Lessons learned from a corporate manufacturer on driving the adoption of nature-based solutions
Bibliographic record
Abstract
Companies are increasingly focused on driving the adoption of nature-based solutions across their organizations. Yet, implementing nature-based solutions within existing regulatory frameworks poses a unique set of challenges. In this paper, we present three nature-based solution case studies from The Dow Chemical Company and The Nature Conservancy's nearly 10-year collaboration. In the first case study, we focus on the potential benefits of reforestation to support the state's air quality improvement efforts. Ultimately, federal and state authorities did not approve of the reforestation project. Following this early setback, the collaboration team developed a suite of science-based tools that could be used to better advocate for government approval for the implementation of nature-based solutions. In the second case study, we highlight how one of these tools, the Ecosystem Services Identification & Inventory Tool, was used to improve communications about the benefits of nature-based solutions with regulatory agencies. In this case, Dow ultimately received approval for the restoration of a wetland to remediate an existing ash pond. Finally, the third case study highlights how engaging the right expertise through collaboration between the private sector and conservationists can improve land management strategies. Overall, this paper emphasizes the importance of robust conservation science, tools and expertise, and thoughtful collaboration as necessary means of driving the adoption of nature-based solutions both within a company and by its regulating entities. Integr Environ Assess Manag 2022;18:74-81. © 2021 SETAC.
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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 | 0.036 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".