Beyond reductionism: rethinking MNEs’ role in environmental crises
Bibliographic record
Abstract
Abstract This counterpoint challenges the view advanced by Yu, Bansal, and Arjaliès (J Int Business Stud 54:1151–1169, 2023), who argue that multinational enterprises (MNEs), by virtue of their cross-border operations, are inherently detrimental to the environment. While Yu et al.’s call for responsible resource use is commendable, we contend that their framework oversimplifies the complex realities of international business (IB). Drawing on New Internalization Theory (NIT), we examine environmental crises through the lens of multilevel complexity—macro-level institutional interdependencies, firm-level heterogeneity, and individual-level cognitive and behavioral constraints. Our approach underscores the role of bounded rationality and bounded reliability across all relevant actors—not just MNE managers—in shaping environmental outcomes. We find that Yu et al.’s proposed strategies give limited attention to the institutional, industry, organizational, and governance conditions under which environmental value is created. By contrast, we apply comparative institutional analysis to investigate MNEs’ impact on the environment as compared to feasible, real-world alternatives. We argue that meaningful environmental progress hinges not on targeting MNEs, but on fostering multilateral coordination among public, private, and civil society actors, with MNEs being well positioned to lead such collaborations. We call for scholarship that avoids ideological overreach, embraces IB theory, and acknowledges MNEs as key agents in advancing environmental sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".