MétaCan
Menu
Back to cohort
Record W2039006293 · doi:10.1068/c0827

Eco-Efficiency and Organizational Practices: An Exploratory Study of Manufacturing Firms

2009· article· en· W2039006293 on OpenAlexaffabout
Jean‐François Henri, Marc Journeault

Bibliographic record

VenueEnvironment and Planning C Government and Policy · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBusinessSustainabilityProductivityIncentiveOrganizational performanceManufacturingEnvironmental economicsSample (material)Exploratory researchOperational efficiencyProduct (mathematics)Industrial organizationMarketingEnvironmental resource managementProcess managementEconomicsEcology

Abstract

fetched live from OpenAlex

As the pressure increases on organizations to improve their eco-efficiency, the identification of organizational practices that can support this improvement has become a major concern for managers and researchers. The aim of this exploratory study is to identify the association between organizational practices (ie managerial and operational) and eco-efficiency in manufacturing organizations. Combining survey and public data from a sample of Canadian manufacturing industries, the results suggest for managerial practices that environmental strategic planning, environmental administrative mechanisms, and incentives are associated with two eco-efficiency ratios—namely, the material intensity ratio and the environmental productivity ratio—while environmental performance indicators are associated with the material intensity ratio. Furthermore, the results suggest that three environmental operational practices—namely, product redesign, reduction, and alliances—are positively associated with the material intensity ratio. This study contributes to the sustainability and eco-efficiency literatures by exploring the role and contributions of organizational practices that support environmental and economic performance.

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 score0.005
Threshold uncertainty score0.791

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 communication0.0000.001
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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

Citations29
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueEnvironment and Planning C Government and PolicySame topicEnvironmental Sustainability in BusinessFrench-language works237,207