How to reduce CO2 emissions from freight transport in France?: Socioeconomic appraisal of three public policies
Bibliographic record
Abstract
France must implement voluntarist policies in order to reduce CO2 emissions, which raises questions regarding the effectiveness of competing options and their costs, for both public finances and society. This paper estimates abatement costs of one ton of CO2 from three scenarios aimed at softening the environmental impacts of freight moved by trucks. Hybrid trucks may generate large CO2 savings (5.7 Mt/year in 2030), at a moderate discounted abatement cost (88 €/t). The option based on natural gas for vehicles leads to a similar abatement cost and to lower environmental gains (3.2 Mt/year in 2030). Whilst inducing small CO2 savings (0.2 Mt/year in 2030), megatrucks present a negative discounted abatement cost (-285 €/t), thus suggesting the existence of one potential double dividend. Latter result is uncertain, however, as stressed by some of the sensitivity tests proposed in this research. Afin de réduire ses émissions de CO2, la France doit aujourd’hui mettre en place des mesures très volontaristes. Se pose alors la question de l’efficacité des politiques et de leurs coûts, tant pour les finances publiques que pour la collectivité. Cet article estime le coût d’abattement d’une tonne de CO2 pour trois scénarios visant à réduire l’impact environnemental des poids lourds (PL) en France. Les PL hybrides permettraient d’obtenir de forts gains d’émissions de CO2 (5,7 Mt/an en 2030), pour un coût d’abattement actualisé de 88 €/t sur la période 2030-2050. Si le coût d’abattement actualisé des PL alimentés au gaz naturel est proche, cette technologie fait économiser moins de CO2 (3,2 Mt/an en 2030). Le scénario sur les PL de 60 t permet quant à lui d’obtenir des gains environnementaux modestes (0,2 Mt/an en 2030) mais pour un coût d’abattement actualisé négatif (-285 €/t), suggérant donc une forme de double dividende. Ce dernier résultat est toutefois incertain, comme l’attestent les nombreux tests de sensibilité que nous proposons.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".