Innovative Business Models in Ocean-Bound Plastic Recycling
Bibliographic record
Abstract
Problem definition: About 30 million tons of plastic waste reaches the oceans each year, mostly from low- and middle-income coastal countries. We study novel business models of firms aiming to reduce ocean plastic pollution with a triple-bottom-line (TBL) objective—a weighted sum of profit, environmental impact, and social impact. These firms sell (a) plastic offsets and (b) segregated plastic. Methodology/results: We develop and analyze models where a firm partners with a local plastic recycling supply chain to sell (a), (b), or both via collecting and recycling ocean-bound plastic. Considering additionality (i.e., that the firm can only sell plastic offsets based on recycled plastic that is additional to the plastic recycled without the firm’s presence), we solve the equilibrium outcomes by maximizing the firm’s TBL objective. For the special case of a for-profit firm, we show that additionality can decrease the firm’s social and environmental impacts when selling (a) only or when selling both (a) and (b). Additionality may also alter the effect of the local recycled plastic market (i.e., the number of collectors and the recycled plastic price) on the firm. We find similar insights under the TBL objective via a numerical study calibrated with real data. Managerial implications: When firms decide whether to integrate and promote additionality, they must be careful because it may not only reduce their profit but also, reduce their social and environmental impacts. Moreover, we find that selling both (a) and (b) can generate a much higher TBL objective value than selling either one alone. We also find that firms employing a TBL objective can generate much larger environmental and social impacts with a slight reduction in profits than profit-maximizing firms. Our model and results provide insights into new initiatives for tackling ocean plastic pollution. Funding: O. Baron and G. Romero are both supported by the Natural Science and Engineering Research Council of Canada. Z. Zhang is partially supported by the Fundamental Research Funds for the Central Universities of Xiamen University [Grant 20720241012]. S. X. Zhou is partially supported by the Hong Kong Research Grants Council General Research Fund [Grant CUHK-14500921], the National Natural Science Foundation of China [Grant 72394395], and the Asian Institute of Supply Chains and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0643 .
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".