Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 268

The DOI matches its Crossref record; 90 of the 90 references it cites were found in a registry or an index.

Renewable energy and CO2 emissions: New evidence with the panel threshold model

Open access
Chen, Chaoyi, Pinar, Mehmet, Stengos, Thanasis·2022·Renewable Energy

Abstract

The increased concerns over climate change led to a large body of literature that examined the impact of energy and economic growth on carbon dioxide (CO2) emissions per capita. The majority of the existing studies employed various linear panel estimation techniques ignoring the potential nonlinear effects of energy and income on CO2 emissions per capita. To fill this gap, this study uses panel data consisting of 97 countries between 1995 and 2015 and examines the nonlinear impact of renewable, non-renewable energy consumption, economic growth on CO2 emissions per capita by using a dynamic panel threshold model that is robust to cross-section dependence. Our findings indicate the effect of growth in renewable energy consumption per capita on the growth of CO2 emissions per capita is negative and significant if countries surpass a certain threshold of renewable energy consumption. This finding mainly holds for developed countries and countries with stronger institutions and is robust to the use of an alternative proxy for renewable energy consumption. Our findings highlight the fact that increased renewable energy consumption would only reduce CO2 emissions per capita if and only if countries surpass a certain threshold of renewable energy consumption.

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 · 10.1016/j.renene.2022.05.095

Cites

90 of 90 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 90 references

Metadata

Authors
Chen, C., Pinar, M., & Stengos, T.
Venue
Renewable Energy
Publisher
Elsevier BV
Published
2022-07
Volume
194
Pages
117-128
ISSN
0960-1481 (ISSN-L)
OpenAlex
W4281259700
Cited by
268
References
90
FWCI
40.89
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: Renewable energy and CO2 emissions: New evidence with the panel threshold model. https://app.cochat.ai/papers/10.1016/j.renene.2022.05.095
The paper
Chen, C., Pinar, M., & Stengos, T. (2022). Renewable energy and CO2 emissions: New evidence with the panel threshold model. Renewable Energy. https://doi.org/10.1016/j.renene.2022.05.095

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.