Table 1. Previous Panel Studies on ICT and CO2 emissions.
|
Regions |
Periods |
Methods |
Signs of ICT Variable on CO2 Emissions |
Higón et al. [4] |
116 developing and 26 developed countries |
1995–2010 |
Pooled Ordinary Least Squares Driscoll–Kraay Fixed Effects model Instrumental variable Fixed Effect model |
Negative |
Lu [5] |
12 Asian countries 1 |
1993–2013 |
Pedroni cointegration test |
Negative |
Ozcan and Apergis [6] |
20 emerging economies 2 |
1990–2015 |
MG estimator GM FMOLS |
Negative |
Haseeb et al. [7] |
BRICS countries 3 |
1994–2014 |
FMOLS and DSUR |
Negative |
Faisal et al. [8] |
Fast emerging countries 4 |
1993–2014 |
FMOLS, DOLS, robust least square |
Negative |
Zhang and Liu [9] |
China |
2000–2010 |
STIRPAT |
Negative |
Park et al. [13] |
EU countries 5 |
2001–2014 |
MG estimator |
Positive |
Lee and Brahmasrene [14] |
ASEAN countries 6 |
1991–2009 |
FMOLS Canonical Cointegrating Regression Dynamic OLS |
Positive |
Salahuddin et al. [15] |
OECD countries 7 |
1991–2012 |
PMG, DOLS, FMOLS |
Positive |
Asongu et al. [16] |
44 countries in Sub-Saharan Africa |
2000–2012 |
Generalized Method of Moments |
Insignificant |
Danish et al. [17] |
11 countries 8 |
1990–2014 |
MG estimator |
Positive |
Amri [18] |
Tunisia |
1975–2014 |
ARDL |
Insignificant |
Shehzad [19] |
Pakistan |
1990–2018 |
STIRPAT and ARDL |
Positive |
Note:
1 Brazil, India, China, and South Africa.
2 Brazil, Chile, China, Colombia, the Czech Republic, Egypt, Hungary, Indonesia, India, Greece, Mexico, Malaysia, Peru, the Philippines, Poland, Russia, South Africa, South Korea, Thailand, and Turkey.
3 Austria, Belgium, Bulgaria, Croatia, Cyprus, the Czech Republic, Denmark, Finland, France, Germany, Greece, and Hungary, Ireland, Italy, Luxembourg, the Netherland, Poland, Portugal, Romania, Slovenia, Spain, Sweden, and the UK.
4 Brazil, Chile, China, Colombia, the Czech Republic, Egypt, Hungary, Indonesia, India, Greece, Mexico, Malaysia, Peru, the Philippines, Poland, Russia, South Africa, South Korea, Thailand, and Turkey.
5 Australia, Hong Kong, Japan, India, Indonesia, Korea, Malaysia, Philippines, Singapore, Thailand, and Turkey.
6 Brazil, China, Russia, India, and South Africa.
7 31 countries of OECD.
8 Bangladesh, Egypt, Indonesia, Iran, South Korea, Mexico, Nigeria, Pakistan, Philippines, Turkey, and Vietnam.
This entry differs from previous studies in several ways. First, the subjects of analysis were OECD countries. Since these countries are generally classified as high-income countries, they are relatively active in addressing climate change policies. Understanding the GHG characteristics of these countries can help establish policies to address climate change in the future. Common characteristics of the OECD countries are that their levels of ICT are higher those in other countries and that they are leaders in related ICT usage. Therefore, it is important to analyze the influence of various factors on CO
2 emissions in OECD countries. Although Salahuddin et al.
[15] analyzed OECD countries, their study has limited relevance, as the analysis period was 10 years prior, and the recent changes in ICT were not reflected.
Second, the supply of renewable energy in OECD countries has gradually increased since 2000. Renewable energy has been an efficient and reliable means of reducing CO
2 emissions. Recently, there were several studies on the role of renewable energy in mitigating CO
2 emissions. Recent previous studies include Menyah and Wolde_Rufael
[24], Apergis et al.
[25], Shafiel and Salim
[26], Jaforullah and King
[27], Bilgili et al.,
[28], Dogan and Seker
[29], Ito
[30], Zoundi
[31], Jebli et al.
[32], Dong et al.
[33], and Inglesi-Lotz and Dogan
[34]. Most of these previous studies have shown that renewable energy has significantly contributed to the mitigation of CO
2 emissions. However, previous empirical models for the impact of ICT on CO
2 emissions do not sufficiently reflect the effects of these renewable energy sources. Specifically, Salahuddin et al.
[15] also did not consider the effect of renewable energy on the GHG emissions in the context of OECD countries. Therefore, this entry considered renewable electricity as a factor influencing CO
2 emissions.
Third, economic growth was included as an important factor influencing CO
2 emissions in most previous studies. However, although trade openness was included as a factor affecting CO
2 emissions in some previous studies (Park et al.
[13], Faisal et al.
[8], Ozcan
[6], Armi
[18], and Shehzad
[19]), there are other studies where it was not included as a factor influencing CO
2 emissions (Haseeb et al.
[7], Lu
[5], Lee and Brahmasrene
[14], Higón et al.
[4], and Zhang and Liu
[9]). Even studies that included trade openness as a factor influencing CO
2 emissions may or may not be statistically significant. As shown in
Figure 1b, most OECD countries are very open to trade and show increasing trends. Therefore, in this entry, trade openness was included and analyzed as a factor affecting CO
2 emission.
Fourth, in terms of analysis methods, the mean group (MG) estimator and FMOLS were mainly used in the existing panel analyses. However, this entry used the pooled mean group (PMG) in line with Salahuddin et al.
[15]. The pooled-mean-group (PMG) estimator of Pesaran et al.
[35] assumes a common long-run equilibrium relationship across countries, allowing country-specific short-run dynamics. Furthermore, the PMG estimators are consistent and asymptotically normal when the regression variables are I(0) and I(1). Therefore, through this methodology, it is possible to identify the common characteristics of the OECD countries.