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28th May 2017

Dear Editors,

The manuscript "CO2 emission efficiency per capita in BRICS countries: Analysis of product

generational dematerialization and convergence" by Yue-Jun Zhang and Ming-Ying Chen is

submitted to Journal of Policy Modeling for possible publication. Please note that:

The manuscript is our own original work, and does not duplicate any other previously

published work, including our own previously published work.

The manuscript has been submitted only to Journal of Policy Modeling; it is not under

consideration or peer review or accepted for publication or in press or published

elsewhere.

The manuscript contains nothing that is abusive, defamatory, libellous, obscene,

fraudulent, or illegal.

Based on the requirements of your esteemed journal, the language of this paper has

been revised by the native English-speaking professors via Elsevier.

Sincerely yours,

Yue-Jun Zhang

Dr., Professor in Energy Economics and Policies

Business School, Hunan University, Changsha 410082, PR China

Center for Resource and Environmental Management, Hunan University, Changsha 410082,

PR China

Tel./ fax : 86-731-88822899.

E-mail: zyjmis@126.com

PAGE \* MERGEFORMAT 3

Biographical notes

Dr. Yue-Jun Zhang is a Professor at Business School of Hunan University, China, as well as the

Director of Center for Resource and Environmental Management, Hunan University. He got his

PhD degree in Energy Economics from Chinese Academy of Sciences (CAS) in 2009. His

research interests mainly cover energy economics and energy policies. Up to now, Dr. Zhang

has published more than 70 articles in peer-reviewed journals, such as Journal of Policy

Modeling, Energy Economics, Energy Policy, Annals of Operations Research, Quantitative

Finance. Dr. Zhang was a visiting scholar at Energy Studies Institute (ESI) of National University

of Singapore (NUS) and Lawrence Berkeley National Laboratory (LBNL) of USA. Now he is also a

member of International Association of Energy Economics (IAEE).

Mr. Ming-Ying Chen is a graduate student at Business School of Hunan University, China. His

research interests focus on energy policy modeling.

CO2 emission efficiency per capita in BRICS countries: Analysis of product generational dematerialization and convergence

Yue-Jun Zhang a,b*, Ming-Ying Chen a,b

a a Business School, Hunan University, Changsha 410082, Chinab Center for Resource and Environmental Management, Hunan University, Changsha 410082, China

Abstract: Under the background of global warming, BRICS countries are facing severe

challenges in CO2 emissions reduction. Mitigating the CO2 emissions per capita and improving

the CO2 emission efficiency per capita have become crucial to addressing climate change in

BRICS countries. Hence, we adopt the product generational dematerialization analysis

method to measure the CO2 emission efficiency per capita (CEEPC) in BRICS countries during

1985-2015. Then, we also extend the convergence analysis approach to detect the time-

varying trend of the gaps in CEEPC among BRICS countries. The empirical results indicate

that, first, on the whole, the CEEPC in South Africa appears relatively high, followed by that in

Russia, and China ranks the last. Second, BRICS countries’ changes in fossil and renewable

energy consumption present different effects on their CEEPC. Finally, there is no

convergence, but β and stochastic convergences are found, in the CEEPC among BRICS

countries.

Keywords: CO2 emission efficiency per capita; BRICS countries; product generational

dematerialization analysis; convergence analysis.

1. Introduction

At present, the process of global warming has been accelerated by an increase in the

amount of greenhouse gases (GHG). Dealing with climate change and reducing the emission

of GHG have become one of the most important issues for science and policy-making (Bella

a * Corresponding author. Tel./ fax : 86-731-88822899. E-mail: zyjmis@126.com (Yue-Jun Zhang). PAGE \* MERGEFORMAT 3

et al., 2014; Pal and Mitra, 2017). The emissions of GHG present a threat to sustainable

development (Cowan et al., 2014). Current research results indicate that over the two-thirds

of GHG derives from the CO2 related to the use of fossil energy (Wu et al., 2015). In fact, the

necessity of the reduction of CO2 emissions and promotion of a low-carbon economy has

become the consensus around the world. Thus, to deal with the climate change, many

countries make efforts to reduce or control CO2 emissions and improve their CO2 emission

efficiency.

The BRICS countries (i.e., Brazil, Russia, India, China, and South Africa) are emerging

economies: in recent years, the BRICS countries, because of their rapid economic

development, play an important global role and have attracted wide societal attention. With

the advance of industrialisation, the increased concern about the low-carbon economy has

made BRICS countries rethink their problems of energy consumption and CO2 emission.

Among BRICS countries, China, Russia, and India are the global top five emitters of CO2 (Sebri

and Ben-Salha, 2014). In 2015, the percentage of BRICS countries’ CO2 emissions reached

41.12% of the global total CO2 emissions (BP, 2016). The current economic development,

energy consumption, and CO2 emissions have not yet been decoupled, in the face of pressure

from the international community to reduce CO2 emissions, BRICS countries have been

burdened with the challenge of emissions reduction. Because BRICS countries are all

undergoing rapid economic growth, they occupy a similar position when dealing with climate

change. In 2013, the fifth BRICS Summit was held in Durban: delegates from the BRICS

countries signed a “multilateral agreement on climate co-operation and the green economy”,

which will ensure the exchange of technical and financial support to curb the negative impact

of climate change (Cowan et al., 2014). However, BRICS countries still largely use fossil fuels

for electricity generation to develop their economy, causing increased CO2 emissions and the

acceleration of global warming. Therefore, how to evaluate the CO2 emission level and

measure the CO2 emission efficiency more efficiently, how to make better policy to reduce

CO2 emissions based on the differences of CO2 emission situations among BRICS countries

have become pressing issues which BRICS countries need to tackle.

Many current studies adopt the data envelopment analysis approach (DEA) to evaluate

CO2 emission efficiency (Wu et al., 2012; Wang et al., 2013; Wang et al., 2015; Chang, 2015;

Meng et al., 2016; Zhang et al., 2016a; Zhang et al., 2016b). This approach measures the

relative efficiency of each decision-making unit (DMU), such as a country or region, based on

certain input indicators (such as energy consumption and size of labour force) and output

indicators (such as GDP and CO2 emission), and then terms these relative efficiency values as

their CO2 emission efficiency. Nevertheless, these studies only measure total and static

efficiency, i.e., measure the CO2 emission efficiency based on data from a given year, but

never consider CO2 emission efficiency at a dynamic, per capita level. Indeed, when making

decisions to reduce total CO2 emissions and promote macro-CO2 emission efficiency, both

developing countries and developed countries need an indicator, or method, suited to

analysis at the per capita level to supervise the dynamic CO2 emission efficiency (Li and Lin,

2013). At present, many scholars use per capita CO2 emissions values to represent the per

capita-level CO2 emissions of different countries or regions (Li and Lin, 2013; Presno et al.,

2015; Dong and Zhao, 2017). However, because the population and its growth path in BRICS

countries are different, only considering the per capita CO2 emissions makes it hard to reflect

the CO2 emission efficiency per capita under the effects of population dynamics (Ziolkowska

and Ziolkowska, 2015). Thus, we use the product generational dematerialization analysis

method (PGD) to measure the CO2 emission efficiency per capita1. This method evaluates the

rate of change in CO2 emissions per capita considering a simultaneous population change,

which can not only indicate the effect of population dynamics on CO2 emission efficiency per

capita, but also compares the change path and direction of population and CO2 emissions per

capita, then reveals whether, or not, there is an emission reduction at the per capita-level in

BRICS countries. Additionally, BRICS policy-makers need to know their relative growth level of

CO2 emission efficiency per capita compared with other countries because different growth

level of CO2 emission efficiency per capita impose different pressures on each BRICS country,

and then this is required to identify a more reasonable, feasible emissions reduction policy.

Besides, policy makers also need to know whether the gap in CO2 emission efficiency per

capita among BRICS countries can be lessened by the implementation of a series of

sustainable climate co-operation strategies, whether the lower-CO2 emission efficiency per

capita countries will “catch up” with the higher-CO2 emission efficiency per capita countries

(Karimu et al., 2017). Hence, we also adopt a convergence analysis to detect the above

issues2.

This research contributes to two aspects covered in the existing literature: first, we

adopt the product generational dematerialization analysis method to measure the CO2

emission efficiency per capita of BRICS countries during 1986-2015 and then analyse the

differences in CO2 emission efficiency per capita among BRICS countries and demonstrate the

1 Following Ziolkowska and Ziolkowska (2015), the term ‘generational’ refers to the word ‘generation’ and emphasises the relevance of population in measuring resource efficiency.2 The convergence analysis method is initially applied to explore the dynamics of gaps in different countries’ income levels (Barro and Sala-I-Martin, 1992). If there is convergence at various income levels, it means that countries with lower initial income levels are expected to experience higher income growth levels and hence “catch up” with higher-income countries (Quah, 1996; Pettersson et al., 2014). In the field of energy and environment studies, the convergence analysis method is an important tool to estimate the time-varying trend of energy or CO2 emission efficiency (or intensity) (Barro and Sala-I-Martin, 1992). In this paper, the convergence analysis is adopted to detect the time-varying trend of gaps in CO 2 emission efficiency per capita among BRICS countries. There are three common types of convergence analysis: convergence, β convergence, and stochastic convergence (Sala-I-Martin, 1996).

underlying reasons, so as to provide the support to BRICS countries’ sustainable

development policy-makers. Second, we estimate the , β, and stochastic convergence in CO2

emission efficiency per capita of BRICS countries during 1985-2015 and detect the time-

varying trend of efficiency differences; then based on the convergence analysis results, we

provide some suggestions to deeply strengthen climate co-operation among BRICS countries.

The remainder of this paper is organised as follows: Section 2 reviews the relevant

literature. Section 3 introduces the data description and research methods. Section 4

presents empirical results and discussions. Finally, Section 5 concludes the paper and puts

forward some suggestions.

2. Relevant literature review

Due to the irreversible harm brought about by climate change, BRICS countries are

urged to develop effective policies to reduce CO2 emissions, improve CO2 emission efficiency,

and achieve their own emission reduction targets. This paper reviews the related literature

from three aspects, i.e., the methods for evaluating CO2 emission level, the dematerialization

analysis method, and the convergence analysis.

2.1 Methods of evaluating CO2 emission level

Current studies mainly adopt two types of methods to measure CO2 emissions: the first

category is the CO2 emission intensity (carbon emission intensity). For example, Chang (2015)

notes that the carbon emissions intensity can be implemented for environmental monitoring

purposes; he measures the BRICS countries’ carbon emission intensities during 2000-2010

and points out that there is more room for improvement in BRICS countries’ carbon emission

intensity than in the G7 countries. Thomakos and Alexopoulos (2016) calculate the carbon

emission intensity and environmental performance index (EPI) of the USA during 1990-2013

and note that the EPI is mainly determined by carbon emissions intensity. Besides, Li and Lin

(2016) argue that carbon emissions intensity is the critical indicator for evaluating the

emissions reduction effect in China. They measure the energy-related carbon emission

intensity of Chinese six productive sectors (i.e., primary industry, mining, manufacturing,

production and supply of electricity, gas, and water, construction, transportation and

warehousing) during 1985-2012 and note that China’s carbon emission intensity declined

during 1985-2012, most of which can be attributed to labour substitution and energy price

increases. Furthermore, Ang and Su (2016) point out that, in terms of environmental

protection and climate change, decreases in carbon emission intensity are expected by the

public. They calculate the global carbon emission intensity during 1990-2013 and argue that

from a global perspective, the carbon intensity only exhibits a 3.8% increase in 2013

compared with 1990; while at the country level, many countries achieve a decrease in

carbon intensity in this period, which is mainly attributed to the decreasing dependence on

fossil fuels and improvements in energy efficiency. Additionally, Zhao et al. (2017) deem

carbon emission intensity as the key criterion for allocating carbon quotas to various national

industries or sectors.

The second category is the CO2 emission efficiency (or carbon emission efficiency) and

environmental efficiency. For example, Meng et al. (2016) argue that carbon emission

efficiency is a crucial indicator to evaluate the efficacy of emission reduction policies. They

adopt six types of DEA model to measure the energy-carbon emission efficiency (EE&CE) of

Chinese regions during 1995-2012 and note that the eastern region of China has the highest

EE&CE while the central region has the lowest. Chang et al. (2013) adopt the size of the

labour force, fixed assets, and energy consumption in Chinese provincial transportation

sectors as inputs, GDP as a desirable output, CO2 emissions as an undesirable output, and

use a non-radial SBM-DEA model to analyse the environmental efficiency of Chinese

provincial transportation sectors in 2009. They point out that in this year, environmental

efficiency levels in most of the provinces are lower than 50% of their ideal, or target, level

and show poor environmental efficiency. Furthermore, Liu et al. (2016) note that improving

carbon emission efficiency is critical to achieving Chinese carbon emission intensity

reductions targets by 2020. They extend a SBM-DEA model to measure the Chinese carbon

emission efficiency during 2000-2011 and argue that Chinese carbon emission efficiency

declined in this period and there is room for greater reductions in emissions. Similarly,

Iftikhar et al. (2016) point out that energy efficiency and carbon emission efficiency are

important indicators for monitoring economic green development. Besides, Zhang et al.

(2016b) think that the national or industrial carbon emission efficiency is closely associated

with its emissions reduction potential. They extend a DEA window analysis to explore

Chinese provincial industrial sectors’ carbon emission efficiency during 2005-2012 and then

argue that the northwest region has more potential to reduce emissions compared with

other regions. In addition, some scholars investigate carbon emission efficiency under the

total-factor structure. For example, Zhang et al. (2015) adopt the non-radial Malmquist index

to measure the Chinese regional transportation sectors’ total-factor carbon emission

efficiency during 2002-2010 and note that the efficiency decreases in this period due to

technological decline.

2.2 Dematerialization analysis

Cleveland and Ruth (1998) argue that dematerialization can be explained as the

absolute or relative reduction in the quantity of materials used and/or the quantity of waste

(such as CO2 emission) generated in the production of a unit of economic output. Similarly,

Sun (2003) and Tapio (2007) also point out that dematerialization can be defined as the CO 2

emissions reduction in an energy production process. In addition, Carolan (2004) states that

dematerialization is like a form of production that is less consumptive of resources than

other more material forms of production. Further, Ziolkowska and Ziolkowska (2011) argue

that, when implementing traditional indicators or methods, the goals of regional, national,

and international policies are defined according to statistical data on the usage of natural

resources without referring to demographic changes, which may lead to misleading results.

Based on the study of Carolan (2004), they extend the concept of the product generational

dematerialization (PGD) indicator and note that this indicator is designed to support

sustainable policy as a supplementary and controlling instrument among other

dematerialization approaches. They use the PGD indicator to analyse the dematerialization

issue of crude oil in the global economy from 1972 to 2010, and then point out that global

crude oil consumption totally presents the dematerialization state in this period. Similarly,

Ziolkowska and Ziolkowska (2015) use the PGD indicator to evaluate the energy efficiency of

the transport sector in the EU-27 nations from 2000-2010 and argue that the sustainable

management policy of the entire European Union was successful in generational

dematerialization of energy in the transport sector (and minimising consumption of fossil

fuels per capita).

2.3 Convergence analysis

In this paper, the convergence issue in CO2 emission efficiency per capita is used to

explore the time-varying trend of gaps in CO2 emission efficiency per capita among BRICS

countries. There are three typical convergence analysis methods: convergence,

convergence and stochastic convergence (Sala-I-Martin, 1996). For the convergence of CO2

emission efficiency per capita, convergence detects the changes in standard deviation of

CO2 emission efficiency per capita of BRICS countries in each year (cross-sectional data). If

the standard deviation decreases with time, it means that there is convergence.

convergence includes two forms: absolute and conditional (relative) convergence. The two

forms of convergence both reflect the fact that countries with lower initial CO2 emission

efficiency per capita are expected to experience higher efficiency growth levels and hence

“catch up” with the higher-efficiency countries (Pettersson et al., 2014). The difference

between the two is that absolute convergence implies that all BRICS countries exhibit the

same growth path and steady-state level of CO2 emission efficiency per capita, while

conditional convergence means that the growth paths differ and therefore all BRICS

countries do not converge to the same steady state (Karimu et al., 2017). Finally, the

stochastic convergence estimates whether the gaps in CO2 emission efficiency per capita

among BRICS countries exhibit an I(0) process, namely, whether these series are stationary

(Evans and Karras, 1997). If these series are stationary, then the CO2 emission efficiency per

capita of BRICS countries exhibits stochastic convergence. The essence of stochastic

convergence is to investigate the shocks in the time paths of the gaps in CO2 emission

efficiency per capita among BRICS countries are permanent or temporary. If shocks that

affect the gaps are temporary, which will make the need for policy of CO2 emissions

reduction slightly mandatory to lessen these gaps and achieve the joint emission reduction

(Wang and Zhang, 2014).

Cross-country convergence in CO2 emissions could influence the political economy of

negotiating multilateral climate agreements (Karimu et al., 2017). For instance, various

countries should make distinct efforts to deal with climate change due to their differences in

CO2 emission efficiency, emissions reduction abilities, and potential (Liddle, 2010).

Additionally, Brännlund et al. (2015) argue that, if the time allowed for the transition to a

lower emissions path is narrowed, overall abatement costs will increase, and in the presence

of slow convergence patterns multilateral agreements may be more difficult to achieve. They

estimate the convergence in CO2 emissions performance of Swedish industrial sectors and

note that there is conditional convergence in CO2 performance among several industrial

sectors in Sweden. Similarly, Herrerias et al. (2016) point out that different regional policy-

makers should consider the convergence paths of energy (or CO2 emissions) intensity or

efficiency when determining their own emissions reduction targets and identify a sustainable

development and climate co-operation strategy.

In summary, many scholars have already worked on CO2 emission efficiency evaluation

and its convergence analysis. Nevertheless, there are still two aspects of shortcomings: on

the one hand, previous studies only measure the total (macro) and the static CO2 emission

efficiency but lack measurement at the per capita level considering population changes. On

the other hand, previous studies seldom explore the differences in CO2 emission efficiency

per capita among various countries and thus hardly reveal the time-varying trend of these

efficiency differences.

Therefore, we extend a product generational dematerialization analysis method to

measure the CO2 emission efficiency per capita of BRICS countries during 1986-2015 which

considers the population changes in these countries and then estimate the , and

stochastic convergence in CO2 emission efficiency per capita of BRICS countries during 1985-

2015 to detect the time-varying trend of efficiency differences. Finally, we provide some

support to BRICS countries’ sustainable development policy-makers.

3. Data and methods

3.1 Data descriptions

Here, we use the annual total population, per capita GDP and the foreign direct

investment in BRICS countries (1985-2015) as reported by the World Bank and the annual

total CO2 emission, fossil and renewable energy consumption and primary energy

consumption during 1985-2015 as reported by BP as our research data.

3.2 Methods

3.2.1 Product generational dematerialization analysis method

We adopt the product generational dematerialization analysis (PGD) method to

measure the CO2 emission efficiency per capita of BRICS countries3. Following Sun (2001) and

Ziolkowska and Ziolkowska (2015), our PGD indicator can be calculated by use of Eq. (1):

where Pi , t and Pi , t0 are the total population of country i at times t and t0 , respectively.

and denote the CO2 emissions per capita of country i at times t and t0 , respectively.

PGDi ,t is the PGD value of country i at time t relative to time t 0 . The greater the PGD

3 In this paper, dematerialization denotes an increase in population that is greater than that in CO 2 emissions per capita in the same time period, i.e., the PGD value is positive; accordingly, the concept of materialization means that the increase in population is less than that in CO2 emissions per capita in the same time period, i.e., the PGD value is negative.

value, the higher the dematerialization level, i.e., the higher the CO2 emission efficiency per

capita. Meanwhile, if the PGD value (PGDi ,t ) is positive, it means that at time t , country i

generates less CO2 emission than at time t 0 , under the per capita-level, and indicates the CO2

emission reduction and environmental improvement; if the PGD value (PGDi ,t ) is negative, it

means that at time t , country i generates more CO2 emission than that at time t 0 , under

the per capita-level, and reflects that there appears no CO2 emission reductions at the per

capita level.

3.2.2 Convergence analysis method

We extend the convergence analysis method to explore the time-varying trend for

differences in CO2 emission efficiency per capita (i.e., the PGD value) among BRICS countries.

This method is the important access to evaluate the differences in a certain variable among

various countries or regions (Presno et al., 2015). The convergence analysis of PGD values is

helpful for BRICS policy-makers to understand the dynamics of their domestic CO2 emission

efficiency per capita (Li and Lin, 2013), and then identify a more effective measure to realise

their emissions reduction targets and strengthen climate co-operation. In our paper, we

estimate the , , and stochastic convergence in CO2 emission efficiency per capita (i.e., the

PGD value) of BRICS countries.

Following Stegman and Mckibbin (2005), the convergence model can be defined as

Eq. (2):

σ t=√∑i=1

(PGD i , t−∑i=1

PGDi ,t

where σ t is the standard deviation of the PGD values of BRICS countries at time t (cross-

sectional data); if series σ decreases over time, we can say that there is convergence in

the PGD values of BRICS countries.

Then, we estimate the convergence in the PGD values of BRICS countries. If β

convergence exists, it means that countries with smaller PGD values (i.e., lower CO2 emission

efficiencies per capita) exhibit higher growth rates than countries with bigger PGD values,

and the growth rate and initial PGD level are negatively correlated. There is a “catch up”

effect from smaller-PGD value countries to bigger-PGD value countries (Pettersson et al.,

2014). Following Bernard et al. (1996), the absolute β convergence can be determined as

follows:

PGDi ,t +1−PGD i , t=α+β×PGDi ,t+εt (3)

where α and β are sample regression coefficients; ε t is an random disturbance term; if β

is significantly negative, then absolute β convergence exists and there is a significant

negative correlation between the growth of PGD value (i.e., CO2 emission efficiency per

capita) and the PGD value of the base period, smaller-PGD value (lower-efficiency) countries

have the trend of catching up with bigger-PGD value (higher-efficiency) countries.

Following Zhang et al. (2017), we select per capita GDP, foreign direct investment, and

primary energy consumption as our conditional variables, the corresponding conditional β

convergence model can be defined as follows:

PGDi ,t +1−PGD i , t=α+β1×PGD i , t+ β2×RGDPi , t+β3×FDI i , t+β 4×ECi , t+ε t (4)

where RGDPi , t ,FDI i ,t and ECi , t denote the per capita GDP, foreign direct investment, and

the primary energy consumption of country i , respectively; , and are sample

regression coefficients; if the value of β1 is significantly negative, then we can say that there

is conditional convergence.

Finally, in this paper, if the gaps of PGD values among BRICS countries form the

stationary series, then we can say that the PGD values of BRICS countries exhibit stochastic

convergence (Wang and Zhang, 2014). Following the concept of relative income defined by

Carlino and Mills (1993), we introduce the concept of the relative PGD value ( i.e., relative CO2

emission efficiency per capita), which can be expressed as Eq. (5):

where PGDt is the average PGD value of BRICS countries. Therefore, to estimate the

stochastic convergence, we can turn to estimate whether the panel data

RPGDi , t ,i=1,2, . .. ,5 ;t=1986 ,1987 , . .. ,2015 , is stationary (Payne et al., 2017), i.e., we need

to extend the unit root test for this panel data (Dickey and Fuller, 1979; Phillips and Perron,

1988; Maddala and Wu, 1999; Im et al., 2003; Hadri, 2000). If this panel data has a unit root,

it means that there is no stochastic convergence, then any shocks that affect the series are

permanent; if this panel data does not have a unit root, it means that stochastic convergence

exists and any shocks that affect the series are temporary and transitory, which will make the

need for policy of carbon dioxide emissions reduction slightly mandatory (Wang and Zhang,

2014).

4. Empirical result analyses

4.1 The CO2 emission efficiency per capita of BRICS countries

Following Ziolkowska and Ziolkowska (2015), we use the PGD value mentioned above to

represent our CO2 emission efficiency per capita of BRICS countries. Fig. 1 presents the PGD

values of BRICS countries in 2015 relative to 1985, our findings are as follows:

On one hand, over the entire sample period, the PGD values of BRICS countries

exhibited a large difference. Among them, the PGD value of South Africa is higher while that

of China is lower. On the other hand, only Russia and South Africa appear to be in a

dematerialization state (i.e., the PGD value is positive) in the whole sample period. This

phenomenon indicates that the increase rate of population is totally higher than that of the

PGD value (i.e., our CO2 emission efficiency per capita) in these countries at the per capita

level, these countries generate less CO2 in 2015 compared with in 1985. The candidate

reasons are technological advancement and the improvement of energy efficiency in these

countries during the sample period (Ziolkowska and Ziolkowska, 2015).

Figure 1 here

Fig. 2 shows the PGD values of BRICS countries from 1986 to 2015. Several findings can

be identified as follows:

Firstly, the PGD values of Russia and South Africa are relatively high among BRICS

countries4. During the whole sample period, the number and level of dematerialization state

of Russia and South Africa are relatively bigger and higher, respectively (see Fig. 2).

Nevertheless, there are some different reasons for this phenomenon in these two countries:

from 1999, Russia is basically in materialization state while South Africa is basically in

dematerialization over the whole sample period. The possible reasons for this are as follows:

for Russia, firstly, the increase rate of population in Russia is relatively lower which severely

curbs the upgrade of PGD values. In Russia, renewable energy consumption is far smaller

than fossil energy consumption (see Fig. 3). However, Russia mainly consumes natural gas

among various fossil energy sources available (to 2015, the percentage of Russian natural gas

consumption in total fossil energy consumption is 60.33%5). Using natural gas will emit less

CO2 compared with using coal and oil. Theoretically, Russia is supposed to exhibit a lower CO2

emissions level. Indeed, after 1990, the increase rate of Russian population slows down,

which causes the PGD values to appear relatively smaller. The population of Russia is

basically decreasing from 1990 (see Fig. 4). The candidate reasons are as follows: for one

thing, the demographic crisis deteriorated after the founding of the Russian nation6, causing

a weakness in population reproduction abilities (Cheng, 2016). After the nation foundation,

Russia experienced a transition period of society and economy. At that time, the recession of

economy, the fluctuation of society and the demotion of living standard make Russian 4 In the whole sample period, none of BRICS countries exhibits the dematerialization in all years, i.e., PGD values of any BRICS countries are not positive all the time. The reason why Russia and South Africa have dematerialization in total is that the number and level of dematerialization-years in these countries are relatively higher, leading to the total-dematerialization. However, there exists significant difference between these two total-dematerialization states that Russia’s long dematerialization-period from 1988 to 1998 contributes to its total-dematerialization (since 1999, Russia basically stays in the state of materialization, i.e., its PGD value is negative), while South Africa sees the dematerialization during almost the whole sample period. 5 http://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy/downloads.html6 http://news.xinhuanet.com/cankao/2013-10/21/c_132817095.htm

citizens difficult to solve the life and employment problems and cannot or unwilling to give

birth to and rear children. Russian young people were trying their best to find work to ease

their plight in life (Vishnevsky, 2009). Even though they want to establish a family and have

children, unemployment, high commodities and housing prices, and the lack of guarantee of

life quality hamper their desire to have a child. Finally, Russian birth rates have remained low

due to high alcohol consumption (Javeline and Brooks, 2012; Balachova et al., 2016). Besides,

poor diet, and water and air pollution also severely impact Russians’ health. Finally, Russia

has a higher death rate (Russia’s death rate is far more than other countries which also

underwent drastic population decrease such as the UK, Germany, and Ireland7) (Peng, 2011).

Secondly, the Russian government intends to increase the amount of coal-fired power plants

to increase employment and then accelerate economic growth (Cowan et al., 2014);

however, the consequence is an increased level of CO2 emissions (EIA, 2012), leading to

Russia frequently manifesting a materialization state after 1990. Therefore, different from

other indicators and methods for evaluating CO2 emission efficiency, PGD values can reveal

the per capita level-CO2 emission efficiency from the dynamics of both population and CO2

emission per capita perspectives.

Figure 2 here

Figure 3 here

Figure 4 here

However, for South Africa, the candidate explanations for its frequent dematerialization

7 From 1976 to 1991 (i.e., the last 16 years of the Soviet era), Russian official recordings show that 36 million people were born, but from 1992 to 2007 (i.e., 16 years after the collapse of the USSR), only 22.33 million people were born, a relative reduction in total population of 40% compared with the previous 16 years. Between 1976 and 1991, the death toll was 24.66 million. However, from 1992 to 2007, the death toll was 34.77 million, a 40% increase compared with 1976-1991. The symmetrical number is even more striking: from 1976 to 1991, the number of births was 11.44 million more than the death toll, but in the following 16 years, the death toll was about 12.40 million more than the number of births, which can be found from http://news.sohu.com/20150719/n417060035.shtml.

state are as follows: on one hand, the growth of South Africa’s population is relatively higher

(see Fig. 4). At present, South Africa is undergoing rapid urbanisation, with an increase in

urban population and the number of young people. According to recent reports from South

Africa’s Security Institute, at the end of 2030, South Africa’s population will increase to 68

million, which is far above the 58 million that its National Development Plan (NDP) forecast8.

In addition, the increasing population appears to be at a relatively low level during 2003-

2004 and correspondingly, the PGD values present a significant slump in these two years,

which indicates that the population dynamics exert a significant impact on South Africa’s

PGD values. On the other hand, the emissions reduction policies of South Africa have some

effects on mitigating CO2. South Africa is classified as a non-annex signatory to the Kyoto

Protocol: it is a country that has no mandatory CO2 emission reduction target (Kohler, 2013),

although South Africa is the world’s most carbon-intensive non-oil-producing country and

the biggest emitter in Africa (EIA, 2010). However, this country still promises to fight against

climate change by instituting several policies and strategies at a national level to reduce CO2

emissions such as increasing the use of wind, solar and other clean energy sources to

gradually replace fossil fuel, and actively develop and implement a carbon tax policy (Chang,

2015). Hence, CO2 emissions in South Africa have been controlled, which contributes to the

relatively higher PGD values therein.

Secondly, the PGD values of India and China are relatively lower among the BRICS

countries. For India, the main possible reasons are as follows: for one thing, India’s economic

growth brings higher demand for electricity. India’s coal-dominated electricity generation

pattern makes its CO2 emissions increase with increasing electricity consumption. Indeed,

8 http://www.mofcom.gov.cn/article/i/jyjl/k/201310/20131000356440.shtml PAGE \* MERGEFORMAT 3

after its independence, India has used a lot of energy to facilitate economic growth (Douglas,

2006; Focacci, 2005). India’s government announced that it must increase energy use to help

drag its population out of poverty. During this period, India’s government policy of energy

price subsidy has caused inefficiency in energy use. For now, India is still passing a period of

high-energy demand and inefficient energy use (Chang et al., 2015). In recent years, India’s

rapidly developing economy brings mounting demand for electricity9, and its electricity

generation heavily depends on coal (from 2012 to 2017, coal will remain the mainstay of

power generation during India’s 12th Five-year Plan, providing at least 50% of base-load

power (Vazhayil and Balasubramanian, 2014)), causing its CO2 emissions to increase with

increasing electricity production. Finally, the increase rate of India’s CO2 emissions is higher

than that of its population. For another, the development and utilisation of clean energy in

India are inadequate. For example, from the perspective of energy endowment, India

possesses abundant solar resource and most parts of India have 300 sunny days in a year, but

the use of solar power is inefficient (Hairata and Ghosh, 2017). In addition, because of the

lack of related facilities and equipment, and the low discovery degree, the risk of mining low-

emitting natural gases (such as shale gas) have been aggravated. Finally, natural gas

consumption remains low (see Fig. 5). To sum up, although the increase rate of India’s

population ranks the top two among BRICS countries (see Fig. 4), its population growth is

hard to promote its PGD values so that it remains at a higher materialization level (or a lower

dematerialization level) over the whole sample period. Thus, India’s government needs to

make efforts to improve energy efficiency, further increase the development and utilisation

of natural gas and other clean energy sources, develop renewable energy-related

9 By the start of 2011, 92.7% of urban households and 55.3% of rural households had access to electricity in India (Census of India, 2011).

technologies to be independent of coal, reduce CO2 emissions, and ensure simultaneous

economic growth.

Figure 5 here

For China, the candidate reasons for its relatively lower PGD values are as follows: on

one hand, in the 1980s, China suffered from a severe shortage of energy supply due to its

rapid economic growth and China’s energy policy was originally oriented at promoting coal

production (Chang et al., 2015). The substantial and inefficient utilisation of coal leads to

mounting CO2 emissions. On the other hand, although China is trying to gradually substitute

other fossil fuels and clean energy for coal (for example, in electricity generation, China is

gradually replacing clean energy with coal (in recent years, the percentage of electricity

generated by clean energy in total electricity generation is approximately 20% (Woo et al.,

2017))), the industrial and transportation sectors still mainly depend on coal and these

sectors emit more CO2 than other sectors in the whole economy (Wang et al., 2017). Besides,

increasing urbanisation leads to a large increase in CO2 emissions in China. Michieka and

Fletcher (2012) point out that urbanisation will positively affect CO2 emissions in BRICS

countries and the effect on China is the most significant. Cities are areas of the most

intensive consumption of energy and resources, and are also intensive CO2 emissions sources

(Zhang et al., 2014). Currently, China’s level of urbanisation has just surpassed 50%, and

there remains a significant gap compared with that of developed countries. The extension of

urbanisation will be the main force behind the emission of CO2 in the future. In 2014, the

Chinese government issued the National New Urbanisation Plan (2014 - 2020): China will add

the concept of ecological civilisation into its urbanisation process and make efforts to

promote green development, recycling, and low-carbon development10. In the following

years, Chinese urbanisation will face the challenges of those issues mentioned above. Last

but not least, influenced by China’s family planning policy, China’s population growth rate has

been controlled which causes its growth rate of population to be lower than that of CO2

emissions. Thus, China’s PGD value is hard to increase significantly.

Thirdly, in terms of energy consumption, BRICS countries’ changes in fossil and

renewable energy consumption present different effects on their PGD values. For example,

for Brazil, its increase in coal consumption significantly affects its PGD values. As Fig. 2 shows,

Brazil’s PGD values experienced frequent fluctuations from positive to negative and vice

versa. During 1990-1991, 2003-2004, and 2009-2010, Brazil’s PGD values all slumped from a

high positive value to a low negative value. From Fig. 6 we find that Brazil’s coal consumption

increased significantly in these years (1991, 2004, and 2010) compared with that in the

successive proceeding years (1990, 2003, and 2009). Although Brazil’s renewable energy

consumption also increased in 1991, 2004, and 2010, it cannot upgrade its PGD value. Then,

we estimate the Pearson correlation coefficient between Brazil’s coal, oil, natural gas, and

renewable energy consumption and its PGD values (see Table 1). The results show that there

is significant negative correlation between Brazil’s coal consumption and its PGD values at

the 10% significance level while there is no significant correlation between Brazil’s other

energy consumption and its PGD values. Furthermore, we test the Granger causality between

the first-order difference series of Brazil’s coal consumption and its PGD values11, and the

results are shown in Table 2. We find that there is a significant unidirectional causality from 10 http://news.chinaChina.com.cn/txt/2015-06/17/content_35847226.htm11 We test the Granger causality between Brazil’s coal consumption and its PGD values while no evidence of Granger causality in any direction between them was found. Hence, we turn to estimate their first-order difference series. Firstly, we calculated the Pearson correlation coefficient between these two series and find that significant negative correlation at the 1% significance level. Then, following Dickey and Fuller (1979) and Phillips and Perron (1988), we extended the unit root test for these two series and the results show that they are all stationary.

the first-order difference series of Brazil’s coal consumption to the first-order difference

series of Brazil’s PGD values12. Thus, for Brazil, the dynamics of changes in coal consumption

significantly negatively affect the dynamics of PGD value changes; specifically, an increase in

coal consumption changes will decrease the changes in the PGD value and even turn Brazil

from a nation in a state of dematerialization, to one in materialization.

Figure 6 here

Table 1 here

Table 2 here

Additionally, BRICS countries’ renewable energy consumption is hard to promote their

PGD values. For example, as shown in Fig. 7, from 2000-2013, renewable energy

consumption in India and China gradually increases and approaches their fossil energy

consumption levels. However, these two countries still basically remain in a materialization

state in this period. Furthermore, as Table 1 shows, for India and China, no evidence of

significant correlation between their renewable energy consumption and their PGD values

was found. The main reasons for this phenomenon are as follows: for one thing, the increase

in population is lower than that of CO2 emissions per capita in these two countries; for

another, renewable energy use in India and China is mainly for hydroelectric power

generation (Yang et al., 2016); however, social life (such as transportation) and production

(such as the steel and chemical industries) still depend heavily on coal and oil (Mi et al.,

2015). Although India and China are striving to develop energy-saving and emission-

reduction measures, the total consumption of coal and oil is difficult to decrease in the short-

term. This result is partially consistent with the findings in Sebri and Ben-Salha (2014). They

12 Based on AIC, SC, and LR statistics, we determined the optimal lag length of the Granger causality model to be one. PAGE \* MERGEFORMAT 3

test the Granger causality between BRICS countries’ renewable energy consumption and

their CO2 emissions, and then point out that for India, no evidence of Granger causality in

any direction between its renewable energy consumption and its CO2 emissions was found.

Figure 7 here

Fourthly, in terms of climate change co-operation negotiation, some climate co-

operation protocols (such as the Kyoto Protocol) do not exert an influence on constraining

signatory countries to reduce CO2 emissions and improve CO2 emission efficiency. For

example, 2005 was the year of implementation of the Kyoto Protocol while Russia’s PGD

value did not increase after 2005; otherwise, although China’s PGD value increased after

2005, it slumped again in 2008. Therefore, the Kyoto Protocol cannot have a long-term,

enduring effect, on promoting PGD values of Russia and China. This result is partially

consistent with the findings in Chang (2015). He measures the carbonisation index

(CO2/energy consumption) of BRICS countries and then finds that BRICS countries’ average

carbonisation index increased after 2005. He points out that the Kyoto Protocol has had

limited effects on CO2 emissions reduction in BRICS countries. Besides, Almer and Winkler

(2017) also note that the Kyoto Protocol exerts little influence on mitigating emissions of its

annex countries.

Finally, BRICS countries’ per capita CO2 emission efficiencies have all improved in recent

years. As shown in Fig. 2, BRICS countries’ PGD values all increase in 2014 and 2015. Except

in India, other BRICS countries are all in a dematerialization state, which means that in these

countries, an average citizen emits less CO2 compared to last year. Possible reasons for this

result are as follows: for one thing, the increase rate of population is higher than that of CO2

emissions per capita in these countries; for another, the implementation of sustainable

development strategies and energy-saving/emission-reduction policies have been improved,

and the intensive support for promoting energy efficiency, leading to better-controlled CO2

emissions (Ziolkowska and Ziolkowska, 2015). Taking China as an example, China’s current

carbon quota system continues to be improved, and China is gradually establishing a carbon

trading market to help achieve its energy-saving and emissions reduction targets. In 2014,

China launched a carbon emission trading (CET) pilot. Zhang et al. (2017) note that this pilot

CET scheme has exerted significant influence on carbon emissions reduction in China in

recent years. However, the CET is still accompanied by an immature market environment,

unsound infrastructure, limited emissions trading volume, and low market liquidity.

Therefore, whether a CET can have a durable effect on China’s carbon emission reduction

should be further investigated.

4.2 Convergence analysis for CO2 emission efficiency per capita of BRICS countries

To detect the time-varying trend of the gaps in CO2 emission efficiency per capita (i.e.,

our PGD value) among BRICS countries, we firstly estimate the convergence of these

countries’ PGD values from 1986 to 2015 (see Fig. 8). We find that during the whole sample

period, the standard deviation of PGD values of BRICS countries (cross-sectional data) does

not decrease over time, which means that there was no convergence in the PGD values of

BRICS countries. Put another way, the gaps in the CO2 emission efficiency per capita among

BRICS countries cannot be continuously decreased over time.

Figure 8 here

Then, we estimate the absolute and conditional convergence of BRICS countries’ PGD

values during 1986-201513, results are shown in Table 3:

Table 3 here

Two finding are as follows: firstly, there exists convergence in PGD value of BRICS

countries and the lower-PGD value countries will “catch up” with the higher-PGD value

countries at a higher growth rate. As Table 3 shows, coefficients of PGD are all significantly

negative, which indicates that the growth in PGD values was negatively correlated with the

initial PGD values, evincing both absolute, and conditional, convergence. The growth in

PGD values of the lower-PGD value countries (such as India and China) appears higher than

that of the higher-PGD value countries. However, it does not mean that India and China can

slacken their emission-reduction efforts because they still have poor PGD values (see Fig. 2).

Hence, India and China should further develop policies to reduce CO2 emissions. Additionally,

although BRICS countries are facing an urgent emission-reduction task, if the time allowed

for a country’s transition to a lower emission path is narrowed, it will be more difficult to

achieve its emission reduction target and it dooms to increase abatement costs (Brännlund

et al., 2015). Herrerias et al. (2016) also note that, when negotiating climate co-operation,

different countries should consider their convergence paths of CO2 emissions and formulate

their own emissions-reduction targets based on these convergence paths.

Secondly, the rate of conditional convergence is greater than the rate of absolute

convergence. As shown in Table 3, after adding the three conditional variables, i.e., per capita

GDP, foreign direct investment, and primary energy consumption, the coefficient of PGD

changes from -0.6858 to -0.7118, meaning that the convergence rate has been increased

(Zhang et al., 2017). Based on this result, we can identify that: for one thing, by considering

13 Following the result of the Hausman test, we adopt the fixed-effect model of panel date to estimate the convergence. The DW values in Table 3 mean that there was no evidence to support the existence of auto-correlation in these models.

the different characteristics in the economies and energy consumption of BRICS countries,

the PGD values of these countries will trend to their own steady-state level and exhibit

imbalanced development; for another, in BRICS countries, economic development, foreign

investment, and primary energy consumption are key factor affecting the convergence rate

of CO2 emission efficiency per capita (PGD value). This conclusion is partially consistent with

the findings in Zhang et al. (2017). They estimate the convergence in the energy efficiency

of China at the regional-level and point out that, after adding the per capita GDP, foreign

investment and other conditional variables, the convergence rates of energy efficiency in

different regions become higher than that of absolute convergence. Furthermore, they note

that, to decrease the gaps in energy efficiency and CO2 emissions, it is necessary to control

the level of these factors. Governments should further attract foreign investment and take

advantage of international markets and international resources to introduce more advanced

technologies to reduce CO2 emissions per capita. Besides, Li and Lin (2013) and Wang and

Zhang (2014) add per capita GDP into their convergence models to estimate the

convergence in CO2 emissions per capita of 110 countries and note that per capita GDP is the

important factor affecting the convergence rate of CO2 emissions per capita.

Finally, we estimate the stochastic convergence of BRICS countries’ PGD values during

1986-2015. Based on the related definition, to estimate the stochastic convergence of PGD

values, we should examine whether the relative PGD values are stationary, i.e., we need to

extend the unit root test of panel data (Dickey and Fuller, 1979; Phillips and Perron, 1988;

Maddala and Wu, 1999; Im et al., 2003; Hadri, 2000). Table 4 presents the results of four

common unit root tests as used on this panel data.

Table 4 here

We find that there is evidence of stochastic convergence of BRICS countries’ PGD values

during the whole sample period. As Table 4 shows, the results of IPS, ADF-Fisher, and PP-

Fisher tests indicate that, in all relative PGD value series the unit root null hypothesis can be

rejected, at the 1% level of significance; the Hadri test accepts the null hypothesis that all

relative PGD value series are stationary. Hence, to sum up, the relative PGD value series of

BRICS countries are all stationary, which means that there is stochastic convergence in CO2

emission efficiency per capita (PGD values) in BRICS countries. The existence of stochastic

convergence shows that any shocks that affect the relative PGD value series are transitory

and temporary, which means that the gaps in BRICS countries’ PGD values (CO2 emission

efficiency per capita) would not be persistently aggregated (Lee and List, 2004; Payne et al.,

2017) and BRICS countries can lessen these gaps by enacting appropriate policies and finally

realising joint emissions reductions. For example, India’s technologies used in the

exploitation of natural gas are relatively backward and investing in the natural gas industry

presents higher risks, which causes the poor use rate of natural gas use (Busby and Shidore,

2017). If India can introduce more advanced technologies into its natural gas industry from

Russia and then, improve the use of natural gas use and gradually reduce the use of coal,

India will be able to further reduce her CO2 emissions and its CO2 emission efficiency per

capita can also be improved.

5. Conclusions and policy implications

To provide some scientific support for BRICS countries’ sustainable development policy-

makers, we adopt the production generational dematerialization method to measure the CO2

emission efficiency per capita of BRICS countries during 1986-2015. Furthermore, we use the

convergence analysis method to detect the time-varying trend of the gaps in CO2 emission

efficiency per capita (i.e., the PGD value) among BRICS countries. Based on the empirical

results set forth above, some main conclusions are obtained as follows.

Firstly, the PGD values of BRICS countries exhibit large differences over the whole

sample period. Among them, South Africa’s PGD values are relatively high due to its rapidly

increasing population and control of CO2 emissions; Russia’s PGD values rank immediately

below South Africa’s, however, Russia is basically in a state of materialization (its PGD value is

negative) after 1999 because of its demographic crisis; in addition, the PGD values of China

are relatively lower.

Secondly, although Russia uses substantial natural gas resources and emits far less CO2

than other countries which depend heavily on coal and oil, the increase in its CO 2 emission

per capita is still higher than that of its population due to its lower birth rate and higher

death rate.

Moreover, the PGD values of India and China are lower than those of the other BRICS

countries: because India’s economy depends heavily on electricity, its coal-dominated

electricity generation pattern makes its CO2 emissions increase with increasing electricity

consumption. For China, its industrial and transportation sectors still largely use fossil fule

energy and these sectors emit far more CO2 than other sectors. Finally, in these two

countries, the increases in CO2 emissions are higher than those in their populations.

In addition, for Brazil, the dynamics of coal consumption changes significantly negatively

affect the dynamics of PGD value changes; specifically, the increase in Brazil’s coal

consumption changes will decrease its PGD value changes and even turn Brazil from

dematerialization state to materialization state.

Finally, the results of convergence analysis indicate that, although the gaps in CO2

emission efficiency per capita (i.e., the PGD value) among BRICS countries do not

continuously decrease with time, the lower-PGD value countries will “catch up” with the

higher-PGD value countries at a higher growth rate. Furthermore, after adding the three

conditional variables, i.e., per capita GDP, foreign direct investment, and primary energy

consumption, the rate of conditional convergence is greater than the rate of absolute

convergence. Besides, any shocks that affect the gaps in PGD values among BRICS countries

are transitory, and temporary, and would not continuously expand these gaps.

The conclusions set forth above have significant implications for BRICS countries’

sustainable development policy-makers. For example, Brazil’s government should control

nationwide coal consumption to promote CO2 emission efficiency per capita. Besides, healthy

lifestyle education and publicity for Russians should be strengthened to improve the overall

citizens’ health level and finally maintain the population birth rate and death rate at a proper

level; otherwise, Russia should pay attention to the control of nationwide coal consumption,

and improve technological levels to enhance productivity, increase infrastructure investment

and construction to improve energy efficiency, and thereby reduce its CO2 emissions. For

China and India, and especially India, they need to gradually lessen their dependence on coal

and increase their utilisation levels of renewable and clean energy (especially hydro, wind,

and nuclear energy) to reduce CO2 emissions and promote their CO2 emission efficiency per

capita, while ensuring coordination between urbanisation and green development

processes. Finally, efforts should be made to further enhance climate co-operation measures

in BRICS countries: low-PGD value countries should extend and implement more efficient

policies to lessen the gaps between themselves and high-PGD value countries, thus achieving

joint emissions reductions.

It should be noted that there is more work to do on the topic of this paper: for instance,

the impact of the implementation of the Paris Agreement on BRICS countries’ PGD values

can be further investigated. In addition, for China, whether the two-child policy will affect its

PGD value can be explored.

Acknowledgments

We acknowledge the financial support from National Natural Science Foundation of

China (Grant nos. 71273028 and 71322103), National Special Support Programme for High-

Level Personnel from the Central Government of China, Changjiang Scholars Programme of

the Ministry of Education of China, and Hunan Youth Talent Programme. We also

acknowledge the seminar participants from the Centre for Resource and Environmental

Management, Hunan University, for their insightful discussions.

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Tables

Table 1 Person correlation coefficients between energy consumption and PGD value.

Countries Correlation Coal Oil Natural gas Renewable energy

BrazilCoefficients -0.3290 -0.2540 -0.2110 -0.1910

P value 0.0760 0.1750 0.2640 0.3120

RussiaCoefficients 0.1320 0.2030 -0.1660 0.2060

P value 0.4860 0.2820 0.3810 0.2750

IndiaCoefficients -0.2970 -0.2380 -0.2190 -0.3010

P value 0.1110 0.2040 0.2460 0.1060

ChinaCoefficients -0.1020 -0.1010 0.1160 -0.0820

P value 0.5930 0.5940 0.5420 0.6660 South Africa

Coefficients -0.2890 -0.1220 -0.0440 -0.2790

P value 0.1210 0.5220 0.8160 0.1350

Table 2 Results of Granger causality estimation.Null Hypothesis F-Statistic P-value

DPGD does not Granger Cause DCOAL 0.0351 0.8529DCOAL does not Granger Cause DPGD 5.5366 0.0268Note: DCOAL and DPGD are the first-order difference series of Brazil’ s coal consumption and the first-order difference series of Brazil’s PGD value, respectively.

Table 3 The estimation for β convergence in PGD values of BRICS countries.Type of β convergence Variable Coefficient Adjusted R2 DW stat. F stat.

Estimation for absolute β convergence

α -0.4009 (0.21)0.3111 2.1015

14.0074 (0.00)PGD -0.6858 (0.00)

Estimation for conditional β convergence

α -0.3036 (0.76)

0.3063 2.05408.6722 (0.00)

PGD -0.7118 (0.00)

RGDP -0.0002 (0.37)FDI 0.0000 (0.90)EC 0.0008 (0.75)

Note: The p-values are reported in parentheses.

Table 4 Results of panel unit root tests.Test Statistic value P value

IPS test -6.87 0.00ADF-Fisher test 61.97 0.00PP-Fisher test 73.36 0.00Hadri test -0.83 0.00

Figures

Fig.1 PGD values of BRICS countries in 2015 related to 1985.

Fig.2 PGD values of BRICS countries during 1986-2015.

Fig.3 Energy consumption of Russia during 1985-2015.

Fig. 4 Changes in population of BRICS countries during 1986-2015.

Fig.5 Fossil energy consumption of India during 1985-2015.

Fig.6 Energy consumption of Brazil during 1985-2015.

Fig.7 Energy consumption of India and China during 1985-2015.

Fig.8 Standard deviation of PGD values for BRICS countries during 1986-2015.

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