensions
Objective: Selecting truly sustainable suppliers for a food company requires balancing cost, quality, and environmental criteria amid data uncertainty.
| Supplier | Carbon | Water | Waste | Organic | Packaging | Transport |
|---|---|---|---|---|---|---|
| A | 8.2 | 7.5 | 6.8 | 9.1 | 7.2 | 5.9 |
| B | 6.7 | 8.9 | 7.1 | 8.3 | 6.5 | 8.2 |
| C | 9.1 | 6.2 | 8.5 | 7.7 | 8.8 | 6.4 |
Table 2: Supplier Ratings (Scale: 0–10, 10 = Best)
Compare sustainability profiles of different suppliers
Essential Resources for Quantifying "Green"
| Tool/Reagent | Function | Field Application |
|---|---|---|
| Life Cycle Assessment (LCA) | Measures cradle-to-grave environmental impacts | Validating carbon claims of biofuels |
| Intuitionistic Fuzzy TOPSIS | Ranks options by distance from "ideal green" | Selecting solar panel suppliers 5 |
| Hesitant Fuzzy Linguistic Terms | Converts expert opinions into quantitative scores | Assessing social sustainability in textiles |
| Z-Numbers | Models reliability of subjective data | Predicting supply chain risks 5 |
| MOORA/CRADIS Models | Solves multi-objective optimization | Steel industry waste reduction 5 |
Table 3: Key Tools for Quantifying "Green"
The gold standard for comprehensive environmental impact assessment across a product's entire lifecycle.
Helps decision-makers choose the best alternative when dealing with vague or incomplete information.
Combines both the restriction and reliability components of uncertain information for better modeling.
The pursuit of sustainability isn't about achieving mythical "green" status. As Subhas Sikdar aptly noted, it's about continuous improvement—using transparent metrics to make processes "cleaner" and supply chains "greener" 1 3 . Fuzzy mathematics isn't just a theoretical exercise; it's a pragmatic toolkit for navigating imperfect data and conflicting priorities.
The next time you see a "100% Eco-Friendly" label, ask: Cleaner than what? Green by which metrics? Only when we replace absolutes with honest comparatives—supported by rigorous, multifaceted science—can we transform "fuzzy green" into genuine planetary progress.
A 2025 study in Axioms combined Z-numbers with fuzzy LMAW to optimize wind turbine recycling—proving that embracing uncertainty can yield concrete sustainability wins 5 .