Sustainable Waste Management Companies with Innovative Smart Solutions: A Systematic Review and Conceptual Model
Bibliographic record
Abstract
Overflowing garbage bins and unnecessary truck visits to collect waste have always been core issues of sustainability and maintaining a green environment. In the recent past, a transition has been observed in waste management towards a better environment and the achievement of sustainability goals. Companies are not only focused on producing less but also transforming waste into energy and reusable products. This transition process needs to evolve through sustainable solutions and innovative marketing initiatives that increase awareness and education among end users. This study used a systematic literature review protocol to identify and review the available research on sustainable waste-management solutions, innovative marketing initiatives, and a proposed conceptual model. It analyzed the latest literature from 1976 to 2022 to assess waste-management trends using the Web of Sciences and Scopus databases. To evaluate the practical perspective, this study analyzed ten waste-management companies offering services in the USA, the UK, Korea, Finland, Ireland, Turkey, Brazil, Slovakia, Portugal, Denmark, and Canada to assess their technological and marketing development for the creation of a better future. It was found that Ecube, Enevo, smart bins, Compology, Bigbelly, Sensoneo, Citibrain, ACO recycling, Evrek, Rico, and BrighterBins focus more on technology and less on user awareness and marketing. There is minimal focus on education and empowerment of end users. Our study’s findings guide academics, practitioners, and policymakers to apply ambidextrousness in energy innovation, particularly in the waste-management sector. By implementing sustainable and innovative solutions, companies can not only reduce waste products, but they can also recover, recycle, and better dispose of the waste. However, to do so, companies also need to educate end users.
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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 | 0.029 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.050 | 0.042 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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".