metadata: authors: 0: author: Dong,Kangyin
1: author: Hochman,Gal
2: author: Timilsina,Govinda R.
count: World
docna: 0: docna: Are driving forces of CO2 emissions
            different across countries? : insights from identity and
            econometric analyses
docty: Policy Research Working Paper
subtopic: Energy Demand,Energy and Mining,Energy and Environment,Economic Theory & Research,Economic Growth,Industrial Economics,Global Environment,Energy Consumption,Environment and Energy Efficiency,Energy Conservation & Efficiency
teratopic: Macroeconomics and Economic Growth,Industry,Environment,Energy
abstracts: cdata!: This paper investigates factors behind
            the growth of carbon dioxide emissions over the 35 years
            between 1980 and 2015 in more than 100 countries, using an
            index decomposition technique (the Logarithmic Mean Divisia
            Index). The results are further confirmed using an
            econometric technique (the general method of moments). The
            study finds that economic growth, measurred in per capita
            gross domestic product, and population growth are the main
            drivers of the growth of carbon dioxide emissions during
            1980–2015. Although economic growth is mainly responsible
            for the growth of emissions in high-, upper-middle-, and
            lower-middle-income countries, population growth that is
            primarily responsilble for it in low-income countries. More
            than 70 percent of the global growth in carbon dioxide
            emissions over the past 35 years was contributed by
            upper-middle-income countries. Improved energy efficiency,
            reflected in the declining energy intensity of gross
            domestic product, has substantially contributed to limit
            global carbon dioxide emissions at the current level;
            otherwise, the world's current carbon dioxide emissions
            would have been 40 percent higher. Despite the recent rapid
            expansion of renewable energy, its contribution to slowing
            the growth of global carbon dioxide emissions is not
            noticeable yet, due to its small share in the global energy
            supply mix.
repnme: repnme: Are driving forces of CO2 emissions
            different across countries? : insights from identity and
            econometric analyses
display_title: Are driving forces of CO2 emissions
            different across countries : insights from identity and
            econometric analyses
listing_relative_url: /research/2018/06/30147563/driving-forces-co2-emissions-different-across-countries-insights-identity-econometric-analyses
topicv3: Economic Growth m1326241 471,Energy and Extractives m1326595 507,Energy Efficiency m1326852 533,Environment, Natural Resources and Blue
            Economy m1327391 566,Population, Aging and Health m1327736 640
publishtoextweb_dt: 2018-06-20T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Environment,Energy
no_of_pages: 41
author_keys: 0: author_key: 1271716
1: author_key: 940446
model_extractions: WPS8477 Policy Research Working Paper 8477 Are Driving Forces of CO2 Emissions Different across Countries? Insights from Identity and Econometric Analyses _Kangyin Dong Gal Hochman Govinda R. Timilsina_ Development Economics Development Research Group June 2018
Policy Research Working Paper 8477

# **Abstract**

This paper investigates factors behind the growth of carbon dioxide emissions over the 35 years between 1980 and 2015 in more than 100 countries, using an index decomposition technique (the Logarithmic Mean Divisia Index). The results are further confirmed using an econometric technique (the general method of moments). The study finds that economic growth, measurred in per capita gross domestic product, and population growth are the main drivers of the growth of carbon dioxide emissions during 1980–2015. Although economic growth is mainly responsible for the growth of emissions in high-, upper-middle-, and lower-middle-income countries, population growth that is primarily responsilble for it in low-income countries. More than 70 percent of the global growth in carbon dioxide emissions over the past 35 years was contributed by upper-middle-income countries. Improved energy efficiency, reflected in the declining energy intensity of gross domestic product, has substantially contributed to limit global carbon dioxide emissions at the current level; otherwise, the world’s current carbon dioxide emissions would have been 40 percent higher. Despite the recent rapid expansion of renewable energy, its contribution to slowing the growth of global carbon dioxide emissions is not noticeable yet, due to its small share in the global energy supply mix.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at gtimilsina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Are Driving Forces of CO2 Emissions Different across Countries? Insights from Identity and Econometric Analyses**<sup>**1**</sup>

**Kangyin Dong, Gal Hochman and Govinda R. Timilsina**<sup>**2**</sup>

**Keywords:** CO2 emissions; Driving forces; system GMM; LMDI method; Incomelevel countries

**JEL Classification** : C43, Q54

> 1 The views and interpretations are of authors and should not be attributed to the World Bank Group and the organizations they are affiliated with. We acknowledge World Bank’s Knowledge for Change (KCP) Trust Fund.

> 2 Dong and Hochman are, respectively, Ph.D student and Associate Professor at the Department of Agricultural, Food and Resource Economics, Rutgers University, New Jersey, USA. Timilsina is a Senior Economist at the Development Research Group, World Bank, Washington, DC, USA.
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Two different approaches are found to be used in the literature to determine the causes of CO2, growth. Broadly, these approaches can be classified into two groups: identity approach and econometric approach. The first decomposes the independent variables to calculate their relative roles in driving the emission growth; it is also called index decomposition analysis, pioneered by Professor B.W. Ang of Singapore National University [3]. The second uses the standard econometric approach to check the causality between the dependent and independent variables.

Within the decomposition or identity approach, there are several specific techniques, of which the Logarithmic Mean Divisia Index (LMDI) method introduced by Ang and Zhang in 2000 [4] has been widely used. The popularity of this technique has increased due to its ability to handle cases with zero values without leaving residuals, consistency in aggregation, and path independency [5-9]. Since the 2000s, a growing body of studies has employed the LMDI approach to identify the factors impacting CO2 at the country and regional levels (see Table A1). For instance, at the regional level, González et al. [10] decompose CO2 in the European Union (EU) from 2000 to 2010 into five factors. Other scholars investigated CO2 at the country level (Hatzigeorgiou et al. [11] for Greece, Tunç et al. [12] for Turkey, Oh et al. [13] for the Republic of Korea, de Freitas and Kaneko [14] for Brazil, O’Mahony et al. [15] for Ireland, Zhang et al. [16] for China, Feng et al. [17] for the United States, and Mousavi et al. [18] for the Islamic Republic of Iran, among others).

On the econometric side, various methods are employed where the focus of these studies is usually the relation between CO2 and income and carried out at the national, regional and global levels. At the national level, Bento and Moutinho [19] explore the linkage between renewables energy adoption and CO2 emissions in Italy using TodaYamamoto causality tests. Similar studies have been conducted by Bélaïd and Youssef [20] for Algeria and Danish et al. [21] for Pakistan. At the regional level, Dong et al. [22, 23] use the VECM panel Granger causality method to estimate how expansion of renewable energy consumption would reduce CO2 emissions in the BRICS countries 3
(i.e., Brazil, Russia Federation, India, China, and South Africa). Other similar regional studies include Dogan and Seker [24] and Jebli et al. [25].

The added value of this paper is to complement the analysis using the LMDI approach with the econometric approach -- the generalized method of moments so that results derived from the former technique can be validated or strengthened with the latter thereby enhancing the robustness of the findings. Unlike many existing studies that determine the drivers of CO2 growth at the national or regional level, this study considers a global analysis using data from 100 plus countries.<sup>3</sup> The data span (19802015) considered in this study is much longer as compared to other existing literature.

The remainder of this paper is structured as follows. Section 2 outlines the methodology and data used. Section 3 discusses the results from the decomposition analysis. Section 4 discusses the results from the econometric analysis. Section 5 conducts a comparison of the results between the two approaches: statistical vs. numerical. Section 6 offers policy discussion and concluding remarks.

# **2. Methodology and data**

Let us define global CO2 emissions from energy consumption in a country for a given year with the following identity: where _C_ denotes the global CO2 emissions; _C ij_ represents the amount of CO2 emissions of energy type _j_ in country _i_ ; _E ij_ stands for the energy consumption by energy type _j_ consumed in country _i_ ;<sup>_E_</sup> _i_<sup>,</sup> _GDPi_ , and<sup>_P_</sup> _i_<sup>refer to the total primary</sup>

> 3 Two other global studies include Wang et al. [26] and Bacon et al. [27]. However, their period of analysis is much shorter than the one used in this paper.

> [26] Wang S, Li G, Fang C. Urbanization, economic growth, energy consumption, and CO2 emissions: Empirical evidence from countries with different income levels. Renew Sust Energ Rev. 2017.

> [27] Bacon RW, Bhattacharya S, Damania R, Kojima M, Lvovsky K. Growth and CO2 emissions: how do different countries fare. Environment Department Papers. 2007;113.

4
energy consumption, GDP, and total population in country _i_ , respectively.

Eq. (1) can then be written as follows: where _ECij_  _Cij Eij_ denotes the emission coefficient of fossil fuel _j_ in country _i_ ; _ECSij_ = _Eij Ei_ denotes the energy mix of fossil fuel _j_ in country _i_ ; _EIi_ = _Ei GDPi_ is the energy intensity in country _i_ ; and _PCGi_ and<sup>_P_</sup> _i_<sup>are the per capita GDP and total</sup> population of country _i_ , respectively.

Below we first present the identity approach -- LMDI methodology-- (Section 2.1) followed by the econometric approach - General Method of Moments (GMM) in Section 2.2. The fundamental difference between these techniques is that the LMDI approach assumes that relationship between the independent and dependent variables already exists, it only measures the relative importance of each independent variable to influence the change in the dependent variable. On the other hand, the econometric approach does not assume the relationship between the independent and dependent variables, instead it investigates if such relationship exists or it estimates the causal links between CO2 emissions and their potential determinants. Since the LMDI is the outcome of a numerical analysis, it does not account for variability and uncertainty. LMDI does not account for all data points, as it only includes the start and ending points, which may result in biased estimates. If the data exhibit good accuracy, averaging across all countries should account for lack of precision in the estimates. However, if the data exhibit lack of precision, then the LMDI estimates yield biased results. And LMDI does not account for some factors due to its limitation in specification (e.g., LMDI cannot accommodate the quadratic term of GDP).

GMM is a statistical method that weighs observations using a measure of precision and controls country- and time-specific effects. Thus, it provides a more robust analysis of the causal links between CO2 emissions and their determinants.

Another key difference between LMDI and GMM especially in the global analysis 5
is that LMDI counts the weight of an individual country in the global effects. Meaning that the global change in CO2 is largely influenced by big countries such as China and _C t_ - _C_ 0 the United States (see the _L_  _Cijt_ , _Cij_ 0   _ij t ij_ 0 component in Equations 4 to 8). This ln _C C_  _ij ij_  causes the results to be biased towards larger countries and effects of driving factors from small countries do not show up. For example, if the energy intensity of GDP in many countries is improving significantly, but decreasing in the large countries, the improvements in many countries will be overshadowed by deteriorations in the aggregate or the global result. The GMM method, on the other hand, corrects this bias by putting an equal weight on each country for a given driver. The results under the GMM are more representative of all countries instead of large countries only. Therefore, complementing LMDI analysis with GMM brings additional insights in the analysis.

# **2.1. The LMDI decomposition method**

Using the LMDI decomposition technique, the following relationship can be derived from Eq. (2): where superscripts _t_ and 0 denote the final year and benchmark year; and  _CEC_ ,  _CECS_ ,  _CEI_ ,  _CPCG_ , and  _CP_ refer to emission coefficient effect, energy mix effect, energy intensity effect, income effect, and population growth effect, respectively. The five effects can be deduced as follows: _C t_ - _C_ 0 where _L_  _Cijt_ , _Cij_ 0   _ij t ij_ 0 , and subscripts _i_ and _j_ are energy and country types, ln _C C_  _ij ij_  6
respectively.

# **2.2. Econometric methodology**

For the GMM approach, we convert Eq. 2 to the relationship expressed in Eq. 9. In addition, we added one more factor, square of GDP per capita to investigate if the environmental Kuznets curve (EKC) ([28] & [29]) occurs. Note in Eq. 9 that we assumed a log-log relation between CO2 emissions and the driving factors.

ln _CO_ 2 _it_   0   1 ln _ECit_   2 ln _ECSit_   3 ln _EIit_   4 ln _PCGit_   5 ln _PCGit_ 2   6 ln _Pit_   _it_ (9) where subscripts _i_ and _t_ denote country and year, respectively;  1- 6 are the parameters to be estimated; _CO_ 2 represents the amount of CO2 emissions (measured in million tonnes, Mt); _EC_ indicates the emission coefficient (measured in tonnes/toe); _ECS_ denotes energy mix (measured in the share of dirty fossil fuels (i.e., coal and petroleum) in total final energy consumption, %); _EI_ describes energy intensity (measured in tonnes/10,000 US$); _PCG_ ( _P C G_ 2 ) stands for per capita GDP (squared) (measured in 1,000 US$); _P_ is population size (measured in billions);  0 is a constant term; and  is a random error term.

The dynamic relationship between _CO_ 2 and its determinants suggests one should use a dynamic panel model, where the first-order lagged term of _CO_ 2 (i.e., _CO_ 2 _it_ -1 ) is taken into account and introduced into our empirical model (into Eq. (9)): ln _CO_ 2 _it_   0   1 ln _CO_ 2 _it_ -1   2 ln _ECit_   3 ln _ECSit_   4 ln _EIit_   5 ln _PCGit_   6 ln _PCGit_ 2   7 ln _Pit_   _it_ (10) An endogenous problem due to the correlation between the independent variable and error term results in biased estimates when conventional panel data estimation methods, such as pooled ordinary least squares (OLS), fixed effect, and random effect, are adopted. However, the GMM estimator proposed by Arellano and Bond [30] and developed by Arellano and Bover [31] and Blundell and Bond [32], provides a solution to the endogeneity and also controls individual- and time-specific effects.

Two different types of GMM estimators are usually used in the literature: (i) difference GMM (i.e., first-difference GMM and orthogonal-difference GMM) developed by Arellano and Bond [30]; and, (ii) system GMM (Arellano and Bover [31] 7
and Blundell and Bond [32]). The main difference between the two methods is in the choice of the endogenous variables and corresponding instrumental variables. As reported by Sung et al. [33], the system GMM estimator is theoretically more efficient than the difference GMM estimator because it allows for a richer set of instruments. The difference GMM estimator may also suffer from weak finite sample bias when the cross-section dimension (N) is large enough. In our sample, the cross-section dimension (N=110) and time dimension (T=8). In this paper we adopt the system GMM estimator.

The efficiency of the system GMM depends on the following two specification tests: (i) the difference-in-Hansen test for too many instruments; and (ii) the Arellano and Bond test for second-order autocorrelation (i.e., AR(2) test). When employing our data, the analysis suggests that there is no instruments proliferation or we do not have too many instruments.

# **2.3. Data**

We use a balanced panel data set for 110 countries, with eight data points covering 1980-2015 corresponding to 1980, 1985, 1990, 1995, 2000, 2005, 2010, and 2015. Prior to the Kyoto Protocol adopted in 1997, CO2 emissions where not much of a concern. However, mounting concerns to the environment and the need to limit CO2 emissions ignited worldwide concerns, (e.g., the establishment of the UNFCCC and the 2015 Paris Climate Change Conference). To understand how changes in awareness of climate change impacts CO2, the analysis not only investigates the whole period of analysis, but also looks at two subsamples: early (1980-1990) and late (2005-2015) periods.

The 110 countries are classified into four groups based on their per capita gross national income (GNI) calculated using the World Bank Atlas method 2016 [34]: lowincome countries (LI countries, less than $1,005), lower-middle-income countries (LMI countries, $1,006-$3,955), upper-middle-income countries (UMI countries, $3,956$12,235), and high-income countries (HI countries, more than $12,236). The lowincome subpanel in this study consists of data for 10 countries, while the lower-middle8
income, upper-middle-income, and high-income subpanels comprise data for 28 countries, 28 countries, and 44 countries, respectively (see Table A2 in Appendix A).

The data on CO2 emissions from fuel combustion are collected from the CO2 Emissions from Fuel Combustion 2017 published by the International Energy Agency (IEA) [35], while IEA’s World Energy Balances [36] provides the data on energy consumption across the globe. In this study, we use primary energy supplied defined as energy production plus energy imports plus increase in stocks (which is negative if the stock decreases), minus energy exports and international bunkers. In addition, the data on GDP and population are obtained from the World Development Indicators (WDI) published by the World Bank [2], in which the data on GDP are in 2010 constant prices.

# **3. Results from the Decomposition Analysis**

# **3.1. Decomposition results of global CO2 emissions changes**

The decomposition results are depicted in Fig. 2a. The group with the highest contribution to global CO2 growth during the 1980-2015 period is UMI countries, contributing 71.3%, followed by the LMI, HI, and LI countries, which contributed 21.7%, 9.8%, and 0.3%, respectively.

As shown in Figs. 2b and 2c, only the energy intensity effect (  _CEI_ ) for the 19802015 period is negative, while the income effect (  _CPCG_ ), population effect (  _CP_ ), emission coefficient effect (  _CEC_ ), and energy mix effect (  _CECS_ ) are positive. These results suggest that, while economic and population growth have been driving up CO2 emissions over the 1980-2015 period, improving energy efficiency is slowing down that growth. CO2 growth would have been even faster had there been no improvements of energy efficiency. Note that global CO2 emissions increased by more than 80% over the last 35 years (1980-2015). Total CO2 emissions added during that time are more than 14,000 million tons (Fig. 1c). If the average energy intensity of GDP remained at the 1980 level, global CO2 emissions in 2015 would have been almost 14,000 million tons (or more than 40%) higher than the actual level in that year.

The income or economic growth effect is the primary factor driving CO2 emissions 9
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It would be interesting to see the relationship between CO2 emission growth and driving factors at different periods. For this, we divide the entire period (1980-2015) into two periods: early period (1980-1990) and the late period (2005-2015) (see Section 2.3).<sup>4</sup> Changes in the global emissions from the four income-based subpanels over the periods 1980-1990 and 2005-2015 are shown in Fig. 3. For both subperiods, countries with the highest increase in global emissions were mainly UMI countries, contributing 7.9% and 6.3% to the increase of global emissions, respectively.

The LMI countries contributed 3.3% and 2.7% over these two periods, respectively, to the increase in global emissions. On the other hand, between 1980 and 1990 HI countries contributed 2.4% to the increase in global emissions. Growth of CO2 emissions in the HI countries is slowing down yet still positive since 2005 (see Fig. 3). In addition, the LI countries’ contribution to CO2 emissions is small; LI countries contributed 0.1% to the increase in global emissions over the two investigated periods. These results have strong policy or international negotiation implications regarding climate change mitigation. While high-income countries are historically responsible for the current level of CO2 concentration in the earth’s atmosphere, upper-middle income countries are more responsible in more recent years (over the last few decades). Therefore, CO2 emission mitigation efforts should be focused on upper-middle income countries. This does not, however, mean other groups of countries do not pay attention to reduce their CO2 emissions.

> 4 The analysis can be carried out for every year for all 110 countries.

11
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to continue emitting large amounts of CO2 in the future in the absence of any policy intervention; this finding is consistent with Lee et al. [38].

**Table 1.** System GMM estimation results for **full sample (1980-2015)** .

|Dependent variab<br>|le:<br>2<br>ln_CO_<br>|||||
|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)|(4)|(5)|
||Global panel|HI countries|UMI countries|LMI countries|LI countries|
|2<br>1<br>ln<br>_it_<br>_CO_<br>|0.718<sup>***</sup>|0.445<sup>***</sup>|0.584<sup>***</sup>|0.640<sup>***</sup>|0.085<sup>**</sup>|
||(35.898)|(15.847)|(16.592)|(16.156)|(2.246)|
|ln<br>_it_<br>_EC_|0.057<sup>***</sup>|0.594<sup>***</sup>|0.059<sup>**</sup>|0.027<sup>***</sup>|0.050<sup>*</sup>|
||(2.960)|(16.444)|(2.275)|(7.270)|(1.780)|
|ln<br>_it_<br>_ECS_|0.331<sup>***</sup>|0.052<sup>**</sup>|0.475<sup>***</sup>|0.507<sup>***</sup>|0.976<sup>***</sup>|
||(14.089)|(2.323)|(11.507)|(9.463)|(11.504)|
|ln<br>_it_<br>_EI_|0.445<sup>***</sup>|0.619<sup>***</sup>|0.509<sup>***</sup>|0.471<sup>***</sup>|0.308<sup>***</sup>|
||(13.891)|(17.098)|(9.586)|(7.369)|(11.927)|
|ln<br>_it_<br>_PCG_|0.290<sup>***</sup>|1.504<sup>***</sup>|0.768<sup>***</sup>|0.334<sup>***</sup>|0.267<sup>***</sup>|
||(9.283)|(18.572)|(7.756)|(6.267)|(3.722)|
|2<br>ln<br>_it_<br>_PCG_|-0.045<sup>***</sup>|-0.201<sup>***</sup>|-0.148<sup>***</sup>|0.049|-0.185<sup>**</sup>|
||(-3.649)|(-14.844)|(-3.163)|(1.647)|(-2.054)|
|ln<br>_it_<sup>_P_</sup>|0.270<sup>***</sup>|0.534<sup>***</sup>|0.838<sup>***</sup>|0.886<sup>***</sup>|0.865<sup>***</sup>|
||(13.040)|(18.528)|(12.071)|(9.131)|(13.223)|
|Constant|2.605<sup>***</sup>|1.253<sup>***</sup>|1.186<sup>***</sup>|0.985<sup>***</sup>|0.844<sup>***</sup>|
||(9.292)|(4.201)|(3.570)|(7.648)|(3.311)|
|Hansen test|0.103|0.194|0.649|0.582|0.412|
|AR(2) test|0.229|0.152|0.172|0.202|0.188|
|Turning point|25,084|42,152|13,391|-|-|

**_Note:_** ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively; the values in

parentheses represent t-statistics; the values of Hansen and AR(2) tests represent p-values; and the unit of turning point is 2010 US$.

16
# **4.1.1 Results from the 110-country panel**

The estimated coefficients of ln _GDP_ and  ln _GDP_  2 are positive and negative, respectively, suggesting the presence of the EKC curve. Note that the EKC is a phenomenon of the long run [39, 40], further suggesting that the countries with different income levels should follow a long-term strategy that achieves the targeted balance between reduced CO2 emissions and high economic growth.

The results further indicate that, for the whole sample of 110 countries, the estimated coefficients of ln _EC_ , ln _ECS_ , ln _EI_ , ln _P C G_ , and ln _P_ are positive and significant at a 1% significance level. Similar to the decomposition analysis presented in Section 3 above (see also Figs. B1 & B2 in Appendix B), the econometric analysis suggests that the change in emission increased with per capita GDP (i.e., economic growth) and population growth. These two factors are the primary drivers of the increase of CO2 between 1980 and 2015, while changes in fuel mix (i.e., increasing share of clean fuels in total primary energy supply) and improving energy efficiency (i.e., decreasing energy intensity of GDP) slowed this increase in growth of CO2 during the same period.

# **4.1.2 Differences in impacts across income levels**

The theoretical EKC is an inverted U-curve, with the coefficients of ln _GDP_ and  ln _GDP_  2 positive and negative, respectively. However, in the results in columns 2-5, only for HI and UMI countries the estimated coefficients of ln _GDP_ and  ln _GDP_  2 are positive, and negative respectively. For HI and UMI countries, the estimated turning points are $42,152 and $13,391, respectively. In contrast, we cannot identify the turning point of the EKC hypothesis for LMI and LI countries. The large gap between the current economic level in LMI and LI countries ($2,030 and $599 in 2015, respectively; see Fig. 7) and the turning point of EKC may result in the data failing to identify the peak in CO2 emissions.

17
<!-- Start of picture text --> 15<br>2@ Hi<br>@ UMI<br><!-- End of picture text --> <!-- Start of picture text --> 2 LMI<br>eu<br>10 | @€ Average value<br><!-- End of picture text -->

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|ln<br>_it_<br>_ECS_|0.425<sup>***</sup>|0.103<sup>*</sup>|0.595<sup>***</sup>|0.563<sup>***</sup>|0.941<sup>***</sup>|
|---|---|---|---|---|---|
||(9.124)|(1.724)|(6.452)|(5.367)|(6.564)|
|ln<br>_it_<br>_EI_|0.476<sup>***</sup>|0.621<sup>***</sup>|0.566<sup>***</sup>|0.481<sup>***</sup>|0.169<sup>***</sup>|
||(7.541)|(7.067)|(4.850)|(4.895)|(8.787)|
|ln<br>_it_<br>_PCG_|0.341<sup>***</sup>|2.012<sup>***</sup>|0.687<sup>***</sup>|0.379<sup>***</sup>|0.158<sup>**</sup>|
||(5.735)|(9.042)|(4.366)|(4.612)|(2.393)|
|2<br>ln<br>_it_<br>_PCG_|0.014<sup>*</sup>|0.226<sup>***</sup>|-0.065|0.052|0.012|
||(1.733)|(6.861)|(-1.670)|(1.250)|(0.049)|
|ln<br>_it_<sup>_P_</sup>|0.344<sup>***</sup>|0.560<sup>***</sup>|0.804<sup>***</sup>|0.839<sup>***</sup>|0.789<sup>***</sup>|
||(8.454)|(9.200)|(6.429)|(5.479)|(9.364)|
|Constant|0.812<sup>***</sup>|2.026<sup>***</sup>|1.186<sup>***</sup>|0.648<sup>***</sup>|1.168<sup>**</sup>|
||(5.830)|(3.955)|(3.570)|(2.986)|(2.076)|
|Hansen test|0.121|0.390|0.493|0.603|0.115|
|AR(2) test|0.224|0.192|0.178|0.149|0.191|
|Turning point|-|-|-|-|-|

**_Note:_** ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively; the values in parentheses represent t-statistics; the values of Hansen and AR(2) tests represent p-values; and the unit of turning point is 2010 US$.

# **4.2.1 The 110-country panel**

According to the first column in Table 2, for the 110-country panel, the estimated coefficients of ln _EC_ , ln _ECS_ , ln _EI_ , ln _P C G_ , and l n _P_ between 1980 and 1990 are positive and significant at a 1% significance level. GDP per capita and population were the primary drivers leading to an increase in CO2 emissions between 1980 and 1990, while the factors that slowed the increase of the emissions over the same period were emission coefficient, energy mix, and energy intensity.

The first column in Table 3 indicates that the estimated coefficients of the variables between 2005 and 2015 are positive and significant. Furthermore, Figs. B1 & B2 in Appendix B suggest that emission coefficient, per capita GDP, and population were the primary driving forces of increasing CO2 emissions between 2005 and 2015, while the factors that slowed the increase of the emissions over the same period were energy mix 20
and energy intensity (these latter variables depict a downward trend over time).

**Table 3.** System GMM estimation results for **late period (2005-2015)** .

|Dependent variab<br>|le:<br>2<br>ln_CO_<br>|||||
|---|---|---|---|---|---|
|Variable|Global panel|HI countries|UMI countries|LMI countries|LI countries|
|2<br>1<br>ln<br>_it_<br>_CO_<br>|0.815<sup>***</sup>|0.248<sup>***</sup>|0.456<sup>***</sup>|0.572<sup>***</sup>|0.104<sup>***</sup>|
||(23.035)|(4.804)|(5.915)|(7.350)|(3.804)|
|ln<br>_it_<br>_EC_|0.017<sup>**</sup>|0.793<sup>***</sup>|0.353<sup>***</sup>|0.033<sup>**</sup>|0.293<sup>***</sup>|
||(2.482)|(15.251)|(4.111)|(2.401)|(3.044)|
|ln<br>_it_<br>_ECS_|0.210<sup>***</sup>|0.017<sup>***</sup>|0.151<sup>***</sup>|0.523<sup>***</sup>|1.073<sup>***</sup>|
||(5.038)|(3.606)|(2.376)|(4.938)|(6.783)|
|ln<br>_it_<br>_EI_|0.401<sup>***</sup>|0.814<sup>***</sup>|0.541<sup>***</sup>|0.773<sup>***</sup>|0.170<sup>***</sup>|
||(7.028)|(15.376)|(5.998)|(4.804)|(5.149)|
|ln<br>_it_<br>_PCG_|0.213<sup>***</sup>|2.611<sup>***</sup>|2.789<sup>***</sup>|0.232<sup>**</sup>|0.229<sup>*</sup>|
||(3.686)|(14.150)|(7.060)|(2.041)|(1.955)|
|2<br>ln<br>_it_<br>_PCG_|-0.001|0.258<sup>***</sup>|0.567<sup>***</sup>|0.219|-0.175|
||(-0.080)|(12.143)|(6.562)|(1.186)|(-0.577)|
|ln<br>_it_<sup>_P_</sup>|0.181<sup>***</sup>|0.738<sup>***</sup>|0.857<sup>***</sup>|0.941<sup>***</sup>|0.870<sup>***</sup>|
||(4.941)|(14.232)|(7.214)|(5.404)|(6.607)|
|Constant|0.943<sup>***</sup>|0.963<sup>**</sup>|0.746<sup>**</sup>|1.193<sup>***</sup>|1.283<sup>***</sup>|
||(7.091)|(2.053)|(2.458)|(4.420)|(5.189)|
|Hansen test|0.163|0.158|0.178|0.156|0.606|
|AR(2) test|0.220|0.106|0.110|0.237|0.122|
|Turning point|-|-|-|-|-|

**_Note:_** ***, **, and * denote statistical significance at 1%, 5%, and 10%, respectively; the values in parentheses represent t-statistics; the values of Hansen and AR(2) tests represent p-values; and the unit of turning point is 2010 US$.

# **4.2.2 Varying income levels across countries**

Fig. 9 details the impact of each of the factors. In general, the main factors affecting the emissions for the four income groups did not change much over time. However, it is noteworthy that over time, the impact of changes in energy mix and 21
<!-- Start of picture text --> a0% - | _i HI countries aan0° 3 HI countries;<br><!-- End of picture text -->

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<u>f / f</u> f if ;
same marginal impact is calculated once under the LMDI method and once under the GMM method. We do this for the whole sample but also for the different income groups (Table 4). From this table, we can see that the level of the marginal impacts of various factors on CO2 growth obtained by the LMDI method are significantly bigger than that of the GMM. This may be because, as addressed previously (i.e., section 2), the system GMM estimator takes into account each country’s characteristics when calculating the marginal impact whereas the LMDI method ignores the individual characteristics. The difference in the results between LMDI and GMM are important to understand how CO2 growth responds to change in a given driver. Since the LMDI results are biased towards the country size (i.e., they are skewed due to China and the United States, the larger countries’ results), the response indicators (or marginal impact here) based on LMDI may be biased. The response indicators estimated based on GMM analysis are more credible.

**Table 4.** A comparison in the marginal impacts of various factors on CO2 growth between system GMM technique and LMDI method for the period 1980-2015.

|Panel|_EC_|_ECS_|_EI_|_PCG_|_PCG_<sup>_2_</sup>|_P_|
|---|---|---|---|---|---|---|
|**GMM m**|**ethod**||||||
|Global|0.057|0.331|0.445|0.290|-0.045|0.270|
|HI|0.594|0.052|0.619|1.504|-0.201|0.534|
|UMI|0.059|0.475|0.509|0.768|-0.148|0.838|
|LMI|0.027|0.507|0.471|0.334|0.049|0.886|
|LI|0.050|0.976|0.308|0.267|-0.158|0.865|
|**LMDI m**|**ethod**||||||
|Global|1.654|0.080|4.042|2.325|NA|0.724|
|HI|0.024|0.646|1.464|0.760|NA|1.086|
|UMI|0.661|3.978|8.904|2.087|NA|1.724|
|LMI|0.362|2.877|4.089|1.983|NA|1.846|
|LI|2.573|4.413|0.658|1.638|NA|0.911|

23
**_Note:_** _EC_ indicates emission coefficient; _ECS_ denotes energy mix; _EI_ describes energy intensity; _PCG_ ( _P C G_ 2 ) stands for per capita GDP (squared); and _P_ is population size.

# **6. Conclusions**

In this study, we measured the role of various factors that have driven CO2 emissions over the past 35 years at the global level and different groups of countries by their current income level (i.e., high income, upper-middle income, lower-middle income, and low income). We employ data from 110 countries in two different techniques: Index decomposition technique (identity analysis) and an econometric technique. We also examined if a factor has influenced CO2 growth differently during different time intervals: early period of the study horizon (1980-2015) and late interval of the study horizon (2005-2015).

The results from our identity and econometric analysis reveal that income (measured in terms of per capita GDP) growth and population growth are the major drivers for the increased CO2 emissions during the period. Improved energy efficiency (i.e., decreasing energy intensity of GDP) has slowed down the growth; otherwise, CO2 emissions would have increased even further.

Improvement of energy efficiency has contributed in all groups of countries to slow down their CO2 emission growth. This factor is more prominent in the highincome and upper-middle-income groups of countries. The fuel mix factor or the increasing share of low carbon (e.g., natural gas) or no carbon (e.g., renewable) fuels in the total primary energy supply mix contributed to slow down CO2 growth in the highincome group of countries. It is income growth that is responsible for CO2 growth in the upper-income and middle-income countries. Population growth is more responsible for CO2 emission growth in low-income countries, although they have a very small role in the growth of global CO2 emissions.

The analysis provides some important policy insights. Although one could argue that upper-middle-income countries’ per-capita emissions are lower than that of highincome countries and therefore pressure should still be on high-income countries, our 24
analysis shows that since the upper-middle-income group of countries is mostly responsible for the recent global CO2 growth, more efforts should be paid to reduce their CO2 emissions. This study does not mean that these countries have to take on most of the financial burden to reduce their CO2 emissions. How to share the global burden of CO2 reduction is a different question and it is beyond the scope of this study. Improving energy efficiency has contributed historically to slow down the CO2 growth and this effect is basically driven through various policies (e.g., incentives for energy efficiency improvements) and technological innovation. Although the world has witnessed a rapid expansion of renewable energy more recently, the expansion is still too small to make a noticeable impact in limiting the growth of global CO2 emissions. Further expansion of clean and renewable energy would be needed for a noticeable impact on slowing down the global CO2 growth.

# **Acknowledgments**

We acknowledge the financial support from Knowledge for Change Trust fund of the World Bank.

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# **Appendix A**

**Table A1.** Selected prior studies on decomposition of CO2 emissions using the LMDI

approach (2008-2018). **_Source:_** Compiled by the authors.

|**Author [ref.]**|**Study area**|**Study period**|**Driving force**|
|---|---|---|---|
|Hatzigeorgiou et al. [11]|Greece|1990-2002|1, 2, 3|
|Tunç et al. [12]|Turkey|1970-2006|2, 4, 5, 6|
|Oh et al. [13]|Korea, Rep.|1990-2005|1, 2, 4, 5|
|Pani and Mukhopadhyay [41]|114 countries|1992-2004|1, 3, 5, 6|
|Vinuya et al. [42]|United States|1990-2004|1, 2, 3, 5, 6|
|de Freitas and Kaneko [14]|Brazil|1970-2009|2, 3, 4, 5, 6, 7|
|Zhang et al. [43]|China|1995-2009|2, 4, 5, 6, 8|
|O’Mahony et al. [15]|Ireland|1990-2010|2, 4, 5, 6, 8|
|O'Mahony [44]|Ireland|1990-2010|1, 2, 3, 5, 6|
|Zhang et al. [16]|China|1991-2009|6, 7, 9, 10, 11, 12|
|González et al. [10]|European Union|2001-2010|1, 2, 3, 5, 6|

28
|Feng et al. [17]|United States|1997-2013|2, 3, 5, 13, 14|
|---|---|---|---|
|Xie et al. [45]|China|1995-2013|2, 5, 6, 15, 16|
|Mousavi et al. [18]|Iran, Islamic Rep.|2003-2014|2, 3, 4, 5, 6|
|Henriques and Borowiecki|Europe, North|1980-2011|1, 2, 3, 5, 6|
|[46]|America and Japan|||
|Shahiduzzaman and Layton|United States|1973-2014|1, 2, 3, 5, 6|
|[47]||||
|Chen et al. [48]|Macao|2000-2011|2, 4, 5, 8|

**_Notes:_ a)** 1. Income effect, 2. Energy mix effect, 3. Population effect, 4. Economic structure effect, 5. Energy intensity effect, 6. Emission coefficient effect, 7. Economic activity effect, 8. GDP, 9. Electricity generation efficiency effect, 10. Thermal power structure effect, 11. Electricity structure effect, 12. Electricity intensity effect, 13. Consumption pattern effect, 14. Production structure effect, 15. Industrial activity effect, and 16. Industrial scale effect; and **b)** EU denotes European Union and US denotes United States.

29
**Table A2.** List of countries based on income level.

|**Subpanel**|**Countries**|
|---|---|
|Low-income countries<br>(10 countries)|Benin, Democratic Republic of Congo, Ethiopia,<br>Haiti, Mozambique, Nepal, Senegal, Tanzania,<br>Togo, and Zimbabwe|
|Lower-middle-income<br>countries<br>(28 countries)|Angola, Bangladesh, Bolivia, Cameroon, Republic<br>of Congo, Côte d'Ivoire, Arab Republic of Egypt, El<br>Salvador, Ghana, Guatemala, Honduras,<br>India,<br>Indonesia,<br>Jordan,<br>Kenya,<br>Morocco,|
||Myanmar,<br>Nicaragua,<br>Nigeria,<br>Pakistan,<br>Philippines, Sri Lanka, Sudan, Syrian Arab<br>Republic, Tunisia, Vietnam, Republic of Yemen,<br>and Zambia|
|Upper-middle-income<br>countries<br>(28 countries)|Albania, Algeria, Argentina, Brazil, Bulgaria,<br>China, Colombia, Costa Rica, Cuba, Dominican<br>Republic, Ecuador, Gabon, Islamic Republic of<br>Iran, Iraq, Jamaica, Lebanon, Libya, Malaysia,<br>Mauritius, Mexico, Panama, Paraguay, Peru,<br>Romania, South Africa, Thailand, Turkey, and<br>Venezuela, RB|
|High-income countries<br>(44 countries)|Australia, Austria, Bahrain, Belgium, Brunei,<br>Darussalam, Canada, Chile, Curacao, Cyprus,<br>Czech Republic, Denmark, Finland, France,<br>Germany, Gibraltar, Greece, Hungary, Iceland,<br>Ireland, Israel, Italy, Japan, the Republic of Korea,|
||Kuwait, Luxembourg, Malta, New Zealand,<br>Netherlands, Norway, Qatar, Oman, Poland,<br>Portugal,<br>Saudi<br>Arabia,<br>Singapore,<br>Slovak<br>Republic, Spain, Sweden, Switzerland, Trinidad<br>and Tobago, United Arab Emirates, United<br>Kingdom,United States,and Uruguay|

30
**Table A3.** Eight possible cases with zero values.

|Case|0<br>_ij_<br>_X_|_t_<br>_ij_<br>_X_|0<br>_ij_<br>_C_|_t_<br>_ij_<br>_C_|<br><br>0<br>0<br>0<br>ln<br>ln<br>_ij_<br>_t_<br>_t_<br>_ij_<br>_ij_<br>_ij_<br>_X_<br>_t_<br>_ij_<br>_ij_<br>_ij_<br>_C_<br>_C_<br>_X_<br>_C_<br>_X_<br>_C C_<br><br><br><br><br><br><br><br><br><br><br><br>|
|---|---|---|---|---|---|
|1|0|+|0|+|_ij_<br>_t_<br>_X_<br>_ij_<br>_C_<br>_C_<br><br>|
|2|+|0|+|0|0<br>_ij_<br>_X_<br>_ij_<br>_C_<br>_C_<br><br>|
|3|0|0|0|0|0|
|4|+|+|0|+|0|
|5|+|+|+|0|0|
|6|+|+|0|0|0|
|7|+|0|0|0|0|
|8|0|+|+|+|0|

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**Table A4.** Decomposition results of the emissions changes and contributions of various driving forces **across the globe** over the early period (1980-1990), late period (20052015), and entire period (1980-2015).

|**Period**|**1980-1990**|**2005-2015**|**1980-2015**|
|---|---|---|---|
|**Emissions change (Mt)**||||
|Overall change|1,898.5|4,952.6|14,299.9|
|_EC_|-141.9|67.3|521.4|
|_ECS_|-468.0|-208.0|108.6|
|_EI_|-2,780.1|-5,909.6|-13,716.7|
|_PCG_|3,719.8|8,758.4|20,655.0|
|_P_|1,568.7|2,244.4|6,731.6|
|**Contribution (%)**||||
|Growth rate|13.7|21.3|103.1|
|_EC_|-1.0|0.3|3.8|
|_ECS_|-3.4|-0.9|0.8|
|_EI_|-20.0|-25.4|-98.9|
|_PCG_|26.8|37.7|148.9|
|_P_|11.3|9.7|48.5|

**_Note:_** _EC_ , _ECS_ , _EI_ , _PCG_ , and _P_ refer to emission coefficient, energy mix, energy intensity, income, and population, respectively.

32
**Table A5.** Decomposition results of the emissions changes and contributions of various driving forces in the **HI countries** over the early period (1980-1990), late period (20052015), and entire period (1980-2015).

|**Period**|**1980-1990**|**2005-2015**|**1980-2015**|
|---|---|---|---|
|**Emissions change (Mt)**||||
|Overall change|338.0|-1,165.1|1,357.6|
|_EC_|-3.5|-267.2|40.5|
|_ECS_|-733.9|-610.3|-1,737.7|
|_EI_|-2,053.6|-2,285.2|-6,184.4|
|_PCG_|2,304.7|928.2|6,081.7|
|_P_|824.2|1,069.4|3,157.5|
|**Contribution (%)**||||
|Growth rate|3.2|-9.0|12.9|
|_EC_|-0.1|-2.1|0.4|
|_ECS_|-7.0|-4.7|-16.6|
|_EI_|-19.6|-17.6|-58.9|
|_PCG_|22.0|7.1|57.9|
|_P_|7.9|8.2|30.1|

**_Note:_** _EC_ , _ECS_ , _EI_ , _PCG_ , and _P_ refer to emission coefficient, energy mix, energy intensity, income, and population, respectively.

33
**Table A6.** Decomposition results of the global emissions changes and contributions of various driving forces in the **UMI countries** over the early period (1980-1990), late <u>period (2005-2015), and entire period (1980-2015).</u>

|**Period**|**1980-1990**|**2005-2015**|**1980-2015**|
|---|---|---|---|
|**Emissions change (Mt)**||||
|Overall change|1,095.4|4,770.7|9,886.4|
|_EC_|-122.8|453.7|391.7|
|_ECS_|131.0|61.9|1,038.6|
|_EI_|-719.9|-2,948.2|-6,756.7|
|_PCG_|1,247.9|6,457.0|12,683.9|
|_P_|559.3|746.4|2,528.9|
|**Contribution (%)**||||
|Growth rate|39.1|60.2|352.5|
|_EC_|-4.4|5.7|14.0|
|_ECS_|4.7|0.8|37.0|
|_EI_|-25.7|-37.2|-240.9|
|_PCG_|44.5|81.5|452.3|
|_P_|19.9|9.4|90.2|

**_Note:_** _EC_ , _ECS_ , _EI_ , _PCG_ , and _P_ refer to emission coefficient, energy mix, energy intensity, income, and population, respectively.

34
**Table A7.** Decomposition results of the global emissions changes and contributions of various driving forces in the **LMI countries** over the early period (1980-1990), late <u>period (2005-2015), and entire period (1980-2015).</u>

|**Period**|**1980-1990**|**2005-2015**|**1980-2015**|
|---|---|---|---|
|**Emissions change (Mt)**||||
|Overall change|456.4|1,318.1|3,012.3|
|_EC_|-15.6|-123.2|82.8|
|_ECS_|132.1|330.7|800.2|
|_EI_|-5.7|-667.0|-767.3|
|_PCG_|167.4|1,360.3|1,881.5|
|_P_|178.2|417.3|1,015.2|
|**Contribution (%)**||||
|Growth rate|82.1|58.6|542.1|
|_EC_|-2.8|-5.5|14.9|
|_ECS_|23.8|14.7|144.0|
|_EI_|-1.0|-29.6|-138.1|
|_PCG_|30.1|60.5|338.6|
|_P_|32.1|18.5|182.7|

**_Note:_** _EC_ , _ECS_ , _EI_ , _PCG_ , and _P_ refer to emission coefficient, energy mix, energy intensity, income, and population, respectively.

35
**Table A8.** Decomposition results of the global emissions changes and contributions of various driving forces in the **LI countries** over the early period (1980-1990), late period <u>(2005-2015), and entire period (1980-2015).</u>

|**Period**|**1980-1990**|**2005-2015**|**1980-2015**|
|---|---|---|---|
|**Emissions change (Mt)**||||
|Overall change|8.7|28.9|43.6|
|_EC_|0.1|4.1|6.4|
|_ECS_|2.8|9.6|7.5|
|_EI_|-0.9|-9.1|-8.2|
|_PCG_|-0.3|13.0|7.9|
|_P_|7.0|11.3|30.0|
|**Contribution (%)**||||
|Growth rate|42.9|82.6|214.8|
|_EC_|0.2|11.6|31.7|
|_ECS_|13.7|27.5|37.1|
|_EI_|-4.3|-26.0|-40.6|
|_PCG_|-1.5|37.1|38.9|
|_P_|34.7|32.4|147.8|

**_Note:_** _EC_ , _ECS_ , _EI_ , _PCG_ , and _P_ refer to emission coefficient, energy mix, energy intensity, income, and population, respectively.

36
**Table A8.** The marginal impacts of various factors on CO2 growth based on the LMDI

decomposition results.

|Factor|Global panel|HI countries|UMI countries|LMI countries|LI countries|
|---|---|---|---|---|---|
|**1980-2015**||||||
|_EC_|1.654|0.024|0.661|0.362|2.573|
|_ECS_|0.080|0.646|3.978|2.877|4.413|
|_EI_|4.042|1.464|8.904|4.089|0.658|
|_PCG_|2.325|0.760|2.087|1.983|1.638|
|_P_|0.724|1.086|1.724|1.846|0.911|
|**1980-1990**||||||
|_EC_|0.178|0.005|4.407|0.339|0.042|
|_ECS_|0.532|0.742|1.895|1.496|1.361|
|_EI_|1.859|1.108|5.686|0.260|2.985|
|_PCG_|2.055|0.851|3.697|1.999|0.318|
|_P_|0.578|1.072|1.070|1.181|1.088|
|**2005-2015**||||||
|_EC_|0.357|0.303|0.857|0.277|0.438|
|_ECS_|0.359|0.425|1.348|1.113|1.311|
|_EI_|3.430|1.181|2.421|1.475|0.227|
|_PCG_|2.450|1.000|1.275|1.089|0.925|
|_P_|0.764|1.161|1.143|1.086|1.048|

**_Note:_** _EC_ indicates emission coefficient; _ECS_ denotes energy mix; _EI_ describes energy intensity; _PCG_ stands for per capita GDP; and _P_ is population size.

37