The past, present, and future of sustainability marketing: How did we get here and where might we go?
Bibliographic record
Abstract
The ever-increasing concerns about global sustainability have sparked intense debates and actions across various sectors, with marketing playing a pivotal role in influencing both the problem and potential solutions. This article maps the historical trajectory of sustainability marketing, analyzes current trends, and explores future directions to enhance sustainability within marketing practice. We show that sustainability marketing has historically centered on resource conservation and green marketing , but in recent years its scope has expanded to encompass a broader range of topics. These include the circular economy , anti-consumption, regulatory frameworks, innovation, carbon emissions , as well as the social and ethical considerations inherent in sustainability marketing. We propose three main areas within which sustainability marketing might expand in the future: system-driven changes, business-driven changes, and consumer-driven changes. Our analysis aims to deepen the understanding of sustainability marketing and inspire marketers to explore questions and solutions that will contribute to a sustainable future.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".