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Record W3130242835 · doi:10.5539/jms.v11n1p88

Environmental Issues in City Logistics: The Case of Low Emission Zones in Europe

2021· article· en· W3130242835 on OpenAlexvenueno aff
Gilles Paché, C. Morel

Bibliographic record

VenueJournal of Management and Sustainability · 2021
Typearticle
Languageen
FieldEngineering
TopicUrban and Freight Transport Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsMetropolitan areaBusinessDistribution (mathematics)Energy consumptionOrder (exchange)Environmental economicsConsumption (sociology)SustainabilityDependency (UML)Greenhouse gasSustainable developmentPoliticsEnvironmental planningEnvironmental resource managementPolitical scienceEngineeringGeographySociologyEconomics

Abstract

fetched live from OpenAlex

The energy transition can be defined as all the transformations of the system of production, distribution and consumption of energy carried out in a territory in order to make it more ecological. The aim is to reduce the environmental impact of an energy system. Inseparable from sustainable development, the energy transition contributes to the fight against global warming, through the implementation of changes based on innovative technologies, but also and above all on new political orientations. The paper focuses in particular on city logistics in Europe as an illustration of energy transition, showing that strategic interactions between city logistics stakeholders are at the heart of new practices, particularly in the implementation of low emission zones (LEZs). A case study was conducted with a large French metropolitan area, which is at the forefront of sustainable city logistics. This case study is based on an analysis of official documents written as part of the implementation of the LEZ. Using a conceptual framework drawn from the SCP paradigm, which is at the origin of the industrial organization’s stream, the results indicate that the success of environmental city logistics strategies depends effectively on interactions between several public and private stakeholders, and not only on virtuous managerial practices from companies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.212
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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