Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 213

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance?

Open access
Chiarini, Andrea·2021·Business Strategy and the Environment

Abstract

This research contributes to the debate about the relevance of Industry 4.0 technologies in improving environmental performance in the manufacturing industry. We employed a qualitative–quantitative approach in which 19 Italian operations managers were interviewed and 260 managers responded to an online questionnaire. The effects of various technologies were ranked using ordinal regression. Comments and suggestions gave context to the quantitative results. Sensors, radio‐frequency identification, artificial intelligence and analytics were found to be most relevant in improving environmental performance, whereas simulation software contributed moderately. Additive manufacturing, cobots, robots, automated mobile robots and automated guided vehicles had a negative effect, augmented reality had no effect and other technologies indirectly affected environmental performance. We also found a lack of knowledge and application as well as scepticism about technologies such as artificial intelligence and augmented reality. Finally, there was concern about the disposal of electrical and electronic waste produced by these technologies.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/bse.2797

Cites

7 of 7 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 7 references

Metadata

Authors
Chiarini, A.
Venue
Business Strategy and the Environment
Publisher
Wiley
Published
2021-04-10
Volume
30
Issue
7
Pages
3194-3207
ISSN
  • 0964-4733 (ISSN-L)
  • 1099-0836
OpenAlex
W3156471857
Cited by
213
References
7
FWCI
15.49
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance? https://app.cochat.ai/papers/10.1002/bse.2797
The paper
Chiarini, A. (2021). Industry 4.0 technologies in the manufacturing sector: Are we sure they are all relevant for environmental performance? Business Strategy and the Environment. https://doi.org/10.1002/bse.2797

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.