New directions for research in green public procurement: The challenge of inter-stakeholder tensions
Bibliographic record
Abstract
Public sector spending represents a significant portion of gross domestic product in most countries, and holds much promise to advance calls to improve the sustainability of goods and services provided by supply chain partners – but only if multiple objectives can be reconciled. Public procurement also tends to heavily emphasize outcome-based specification practices that rely on traditional tendering for supplier selection, thereby stifling potentially innovative improvements. Drawing on stakeholder theory, we consider how potential inter-stakeholder tensions contribute to both the challenges and opportunities for green public procurement (GPP) practices. In addition to conventional categories of internal and external stakeholders, we identify a third category of stakeholders who ‘bridge’ across these two groups. This framing helps to delineate complex interactions among multiple stakeholder groups and enables a mapping of each group’s weighting of priorities and influence in decision making. Doing so highlights potential sources of inter-stakeholder tensions that must be balanced or resolved to advance GPP. Moreover, process-based collaboration can engage multiple groups of stakeholders, attenuate inter-stakeholder tensions, and foster cooperative, novel solutions for improved environmental outcomes. Drawing from an initial case study, new research directions emerge when we combine both process- and outcome-based practices that engage supply chain partners and multiple stakeholders to develop and advance new green technologies and evaluate complex considerations in public sector procurement.
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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 | 0.065 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.027 | 0.057 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".