Greenwashing: The Ethics of Green Marketing
Bibliographic record
Abstract
In this paper, I argue that companies who make exaggerated claims about the environmental impact of their products act unethically. The public has become more ecologically conscientious than ever before, and firms have been quick to exploit this new interest to their economic advantage. Many of the claims conveyed in these green advertisements attempt to convince consumers that through purchasing a product, they can create a tangible benefit on the environment relative to similar products they would have otherwise purchased. However, consumers are often manipulated by false information conveyed through explicit and implicit advertising cues. Even from a permissive ethical framework, companies engaged in marketing have a duty to avoid misleading consumers. By engaging in these deceptive advertising campaigns, firms violate their basic duties. According to a 2010 study published by a consumer watchdog group, 95% of environmentally beneficial claims on products marketed to consumers in the United States and Canada contained at least some level of misrepresentation (TerraChoice, 2010, p. 6). Companies preying upon recent positive developments in increasing environmental awareness of consumers are fraudulent, but could also bring about negative consequences for society. In “Greenwashing in the New Millenium,” author Nancy Furlow lists the three major problems with greenwashing: 1) It misleads consumers; 2) It harms the truly green companies; and, 3) It causes consumers to disregard environmental claims in general (Furlow, 2011). As a consumer-driven society, fostering distrust of claims relating to the ecological impact of products could severely impede the future progress of environmental reform, rob individuals of their money, and prevent consumers from accurately gauging their environmental footprint—all under the guise of ethics-based consumption. Because of these harms, everyone in society has a stake in preventing fraudulent messages from being transmitted to the public by ethically remiss businesses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.023 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".