Business models for the Anthropocene: accelerating sustainability transformations in the private sector
Bibliographic record
Abstract
The rapid pace and escalating severity of climate change impacts have made clear that current incremental approaches to pressing global socio-ecological challenges are insufficient to address the root causes of unsustainable development. This has spurred increasing interest in the dynamics of transformation: the actors, capacities and resources needed to fundamentally shift development paths. The private sector is at the core of essential transformative processes necessary to build a future premised on environmental integrity, social inclusivity, and resilience. The activities of the private sector are structured and driven by their underlying business model, which is at its core a set of assumptions about how a business creates, extracts and delivers value. Dominant conceptualizations of the business model remain a narrow imagining of how business interacts with societal processes and shape development patterns. In this article we call for the conceptualization and design of business models anchored in societal purpose and operating within planetary boundaries, apt for the Anthropocene. We identify five building blocks for business models where transdisciplinary sustainability research can accelerate entrepreneurial activity that fosters desirable sustainable pathways by enabling the creation of new capabilities in support of broader transformational processes. This article seeks to inform (and potentially re-orient the efforts of) transdisciplinary scholars engaging the private sector in the co-production of community-based sustainability and resilience-building initiatives. Likewise, the building blocks provide a guide for businesses who aim to deepen their capacity to build new partnerships, identify, and incorporate new information on climate risk into their operations and develop practices, sequences and procedures oriented toward the sustainable development goals and disaster resilience.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".