The spatial representation of business models for climate adaptation: An approach for business model innovation and adaptation strategies in the private sector
Bibliographic record
Abstract
Abstract Climate change adaptation requires organizations to recognize the numerous natural and social dimensions of climate risk. In the private sector, local adaptation responses to climate change are observed as changes to the limited capacities of organizational processes to plan for social aspects of adaptation. This article applies a methodology to map these connections and presents empirical evidence of a firm's autonomous adaptation measures along their supply chain in Baja California, Mexico. The spatial conceptualization of the business model illustrates the potential to identify sources of climate‐related risks, autonomous adaptation actions, and the barriers to improving the feedback loops to facilitate the integration of local knowledge for business model innovation. The results suggest that coproduction of innovations is a mechanism for organizational learning that can help to overcome the challenges for business strategy to identify the wide array of local factors associated to climate adaptation and normalize adaptation planning into business models. This approach might accelerate leveraging the capabilities of the private sector for socially oriented forms of adaptation that amplify the transformational value of business model approaches for improved adaptation strategies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".