A Transatlantic Analysis of EU and U.S. Strategies In 'Green Procurement'
Bibliographic record
Abstract
As governments the world over move to reduce global warming, public procurement has become an increasingly important means of leveraging governments’ vast purchasing power to reduce greenhouse gas (GHG) emissions through “green” or environmentally sustainable procurement. This article reviews emerging strategies in green procurement in the European Union and the United States. The article notes that those green procurement strategies are remarkably consistent on both sides of the Atlantic, from sector-specific preferences for low-carbon products to eco-labels to life-cycle cost analyses which take into account broader environmental impacts. On both sides of the Atlantic, however, parallel problems have emerged as well. While initial efforts have been made to force firms to chronicle their products’ and services’ GHG emissions so that those emissions can be assessed (including in awarding contracts), those efforts have faltered politically in both the United States and the European Union because of the high costs of implementation. These initial results from both continents suggest that while green procurement can evolve in parallel around the world, using common strategies and devices, the costs of implementation — until now, a largely overlooked variable — may play a critical role in deciding which environmentally sustainable strategies are likeliest to succeed, at least in the short run.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".