Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 459

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Income and emission: A panel data-based cointegration analysis

Open access
Dinda, Soumyananda, Coondoo, Dipankor·2006·Ecological Economics

Abstract

This paper presents the results of an investigation of the causality issue of income-emission relationship based on time series econometric techniques of unit root test, co-integration and related error correction model for a panel data set. Here, the nature of causality between per capita CO2 emission (PCCO2) and per capita GDP (PCGDP) has been examined using a cross country panel data set covering 88 countries for the period 1960 - 90. Using the panel unit root test procedure of Im et al. (1997) (IPS), we have found that the hypothesis of unit root (i.e., non-stationarity) of the time series of PCGDP and PCCO2 can not be rejected for individual country groups. As both the variables are found to follow I(1) process, we next have performed the panel data co-integration test and finally, we have estimated the ECM (for these country groups for which significant income-emission cointegration was obtained) to explore the nature of dynamics implicit in the given panel data set. Our findings suggest that there is more or less a bi-directional causal relationship between income (PCGDP) and CO2 emission (PCCO2) for Africa, Central America, America as a whole, Eastern Europe, Western Europe, Europe as a whole and the World as a whole. That means, the movement of the one variable directly affects the other variable through a feedback system. Thus, the policy makers should be cautious to make proper decision about the control of emission level.

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.ecolecon.2005.03.028

Cites

3 of 3 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

Cited by

See all 459

Metadata

Authors
Dinda, S., & Coondoo, D.
Venue
Ecological Economics
Publisher
Elsevier BV
Published
2006-05
Volume
57
Issue
2
Pages
167-181
ISSN
0921-8009 (ISSN-L)
OpenAlex
W1986789017
Cited by
459
References
3
FWCI
7.27
Type
journal article
Language
en
Open access
green

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

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.