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Review of Economic Analysis 16 (2024) 343-370 1973-3909/2024343
343
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Cross-country distribution dynamics of carbon emissions and
intensity: Before and after the global financial crisis
YIGANG WEI
Beihang University and Beijing Key Laboratory of Emergency Support Simulation
Technologies for City Operation, Beijing, China
MICHAŁ WOJEWÓDZKI
Lingnan University, Hong Kong
TSUN SE CHEONG
Hang Seng University of Hong Kong
XUNPENG SHI
University of Technology Sydney
This study aims to explore the levels of regional disparity in carbon emissions and intensity
among different countries. Our study employs the distribution dynamics approach to
uncover transition probabilities and the long-term evolution of relative per capita carbon
emissions (REPC) and relative carbon intensity (REPGDP) across 204 countries. We split
the analysis period into pre-crisis (2000-2007) and post-crisis (2007-2016) and divided
countries into four income groups. The results indicate the emergence of new convergence
clubs post-crisis in both REPC and REPGDP. Furthermore, the majority (many) of the
low- (high) income countries congregate to extremely low (above the global average)
REPC levels in the long run. Finally, using mobility probability plots, we identify low-
Wojewódzki: corresponding author, Department of Finance, Faculty of Business, Lingnan University,
8 Castle Peak Rd - Lingnan, Tuen Mun, New Territories, Hong Kong; michalwojewodzki2@ln.edu.hk.
Wei: School of Economics and Management, Beijing, China and Beijing Key Laboratory of Emergency
Support Simulation Technologies for City Operation, Beijing, China.weiyg@buaa.edu.cn; Cheong: The
Hang Seng University of Hong Kong, School of Business, Hang Shin Link, Shatin, New Territories,
Hong Kong SAR, jamescheong@hsu.edu.hk; Shi: The Australia-China Relations Institute, University
of Technology Sydney, ACRI, 15 Broadway, Ultimo, NSW, 2007, Australia, xunpeng.shi@uts.edu.au.
Competing interests and funding: none
Acknowledgements: The research is supported by grants from the Beijing Social Science Fund
(20GLC054). The authors also thank the anonymous reviewers for insightful comments that helped us
improve the quality of the paper.
© 2024 Yigang Wei, Michał Wojewódzki, Tsun Se Cheong and Xunpeng Shi. Licensed under the
Creative Commons Attribution - Noncommercial 4.0 Licence
(http://creativecommons.org/licenses/by-nc/4.0/. Available at
http://rofea.org.
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(high-) income countries with REPC (REPGDP) levels of 2.3 (3.8) times the global
average to have the highest probabilities of around 100 (65) per cent of diverging further
above the worldwide average in the coming years. The study expands knowledge about
convergence-divergence patterns in carbon emissions and intensity, which is crucial for
energy management and effective climate policies. Moreover, it can aid in climate change
projections and promote a fairer climate framework, encouraging high-emission countries
to take greater responsibility
Keywords: Carbon emissions; Global financial crisis; Distribution dynamics; Convergence
clubs; Income levels
JEL Classifications: C14; G01; O13; Q54
1 Introduction
Greenhouse gas (hereafter GHG) emitted into the atmosphere from burning fossil fuels
significantly contributes to global climate change. Thus, effective control of the relentless
growth of GHG emissions has become an irreplaceable mission for global society (Shahrour et
al 2023). Accordingly, 196 countries signed the Paris Agreement in 2015 with the primary
target of slowing down global warming to below 2°C (if possible 1.5°C) compared to pre-
industrial levels (Cheng et al 2023). The signatories also pledged to define their climate actions
known as Nationally Determined Contributions (hereafter NDCs). However, Climate Action
Tracker (2021) forecasted that assuming all of the existing NDCs were delivered, the global
temperature would still increase by 2.4°C. Furthermore, the NDCs are voluntary actions,
thereby giving rise to countries under-committing, which, together with the implementation gap
in numerous countries, makes reaching the global warming goals unlikely.
The knowledge of convergence-divergence patterns in carbon emissions is significant in
energy management and formulation of efficient climate measures and policies (e.g., Kounetas
2018, Li and Wei 2021). Knowing the dynamic trajectory of emissions also helps facilitate
climate change projections. Moreover, the convergence could result in a fairer climate
framework, thereby increasing the odds of countries with high emissions assuming a greater
responsibility for climate commitments (Aldy 2006, Delgado 2013, Erdogan and Solarin 2021).
Indeed, the Kyoto and Paris Accords highlight the substantial positive impact of emissions
convergence and shared environmental policies on the probability of reaching the desired goals.
Against this backdrop, the convergence in cross-country carbon emissions has attracted
growing attention in the environmental economics literature (e.g., Rios and Gianmoena 2018,
Li et al 2020). On the one hand, numerous studies test the presence of convergence in per capita
carbon emissions measures (e.g., Westerlund and Basher 2008, Li et al 2020). On the other
hand, the recent surveys by Acar et al (2018) and Payne (2020) show that the convergence of
CO2 intensity (per unit of GDP emissions) remains relatively unexplored, and the empirical
results are mixed at best. Such gaps in the literature are worrying, given that many countries
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state carbon commitments in terms of either CO2 emissions or CO2 intensity
1
(Bhattacharya et
al 2020). Furthermore, the two measures of a country’s carbon emissions can take opposite
routes (Parker and Bhatti, 2020).
In terms of research methods, many studies test the presence of sigma, beta, and stochastic
convergence (e.g., Jobert et al 2010, Li and Lin 2013, Erdogan and Solarin, 2021). Other
parametric, semi-parametric, and nonparametric methods have also been used to test absolute
vis-à-vis conditional convergence (e.g., Brock and Taylor 2010, Delgado 2013), stochastic
convergence (Chang and Lee 2008), spatial convergence (Rios and Gianmoena 2018), and club
convergence (Parker and Bhatti 2020). However, fewer studies employ the distribution
dynamics approach (hereafter DDA) developed by Quah (1993, 1997) (e.g., Aldy 2006, Criado
and Grether 2011).
To the best of our knowledge, Kounetas (2018), Li et al (2021) and Wojewodzki et al (2023)
are the only studies examining the convergence in both measures of carbon emissions using the
DDA. However, they neither investigated the transitional dynamics of carbon emissions across
countries focusing on different income levels nor examined the effect of the GFC. Moreover,
Kounetas (2018) uses a relatively small sample limited to 23 EU countries, and his analysis
does not include the MPP tool.
Considering the above-outlined gaps in environmental research, this study makes three new
contributions to the environmental economics literature. To the best of our knowledge, we are
first to examine the long-run, dynamic convergence-divergence pattern of (1) relative per capita
carbon emissions (hereafter REPC) and (2) relative carbon intensity (hereafter REPGDP)
across 204 countries
2
. Furthermore, we deliver a nascent analysis of the impacts of the 2008
global financial crisis (hereafter, GFC) and unprecedented expansionary fiscal and monetary
policies on the future evolution of global REPC and REPGDP levels. For instance, we
document worrying developments in transitional dynamics and long-run steady-state equilibria
of both REPC and REPGDP measures post-GFC period compared with the pre-GFC period.
Such findings might indicate the negative effect of GFC-related expansionary policies (e.g.,
massive borrowing, quantitative easing, and investment in carbon-intensive industries) on both
carbon emissions measures' long-run steady-state equilibria and convergence process.
Third, this paper delves into the profound influence of carbon emissions and intensity on
the distribution dynamics of nations grouped by income levels. Specifically, along with the
1 For example, China and India have stated their emissions reduction goals regarding CO2 intensity
(Bhattacharya et al. 2020).
2
REPC (REPGDP) is a ratio of country-specific annual per capita carbon emissions (intensity) to the
average carbon emissions (intensity) of all sampled countries in a given year. Because of the global
scope of this research (204 countries), we treat the sample’s annual average value as a proxy for the
global averages. Therefore, a country’s REPC or REPGDP above (below) one implies that this
country’s REPC or REPGDP is above (below) the global average in a given year.
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ergodic distribution, we also use a novel display tool of DDA introduced by Cheong and Wu
(2018), the Mobility Probability Plot (hereafter, MPP). This new tool offers specific visual
information regarding the probability mass distribution in the coming years. We document that
the low- (high) income countries with REPC (REPGDP) values of around 2.3 (3.8) times the
global average emissions have the most significant probabilities of around 100 (65) per cent of
diverging further above the worldwide mean. This information, in turn, translates into a “policy
priority list” of countries meriting the most urgent climate policies and actions.
The rest of this study is organised as follows. Section 2 provides an extensive review of
relevant literature. Section 3 introduces data and research methods. Section 4 discusses the
empirical results. The last section concludes the research findings and policy implications.
2 Literature Review
A burgeoning body of environmental literature has tested the convergence hypothesis of cross-
country per capita carbon emissions. For example, Ezcurra (2007) use the DDA and find strong
evidence of convergence towards the mean across 140 countries from 1960 to 1999. On the
contrary, based on the same method and a panel of 166 countries from 1960 to 2002, Criado
and Grether (2011) document an increased divergence and higher levels of CO2 emissions in
the long run. El-Montasser et al (2015) also report the results inconsistent with emissions
convergence in a study of high-income G7 countries. Most recently, Lee et al (2023) employed
stochastic convergence to analyse per capita emissions for 30 OECD countries from 1960 to
2018, revealing the lack of convergence.
On the contrary, Chang and Lee (2008) find significant evidence of stochastic convergence
in a panel of 21 high-income OECD countries using the minimum LM unit root tests. More
recently, Rios and Gianmoena (2018) test the spatial convergence clubs hypothesis vis-à-vis
the conditional convergence hypothesis in a sample of 141 countries and document the
emergence of three clubs. However, in a worldwide sample of countries, Fallahi (2020) show
that per capita carbon emissions are non-stationary and highly persistent at both the global and
regional levels.
Many researchers examine per capita carbon emissions, focusing on countries’ economic
development and income levels. For instance, in the sample of 100 countries, Nguyen Van
(2005) employs the DDA and shows a long-run convergence only across 26 developed
countries. Similarly, Aldy (2006) documents converge in emissions across only 23 developed
OECD members but find an opposite pattern (divergence) among 88 countries. Payne and
Aspergis (2021) identify three convergence groups among 27 low-income countries and five
clubs among 38 lower-middle-income countries from 1972 to 2014. In a sample spanning from
1870 to 2002, Westerlund and Basher (2008) and Delgado (2013) find evidence of the
convergence across developed and developing countries alike. Similarly, Ahmed et al (2017)
study 162 countries and show convergent patterns in 20, 13 and 5 high-income, middle-income,
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and low-income countries, respectively. According to Li et al (2020), convergence is
significantly faster across developed countries vis-à-vis developing countries.
The empirical literature suggests that the episodes of financial crises significantly influence
global carbon emissions, intensity, and convergence patterns. In a study of fourteen Asian
countries, Parker and Bhatti (2020) document different transition paths in the convergence
process of per capita carbon emissions before and after the 1997 Asian financial crisis. The
authors observe the emergence of four (three) convergence clubs pre- (post) crisis. According
to Wang et al (2021), the shock of GFC brought an initial U-turn in an ongoing upward
(downward) trend in global carbon emissions (intensity), followed by a return to the previous
pattern between 2009 and 2011. Li et al (2020) argue that the GFC and unprecedented post-
crisis policies adopted by different countries have led to cross-country divergence in emissions.
Recent surveys of empirical studies (Acar et al 2018, Payne 2020) agree that while the
convergence hypothesis has been tested extensively for cross-country per capita carbon
emissions, relatively few researchers have examined the convergence of CO2 intensity. For
instance, Lindmark (2004) graphically examines the patterns of carbon intensity across 56
countries during the 1870-1992 period and reports high-income countries converging with their
low-income counterparts. Camarero et al (2013) use Phillips and Sul's (2007) convergence-club
approach to identify four clubs of carbon intensity across 19 OECD countries and a non-
convergent pattern in four industrialised countries. Zhu et al (2014) find evidence of
convergence in a panel of 89 countries between 1980 and 2008. Zang et al (2018) examine a
sample of 201 countries from 2003 to 2015 and document club convergence among all three
groups of countries: low-, middle-, and high-income. According to Bhattacharya et al (2020),
two convergence clubs exist in cross-country consumption-based carbon emissions. Moreover,
their forecast based on a panel of 70 countries suggests that between 2014 and 2030, the number
of convergence clubs in consumption-based carbon intensity will increase.
Nevertheless, the dynamic aspects of per capita CO2 emissions vis-à-vis CO2 intensity
remain largely unexplored. Only three cross-country studies employ the DDA approach to
carbon emissions and intensity at the same time. In a sample of 23 EU countries during the
1970-2010 period, Kounetas (2018) find no evidence of convergence. Li et al (2021) investigate
the REPC and REPGDP measures in 178 countries. They document significant disparities in
the transitional dynamics of both measures between 71 countries that signed the Belt and Road
Initiative (hereafter BRI) cooperation with China and 107 non-BRI countries. Wojewodzki et
al (2023) study transitional dynamics and evolution of per capita CO2 emissions and CO2
intensity across countries with different urbanisation levels and agrarian orientations. However,
none of the three studies examine (1) the dynamics and future evolution of carbon emissions
and intensity concerning countries’ income levels and (2) the effect of the GFC on transitional
dynamics and long-run steady-state equilibrium.
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The existing body of environmental literature has yielded conflicting results
3
and left some
critical gaps. We aim to fill the abovementioned gaps by investigating the transitional dynamics
of carbon emissions and intensity in a global sample of 204 countries from 2000 to 2016. This
study adopts the DDA approach and the MPP tool that Cheong and Wu developed (2018).
3 Methodology and Data
The data are compiled from the World Bank’s World Development Indicators (WDI) database.
Two measurements of carbon emissions are employed: relative CO2 emissions per capita
(REPC) and relative CO2 intensity (REPGDP). REPC is measured as metric tons of CO2 per
capita in each country divided by the world average in a particular year. Similarly, REPGDP is
measured as kg of CO2 emissions per 2010 USD of GDP in each country divided by the world
average for this year. Using relative values, the disparity amongst the nations can be displayed
directly as a REPC or REPGDP value of 1, which indicates that a country’s CO2 emissions or
intensity is equal to the global average. In contrast, a value smaller (greater) than 1 means the
level below (above) the global average. The unbalanced panel covers 204 countries, starting in
2000 and ending in 2016
4
. That is because the data for 2017 and beyond were unavailable for
many countries at the time of collection.
Table 1 presents annual descriptive statistics for both variables. We can observe one
measure of central location (median) and one measure of dispersion (the coefficient of variation,
hereafter, CV).
Table 1. Annual (2000-2016) descriptive statistics for the REPC and REPGDP variables
Variable
Year (2000-2016)
Global
REPC
00
01
02
03
04
05
06
07
08
09
10
11
12
13
14
15
16
Median
0.59
0.59
0.59
0.58
0.53
0.54
0.59
0.55
0.55
0.60
0.55
0.56
0.55
0.57
0.57
0.57
0.58
CV
1.43
1.45
1.42
1.39
1.36
1.37
1.38
1.33
1.28
1.28
1.25
1.25
1.26
1.23
1.26
1.25
1.22
Global
REPGDP
Median
0.78
0.79
0.77
0.75
0.74
0.75
0.74
0.75
0.70
0.74
0.72
0.71
0.71
0.72
0.74
0.75
0.78
CV
1.07
1.03
1.04
1.03
1.00
0.95
0.94
0.93
0.90
0.84
0.84
0.86
0.92
0.94
0.82
0.79
0.79
Source: Authors’ calculations based on the World Bank’s World Development Indicators
(WDI)
3
Mixed results in prior studies can be attributed to different sample sizes and research methods.
Specifically, studies using larger samples and dynamic analyses tend to find evidence against cross-
country convergence.
4
The number of countries is the lowest (199) in 2000, while 204 were covered between 2008 and 2016.
This means that each country has a minimum of 9 consecutive annual observations.
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Table 1 shows similar decreasing trends regarding the variability in global carbon emissions
and intensities. Specifically, the CV values for REPC (REPGDP) dropped from 1.43 or 143%
(1.07 or 107%) to 1.22 (0.79). This means a presence of sigma convergence in two major
proxies for emissions over time, similar to the results of Zang et al (2018). This, in turn,
constitutes good news from the perspective of desirable cross-country convergence to the global
mean. However, the percentage change (decrease) in CV between 2000 and 2016 was larger
(85%) for REPC than that for REPGDP (74%), at odds with the changes documented by Zang
et al (2018) for the 2003-2015 period. Notwithstanding, global annual variability in the
REPGDP has remained substantially larger than that of REPC throughout the sampled period.
As for the median values, these remained virtually unchanged between 2000 and 2016,
ranging from 0.6 to 0.53 (0.79 to 0.7) in 2009 and 2004 (2001 and 2008) for the REPC
(REPGDP) variable. Since the global annual mean value for both measures of relative carbon
emissions equals one, we can conclude that the distribution of both variables is negatively
skewed, especially for the REPC measure, due to its median values being significantly below
one. This highlights relatively fewer (more) countries with REPC and REPGDP above (below)
the global average during the investigation period.
Time series econometric analyses are frequently used in forecasting. However, it is essential to
acknowledge that econometric models are limited in their ability to forecast the dependent
variable, as they do not provide insights into the shape of the underlying distribution (Liu et al
2022). Instead, such methods only offer insights into several significant distribution features
since they solely focus on predicting the dependent variable. Notwithstanding, since
distribution is a two-dimensional entity, it is not feasible to predict the future distribution's
overall shape solely through time series econometrics. Consequently, by ignoring information
regarding, e.g., multimodal distributions, the econometric analysis may lead to contaminated
or misleading results (Quah 1997, Maasoumi et al 2007). Likewise, traditional (econometric)
methods cannot comprehensively overview the distribution pattern and its dynamic changes
(Cheong and Wu 2018).
In contrast, the DDA developed by Quah (1993, 1997) focuses on examining the shape of
the distribution and how it changes over time. Thus, while traditional econometric techniques
can be utilised to calculate the slope parameter and assess the impact of a driving factor, the
DDA allows for examining the effects of determinants on the entire distribution. This involves
dividing the data into smaller datasets and applying the DDA to each subset individually. By
comparing the distributions of these datasets, one can better understand the impacts of different
driving factors
5
.
5
However, one limitation of the DDA is that the study of determinants can only be conducted one at a
time.
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Furthermore, the DDA possesses a significant advantage over traditional econometric
analysis regarding its resilience to outliers. This stems from the fact that the computation of the
DDA relies on the probability of entities transitioning between different states, which is
contingent upon the occurrence of the entities rather than their measured values. In stark
contrast, traditional econometric analysis is susceptible to the influence of outliers, as it relies
on calculating the slope parameter, which can be significantly affected by outliers. Moreover,
one of the tools of the DDA is the ergodic distribution, which represents the future trend's
steady-state distribution (Wei et al 2020). This analytical technique can comprehensively depict
the underlying trend and the future evolution and intensity of, for example, carbon emissions.
Quah’s DDA can be divided into two major categories: the traditional Markov transition
matrix analysis and the stochastic kernel approach. One issue of the traditional Markov
transition matrix analysis is the arbitrary boundary of the state associated with the selection of
grid values. In contrast, demarcation can be achieved objectively in the stochastic kernel
approach, which can be viewed as an improvement of the traditional Markov transition matrix
approach. As a result, this study employs the stochastic kernel approach. The bivariate kernel
estimator can be represented as the equation (1).
(1)
where n stands for the number of observations, and x (y) stands for the relative CO2 emissions
of an entity at period t (t+1). Xi,t (Xi,t+1) represents an observed value of relative CO2 emissions
at time t (t+1). Furthermore, terms h1 and h2 correspond to the bandwidths computed using the
approach established by Silverman (1986), and K is the normal density function. Due to the
data sparseness, we use the adaptive kernel with flexible bandwidth, which was first proposed
by Silverman (1986). There are two steps in implementation. It involves the computation of a
pilot estimate at the beginning, and then the bandwidth is adjusted by a factor that reflects the
kernel density. Under restrictive assumptions that the studied variable’s distribution at time t
+τ depends on t only, the process is of first order and doesn’t change over time; the relationship
between the distributions at periods t and t +τ can be represented by the equation (2).
(2)
where represents the transition probability kernel, which plots the distribution from
period t to t +τ, while the term
stands for the kernel density function of the variable’s
distribution at period t. Moreover, the term
captures the τ-period-ahead density function
of z conditional on x. Because annual transitions are used in the analysis, the sample size will
be larger, and the estimation results will be more reliable. Given that it exists, the ergodic
density function can be calculated from equation (3).
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(3)
where
represents the ergodic density function given infinite τ. The ergodic distribution
can be viewed as a forecast of the steady-state distribution of relative CO2 emissions. The MPP
display tool was developed by Cheong and Wu (2018) to interpret detailed mobility probability,
which can be used in conjunction with the traditional distribution dynamics approach. The MPP
is obtained through the computation of representing the net upward mobility probability
as per equation (4).
(4)
The MPP is expressed in percentages ranging from -100 to 100 and plots the net upward
mobility probability against the REPC and REPGDP variables. A positive (negative) value
highlights that the entity has a net probability of an upward (downward) shift in the distribution
of the analysed variable (Wei et al 2020). The MPP has advantages over traditional display
tools of distribution dynamics, such as three-dimensional plots or contour maps of transition
probability kernels. Specifically, the MPP offers detailed information about the transitional
dynamics of the variable even if the distribution’s probability mass is highly concentrated.
Moreover, we can superimpose several MPPs in one graph, which, in turn, facilitates effective
comparisons of the REPC and REPGDP transitional dynamics between pre- and post-GFC
periods as well as across countries grouped by income levels. Due to its merits, the MPP has
been employed to study transitional dynamics of energy consumption (Shi et al 2021a, b), per
capita GDP (Wu et al 2021), housing affordability (Liu et al 2022), information transparency
(Williams et al 2022), and credit ratings (Lee et al 2021).
4 Empirical Results and Discussions
To better understand the transitional dynamics of carbon emissions and intensities worldwide,
this section presents and discusses ergodic distributions and the MPPs of the REPC and
REPGDP variables. As for the order of presentation, first, we divide the dataset into two
subperiods: pre-GFC (2000-2007) and post-GFC (2008-2016). Next, we split the overall
sample into four subgroups based on countries' income levels following the World Bank’s
classification.
4.1 Carbon emissions and intensities before and after the GFC
The GFC was followed by unprecedented fiscal and monetary policies undertaken by many
developing (e.g., China) and developed (e.g., the US and the UK) governments to revive their
economies. This section analyses the transitional dynamics and future evolution of the REPC
variable before the GFC (2000-2007) and afterwards (2007-2016). Figure 1 presents the ergodic
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distributions of the REPC variable. It should be remembered that the ergodic distribution
presents the future long-run steady-state equilibrium under the assumption that the transitional
dynamics remain the same. Moreover, the vertical (horizontal) axis represents the proportion
(REPC values).
Figure 1 shows two striking differences between the two periods in the future evolution of
REPC. First, the pre-GFC (post-GFC) distribution is bi-modal (tri-modal), i.e., we can observe
the emergence of an additional convergence club in the long-run steady-state equilibrium based
on the post-GFC period. Specifically, panel A's major and minor peaks correspond to a REPC
value of around 0.2 and 2, respectively. However, in the post-GFC distribution, the three peaks
occur at the REPC values of 2 (major peak), 0.2, and 2.7 (minor peaks). Second, the distribution
in panel B is significantly more spread out. For instance, the post-crisis major peak has a height
of 0.18, while the pre-crisis major peak has a height of 0.85.
The emergence of an additional (third) convergence club in the post-GFC period indicates
increased heterogeneity in countries' REPC in the long run and aligns with Rios and Gianmoena
(2018), who show the emergence of three convergence clubs in a worldwide study. This
suggests that the disparities in emissions widened (Criado and Grether, 2011), potentially due
to varying responses to the financial crisis (e.g., investments in carbon-intensive industries,
reduced taxes, ultra-low interest rates and quantitative easing) and subsequent economic
developments.
Furthermore, significantly more spread-out post-crisis ergodic distribution and the
emergence of additional convergence clubs at higher (nearly three times the average) REPC
Figure 1 Ergodic distributions for REPC before and after GFC
A Pre-crisis (2000-2007) B Post-crisis (2007-2016)
Notes: The horizontal and vertical axes show the value of the REPC variable and the
proportion, respectively.
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values pose opportunities and challenges for global climate policy efforts, respectively (Li et al
2020). On a positive note, substantially more dispersed post-crisis distribution signalises
improved flexibility regarding desirable carbon emissions reduction and convergence to the
mean. Conversely, achieving consensus and coordination among countries to reduce carbon
emissions may be more complex, as countries converging towards significantly above-average
REPC may have divergent interests and priorities. This, in turn, highlights the need for
policymakers to tailor their approaches to address the specific circumstances and drivers of
carbon emissions in countries experiencing different convergence dynamics. Overall, Figure 1
suggests a shifting landscape in global REPC patterns, requiring nuanced and adaptive
approaches in formulating and implementing climate policy.
Figure 2 presents the MPPs of cross-country REPC pre- and post-GFC, which allows us to
directly compare the distributional dynamics and probability mass of emissions in all countries.
The findings are mixed regarding desirable reduction and convergence in CO2 emissions. On
the one hand, the post-GFC MPP lies underneath the pre-GFC MPP for countries with REPC
values from 1.2 to 2.3, 3.8 to 7 and above 9.3. Based on the post-crisis sample period, this
translates to a greater probability of reducing carbon emissions in years to come for countries
with such emissions levels. Therefore, decision-makers could focus on reinforcing existing
policies or implementing new measures to strengthen or sustain this positive trend. The policies
could include incentivising renewable energy investments, implementing stricter emissions
regulations, or promoting energy efficiency measures tailored to these countries' needs.
Such an encouraging pattern is particularly pronounced for the outliers with the highest
REPC values of around 12. Specifically, we can observe that the probability of future carbon
emissions converging towards the global average increases from 17% before the GFC to
approximately 100% after the crisis. Such a maximum probability constitutes a so-called
“development trap”, i.e., good news from the perspective of CO2 emissions convergence; that
is because the biggest emitters, upon approaching this threshold level, would encounter a
decline in their REPC in the following year. The concept of a "development trap" highlights
the importance of recognising when a country's REPC approaches such a threshold level and
ensuring that appropriate measures are in place to sustain and reinforce emission reduction
efforts during this critical development phase. This could involve providing targeted support to
the identified countries, such as technological transfers, capacity-building programs, or
financial incentives for transitioning to cleaner energy sources.
On the other hand, countries with above-average REPC values from 2.3 to 3.8 and 7 to 9.3
are less likely to reduce their CO2 emissions in the coming years during the post-GFC period
than in the pre-GFC period. This means that policymakers may need to reassess existing
strategies and consider implementing tailored policies to address specific challenges hindering
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Figure 2 MPPs for REPC before and after GFC
Notes: The horizontal and vertical axes show the value of the REPC variable and the
MPP, respectively.
emission reduction efforts in these countries. This could include enhancing access to clean
energy technologies, promoting sustainable land-use practices, or providing financial
incentives for adopting low-carbon technologies.
Figure 3 shows ergodic distributions of global, country-level annual carbon intensities
(REPGDP). We can observe a high resemblance between both graphs in panels A and B. The
only apparent difference can be ascribed to the appearance of two additional minor peaks
around the REPGDP value of 0.4 and 1 in panel B. This means the emergence of two additional
clubs in the post-GFC period, consistent with Bhattacharya et al (2020), who predict that
assuming a “business as usual scenario” between 2014 and 2030, the number of convergence
clubs in cross-country carbon intensity would increase. Such results also corroborate Li et al
(2020) argument that the GFC and extraordinary post-crisis policies adopted by governments
worldwide have led to cross-country divergence in carbon emissions. These findings could
offer valuable insights to policymakers. By identifying and monitoring countries belonging to
emerging clubs, policymakers can gain a deeper understanding of the factors influencing
variations in carbon intensity. This understanding can then inform the design of tailored policies
and implementation of targeted measures to address each club member’s specific challenges
and opportunities, such as promoting cleaner technologies, enhancing energy efficiency, or
fostering sustainable development practices.
Additionally, the emergence of a peak around the REPGDP value of one suggests that some
countries are likely to converge to the global average carbon intensity level in the long run.
Policymakers can view this as a positive development and leverage it to inform and guide their
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environmental strategies. This could involve sharing best practices, providing technical
assistance, or facilitating knowledge exchange to support countries in transitioning towards
more sustainable and environmentally friendly practices. Additionally, policymakers may
consider implementing policies that incentivise and reward countries for achieving or
maintaining carbon intensity levels close to the global average.
Figure 4 highlights that two MPPs are near-identical for REPGDP values below 2.65.
Additionally, only countries with REPGDP values far below the global average for both
subperiods have a significant positive net probability of moving upward (closer to the global
average value of one). However, the two plots are highly divergent for the remaining range of
relative carbon intensities. Specifically, the post-GFC MPP lies above the pre-GFC MPP for
the range of REPGDP values from 3.3 to 4.9. This suggests a reduced probability of lowering
carbon relative intensities (convergence to the mean) for countries with such emissions levels
in years to come. Therefore, such countries' decision-makers should focus on identifying the
culprits behind this worrying trend and implementing new, more efficient measures to reverse
On a positive note, we can observe significantly greater and generally increasing
probabilities of reducing carbon intensities (negative values on the vertical axis) post-crisis
compared to pre-crisis for countries with the most extreme relative CO2 intensities (above 5).
This is good news from the perspective of global convergence in emissions, which would
motivate countries to fulfil their environmental commitments and facilitate the adoption of
unified environmental policies or new initiatives (Erdogan and Solarin 2021).
Moreover, focusing on the post-GFC MPP (blue-coloured plot), we can identify three
intersections (and two tangent points) with the horizontal axis around the REPGDP values of
0.67, 0.92 and 1 (0.48 and 1.7). These broadly correspond with the location of four post-crisis
peaks in panel b of Figure 3. This, in turn, underscores that the shape of the ergodic distribution
is primarily determined by transitional dynamics, as indicated by the MPP. Thus, Figure 4
further supports the policies proposed in the previous paragraphs.
Figure 3 Ergodic distributions for REPGDP before and after GFC
A Pre-crisis (2000-2007) B Post-crisis (2007-2016)
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Notes: The horizontal and vertical axes show the value of the REPGDP variable and the
proportion, respectively.
Figure 4. MPPs for REPGDP before and after GFC
Notes: The horizontal and vertical axes show the value of the REPGDP variable and the
MPP, respectively.
4.2 Carbon emissions and intensities across countries with different income levels
In a seminal study, Grossman and Krueger (1991) find evidence of the Environmental Kuznets
Curve (hereafter EKC) hypothesis, i.e., an inverted U-shaped relationship between economic
growth and environmental indicators (e.g., air pollution). Since then, the EKC has been tested
empirically in studies on carbon emissions, but the results remain inconclusive and mixed (e.g.,
Atasoy 2017, Nam et al 2020).
Figure 5 plots the annual CVs of the REPC variable for countries grouped by their income
levels according to the World Bank’s classification updated annually. We can observe a rapid
global cross-group convergence resulting primarily from a massive decrease in variability
among the poorest countries, especially post-2006 (from 2.13 to 0.94). Interestingly, the lower-
middle-income countries displayed a reversed (increased) trend between 2009 and 2013, which
could be associated with unprecedented expansionary policies (monetary and fiscal alike) of
many developing economies (e.g., China) to resist the GFC of 2008 (Wang et al 2021). The
smallest decrease in CV during the sampled period encountered a group of upper-middle-
income countries (from 0.71 to 0.68) followed by the high-income economies (from 0.81 to
0.72).
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Figure 5. Time trends in annual CVs of REPC variable by different income groups
Source: Authors’ calculations based on the World Bank’s World Development Indicators (WDI)
The picture painted in Fig 6. largely contrasts that in Fig 5. First, we can observe that the lowest
variability, with an overall decreasing trend over time, can be attributed to the low-income
countries.
Figure 6. Time trends in annual CVs of REPGDP variable by different income groups
Source: Authors’ calculations based on the World Bank’s World Development Indicators (WDI)
0.6
0.8
1
1.2
1.4
1.6
1.8
2
2.2
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
C.V. of REPC
High-income Low-income
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
C.V. of REPGDP
High-income Low-income
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On the contrary, the annual CVs for the most affluent economies (yellow-coloured plot)
followed an increasing trend (from 0.76 to 0.95), translating into a robust divergence between
2000 and 2016. While we can observe a similar reversed (increasing) pattern in the blue-
coloured plot (lower-middle-income) to that in Figure 5, this group of countries made the most
significant progress regarding within-group convergence in carbon emissions.
While Figure 5 and 6, based on the sigma convergence tool (CV), are highly informative,
they are limited in their ability to forecast the dependent variable, as they do not provide insights
into the shape of the underlying distribution. Given the above backdrop, we are the first to
employ the ergodic distribution and the MPP to analyse REPC and REPGDP across countries
divided into four groups based on income levels
6
.
Figure 7 indicates that most low-income and many lower-middle-income countries
congregate around minimal REPC levels in the long-run steady-state equilibrium. On the other
hand, most high-income economies converge to above the global average REPC levels. Such
apparent divergent long-run trend between the poorest and wealthiest countries is grim news
and at odds with the EKC. This, together with pre/post-GFC analysis (see Fig. 1), suggests that
the GFC and expansionary policies introduced by many industrialised countries could result in
a long-run increase in REPC and the persistent income gap between the rich and the poor.
Figure 7 also highlights by far the most (least) significant convergence process for the low-
(high-) income group of countries captured by the tallest (shortest) and the least (most) spread
out ergodic distribution. Such findings starkly contrast with the evidence reported by Li and
Lin (2013). Furthermore, we can observe that except for upper-middle-income countries,
multiple convergence clubs emerge, whilst all peaks in panels A and B (D) are situated below
(above) the global average REPC equal to one. Thus, assuming the transitional dynamics
remain unchanged, we can expect only conditional convergence in REPC across the three
groups of countries: low-, lower-middle-, and high-income. Such a finding contrasts that of
Zang et al (2018), who examined a sample of 201 countries from 2003 to 2015 and found
evidence of club convergence only among high-income countries.
In addition, we can observe that ergodic distributions become more spread out as we move
up the income ladder, i.e., from panel A to panel D. This observation, in turn, is contrary to the
results of Li et al (2020) and suggests the least and the most significant convergence process
across the richest and the poorest countries, respectively. The findings in Fig 7. highlight the
complex relationship between income levels and carbon emissions, emphasising the need for
targeted policies and international cooperation to address climate change effectively across
diverse economic contexts.
6
See Table A1 in the Appendix for the list of countries by their income levels as of the end of 2016.
During the 2000-2016 period, numerous (a few) countries have moved into higher (lower) income
brackets in line with the World Bank’s annual classification. As of 2016, 30, 53, 55, and 66 low-, lower-
middle, upper-middle, and high-income countries are in the sample.
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Figure 7. Ergodic distributions for REPC by different income groups
A Low-income B Lower-middle-income
C Upper-middle-income D High-income
Notes: The horizontal and vertical axes show the value of the REPC variable and the
proportion, respectively.
Figure 8 shows that the MPP for the high (low) income countries is the least (the most) volatile,
translating into the least (the most) significant aggregate net mobility probability in years to
come. The weakest aggregate net mobility probability suggests a lower likelihood of substantial
shifts in high-income countries' per capita carbon emissions patterns over time. Instead, we can
expect a relatively stable or stagnant emissions trajectory, with gradual or incremental changes
in REPC. This, in turn, could result from the interplay of established infrastructure, mature
economies, and strict environmental regulations that limit rapid changes in carbon emissions of
the high-income countries. On the contrary, more volatile aggregate net mobility probability
signals a more dynamic and evolving REPC landscape, reflecting rapid changes in economic
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
0.00
0.10
0.20
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0.40
0.50
0.60
0.70
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
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activities, rapid industrialisation, technological transitions, policy interventions, and limited
regulatory oversight across many low-income countries.
Figure 8. MPPs for REPC by different income groups
Notes: The horizontal and vertical axes show the value of the REPC variable and the
MPP, respectively.
Additional important implications can be drawn from identifying (1) the sections of MPPs
above the global average REPC values positioned above the horizontal axis and (2) the
intersections and tangent points between the MPPs and the horizontal axis. That is because (1)
it enables us to pinpoint carbon emitters above the global average with a specific range of REPC
levels and exact probabilities to diverge further away/above the global average in the coming
years. Similarly, (2) indicates sticky and above the global average REPC values around which
the entities from different income groups would congregate in years to come.
Given limited resources and environmental policy goals of convergence/reduction in CO2
emissions (Wei et al 2022, Wei et al 2023), countries with above-average REPC levels and net
upward mobility probabilities ranging from 100 to zero should be placed on the policy priority
list. Figure 9 indicates that for the low- (lower-middle) income countries, the alarming ranges
of REPC occur from 1.5 to 2.6 (1.25 to 1.4 and 2 to 2.45), while for the upper-middle- (high)
income countries around a REPC value of 1.5 and from 2.45 to 2.6 (below 1.25). However, the
top spot in the policy priority list should be reserved for low-income countries with REPC
values of around 2.3. This is because the MPP representing these entities reaches the maximum
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positive net mobility probability of 100 at a REPC value of 2.3. This, in turn, implies that upon
reaching such a REPC level, the country’s emissions have a 100 per cent probability of moving
even further above the global average in years to come.
Moreover, Figure 8 indicates that low- and upper-middle-income countries experience a
development trap in their REPC. This is a positive piece of information because it means that
whenever low- and upper-middle-income countries achieve REPC values of 2.9 and 4, they
will encounter a reduction in REPC and a downward move within the distribution in the
following years. However, the green and yellow plots representing more affluent economies
approach the development trap at higher REPC levels of 9 and 21.5, respectively.
Summing up, findings based on the first measure of CO2 emissions (REPC) are essential
from the perspective of future environmental policies aiming at emissions reduction and
convergence. For instance, the results imply the onus on high-income countries to reduce their
per capita carbon emissions. Furthermore, more significant variability in the REPC observed
across the rich countries suggests they would have more spare capacity and flexibility in
reducing CO2 emissions. On the other hand, the poorer countries appear to have ample time
before converging to the global average, except for the outliers with a net mobility probability
of 100 at a REPC value of 2.3, identified in Figure 8.
Figure 9 shows that the ergodic distribution for the low-income (lower-middle-income)
group is denser (more spread out), with more entities converging around lower (higher)
REPGDP values corresponding to three peaks located at 0.35, 0.6 and 1.8 (0.7, 1.1 and 2).
Furthermore, the distribution in panel C for the upper-middle-income countries is the most
dispersed, with many countries congregating around REPGDP values far above the mean (2.4).
However, the most concentrated is the ergodic distribution for the wealthiest group of countries,
with a single peak significantly below the global mean (REPGDP value of 0.32) and a long,
thin right tail. Concerning the emergence of convergence clubs, these appear in panels A to C
only, signifying conditional (absolute) convergence at best across low- and medium-income
(high-income) countries. Such observation only partially corroborates Zang et al (2018) results
of clubs across all income groups in their study of 201 countries.
Overall, findings from Figure 9 suggest that the onus is on the upper-middle-income
emitters to reduce carbon intensity because some countries from this income group converge
toward the long-run carbon intensities significantly above the global average. Such a finding
corroborates Dong et al (2020), who document that the upper-middle-income is the primary
source of worldwide CO2 emissions post-2008 financial crisis. Moreover, the results suggest
that middle-income economies incur relatively higher environmental externalities (emissions
per USD of GDP) for their economic development (Zang et al 2018, Wang et al 2021).
Nevertheless, the most dispersed ergodic distribution for upper-middle-income countries
implies greater flexibility in reducing carbon intensity. Policymakers could leverage this
flexibility to implement policies and strategies tailored to specific country contexts, including
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investment in clean technologies, renewable energy, energy efficiency, and sustainable
infrastructure.
Figure 9. Ergodic distributions for REPGDP by different income groups
A Low-income B Lower-middle-income
C Upper-middle-income D High-income
Notes: The horizontal and vertical axes show the value of the REPGDP variable and the
proportion, respectively.
The MPPs in Figure 10 indicate that countries with REPGDP values below 0.35, irrespective
of income levels, experience a positive net probability of moving upward in the distribution in
future years. From the policy perspective, Figure 10 is interesting because it pinpoints the
countries with specific above-the-global average REPGDP values and positive net probability
of moving higher in the future distribution. Therefore, assuming the emissions reduction goals
are based on CO2 intensity, such countries merit special attention, i.e., they should enter the
climate policy priority list.
We can observe that low-income countries with a range of REPGDP values around 1.65,
3.15, and 6.3 should be placed on the priority list due to the stickiness of their above-average
relative emissions. By the same token, high-income countries with relative carbon intensities
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between 3.3 and 4.5 are also problematic. In particular, industrialised economies with REPGDP
values around 3.8 have the most significant (65%) probability of further diverging from and
above the global average regarding their REPGDP in years to come. This, in turn, places them
high on the policy priority list. On the contrary, two middle-income groups appear to be the
least problematic.
Figure 10. MPPs for REPGDP by different income groups
Notes: The horizontal and vertical axes show the value of the REPGDP variable and the MPP,
respectively.
5 Conclusion and Policy Implications
The main findings of this study can be summarised in three points. First, the post-GFC period
ergodic distributions are characterised by the emergence of additional convergence clubs in
relative per capita carbon emissions (REPC) and relative carbon intensity (REPGDP). Besides,
the post-GFC distribution is significantly more spread out, suggesting that the global long-run
convergence process in REPC ex-post-GFC becomes less significant. Such results, in turn,
might imply the adverse effects of GFC and post-crisis expansionary policies aimed at
economic recovery undertaken by developing and developed countries (e.g., in China and the
US). Overall, the results are pessimistic from the perspective of global warming and
environmental policies because the more significant number of clubs makes future international
climate negotiations and ambitious targets more complex and challenging.
Second, regarding countries grouped by income levels, the results are very different based
on the analyses for the REPC vis-à-vis the REPGDP variable. For instance, a solid divergent
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trend exists in the long-run distribution of REPC. With a vast majority (most) of the poorest
(richest) countries congregating at extremely low (above the global average) REPC levels.
From the perspective of environmental policies aiming at reduction/convergence in global CO2
emission, the results based on the REPC variable suggest that the onus should be majorly on
the industrialised (high-income) countries. However, using the REPGDP variable, the
convergence process is the least significant among upper-middle-income countries, with many
countries clustering around REPGDP values far above the mean (2.4). Therefore, based on the
REPGDP measure, upper-middle-income countries should bear a more significant share of the
future carbon emissions commitments.
Third, using the MPP tool, we identify countries with specific REPC and REPGDP levels
that merit a place in the environmental policy priority list. Specifically, low-income countries
with REPC values of around 2.3 have around 100 per cent probability of moving further up in
the distribution in future years. By the same token, high-income countries with REPGDP values
around 3.8 have the most significant (65 per cent) probabilities of diverging further from and
above the global average. Therefore, in line with the global warming environmental policies
aiming at CO2 emissions reduction/convergence, the above countries should be at the top of the
priority list.
This study offers several policy implications. First, the results deliver nascent evidence
supporting the usefulness of the MPP display tool. For instance, an MPP-based “policy priority
list” can inform policy prioritisation efforts by highlighting areas where emissions abatement
is most urgently required. Policymakers, in turn, could employ this information to allocate
limited resources and prioritise interventions targeting specific countries exhibiting
unfavourable emissions trends, thereby maximising the effectiveness of climate mitigation
efforts globally. Therefore, we advocate the periodic/annual implementation of the MPP
framework as an integral part of the global reference system, helping countries update, optimise
and manage their climate policies and carbon regulations.
Second, we analysed the transitional dynamics and long-run trends in two variables: (1)
REPC, which focuses on carbon emissions per capita, and (2) REPGDP, which accounts for
emissions relative to economic output. Thus, the documented divergence in the forecast trends
between REPGDP and REPC has several implications for multilateral climate negotiations and
global environmental policies. (1) countries with high per capita emissions (as reflected in
REPC) may argue for policies prioritising emission reductions based on individual
consumption patterns. Conversely, countries with high carbon intensity relative to GDP (as
indicated by REPGDP) may advocate for policies that focus on reducing emissions associated
with economic activities. Balancing these competing interests will be crucial for achieving
equitable and effective climate agreements. (2) the results suggest the need for sector-specific
approaches to emission reduction. For instance, economies with high carbon intensity may need
to decarbonise specific sectors (e.g., energy production, industry, or transportation). Tailoring
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mitigation efforts to address sector-specific challenges can enhance the effectiveness of climate
policies and facilitate smoother negotiations. Meanwhile, countries with high REPC may
require policies targeting lifestyle changes, consumption patterns, and urban development.
Third, estimating the distribution of the DDA and the MPP methods among all the countries
can help commensurate intergovernmental cooperation plans by prioritising carbon tax policies
across the countries. For instance, findings based on the pre- versus post-crisis dynamics in
global CO2 emissions align with predictions that assuming a "business as usual scenario" will
increase the number of clubs in cross-country carbon intensity (REPGDP) and emissions per
capita (REPC) alike. We advocate that policymakers consider these projections when designing
long-term environmental policies and strategies. This could include implementing proactive
measures to mitigate carbon intensity growth, such as setting ambitious emission reduction
targets, promoting renewable energy adoption, or implementing carbon pricing mechanisms.
Furthermore, the research outcomes depict the future development trend of national carbon
emissions and intensity, thereby guiding the government to allocate capital and technical
resources more efficiently to promote energy transition. Climate change cooperation
organisations can encourage and prioritise their investment in these countries and regions and
improve knowledge diffusion, particularly in regions with outdated and imbalanced carbon
emissions and intensity.
The study employs the visual tools of the DDA method, which is inherently limited due to
restrictive assumptions of no changes in the transitional dynamics of the study variable. This,
in turn, may influence the accuracy of transition probabilities and long-term evolution
projections. Moreover, due to data limitations, our sample spans between 2000 and 2016.
However, the unprecedented changes in the global social and political economy, such as the
COVID-19 worldwide pandemic and the outbreak of the military conflict in Ukraine,
substantially impact the energy market and the trajectory of global emissions. Given the above
backdrop, exploring the potential structural changes and convergence patterns in carbon
emissions and intensities for the most recent period is imperative.
Moreover, by focusing on carbon emissions among countries with different income levels,
we possibly overlook other factors influencing emissions disparities, such as socioeconomic,
political, and environmental variables. The analysis of pre-crisis and post-crisis periods may
not capture all relevant economic or ecological shocks impacting emissions trajectories.
Consequently, future research could expand the scope of study to incorporate a broader range
of factors influencing carbon emissions convergence-divergence patterns, such as technological
advancements, policy interventions, and socio-economic indicators.
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Appendix
Table A1. 204 countries grouped by income level
Income levels
Countries
High-income
Andorra, Antigua and Barbuda, Aruba, Australia, Austria, Bahamas, Bahrain, Barbados,
Belgium, Bermuda, British Virgin Islands, Brunei Darussalam, Canada, Cayman Islands,
Chile, Cyprus, Czech Republic, Denmark, Estonia, Faeroe Islands, Finland, France, French
Polynesia, Germany, Gibraltar, Greece, Greenland, Hong Kong, Hungary, Iceland, Ireland,
Israel, Italy, Japan, Kuwait, Latvia, Liechtenstein, Lithuania, Luxembourg, Macao, Malta,
Netherlands, New Caledonia, New Zealand, Norway, Oman, Palau, Poland, Portugal, Qatar,
Saudi Arabia, Seychelles, Singapore, Slovak Republic, Slovenia, South Korea, Spain, St.
Kitts and Nevis, Sweden, Switzerland, Trinidad and Tobago, Turks and Caicos Islands,
United Arab Emirates, United Kingdom, United States, Uruguay
Upper-middle-
income
Albania, Algeria, Argentina, Azerbaijan, Belarus, Belize, Bosnia and Herzegovina,
Botswana, Brazil, Bulgaria, China, Colombia, Costa Rica, Croatia, Cuba, Dominica,
Dominican Republic, Ecuador, Equatorial Guinea, Fiji, Gabon, Grenada, Guyana, Iran, Iraq,
Jamaica, Kazakhstan, Lebanon, Libya, Malaysia, Maldives, Marshall Islands, Mauritius,
Mexico, Montenegro, Namibia, Nauru, North Macedonia, Panama, Paraguay, Peru,
Romania, Russian Federation, Samoa, Serbia, South Africa, St. Lucia, St. Vincent and the
Grenadines, Suriname, Thailand, Tonga, Turkey, Turkmenistan, Tuvalu, Venezuela
Lower-middle-
income
Angola, Armenia, Bangladesh, Bhutan, Bolivia, Cabo Verde, Cambodia, Cameroon, Congo,
Rep., Côte d'Ivoire, Djibouti, Egypt, El Salvador, Eswatini, Georgia, Ghana, Guatemala,
Honduras, India, Indonesia, Jordan, Kenya, Kiribati, Kosovo, Kyrgyz Republic, Lao PDR,
Lesotho, Mauritania, Micronesia, Moldova, Mongolia, Morocco, Myanmar, Nicaragua,
Nigeria, Pakistan, Papua New Guinea, Philippines, São Tomé and Principe, Solomon Islands,
Sri Lanka, Sudan, Syria, Tajikistan, Timor-Leste, Tunisia, Ukraine, Uzbekistan, Vanuatu,
Vietnam, West Bank and Gaza, Yemen, Zambia
Low-income
Afghanistan, Benin, Burkina Faso, Burundi, Central African Republic, Chad, Comoros,
Congo, Dem. Rep., Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Haiti, North Korea,
Liberia, Madagascar, Malawi, Mali, Mozambique, Nepal, Niger, Rwanda, Senegal, Sierra
Leone, Somalia, Tanzania, Togo, Uganda, Zimbabwe
Note: The list of countries follows the World Bank’s classification as of the end of 2016.
