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ABSTRACT
This paper examines the relationships among per capita CO2 emissions, per capita GDP and international trade based on panel data spanning the period 1960–2008 for 150 countries. A distinction is also made between OECD and non-OECD countries to capture the differences of this relationship between developed and developing economies. We apply panel unit root and cointegration tests and estimate a panel error correction model. The results from the error correction model suggest that there are long-term relationships between the variables for the whole sample and for non-OECD countries. Finally, Granger causality tests show that there is bidirectional short-term causality between per capita GDP and international trade for the whole sample and between per capita GDP and CO2 emissions for OECD countries.
Acknowledgments
The author would like to thank Prof. Kurt Brännas for helpful comments and suggestions, Prof. Joakim Westerlund and Dr. Damiaan Persyn for their explanations about the panel cointegration tests, the two referees who contributed to improve the quality of this article.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes
1. We omitted 16 countries for which we had insufficient historical data on international trade and CO2 emissions.
2. We take the log of GDP for scale reasons and to facilitate interpretation of the coefficients.
3. Monte Carlo simulation in the IPS study show that the small sample properties of IPS test are superior to those of the best-known Levin and Lin (Citation1993) panel test.
4. ,
;
where
and
are the usual Newey and West (Citation1994) standard error corresponding to the long-run variance estimators
, where
is a bandwidth parameter that determines how many covariances to estimate in the kernel.
may be obtained as above using kernel estimation with
replaced by
for (2) and so on for (3) and (4).
,
.
5. The group-mean and panel tests are constructed in different ways and can therefore give different results. They require large N and large T data sets. These tests are also very sensitive to the specific choice of parameters such as lag and lead lengths, and the kernel width.
6. We also estimated the group-mean error-correction model, averaging coefficients of the error-correction equation over all cross-sectional units, together with the implied long-run relationship. However, these results are not reported here because the long-run coefficients were not significant.
7. MENA countries refers to Middle East and North African countries.