Industrial policy, green challenges, and international business
Bibliographic record
Abstract
Abstract Nation-states are designing their industrial policies increasingly to not only enhance national competitiveness, but also to simultaneously address “Green Challenges”, concerns about the natural environment that require concerted action among different actors in society, including domestic and foreign multinational enterprises (MNEs). This blending of global and national policy objectives is leading to a new wave of industrial policies in advanced economies that are informed by scholarly discourses in evolutionary economics, innovation systems, and 'wicked problems'. We discuss the implications of these sustainability-oriented industrial policies for MNEs. They operate in increasingly diverse local ecosystems shaped by local actors and local policies as we illustrate for two such ecosystems in Nordic countries: circular economy and energy transition. Many MNEs face a tension between capabilities they could use to help nations achieve their sustainability goals and incentives to protect existing rents and business models. They may thus engage pro-actively or reactively in both market and nonmarket realms in each country in which they operate. We discuss the interactions between MNEs, governments, and other actors in host countries pursuing both sustainability and competitiveness objectives, and outline how ensuing tensions create new challenges and opportunities for international business scholarship.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| 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".