Does increasing investment in research and development promote economic growth decoupling from carbon emission growth? An empirical analysis of BRICS countries

Journal Pre-proof Does increasing investment in research and development promote economic growth decoupling from carbon emission growth? An empirical analysis of BRICS countries

Qiang Wang, Fuyu Zhang PII:

S0959-6526(19)34723-7

DOI:

https://doi.org/10.1016/j.jclepro.2019.119853

Reference:

JCLP 119853

To appear in:

Journal of Cleaner Production

Received Date:

15 October 2019

Accepted Date:

22 December 2019

Please cite this article as: Qiang Wang, Fuyu Zhang, Does increasing investment in research and development promote economic growth decoupling from carbon emission growth? An empirical analysis of BRICS countries, Journal of Cleaner Production (2019), https://doi.org/10.1016/j.jclepro. 2019.119853

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Graphical abstract

Journal Pre-proof

Does increasing investment in research and development promote economic growth decoupling from carbon emission growth? An empirical analysis of BRICS countries

Qiang Wang 1,2*, Fuyu Zhang 1,2 1. School of Economics and Management, China University of Petroleum (East China), Qingdao, Shandong 266580, People’s Republic of China 2. Institute for Energy Economics and Policy, China University of Petroleum (East China), Qingdao, Shandong 266580, People’s Republic of China *Corresponding author: [email protected], Tel/Fax: 86+532-86983286

1

Journal Pre-proof Abstract It is generally recognized that increasing research and development investment is an effective measure to promote economic and social progress. An open question is if increasing research and development investment help economic growth decoupling from carbon emission. This study investigates research and development investment and carbon emission in BRICS countries by using the Fully Modified Ordinary Least Squares for empirical estimation from 1996 to 2014. The results show that every 1% increase in research and development investment, carbon emissions will be decreased by 0.8122% for the BRICS as a whole, which indicates increasing research and development investment has a positive impact on decoupling economic growth from environmental pressure. For individual, this impact is most significant in China, weak in Russia and India. However, it also should be noted that only increasing research and development investment cannot achieve the decoupling between economic growth and carbon emission, because other factors, such as economic activity, industrialization and urbanization, renewable energy also influences the decoupling economic growth from carbon emission. The results also show that economic activity, industrialization and urbanization pose negative impact on the decoupling, whereas renewable energy consumption promote the decoupling.

Keywords: research and development investment; environmental pressures; BRICS; urbanization; decoupling

2

Journal Pre-proof 1.Introduction As we all know, with the integration of technology and productivity, science and technology is driving economic and social development rapidly. Development of science and technology become support for economic growth. Research and development (R&D) has greatly helped the economic and social improve.

However,

whether R&D help economic growth decoupling from carbon emission remains still an open problem. For developing countries, they are in a critical period of economic development. In order to eliminate poverty, improve infrastructure and industrial structure, they must maintain rapid economic growth.

BRICS as a typical representative of developing

countries, contains Brazil, Russia, India, China, and South Africa. As shown in Fig.1. Over the past 30 years, the GDP of the BRICS countries has increased from 416.4 billion in 1990 (constant 2010 US$) to 18,188.4 billion in 2018, contributing 22% of global GDP. The average annual growth rate was 5.42%, more than double that of the world's largest economy(United States). The contribution to world economic growth should not be underestimated. However, the deteriorating environment also made us aware of the excessive carbon emissions that come with it. Sacrificing environmental quality to maintain economic growth is not sustainable. Coordinating the relationship between the economy and the environment is a huge challenge for the BRICS countries. On the one hand, because developing countries are in the early stages of industrialization, the production process is often accompanied by carbon emissions. 3

Journal Pre-proof The technological upgrade brought by R&D investment will make the factory expand production scale, which may lead to increased carbon emissions. On the other hand, R&D investment will also promote the development of low-carbon technologies. The use of low-carbon technologies will save energy and reduce carbon emissions. Therefore, for the BRICS countries, whether R&D investment will contribute to economic growth decoupling from carbon emissions is an open and important question. Not only provide reference for policy makers in developing countries to formulate carbon emission reduction policies, but also have important significance for global sustainable development. To solve this open problem, we have taken a series of effective measures. First, we select the Tapio decoupling model to check the decoupling state between economic growth and carbon emissions. Then, the unit root test, cointegration test, model setting test, FMOLS regression estimation and Granger causality test are performed to investigate R&D investment and carbon emissions. Summarize the ways to economic growth decoupling from economic growth and provide reference for global sustainable development. This paper consists of five parts, the rest of which are organized as follows: Section 2 reviews and summarizes the existing relevant literature. Section 3 provides methods and data description. Section 4 shows the empirical results. Section 5 provides discussion. Section 6 summarizes the full text and puts forward the policy implications on the basis of discussion. 4

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Fig. 1 GDP trends of the BRICS countries from 1990 to 2018 2. Literature review 2.1 Economic growth decoupling from carbon emission. In recent years, coordinated economic growth and carbon emission have attracted the attention of many scholars(Csereklyei and Stern (2015); (Wang and Jiang, 2019)). Using the decoupling theory to study the relationship between economic growth and carbon emissions, the earliest method was OECD theory, which first studied carbon emission from the perspective of economics. Next, Moldan et al. (2012) introduce the concept of first-level decoupling and second-level decoupling. Then, the Tapio decoupling model began to appear, which greatly improved the decoupling research system. Subsequently, it was widely used at various levels. At the global level , (Schandl et al., 2016); Wu et al. (2018) used the Tapio decoupling model to analyze the relationship between environmental pressure and economic growth. Similarly, many scholars have conducted research at the national level. Wang et al. (2019a) chose China 5

Journal Pre-proof and India as representatives of developing countries to study the relationship between economy and energy consumption. De Freitas and Kaneko (2011) investigated the relationship between economic growth and carbon emissions in Brazil in 2004-2009. Andreoni and Galmarini (2012) studied Italy. Gray et al. (2006) studied Scotland. Tapio decoupling model has also attracted many scholars to study the industry level. Wang, Y. et al. (2018) use this model to study the relationship between carbon emissions and economic growth in China's transportation industry. In addition, Guivarch and Mathy (2012) studied in India's power sector. Tang et al. (2014) studied the decoupling state of China's tourism industry. Feng et al. (2013),Huang et al. (2019) studied the decoupling state of China's transportation industry. In brief, wide application of Tapio decoupling model at various levels reflects the maturity of the model. However, few studies have conducted comparative decoupling analysis among BRICS. Through decoupling analysis between the BRICS, recent decoupling trend can be confirmed, disadvantages can be improved in the comparison. Therefore, this paper selects Tapio decoupling model to determine economic growth decoupling from carbon emission in BRICS countries. Decoupling analysis only reflect the real-time relationship between carbon emissions and economic growth, while this paper is to study does increasing R&D investment help carbon emission reduction. It is necessary to introduce econometric methods for further research. 2.2 Factors affecting carbon emissions 6

Journal Pre-proof Presently, LMDI decomposition and econometrics are two of the most famous methods for studying the factors that affect carbon emissions (Moutinho et al. (2018),Wang and Su (2020), Hossain (2011),). Moreover, Zang et al. (2017) used the LMDI decomposition technique to conclude that the main drivers of carbon emissions are household income and population. Chen et al. (2018) , Jeong and Kim (2013)decomposed the effects of fossil energy, energy consumption structure, energy intensity, per capita GDP, and population size on carbon emissions. Wang et al. (2005) study the effects of fuel switching and renewable energy penetration on carbon emissions. Cansino et al. (2015) and Yi and Li (2013) focused on population, industrial structure, energy intensity, energy structure. Zhao et al. (2015) decomposed the land area and the impact of the population. Through above literature review, among many factors in decomposition of LMDI decomposition, urbanization and industrialization to be rarely studied due to the limitation of methods. On the contrary, the impact of urbanization and industrialization on carbon emissions can be measured flexibly by econometrics. Parikh and Shukla (1995) used fixed-effect models of panel data to study impact of urbanization on carbon emissions. Alam et al. (2007) used the STIRPAT model to study the impact of urbanization on carbon emissions in Pakistan, Liu (2009) found the positive influence of urbanization on carbon emissions is weakening. Newman and Kenworthy (1989), DHAKAL et al. (2002) and Zhang et al. (2014) also focused on the impact of urbanization on carbon emissions. Wang et al. (2019b) studies the impact of 7

Journal Pre-proof urbanization on energy consumption in 186 countries In research for industrialization. Shahbaz et al. (2014) used ARDL method to capture the impact of industrialization on Bangladesh's carbon emissions. Asumadu-Sarkodie and Owusu (2016) focused on the industrialization of Benin by ARDL. What’s more, Wang, Q. et al. (2018),Shahbaz et al. (2014), Destek and Okumus (2019) use econometric methods to measure the impact of industrialization on carbon emissions. As is known to all, BRICS countries are newly industrialized countries. Therefore, urbanization and industrialization have become indispensable factors to study the carbon emissions of BRICS countries. Compared with LMDI, the FMOLS panel regression selected in this paper can accurately measure the impact of factors on carbon emissions. In recent years, a large number of studies have used econometrics to study the carbon emission influencing factors, which are summarized in Table 1. In recent studies, Chen et al. (2018) found that for 17 OECD countries, energy intensity and GDP will increase carbon emissions, Ito (2017) focused on the impact of energy structure on carbon emissions in 42 developed countries. In the past two decades, R&D investment has increased significantly around the world. Many scholars began to focus on the impact of R&D investment on carbon emissions. According to Table 1, we found that most of the papers focusing on the relationship between R&D investment and carbon emissions were concentrated in developed countries. Wang and Wang (2019) studied the United States. Corradini et al. (2014) and Greaker and Pade (2009) focused on the EU, mostly developed countries. Ouchida and Goto (2016) investigated Japan and the 8

Journal Pre-proof United States, but few papers focused on developing countries. However, the contradiction between environment and economy in developing countries is more acute. It is of great significance to examine the impact of research and development on the decoupling of economic development from carbon emissions. In order to expand the research in this field, we have selected the typical BRICS countries of developing countries as the research objects. In summary, the contribution of this paper to the issue of economic growth decoupling from carbon emission is as follows: Firstly, by combining Tapio decoupling model, time series data, and panel model, we construct a comprehensive and systematical framework. In addition to R&D investment, industrialization and urbanization is included in the research system, and the reasons why the BRICS countries cannot be decoupled are deeply explored. Secondly, different from previous studies that focused on the relationship between R&D investment and carbon emissions, the research object transferred from developed countries to developing countries. The BRICS countries are in a difficult period of economic growth decoupling from carbon emissions. Finding out the role of R&D investment in decoupling economic growth from carbon emissions is of great significance to the sustainable development of developing countries. Table 1. Summary of relevant literature. Authors

Country/Territory

Period

Methodology

Results

A STIRPAT model,

URB→𝐶𝑂2↑

A: Transnational and national level research Cole and Neumayer

86 countries

1975–1998

(2004) Poumanyvong and

balanced panel 99 countries

1975-2005

A STIRPAT model,

URB→𝐶𝑂2↓ 9

Journal Pre-proof Kaneko (2010)

balanced panel

(Low income group) URB→𝐶𝑂2↓ (High income group)

Hossain (2011)

China

1980-2014

NARDL

IR→𝐶𝑂2↑

9 newly

1971–2007

Panel vector error

EC→𝐶𝑂2↑

industrialized

correction model

countries Liddle (2013)

54 developing

1971–2007

countries and 31

A STIRPAT model

GDP, URB, TRADE

and FMOLS

→𝐶𝑂2↑

Multivariate

HE, PD→𝐶𝑂2↑

developed countries Ala-Mantila et al.

Finland

2006

(2014) Parikh and Shukla

Regression model 86 countries

1985-1986

A fixed-effects model

URB→𝐶𝑂2↑

EU-15 countries

1980–2012

panel Granger

ES→𝐶𝑂2↓

(1995) Dogan and Seker (2016) Shuai et al. (2017)

causality 125 countries

1990-2011

STIRPAT model ,

POP, GDP→𝐶𝑂2↑

panel and time-series

RD→𝐶𝑂2↓

data Omri et al. (2014)

54 countries

1990-2011

panel Granger

FDI↔ 𝐶𝑂2

causality Paramati et al.

G20 countries

1991–2012

FMOLS

FDI→𝐶𝑂2↓

Apergis and Payne

7 Central American

1980-2010

vector error

ES→𝐶𝑂2↓

(2014)

countries

Zoundi (2017)

25 selected African

(2017)

correction model 1980-2012

ARDL

1980-2009

VECM,

ES→𝐶𝑂2↓

countries Saboori and

Malaysia

Sulaiman (2013) Jaforullah and King

US

1965-2012

Cointegration,

ES→𝐶𝑂2↓

Granger-causality test US

1960–2007

modified version of

Wolde-Rufael

the Granger causality

(2010)

test

Alkhathlan and

EC→𝐶𝑂2↑

causality

(2015) Menyah and

Granger

ES→𝐶𝑂2↓

Saudi Arabia

1980 - 2011

ARDL

FC→𝐶𝑂2↑

Portugal

1977-2003

VAR model

FC→𝐶𝑂2↑

Iran

1967–2007

Granger causality

GDP,FC→𝐶𝑂2↑

China

1977-2011

ARDL, Johansen

ES→𝐶𝑂2↓

Javid (2013) Pereira and Pereira (2010) Lotfalipour et al. (2010) Lin and Moubarak

10

Journal Pre-proof (2014) Yavuz (2014)

cointegration Turkey

1960-2007

Johansen

ES→𝐶𝑂2↓

cointegration Asumadu-Sarkodie

Beninese

1980-2012

ARDL

IND →𝐶𝑂2↑

China

1978-2011

ARDL

URB, IND→𝐶𝑂2↑

1995-2014

FMOLS, panel DOLS

GDP →𝐶𝑂2↑

estimations

NC→𝐶𝑂2↓

A STIRPAT model,

URB, IND, GDP→

ridge regression

𝐶𝑂2↑

and Owusu (2016) Zhang et al. (2014)

B: Regional and city level research Dong et al. (2017)

Wang et al. (2013)

China’s provinces

Guangdong province,

1980-2010 China

Hossain (2011)

3 regions in China

1995–2010

A STIRPAT model

URB→𝐶𝑂2↑

Lariviere and

45 cities in Canada

1991

OLS

UD→𝐶𝑂2↓

4 fastest-growing

1990-2010

GLS

UD→𝐶𝑂2↓

Lafrance (1999) Ou et al. (2013)

cities in China Ji and Chen (2017)

29 cities in China

1998-2010

A STIRPAT model

URB→𝐶𝑂2↑

Wang et al. (2012)

Beijing in China

1997-2010

A STIRPAT model

URB, IND, GDP→ 𝐶𝑂2↑ RD →𝐶𝑂2↓

Miao (2017)

216 prefecture-

2013

2SLS

URB→𝐶𝑂2↑

level cities in China Notice: “↑”: positive effect, “↓”: negative effect, “↔”: bidirectional causality effect. Variables: Urbanization rate (URB), Urbanization density (UD), per capita gross domestic product (GDP), industrialization (IND), industrial structure (IS), trade openness (TRADE), energy structure (ES), population size (POP), R&D investment (RD), NC, FC, foreign direct investment (FDI), energy consumption (EC), home energy(HE), private driving(PD), fossil fuel consumption(FC), natural gas consumption(NC). innovation (INO), financial development(FD), gross capital formation (GCF), inflow of remittances(IR) Methods: Ordinary Least Squares (OLS), additional autoregressive distributed lag (ARDL), Fixed Effects (FE), panel Fully Modified OLS (FMOLS), Generalized method of moments (GMM), Two stage least squares (2SLS), Generalized least squares regression (GLS), vector error correction model(VECM), NonLinear Auto Regressive Distribution Lag (NARDL)

3.Methodology and data 3.1 Decoupling index model In the current academic field, there are two main approaches to studying decoupling models: OECD(Co-operation and Development, 2001) and Tapio(Tapio, 2005) respectively. This paper chose Tapio decoupling model to confirm the 11

Journal Pre-proof decoupling state of BRICS countries. The formula is as follows: e(CO2) = ∆CO2%/ ∆ GDP%

(1)

Where e(CO2) represents the elasticity coefficient of decoupling between economic growth and carbon emissions, ∆CO2% represents the growth range of total carbon emissions from the base period to the end, and ∆GDP% represents the growth range of regional GDP from the base period to the end. Tapio model subdivides the decoupling state into eight states according to the value of decoupling elastic value (Fig . 2).

Fig. 2. The schematic diagram of decoupling states 3.2 Empirical model of economic growth and carbon emissions The environmental Kuznets hypothesis shows that there is an inverted u-shaped relationship between economic growth and environmental pollution, and the environmental quality deteriorates first and then improves with the accumulation of 12

Journal Pre-proof economic growth. Based on the test of EKC hypothesis, this paper further studies the impact of economic growth on carbon emissions. The empirical equation is expressed as follows: 𝐶𝑛𝑡 = 𝑓(𝑃𝐺𝐷𝑃𝑛𝑡,𝑃𝐺𝐷𝑃2𝑛𝑡,x𝑛𝑡)

(2)

We convert the model into logarithmic form: 𝑙𝑛𝐶𝑛𝑡=𝛼1(𝑙𝑛𝑃𝐺𝐷𝑃𝑛𝑡)2+𝛼2 𝑙𝑛𝑃𝐺𝐷𝑃𝑛𝑡+X𝑛𝑡𝛽+δt+𝜂𝑛+𝜀𝑛𝑡

(3)

Where n is the country, n = 1，2，…，5；t is the time(1996-2014); 𝑙𝑛𝐶𝑛𝑡 is the natural logarithm of the total carbon emissions; 𝑙𝑛𝑃𝐺𝐷𝑃𝑛𝑡 is the natural logarithm of the GDP per capita of each country, reflecting the per capita income of each country; δt

represents the time non-observation effect, mainly reflecting the impact of factors

other than economic growth over time, such as environmental policies, energy price changes, and changes in energy conservation and emission reduction technologies. 𝜂𝑛 represents regional non-observation effects, reflecting the persistence of differences among countries, such as regulatory differences, and preferences differences. 𝜀𝑛𝑡 is a random error term that is independent of time and region. X is another control variable, including energy structure, industrial structure, urbanization rate, and R&D investment. 3.3 Estimation techniques In order to ensure the reliability of the results, this paper adopts a rigorous process (Fig . 3) to panel data, which is mainly divided into five steps.

13

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Start

Unit root test Step1 Stationary

Non-stationary

Termination

Cointegration test

Not-cointegrated

Step2

Cointegrated

Termination

Dimension test Step3

Period dimension

Cross-section dimension

Dual Dimensions

Hausman test Step4 Fixed effect

Random effect

OLS estimaton Step5 FMOLS estimaton estimation

Panel Granger causality test estimation

Step6

End

Fig. 3. Estimation procedure for analyzing the nexus of CO2 emissions and economic growth.

14

Journal Pre-proof The first step is unit root test (Section 3.3.1) to check whether the selected data is stable and available. In this paper, four methods (LLC, IPS, fisher-ADF, fisher-pp) are selected. The next step is the co-integration test (Section 3.3.2). Through the cointegration test (Pedroni panel co-integration test and Kao panel co-integration test), it can be certain whether a group of stable or same-order single integration data has a long-term stable relationship. If it does not exist, regression estimation is not necessary. On the contrary, if there is a long-term stable relationship, the thirddimension test (Section 3.3.3) can be carried out. This step is to select an appropriate method for regression estimation. If this step is ignored, it may bring inaccurate results. The fourth step is Hausman test (Section 3.3.4), which is used to determine whether the regression chooses fixed effect or random effect. In the fifth step, OLS estimation and FMOLS estimation were selected to examine the relationship between variables (Section 3.3.5). In the sixth and final step, panel granger causality test was used to examine the causal relationship between variables. 3.3.1. Panel unit root tests Before using panel regression to do empirical study, the first step is unit root test. If the data has unit root, the set of data is a non-stationary sequence, in which case the regression may be a pseudo-regression. This paper adopts four panel unit root test methods: The first is the LLC panel unit root test developed by Levin et al. (Levin et al., 2002) The model of LLC panel unit root test is as follows: 15

Journal Pre-proof ∆𝑓𝑖𝑡 = 𝛼𝑖𝑦𝑖𝑡 ― 1 +

∑

𝑚𝑛

𝛽𝑖𝑝∆𝑦𝑖𝑡 ― 𝐿 + 𝛿𝑝𝑖𝛾𝑝𝑡 + 𝜇𝑖𝑡，p = 1,2,3

(4)

𝐿=1

Where 𝛼𝑖、𝛿𝑝𝑖、𝛾𝑝𝑡、𝜇𝑖𝑡 it represent the autoregressioncoefficients of the model, and the corresponding vectors of the regression coefficients and the corresponding vectors of the regression parameters are p=1,2,3。The null hypothesis is 𝛼𝑖=0 there is a unit root; If 𝛼𝑖<0，there is no unit root. The second is the IPS panel unit root test (Im et al., 2003)developed by Im et al. IPS test overcomes the limitation of LLC test and allows 𝑝𝑖 of different individuals (sequences) in the panel to be different. The verification formula of IPS is as follows: ∆𝑦𝑖𝑡 = 𝑝𝑖𝑦𝑖𝑡 ― 1 +

∑

𝑘𝑖

𝛾𝑖𝑗∆𝑦𝑖𝑡 ― 𝑗 + 𝜇𝑖𝑡𝛼，i = 1,2,…N;t = 1,2,…,T

(5)

𝑗=1

Null hypothesis 𝑝𝑖=0 i=1,2,…N unit root exists; If 𝑝𝑖<0 , there is no unit root. The third is fisher-ADF panel unit root test developed by Maddala and Wu(Maddala and Wu, 1999) and the fourth is fisher-pp panel unit root test developed by Phillips and Perron(Phillips and Perron, 1988) 𝑁

∑ log(qi) → 𝑥 1 𝜎 (qi) →M (0,1) 𝑀 ∑ 2 2𝑁

Fisher ― ADF = ―2

(6)

𝑖

𝑀

Choi ― ADF =

―1

𝑖―1

(7)

𝑖―1

Where i、 𝜎 ―1 Represents the reciprocal of the normal distribution function, and qi represents the p value of ADF unit root test. The null hypothesis is α=0and unit root exists. If 𝛼𝑖<0 then there is no unit root. 3.3.2. Panel cointegration tests If the unit root test indicates that the data is stable or homogenous, then a panel cointegration test can take to determine whether there is a long-term stable relationship 16

Journal Pre-proof between the stationary or first-order single-integrated data. If there is a long-term stable relationship, you can choose the appropriate method to estimate; if it does not exist, you can't continue, you need to consider re-selecting the variable. This paper uses the Pedroni panel cointegration test developed by Pedroni (Pedroni, 2001b) and the Kao cointegration test proposed by Kao (Kao, 1999) to identify the existence of the panel cointegration relationship. In the cointegration test proposed by Pedroni, there can be non-uniform intercepts and trend coefficients across the cross section. The Kao cointegration test is similar to that of Pedroni, both developed from the Engle-Granger test, but in the first-stage regression it is assumed that there are specific intercept and homogeneity coefficients between the sections. In Kao's two-variable test, the model is: 𝑌it = 𝑎𝑖 +𝑏𝑥𝑖𝑡 + 𝑐𝑖𝑡

(8)

Where 𝑌𝑖𝑡 = 𝑦𝑖,𝑡 ― 1 + u𝑖𝑡,𝑥𝑖𝑡 = 𝑥𝑖,𝑡 ― 1 + 𝑣𝑖𝑡,t=1,…T; i=1,…N. Generally, the first stage regression can be performed with the model. The 𝑎𝑖 between the intercepts is not the same, b is the same, and all the trend coefficients are zero. Similarly, Kao performs a mixed auxiliary regression on the residual term. 3.3.3 Model dimension test Previous literatures usually set models through qualitative analysis to determine whether individual variable intercept or time variable intercept, fixed effect or random effect, rather than statistical test. However, only through qualitative analysis is not rigorous, and it is very likely that the actual data does not conform to the usual methods 17

Journal Pre-proof of qualitative analysis. Therefore, we set up the model through statistical test. Strictly speaking, the dimension test should be carried out first. We regard time point or individual as a dimension. If the individual time point changes are called two dimensions. Finally, the Hausman test was used to determine whether the effect was fixed or random. The test of the model dimension is implemented by a likelihood ratio test, which is uses a likelihood function to detect whether a hypothesis is valid. To set the model as a double fixed effect, the alternative hypothesis of the test is h1：the individual timepoint double fixed effect model, and the model dimension can be determined simply by judging whether the original hypothesis is rejected or not. The basic idea is as follows: suppose the variable( 𝑥1，𝑥2，…，𝑥𝑛 ) consisting of n observed values comes from the function f(x,a), where a is the unknown parameter. The invalid hypothesis to be tested is ℎ0： a=𝑎0, and the alternative hypothesis is ℎ1：a ≠ 𝑎0 . The inspection level is b. Therefore, the ratio of the value of the likelihood function at a=𝑎0 to the value at the maximum point (that is, the maximum) is calculated, which is denoted as c. It can be concluded that: (1) The ratio λ of the two likelihood function values is only a function of the sample observation value and does not contain any unknown parameters. (2)0≤ c≤ 1, because the likelihood function value is not negative, and the denominator of c is the maximum value of the likelihood function, not less than the numerator. 18

Journal Pre-proof (3)The closer to 𝑎0 the larger c is; conversely, the larger the difference from 𝑎0 the smaller c is. Therefore, if the significant boundary value c can be obtained from a given b, statistical inference can be made according to the following rules: When c ≤ 𝑐0, ,ℎ0 is rejected, ℎ1 is accepted; when c>𝑐0, ℎ0 is not rejected. Here p(c≤𝑐0)=b。 3.3.4 Hausman test For panel data models, generally estimated with fixed effects or with random effects. In the actual application, certain which estimation method should be selected, and you need to do Hausman test first (Mutl and Pfaffermayr, 2011). And then select according to the test results. The Hausman test principle is as follows: For the null hypothesis ℎ0: 𝑎𝑖、𝑏𝑡 are not related to all explanatory variables. The null hypothesis implies that the random effects model is the correct model. In the case where the null hypothesis is established, both the fixed effect estimate and the random effect estimate can be uniformly estimated, that is, the FE and RE estimators will converge to the true parameter value according to the probability, so that 𝐶𝐹𝐸-𝐶𝑅𝐸→0. If not, the FE estimator will converge to the true parameter value according to the probability, but the RE estimator will not, which means that 𝐶𝐹𝐸-𝐶𝑅𝐸 will no longer converge to zero. Therefore, as long as the gap between 𝐶𝐹𝐸-𝐶𝑅𝐸 is too large, it tends to reject the null hypothesis. Based on this process, the null hypothesis can be transformed into ℎ0:𝐶𝐹𝐸-𝐶𝑅𝐸 = 0 , and the Hausman test statistic is constructed therefrom. If the statistic is greater than the critical value, the null hypothesis is rejected and a fixed effect is used. 19

Journal Pre-proof 3.3.5 Panel regression estimation After completing the specific setting of the model according to likelihood ratio test and Hausman test results, we select effective methods to conduct regression estimation on the panel data model. In this paper, three models are selected for estimation. Equation (3) is the basic quantitative regression model in this paper. In this paper, the OLS method is used to regress the model 1. In order to make Model 1 more robust, we added four control variables to Model 2 based on Model 1. Furthermore, we consider that Model 2 may have endogeneity problems, Model 3 uses FMOLS estimation method, and FMOLS can correct sequence correlation. (Pedroni, 2001a) proposed cointegration system as follows: (9)

𝑦𝑚𝑛 = 𝛽𝑚 + 𝛼𝑥𝑚𝑛 + 𝜃𝑚𝑛

In this equation, x and 𝑦𝑚𝑛 have a cointegration relationship, (Pedroni, 2001a) also proposes an equation in which regressions and cointegration regression are added to control the effects of endogeneity. 𝑃𝑚

y𝑚𝑛 = 𝛽𝑚 + 𝛼𝑥𝑚𝑛 +

∑

(10)

𝜇𝑚𝑝∆𝑥𝑚𝑛 ― 𝑝 + 𝜃𝑚𝑛

𝑝 = ― 𝑃𝑚

Definition 𝜌mn = (𝜃𝑚𝑛,∆𝑥𝑚𝑛)、𝛿mn = lim 𝐸[ 𝐷→∞

1 𝐷 𝐷 𝐷(∑𝑑 = 1𝜌𝑚𝑛)(∑𝑑 = 1𝜌𝑚𝑛)

], definition𝛿mn

is the long-term covariance of the FMOLS regression process. The long-term covariance can be decomposed into 𝛿m = 𝛿𝑜m = 𝜔𝑚 = 𝜔'𝑚，where 𝜔𝑚is the automatic covariance and 𝛿𝑜m is the weighted sum of the covariance and 𝜔𝑚 The FMOLS estimation criteria are as follows:

20

Journal Pre-proof 1 𝛼𝐹𝑀𝑂𝐿𝑆 = 𝐵

𝐵

∑[ ∑ m

(

𝐷

1 𝐷 𝑥 𝑑 = 1 𝑚𝑛

― 𝑥𝑚

)

(

2

∑ (𝑥

𝑚𝑛

(11)

∗ ― 𝑥𝑚)𝑦𝑚𝑛 ― 𝜔𝜇𝑚)]

𝑑=1

𝑜

∗ Where 𝑦𝑚𝑛 = 𝑦mn ― 𝑦𝑚 ―(𝛿2,1,𝑚/𝛿2,2,𝑚)∆𝑥𝑚𝑛、𝛾m = 𝜔2,1,𝑚 + 𝛿2,1,𝑚 ―(𝜔2,1,𝑚/

𝜔2,2,𝑚)(𝜔2,2,𝑚 + 𝛿2,2,𝑚) 3.3.6 Panel Granger causality test In the process of panel data operation, co-integration relationship tests the causality between variables in one direction. So as to test the causality between variables in two directions, this paper uses panel-based Granger causality test by Engel and Granger to test the causal relationship between variables(Engle and Granger, 1987). This method is divided into two steps. The first step uses the OLS regression to estimate the residual according to the long-term parameters given by equation (3), and the residual is used as the right variable. The second step uses the right variable to estimate the short-term error correction model. The Granger causality test formula is as follows:

∑γ + ∑γ + ∑γ + ∑γ + ∑γ + ∑γ

∆𝐶m 𝑛 = γ1𝑚 +

11𝑚𝑡∆𝐶m 𝑛 ― 𝑡

∑γ + ∑γ

+

𝑡

𝑡

14𝑚𝑡∆𝐼𝑆m 𝑛 ― 𝑡

∑γ + ∑γ

12𝑚𝑡∆𝑃𝐺𝐷𝑃m 𝑛 ― 𝑡 15𝑚𝑡∆𝐸𝑆m 𝑛 ― 𝑡

𝑡

𝑡

16𝑚𝑡∆𝑈𝑅𝐵𝐴𝑁m 𝑛 ― 𝑡

𝑡

17𝑚𝑡∆𝑅𝐷m 𝑛 ― 𝑡

2 13𝑚𝑡∆𝑃𝐺𝐷𝑃 m 𝑛 ― 𝑡

+

𝑡

+ 𝛼1𝑚𝐸𝐶𝑇𝑚𝑛 ― 1 + 𝛽1𝑚𝑛

𝑡

∆𝐶m 𝑛 = γ2𝑚

21𝑚𝑡∆𝐶m 𝑛 ― 𝑡

∑γ + ∑γ

+

𝑡

24𝑚𝑡∆𝐼𝑆m 𝑛 ― 𝑡

𝑡

27𝑚𝑡∆𝑅𝐷m 𝑛 ― 𝑡

∑γ + ∑γ

22𝑚𝑡∆𝑃𝐺𝐷𝑃m 𝑛 ― 𝑡

𝑡

25𝑚𝑡∆𝐸𝑆m 𝑛 ― 𝑡

𝑡

2 23𝑚𝑡∆𝑃𝐺𝐷𝑃 m 𝑛 ― 𝑡

+

𝑡

26𝑚𝑡∆𝑈𝑅𝐵𝐴𝑁m 𝑛 ― 𝑡

𝑡

+ 𝛼2𝑚𝐸𝐶𝑇𝑚𝑛 ― 1 + 𝛽2𝑚𝑛

𝑡

21

Journal Pre-proof

∑γ + ∑γ + ∑γ + ∑γ + ∑γ + ∑γ + ∑γ + ∑γ + ∑γ

∆𝐶m 𝑛 = γ3𝑚 +

31𝑚𝑡∆𝐶m 𝑛 ― 𝑡

∑γ + ∑γ

+

𝑡

𝑡

34𝑚𝑡∆𝐼𝑆m 𝑛 ― 𝑡

∑γ + ∑γ

32𝑚𝑡∆𝑃𝐺𝐷𝑃m 𝑛 ― 𝑡 35𝑚𝑡∆𝐸𝑆m 𝑛 ― 𝑡

𝑡

𝑡

36𝑚𝑡∆𝑈𝑅𝐵𝐴𝑁m 𝑛 ― 𝑡

𝑡

37𝑚𝑡∆𝑅𝐷m 𝑛 ― 𝑡

2 33𝑚𝑡∆𝑃𝐺𝐷𝑃 m 𝑛 ― 𝑡

+

𝑡

+ 𝛼3𝑚𝐸𝐶𝑇𝑚𝑛 ― 1 + 𝛽3𝑚𝑛

𝑡

∆𝐶m 𝑛 = γ4𝑚

41𝑚𝑡∆𝐶m 𝑛 ― 𝑡

∑γ + ∑γ

+

𝑡

𝑡

44𝑚𝑡∆𝐼𝑆m 𝑛 ― 𝑡

∑γ + ∑γ

42𝑚𝑡∆𝑃𝐺𝐷𝑃m 𝑛 ― 𝑡 45𝑚𝑡∆𝐸𝑆m 𝑛 ― 𝑡

𝑡

𝑡

46𝑚𝑡∆𝑈𝑅𝐵𝐴𝑁m 𝑛 ― 𝑡

𝑡

47𝑚𝑡∆𝑅𝐷m 𝑛 ― 𝑡

2 43𝑚𝑡∆𝑃𝐺𝐷𝑃 m 𝑛 ― 𝑡

+

𝑡

+ 𝛼4𝑚𝐸𝐶𝑇𝑚𝑛 ― 1 + 𝛽4𝑚𝑛

𝑡

∆𝐶m 𝑛 = γ5𝑚

51𝑚𝑡∆𝐶m 𝑛 ― 𝑡

∑γ + ∑γ

+

𝑡

54𝑚𝑡∆𝐼𝑆m 𝑛 ― 𝑡

𝑡

57𝑚𝑡∆𝑅𝐷m 𝑛 ― 𝑡

∑γ + ∑γ

52𝑚𝑡∆𝑃𝐺𝐷𝑃m 𝑛 ― 𝑡

𝑡

55𝑚𝑡∆𝐸𝑆m 𝑛 ― 𝑡

𝑡

2 53𝑚𝑡∆𝑃𝐺𝐷𝑃 m 𝑛 ― 𝑡

+

𝑡

56𝑚𝑡∆𝑈𝑅𝐵𝐴𝑁m 𝑛 ― 𝑡

𝑡

+ 𝛼5𝑚𝐸𝐶𝑇𝑚𝑛 ― 1 + 𝛽5𝑚𝑛

(12)

𝑡

Where ECT、t， ∆ denotes the error correction term, hysteresis length and firstorder difference of the variable respectively. In this paper, the Akaike information standard is used to determine the optimal lag length. 3.4 Data and descriptive statistics Because the authoritative data update is up to 2014, this paper selects the long panel data of the BRICS countries from 1996 to 2014 for empirical analysis. The data for this study was derived from the World Bank. To reduce the effects of heteroscedasticity, we did logarithmic processing on the data. Table 2 shows the definition of the variables and the source display, and Table 3 shows the descriptive statistics of the data. Table 2. Definitions and sources of data in the model. Variables Symbol Definition Total 𝑐𝑜2 emissions(Ten thousand tons) Total carbon emissions C GDP per capita PGDP 2010 US$

Source WB(2019) WB(2019) 22

Journal Pre-proof R&D investment

Research and development expenditure WB(2019) (% of GDP) Energy structure ES Renewable energy consumption (% of WB(2019) total energy) Urbanization URB urban population (% of total population) WB(2019) Industrial structure IS Industrial value added (% of GDP) WB(2019) Table 3. Descriptive statistics of variables. Country Statistics lnC lnPGDP lnRD lnES lnURB lnIS Brazil

Mean Std. Dev Max Min

Russia

Mean Std. Dev Max Min

India

Mean Std. Dev Max Min

China

Mean Std. Dev Max Min

South

Mean

Africa

Std. Dev Max Min

Panel

Mean Std. Dev Max Min

RD

10.5062 0.17610 10.8776 10.2569 12.0020 0.05649 12.1177 11.9153 11.7899 0.30313 12.3186 11.3877 13.2493 0.44357 13.8442 12.7123 10.6618 0.11897 10.8259 10.4818

9.1970 0.1257 9.3921 9.0541 9.0444 0.2755 9.3673 8.6135 6.9572 0.2686 7.4025 6.5679 7.9442 0.5024 8.7154 7.1947 8.8020 0.1097 8.9336 8.6532

0.0576 0.0801 0.2389 -0.0372 0.0816 0.0864 0.2515 -0.0472 -0.2482 0.0802 -0.1422 -0.4343 0.1771 0.4044 0.7036 -0.5740 -0.3004 0.1401 -0.1074 -0.5978

3.8016 0.0470 3.8941 3.7251 1.2595 0.0491 1.3404 1.1717 3.8303 0.1335 3.9846 3.6014 2.9535 0.3839 3.4189 2.4592 2.8336 0.0566 2.9508 2.7453

4.4133 0.0256 4.4484 4.3605 4.2976 0.0026 4.3033 4.2951 3.3774 0.0605 3.4776 3.2890 3.7396 0.1708 3.9937 3.4631 4.0859 0.0496 4.1637 4.0067

3.1193 0.0420 3.1907 3.0190 3.4385 0.0731 3.5652 3.3345 3.3588 0.0572 3.4383 3.2749 0.1771 0.4044 0.7036 -0.5740 3.3321 0.0494 3.4278 3.2671

11.6419 1.0355 13.8442 10.2569

8.3890 0.8886 9.3921 6.5679

-0.0464 0.2754 0.7036 -0.5978

2.9357 0.9573 3.9846 1.1717

0.3905 3.9828 4.4484 3.2890

0.2377 3.4149 3.8619 3.0190

4. Empirical results and discussion 4.1 Analysis of decoupling status of BRICS countries The decoupling status of BRICS from 1996 to 2014 is shown in Fig .4. We found that only seven decoupling states in BRICS during the study period. Including four states under economic growth: strong decoupling, weak decoupling, expansionary 23

Journal Pre-proof decoupling, expansionary negative decoupling. And three states under economic recession: strong negative decoupling, weak negative decoupling, recessionary decoupling, recessionary linkage did not appear. Overall, BRICS countries have experienced best strong decoupling state, but the length of appearance varies greatly. For example, China only has a strong decoupling state for one year, while South Africa has a strong decoupling for nine years. This reflects the gap between the decoupling capabilities of countries. Next, this paper will analyze the decoupling state of economic growth and carbon emission in BRICS from 1996 to 2014 respectively. 4.1.1 China As the largest economy in BRICS, China’s economy continued to grow during the period 1996-2014. There are only four states: expansionary negative decoupling, expansionary decoupling, weak decoupling and strong decoupling. In general, decoupling state of China's total carbon emissions has undergone a process from best to worst and gradually improved. During 1997-1999, China's reform and opening up had just been carried out for two decades. The level of industrialization is low and the total amount of carbon emissions is declining. Therefore, the relationship between the total amount of carbon emissions and economy has reached a strong decoupling state, that is, the best decoupling state. Since 2000, however, world situation changed. The pace of three major economies (United States, Japan, European Union) has slowed down, China began to develop rapidly. At the same time, with the combustion of a large number of fossil fuels and the improvement of industrialization level, total carbon 24

Journal Pre-proof emissions began to soar. Decoupling gradually changed from weak decoupling to expansionary decoupling, and even degenerated into expansionary negative decoupling in 2003-2004. Until China signed the Kyoto protocol in 2005, the development of clean energy is accelerating, growth rate of total carbon emissions begins to decline. Consequently, decoupling of China's total carbon emissions has gradually returned to a weak decoupling state. Overall, it is not difficult for China to maintain such a good decoupling state in future. 4.1.2 Brazil Brazil as the second largest economy among BRISC from 1996 to 2014. Brazilian economy has experienced a downward trend affected by the financial crisis. Compared with China, more decoupling states of carbon emissions appear in Brazil. Including weak decoupling, strong decoupling, expansionary connection, expansionary negative decoupling and declining connection. As shown in the Fig .3, decoupling of carbon emissions and economic growth in Brazil is not optimistic. Except for a period of decoupling in 1999-2000, 2001-2003 and 2005-2007, other years were in the state of expansionary negative decoupling and expansionary linkage. That is to say, in most years, the total carbon emission increased with economic growth at a higher speed. This situation may be related to the slow upgrade of Brazilian manufacturing industry. Under the influence of the financial crisis in 2008-2009, the rate of negative growth of carbon emissions was greater than economic contraction, leading to recessionary decoupling. In general, Brazil’s ability to decouple is weak. 25

Journal Pre-proof 4.1.3 India Among the BRICS countries, only India and China have maintained economic growth in 1996-2014. Therefore, decoupling state of India is distributed in the first and fourth quadrants (expansion negative decoupling, expansionary connection, weak decoupling, strong decoupling). Specifically, between 1996 and 2005, India basically maintained a weak decoupling between total carbon emissions and economic growth. However, from 2005 to 2014, India began to develop its economy. The unreasonable industrial structure led to a rapid increase in carbon emissions, which led to an expansionary connection and negative decoupling. Further analysis shows that there were two major fluctuations during 2009-2014. The best strong decoupling state appeared. However, this state only lasted for one year and soon returned to the state of expansionary negative decoupling. Although the decoupling of India's total carbon emissions fluctuated a lot, India still has the ability to decouple. 4.1.4 Russia Russia also failed to escape economic crisis. Both the Asian financial crisis and world financial crisis have caused Russia's economy to contract. From 1996 to 2014, six kinds of decoupling states appeared (strong decoupling, weak decoupling, expansionary decoupling, negative decoupling, strong negative decoupling and recessionary decoupling). From 1997 to 1998, Russia experienced first financial crisis. The devaluation of the ruble caused the national debt to be unable to repay. Carbon emission reduction and economic reduction occurred. The total carbon emissions and 26

Journal Pre-proof economy have been weakly decoupled, but crisis is temporary. With the growth of oil demand in Asia's industrial development, Russia's energy exports are picking up. In 1999, carbon emissions and economic growth returned to the state of decoupling. Global financial crisis once again affected Russia in 2008. The rate of carbon emission reduction exceeded the rate of economic decline. recessionary linkage appeared. In the following five years, stricken Russian economy gradually returned to normal. The relationship between total carbon emission and economy recovered from the expansionary negative decoupling to the expansionary linkage. From 2012 to 2014, Russia reached a strong decoupling. It is not difficult to see that despite the impact of the economic crisis, decoupling remains the norm of Russia.

27

Fig .4. The decoupling states of carbon-related and economic output for BRICS 28

Journal Pre-proof 4.1.5 South Africa As the smallest economy among BRICS countries, South Africa also have fewest carbon emissions. From 1996 to 2014, strong decoupling, weak decoupling, strong decoupling and expansionary decoupling have appeared. During this period, decoupling state experienced a huge fluctuation. From 1997 to 2002, South Africa maintained best decoupling state. However, expansionary negative decoupling began to appear continuously in 2003-2008. Due to financial crisis of 2008, total carbon emissions and economic aggregates were strongly decoupled. Fortunately, decouple state began to improve after that. South Africa returned to a strong decoupling state in 2009-2013. We concluded that South Africa is the country with the longest period of strong decoupling in BRICS. Briefly, despite the large fluctuations, South Africa's carbon emissions and economic growth still have strong decoupling ability.

4.2 Panel unit root test result Results of four unit root tests (LLC test, IPS test, Fisher-ADF test, Fisher-PP test) are shown in Table 4. Results obtained by LLC test and IPS test are similar, results obtained by the Fisher-ADF test and Fisher-PP test are similar. Under the condition of no difference, all the P values in the test results are close to 1, indicating that the data has unit root. Therefore, it is non-stationary and cannot be directly regressed. However, after first difference, P value obtained is less than 0.05, which means null hypothesis is rejected. There is no unit root for each group of variables after first difference. The results of four test methods are consistent. Therefore, it can be considered that each group of variables selected is stable after the first difference. All variables are single integration of first order. Make sure the selected variables are integrated in the same order. Next, a cointegration test is performed to determine if there is a long-term stable 29

Journal Pre-proof relationship between each set of data. Table 4.Panel data unit root test results At level At 1st difference Order of t-Statistic Prob. t-Statistic Prob. integration LLC 0.3710 0.6447 -2.9298*** 0.0017 I(1) IPS 3.0795 0.9990 -3.3266*** 0.0004 I(1) lnC ADF 3.0112 0.9812 28.9158*** 0.0013 I(1) PP-Fisher 1.7750 0.9978 49.0949*** 0.0000 I(1) LLC -0.3098 0.3784 -2.9340*** 0.0017 I(1) IPS 2.8440 0.9978 -2.3981*** 0.0082 I(1) lnPGDP ADF 1.3124 0.9994 21.4673** 0.0181 I(1) PP-Fisher 0.6024 1.0000 25.5305*** 0.0044 I(1) LLC 0.3052 0.6199 -3.0730*** 0.0011 I(1) IPS 3.2650 0.9995 -2.3858*** 0.0085 I(1) (LnPGDP)2 ADF 1.0361 0.9998 21.3279** 0.0189 I(1) PP-Fisher 0.4541 1.0000 24.9361*** 0.0055 I(1) LLC -0.0252 0.4900 -1.0782 0.1405 I(1) IPS 0.6847 0.7532 -1.9573** 0.0252 I(1) lnES ADF 7.6945 0.6587 19.9706** 0.0295 I(1) PP-Fisher 11.3980 0.3274 81.9489*** 0.0000 I(1) LLC -2.0659** 0.0194 -2.6505*** 0.0040 I(1) IPS 0.0618 0.5247 -2.3574*** 0.0092 I(1) lnRD ADF 10.4202 0.4044 21.9548** 0.0153 I(1) PP-Fisher 33.5826*** 0.0002 49.1469*** 0.0000 I(1) LLC -0.2268 0.4103 -3.1372*** 0.0009 I(1) IPS 0.2444 0.5965 -2.3710*** 0.0089 I(1) lnIS ADF 6.3995 0.7807 24.4842*** 0.0064 I(1) PP-Fisher 5.2980 0.8704 42.4878*** 0.0000 I(1) LLC -2.2624** 0.0118 -23.2883*** 0.0000 I(1) IPS 1.8248 0.9660 -12.9078*** 0.0000 I(1) lnURB ADF 5.2132 0.8765 22.1054** 0.0146 I(1) PP-Fisher 18.6638** 0.0447 5.7829 0.8332 I(1) Note: *, **, *** represent significant at 1%, 5%, and 10% inspection levels, respectively. variable

Test method

4.3 Panel cointegration test result Results of cointegration test are shown in Tables 5 and 6. According to results of Pedroni panel cointegration test. In all seven test values, the number of results rejecting null hypothesis exceeds the number of results that accept null hypothesis. The Pedroni panel cointegration test results reject the null hypothesis. Considering that the null hypothesis is no long-term cointegration relationship between the data of each group. 30

Journal Pre-proof Therefore, results of Pedroni panel cointegration test confirm that selected variables have a long-term cointegration relationship. Another Kao cointegration test showed that variables rejected null hypothesis. There was no cointegration relationship at 1% and 5% significance levels. This confirms that there is a long-term equilibrium relationship between lnC and lnPGDP, (LnPGDP)2, lnES, lnIS, lnRD, lnURB. The result is consistent with results of Pedroni panel cointegration test. After long-term equilibrium relationship between variables is determined, next test can be carried out. Table 5. Johansen co-integration test results Test Value P-value Panel v-Statistic -1.8706 0.9693 Panel rho-statistic 1.3218 0.9069 Panel PP-statistic -2.9240*** 0.0017 Panel ADF-statistic -2.5019*** 0.0062 Group rho-statistic 2.3881 0.9915 Group PP-statistic -2.3553*** 0.0093 Group ADF-Statistic -2.2777** 0.0114 Note: *, **, *** represent significant at 1%, 5%, and 10% inspection levels, respectively. Table 6. Kao panel cointegration tests results Statistic P-value ADF -2.9077*** 0.0018 Residual variance 0.0018 HAC variance 0.0011 Note: *, **, *** represent significant at 1%, 5%, and 10% inspection levels, respectively. 4.4 Model setting test results 4.4.1 Dimension test results In order to set model according to statistical methods, dimension test should be conducted first. The likelihood ratio test is used to test the model dimensions (periodonly, individual-only, or both). We first set the model as fixed effect (dual fixed effect) of period and individual, and then conducted likelihood ratio test. If the test results did not reject the null hypothesis, we could establish a mixed model. The test results are shown in table 7. All the above tests reject null hypothesis. Results indicating that model is a two-dimensional model. However, this result does not represent a fixed-effect 31

Journal Pre-proof variable-intercept panel model. We still need to test whether it is a fixed effect, a random effect or both, so we conduct Hausman test. Table 7. likelihood ratio tests result Effects Test

Statistic

Cross-section F Cross-section Chi-square Period F Period Chi-square Cross-Section/Period F Cross-Section/Period Chi-square

P-value

2386.871860 467.670093 2.436567 46.213564 524.728461 485.589988

0.0000 0.0000 0.0042 0.0003 0.0000 0.0000

4.4.2 Hausman test results Before Hausman test, we set model as a double-random model. Through results see whether null hypothesis is rejected or not to determine model setting. Test results are shown in table 8. In the Hausman test with the dependent variable LnC, individual random hypothesis is accepted. Hypothesis that both random point and individual time point are random is rejected. Therefore, the model with the dependent variable LnC should select the fixed-time effect model. Table 8. Hausman tests result Test Summary Chi-Sq. Statistic Cross-section random 0.157396 Period random 8.157857 Cross-section and period random 7.064622

P-value 0.9243 0.0169 0.0292

4.5 Panel data regression results In this subsection, we use panel data model FMOLS regression methods to study the impact of R&D investments on carbon emission in BRICS. This paper selects economic growth, industrial structure, urbanization, and energy structure for auxiliary research. The impact of lnRD, lnPGDP, lnIS, lnURB and lnES on the lnC are estimated with the fixed- effect model and FMOLS method. The regression results are presented in Table 9. Panel model took lnC as the dependent variable for FMOLS regression. According 32

Journal Pre-proof to the test results set by the model in section 4.4, Model 1 adopted time-fixed effect for regression. To be specific, the results of Model 1 show that the natural logarithmic coefficient of lnPGDP is 3.6001, and the natural logarithmic coefficient of (LnPGDP)2 is -0.1859. Both are significant at 1%. Results of Model 2 and Model 3 are similar. In other words, total carbon emission of BRICS countries is inversely proportional to GDP per capita and directly proportional to the quadratic term of GDP per capita. Moreover, according to Model 3 regression results, regression coefficient of R&D investment is significantly negative at the level of 1%. R&D investment has a negative impact on carbon emissions. For each 1% increase in the proportion of R&D investment in total GDP, carbon emissions of BRICS countries will decrease by 0.8122%. Moreover, regression coefficient of energy structure is negative, which means it has a negative impact on carbon emissions. Renewable energy consumption increased by 1%, carbon emission will be decreased by 0.8122%. On the contrary, regression coefficient of industrial and urbanization structure is 0.7332 and 0.4067, that is, it has a positive impact on carbon emissions. Every 1% increase in industrialization and urbanization, carbon emissions will be increased by 0.7332% and 0.4067% respectively, In summary, in both models, the results show that the impact of R&D investment on carbon emissions is negative and significantly correlated, hence R&D investment is negatively correlated with carbon emissions in BRICS. In other words, R&D investment help economic growth decoupling from carbon emission. Table 9.Dependent variable was the amount of carbon emissions from the natural logarithm (lnC) regression results Country Brazil Russia India China

Individual estimate lnPGDP (𝐥𝐧𝐏𝐆𝐃𝐏)𝟐

lnRD

lnIS

lnURB

6.3516* (0.7158) 0.0656* (1.8722) 21.203** (2.6341) 10.346** (2.6588)

-1.7641** (2.6164) -0.0442* (-0.8180) -0.0493* (2.5737) -0.2069*** (5.3722)

0.4628*** (3.4215) 0.0204*** (-0.0911) 0.4548** (-0.4030) 0.5553** (2.7626)

0.2836** (-2.0811) 4.0036*** (4.0719) 0.4839* (1.7006) 4.8768** (2.2081)

-0.3237* (-0.6753) -0.0090** (3.8439) -1.5277** (-2.6255) -0.6846** (-2.8375)

lnES -0.7189*** (-3.5269) -0.0801* (-0.8099) -0.6135** (-2.8346) -0.5133*** (-5.5032)

𝑹𝟐

C -23.2077 (-0.5801) -48.6608*** (-3.3356) -1.3079 (-1.2137) -2.2317** (-2.9351)

0.8191 0.8980 0.5383 0.8962 33

Journal Pre-proof South Africa Panel Model 1 Panel Model 2 Panel Model 3

0.6272*** (3.8839) Panel estimate 3.6001*** (9.4371) 1.4801*** (4.5302) 1.3474*** (3.3348)

-0.0602** (-1.8172)

-0.1610* (1.0030)

-0.1859*** (-7.5934) -0.0701*** (-3.9376) -0.0795*** (-3.7133)

-0.1829*** (-2.6733) -0.8122*** (-7.3778)

0.1311* (-0.4694)

5.4450* (-0.1850)

1.4190*** (1.2532) 0.7332*** (4.7639)

0.1763** (5.9866) 0.4067*** (4.0459)

-0.5279** (-2.0361)

-0.3330*** (-4.3299) -1.0919*** (-3.6488)

7.2749** (3.0596)

0.9133

-5.3321*** (-3.5328) -1.2231 (-0.8075)

0.9930 0.9947 0.9750

Note: *, **, *** represent significant at 1%, 5%, and 10% inspection levels, respectively. 4.6 Panel Granger causality test results Table 9 does not provide information that shows causal relationship between variables. We can analyze causal relationship between the variables by analyzing Granger causality test results in Table 10. As shown in Fig. 5, results show the causal relationship as follows. Per capita GDP, energy structure have a bidirectional causality to R&D intensity, indicating that R&D intensity has an impact on economic growth and energy structure. Carbon emissions, industrial structure, and urbanization have a unidirectional causality to R&D intensity. Moreover, per capita GDP, industrial structure, energy structure have a unidirectional causality to carbon emissions, indicating that per capita GDP, industrial structure, and energy structure have an important impact on carbon emissions. However, there is no causal relationship between carbon emissions and per capita GDP, which indicates that existing methods enable BRICS to reduce carbon emissions while developing the economy. This conclusion is consistent with (Hwang and Yoo, 2014). Table 10．Results of panel Granger causality test Dependent variables ∆𝐥𝐧𝐂 ∆𝐥𝐧𝐏GDP ∆𝐥𝐧𝐈𝐒 ∆𝐥𝐧𝐄𝐒 ∆𝐥𝐧𝐔𝐑𝐁𝐀𝐍 ∆𝐥𝐧𝐑𝐃

Independent variables ∆𝐥𝐧𝐂 ∆𝐥𝐧𝐏GDP 7.0120*** (0.0016) 1.3535 (0.2642) 0.1590 1.0246 (0.3636) (0.8532) 8.9081 4.4856** (0.1003) (0.0142) 1.6016 0.2556 (0.2080) (0.7751) 3.2968** 6.3347*** (0.0421) (0.0028)

∆𝐥𝐧𝐈𝐒 0.0030* (0.0970) 6.7955*** (0.0019) 6.9369*** (0.0017) 1.6115 (0.2060) 6.6756*** (0.0021)

∆𝐥𝐧𝐄𝐒 5.7871*** (0.0045) 1.1094 (0.3348) 0.9136 (0.4052) 1.2564 (0.2902) 3.4286** (0.0373)

∆𝐥𝐧𝐔𝐑𝐁𝐀𝐍 3.5911** (0.0321) 5.1487*** (0.0079) 2.1586 (0.1222) 8.3482*** (0.0005) 7.4930*** (0.0010)

∆𝐥𝐧𝐑𝐃 0.5715 (0.5669) 7.3894*** (0.0011) 0.0080 (0.9919) 7.5116*** (0.0010) 2.0024 (0.1417) -

Note: *, **, *** represent significant at 1%, 5%, and 10% inspection levels, 34

Journal Pre-proof respectively.

Fig.5. Causal relationship between variables 5. Discussion 5.1 EKC hypothesis verification All regression results reveal that GDP per capita has a negative impact on total carbon emissions of BRICS countries. In Model 3, lnPGDP and (LnPGDP)2 are significant at test level of 1%, with coefficients of 1.3474 and -0.0796, respectively. It shows that the relationship between carbon emissions and GDP per capita of BRICS countries is in an inverted U-shape, which is consistent with EKC hypothesis. Moreover, an interesting result is found when comparing with the EKC hypothesis of the different countries by time-series regression results. It is suggested that individual time-series results also conform to EKC hypothesis. That is, EKC exists in every country of the BRICS countries. In other words, the EKC can arise in every country, regardless of whether it is relatively wealthy or poor country of BRICS countries. Additionally, results suggest the current relationship between economic growth and carbon emissions is in the first half of the EKC curve (the part with positive slope) and has not reached the inflection point. To sum up, with the continuous development of BRICS economies, carbon emissions show a trend of first rising and then falling. 5.2 Spatial distributions of the impact of R&D investment and other factors on 35

Journal Pre-proof carbon emission reduction The FMOLS regression results of the time-series data show that the impact of R&D investment on carbon emissions have different effects on different countries. Spatial distributions of the impact of R&D investment, industrial structure, urbanization, and energy structure on carbon emission reduction were shown in Fig .6. R&D intensity has a stronger impact on Russia, India and China than on the other two countries, which may be related to the importance attached to environmental patents and applications. Among the carbon emission influencing factors studied in this paper. The energy structure is a very significant carbon emission inhibitor for five countries. Specifically, the impact on China and Brazil is more significant, but the impact on Russia is relatively weak. Possible reason is that in Russia's energy consumption structure. Oil and natural gas occupy a major position, and renewable energy only occupies a small part. Compared with Brazil, renewable energy plays a vital role in the energy structure. In addition, among the factors contributing to carbon emissions, the industrial structure has a stronger impact on Brazil, Russia, India and China than in South Africa. The negative impact of economic growth on carbon emissions is most evident in India and South Africa. This can be explained by the EKC hypothesis. In addition, for Russia and China, the impact of urbanization on carbon emissions is more significant than in the other three countries. The development process of the urbanization has affected this.

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Fig. 6. Spatial distribution of various factors affecting carbon emission reduction

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Journal Pre-proof 5.3 Impact of R&D investment and other factors on economic growth decoupling from carbon emission in BRICS.

Fig. 7. The impact of various factors on carbon emission reduction in FMOLS estimation results. Fig.7. visually shows the FMOLS estimation results of the panel model. The results in Section 4.5 show that R&D investment has a positive impact on reducing carbon emissions. We discussed the practical implications of the findings for sustainable development. R&D investment of the BRICS countries will inevitably lead to technological advances. The development of low-carbon technology can reduce carbon intensity and reduce energy use, thereby reducing carbon emissions. Therefore, R&D investment will promote the decoupling of carbon emissions from economic development in BRICS countries, thereby driving towards sustainable development. Compared with the regression coefficients of model 2, the contribution of R&D investment to carbon emissions has been increased. The regression coefficients has been increased from 0.1829 to 0.8122, indicating its important status in achieving sustainable development. In addition, The results in Section 4.5 show that the industrial structure and urbanization has a negative impact on carbon emission reduction. Possible reasons are as follows: BRICS members are all newly industrialized countries. More industrial 38

Journal Pre-proof products are produced in newly industrialized countries, Therefore, the increase in industrialization is not conducive to the sustainable development of BRICS countries. Similarly, The urbanization of the BRICS countries is also in its infancy. When the population is moving from rural to urban areas, the labor force will be transferred from the agricultural sector to the industrial sector. The increase in industrialization will increase carbon emissions and have a negative impact on the coordinated development of the economy and the environment. Furthermore, results show that the energy structure has a positive impact on carbon emission reduction. The share of renewable energy consumption in total energy will help reduce carbon emissions. In order to achieve sustainable development, it is important to adjust the energy structure by increasing the consumption of renewable energy. 6. Conclusions and policy implications 6.1 Conclusions Purpose of this paper is to analyze whether increased R&D investment contributes to economic growth decoupling from carbon emissions, and then to study the role of research and development investment in sustainable development. For this goal, Tapio decoupling model is used to study the decoupling status of the BRICS from 1996 to 2014. The panel FMOLS estimates the impact of R&D investment on carbon emission in BRICS countries. The main findings are as follows: (i) Decoupling ability between economic growth and carbon emissions of BRICS is different. Among them, China, South Africa and Russia have relatively strong decoupling ability. Brazil and India have relatively weak decoupling ability and decoupling situation has not improved. (ii) R&D investment has a positive impact on economic growth decoupling from carbon emssion in BRICS. For BRICS as a whole, every 1% increase in R&D 39

Journal Pre-proof investment, carbon emissions will be decreased by 0.8122%, for individual, this impact is most significant in China, weak in Russia and India. (iii) Presently, Every 1% increase in economic growth, industrialization and urbanization, carbon emissions will be increased by 1.3474%, 0.7332% and 0.4067% respectively, renewable energy consumption increased by 1%, carbon emission will be decreased by 0.8122%. That is, economic growth, industrialization and urbanization have a negative impact on achieving sustainable development, and the consumption of renewable energy has a positive impact on achieving sustainable development in BRICS. 6.2 policy implications In the current economic growth stage of the BRICS countries, in order to achieve economic growth decoupling from carbon emission, policymakers can develop policies based on the following recommendations. (i) Increase R&D investment. On the one hand, government fund the development of low carbon technology and improve energy efficiency. On the other hand, increase R&D investment to develop compact cities. Will contribute to the sustainable development of urban areas. (ii) Optimize the industrial structure. Government should appropriately reduce the proportion of industry and focus on the development of modern service industry with high technology content. Make the modern service industry the main driving force of economic growth, so as to coordinate the industrial structure and the environment. (iii) Adjust the structure of energy consumption. Renewable energy is key to sustainable development in BRICS countries On the one hand, the government can control the use of fossil energy by imposing a carbon tax to achieve low-carbon development. On the other hand, the government formulates relevant policies to 40

Journal Pre-proof encourage various industries to use renewable energy (wind, solar, geothermal, bioenergy) instead of non-renewable energy (coal, petroleum) in the manufacturing process. Although our study has made some contributions, there are still shortcomings. In this study, we select BRICS countries to build a panel model, but due to the trend of world economic integration, there may be correlations in some aspects of the development of various countries. In this case, cross-sectional dependence may be caused, and this problem may have a small impact on the real results, causing the limitation of this model. In the further work, we will add a cross-section dependency test to further improve the accuracy of the results.

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Journal Pre-proof Acknowledgement The authors would like to thank the editor and these anonymous reviewers for their helpful and constructive comments that greatly contributed to improving the final version of the manuscript. This work is supported by National Natural Science Foundation of China (Grant No. 71874203), Humanities and Social Science Fund of Ministry of Education of China (Grant No.18YJA790081), Natural Science Foundation of Shandong Province, China (Grant No. ZR2018MG016), and Social Science Planning Project of Shandong Province, China (Grant No. 19CQXJ45).

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Journal Pre-proof References Ala-Mantila, S., Heinonen, J., Junnila, S., 2014. Relationship between urbanization, direct and indirect greenhouse gas emissions, and expenditures: A multivariate analysis. Ecological Economics 104, 129-139. Alam, S., Fatima, A., Butt, M.S., 2007. Sustainable development in Pakistan in the context of energy consumption demand and environmental degradation. Journal of Asian Economics 18(5), 825-837. Alkhathlan, K., Javid, M., 2013. Energy consumption, carbon emissions and economic growth in Saudi Arabia: An aggregate and disaggregate analysis. Energy Policy 62, 1525-1532. Andreoni, V., Galmarini, S., 2012. Decoupling economic growth from carbon dioxide emissions: A decomposition analysis of Italian energy consumption. Energy 44(1), 682691. Apergis, N., Payne, J.E., 2014. Renewable energy, output, CO2 emissions, and fossil fuel prices in Central America: Evidence from a nonlinear panel smooth transition vector error correction model. Energy Economics 42, 226-232. Asumadu-Sarkodie, S., Owusu, P.A., 2016. Carbon dioxide emission, electricity consumption, industrialization, and economic growth nexus: The Beninese case. Energy Sources, Part B: Economics, Planning, and Policy 11(11), 1089-1096. Cansino, J.M., Sánchez-Braza, A., Rodríguez-Arévalo, M.L., 2015. Driving forces of Spain‫ ׳‬s CO2 emissions: A LMDI decomposition approach. Renewable and Sustainable Energy Reviews 48, 749-759. Chen, J., Wang, P., Cui, L., Huang, S., Song, M., 2018. Decomposition and decoupling analysis of CO2 emissions in OECD. Applied energy 231, 937-950. Co-operation, O.f.E., Development, 2001. OECD Environmental Strategy for the First 43

Journal Pre-proof Decade of the 21st Century: Adopted by OECD Environmental Ministers. OECD. Cole, M.A., Neumayer, E., 2004. Examining the impact of demographic factors on air pollution. Population and Environment 26(1), 5-21. Corradini, M., Costantini, V., Mancinelli, S., Mazzanti, M., 2014. Unveiling the dynamic relation between R&D and emission abatement: national and sectoral innovation perspectives from the EU. Ecological Economics 102, 48-59. Csereklyei, Z., Stern, D.I., 2015. Global energy use: Decoupling or convergence? Energy Economics 51, 633-641. De Freitas, L.C., Kaneko, S., 2011. Decomposing the decoupling of CO2 emissions and economic growth in Brazil. Ecological Economics 70(8), 1459-1469. Destek, M.A., Okumus, I., 2019. Does pollution haven hypothesis hold in newly industrialized countries? Evidence from ecological footprint. Environmental Science and Pollution Research, 1-7. DHAKAL, S., KANEKO, S., IMURA, H., 2002. An analysis on driving factors for CO2 emissions from energy use in Tokyo and Seoul by factor decomposition method. Environmental Systems Research 30, 295-303. Dogan, E., Seker, F., 2016. Determinants of CO2 emissions in the European Union: The role of renewable and non-renewable energy. Renewable Energy 94, 429-439. Dong, K., Sun, R., Hochman, G., Zeng, X., Li, H., Jiang, H., 2017. Impact of natural gas consumption on CO2 emissions: Panel data evidence from China’s provinces. Journal of cleaner production 162, 400-410. Engle, R.F., Granger, C.W., 1987. Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276. Feng, Y., Chen, S., Zhang, L., 2013. System dynamics modeling for urban energy consumption and CO2 emissions: A case study of Beijing, China. Ecological Modelling 44

Journal Pre-proof 252, 44-52. Gray, D., Anable, J., Illingworth, L., Graham, W., 2006. Decoupling the link between economic growth, transport growth and carbon emissions in Scotland. Greaker, M., Pade, L.-L., 2009. Optimal carbon dioxide abatement and technological change: should emission taxes start high in order to spur R&D? Climatic Change 96(3), 335-355. Guivarch, C., Mathy, S., 2012. Energy-GDP decoupling in a second best world—a case study on India. Climatic change 113(2), 339-356. Hossain, M.S., 2011. Panel estimation for CO2 emissions, energy consumption, economic growth, trade openness and urbanization of newly industrialized countries. Energy Policy 39(11), 6991-6999. Huang, F., Zhou, D., Wang, Q., Hang, Y., 2019. Decomposition and attribution analysis of the transport sector’s carbon dioxide intensity change in China. Transportation Research Part A: Policy and Practice 119, 343-358. Hwang, J.-H., Yoo, S.-H., 2014. Energy consumption, CO 2 emissions, and economic growth: evidence from Indonesia. Quality & Quantity 48(1), 63-73. Im, K.S., Pesaran, M.H., Shin, Y., 2003. Testing for unit roots in heterogeneous panels. Journal of econometrics 115(1), 53-74. Ito, K., 2017. CO2 emissions, renewable and non-renewable energy consumption, and economic growth: Evidence from panel data for developing countries. International Economics 151, 1-6. Jaforullah, M., King, A., 2015. Does the use of renewable energy sources mitigate CO2 emissions? A reassessment of the US evidence. Energy Economics 49, 711-717. Jeong, K., Kim, S., 2013. LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector. Energy Policy 62, 1245-1253. 45

Journal Pre-proof Ji, X., Chen, B., 2017. Assessing the energy-saving effect of urbanization in China based on stochastic impacts by regression on population, affluence and technology (STIRPAT) model. Journal of Cleaner Production 163, S306-S314. Kao, C., 1999. Spurious regression and residual-based tests for cointegration in panel data. Journal of econometrics 90(1), 1-44. Lariviere, I., Lafrance, G., 1999. Modelling the electricity consumption of cities: effect of urban density. Energy economics 21(1), 53-66. Levin, A., Lin, C.-F., Chu, C.-S.J., 2002. Unit root tests in panel data: asymptotic and finite-sample properties. Journal of econometrics 108(1), 1-24. Liddle, B., 2013. Urban density and climate change: a STIRPAT analysis using citylevel data. Journal of Transport Geography 28, 22-29. Lin, B., Moubarak, M., 2014. Renewable energy consumption–Economic growth nexus for China. Renewable and Sustainable Energy Reviews 40, 111-117. Liu, Y., 2009. Exploring the relationship between urbanization and energy consumption in China using ARDL (autoregressive distributed lag) and FDM (factor decomposition model). Energy 34(11), 1846-1854. Lotfalipour, M.R., Falahi, M.A., Ashena, M., 2010. Economic growth, CO2 emissions, and fossil fuels consumption in Iran. Energy 35(12), 5115-5120. Maddala, G.S., Wu, S., 1999. A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and statistics 61(S1), 631-652. Menyah, K., Wolde-Rufael, Y., 2010. CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy 38(6), 2911-2915. Miao, L., 2017. Examining the impact factors of urban residential energy consumption and CO2 emissions in China–Evidence from city-level data. Ecological indicators 73, 29-37. 46

Journal Pre-proof Moldan, B., Janoušková, S., Hák, T., 2012. How to understand and measure environmental sustainability: Indicators and targets. Ecological Indicators 17, 4-13. Moutinho, V., Madaleno, M., Inglesi-Lotz, R., Dogan, E., 2018. Factors affecting CO2 emissions in top countries on renewable energies: a LMDI decomposition application. Renewable and Sustainable Energy Reviews 90, 605-622. Mutl, J., Pfaffermayr, M., 2011. The Hausman test in a Cliff and Ord panel model. The Econometrics Journal 14(1), 48-76. Newman, P.G., Kenworthy, J.R., 1989. Cities and automobile dependence: An international sourcebook. Omri, A., Nguyen, D.K., Rault, C., 2014. Causal interactions between CO2 emissions, FDI, and economic growth: Evidence from dynamic simultaneous-equation models. Economic Modelling 42, 382-389. Ou, J., Liu, X., Li, X., Chen, Y., 2013. Quantifying the relationship between urban forms and carbon emissions using panel data analysis. Landscape ecology 28(10), 1889-1907. Ouchida, Y., Goto, D., 2016. Environmental research joint ventures and time-consistent emission tax: Endogenous choice of R&D formation. Economic Modelling 55, 179-188. Paramati, S.R., Mo, D., Gupta, R., 2017. The effects of stock market growth and renewable energy use on CO2 emissions: evidence from G20 countries. Energy Economics 66, 360-371. Parikh, J., Shukla, V., 1995. Urbanization, energy use and greenhouse effects in economic development: Results from a cross-national study of developing countries. Global environmental change 5(2), 87-103. Pedroni, P., 2001a. Fully modified OLS for heterogeneous cointegrated panels, Nonstationary panels, panel cointegration, and dynamic panels. Emerald Group 47

Journal Pre-proof Publishing Limited, pp. 93-130. Pedroni, P., 2001b. Purchasing power parity tests in cointegrated panels. Review of Economics and Statistics 83(4), 727-731. Pereira, A.M., Pereira, R.M.M., 2010. Is fuel-switching a no-regrets environmental policy? VAR evidence on carbon dioxide emissions, energy consumption and economic performance in Portugal. Energy Economics 32(1), 227-242. Phillips, P.C., Perron, P., 1988. Testing for a unit root in time series regression. Biometrika 75(2), 335-346. Poumanyvong, P., Kaneko, S., 2010. Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis. Ecological Economics 70(2), 434-444. Saboori, B., Sulaiman, J., 2013. Environmental degradation, economic growth and energy consumption: Evidence of the environmental Kuznets curve in Malaysia. Energy Policy 60, 892-905. Schandl, H., Hatfield-Dodds, S., Wiedmann, T., Geschke, A., Cai, Y., West, J., Newth, D., Baynes, T., Lenzen, M., Owen, A., 2016. Decoupling global environmental pressure and economic growth: scenarios for energy use, materials use and carbon emissions. Journal of cleaner production 132, 45-56. Shahbaz, M., Uddin, G.S., Rehman, I.U., Imran, K., 2014. Industrialization, electricity consumption and CO2 emissions in Bangladesh. Renewable and Sustainable Energy Reviews 31, 575-586. Shuai, C., Shen, L., Jiao, L., Wu, Y., Tan, Y., 2017. Identifying key impact factors on carbon emission: Evidences from panel and time-series data of 125 countries from 1990 to 2011. Applied Energy 187, 310-325. Tang, Z., Shang, J., Shi, C., Liu, Z., Bi, K., 2014. Decoupling indicators of CO2 emissions from the tourism industry in China: 1990–2012. Ecological indicators 46, 48

Journal Pre-proof 390-397. Tapio, P., 2005. Towards a theory of decoupling: degrees of decoupling in the EU and the case of road traffic in Finland between 1970 and 2001. Transport policy 12(2), 137151. Wang, C., Chen, J., Zou, J., 2005. Decomposition of energy-related CO2 emission in China: 1957–2000. Energy 30(1), 73-83. Wang, P., Wu, W., Zhu, B., Wei, Y., 2013. Examining the impact factors of energyrelated CO2 emissions using the STIRPAT model in Guangdong Province, China. Applied Energy 106, 65-71. Wang, Q., Jiang, R., 2019. Is Carbon Emission Growth Decoupled from Economic Growth in Emerging Countries? New Insights from Labor and Investment Effects. Journal of Cleaner Production, 119188. Wang, Q., Jiang, R., Zhan, L., 2019a. Is decoupling economic growth from fuel consumption possible in developing countries?–A comparison of China and India. Journal of Cleaner Production 229, 806-817. Wang, Q., Su, M., 2020. Drivers of decoupling economic growth from carbon emission – an empirical analysis of 192 countries using decoupling model and decomposition method. Environmental Impact Assessment Review, EIR_106356. Wang, Q., Su, M., Li, R., 2018. Toward to economic growth without emission growth: The role of urbanization and industrialization in China and India. Journal of cleaner production 205, 499-511. Wang, Q., Su, M., Li, R., Ponce, P., 2019b. The effects of energy prices, urbanization and economic growth on energy consumption per capita in 186 countries. Journal of Cleaner Production 225, 1017-1032. Wang, Q., Wang, S., 2019. Decoupling economic growth from carbon emissions 49

Journal Pre-proof growth in the United States: the role of research and development. JOURNAL OF CLEANER PRODUCTION 234, 702-713. Wang, Y., Zhou, Y., Zhu, L., Zhang, F., Zhang, Y., 2018. Influencing factors and decoupling elasticity of China’s transportation carbon emissions. Energies 11(5), 1157. Wang, Z., Yin, F., Zhang, Y., Zhang, X., 2012. An empirical research on the influencing factors of regional CO2 emissions: evidence from Beijing city, China. Applied Energy 100, 277-284. Wu, Y., Zhu, Q., Zhu, B., 2018. Decoupling analysis of world economic growth and CO2 emissions: A study comparing developed and developing countries. Journal of cleaner production 190, 94-103. Yavuz, N.Ç., 2014. CO2 emission, energy consumption, and economic growth for Turkey: Evidence from a cointegration test with a structural break. Energy Sources, Part B: Economics, Planning, and Policy 9(3), 229-235. Yi, Q., Li, H., 2013. Decomposition Analysis on CO_2 Emission from Energy Consumption in Guangdong Province Based on LMDI Method [J]. Science and Technology Management Research 12, 224-227. Zang, X., Zhao, T., Wang, J., Guo, F., 2017. The effects of urbanization and householdrelated factors on residential direct CO2 emissions in Shanxi, China from 1995 to 2014: A decomposition analysis. Atmospheric Pollution Research 8(2), 297-309. Zhang, Y.-J., Liu, Z., Zhang, H., Tan, T.-D., 2014. The impact of economic growth, industrial structure and urbanization on carbon emission intensity in China. Natural hazards 73(2), 579-595. Zhao, R., Huang, X., Liu, Y., Zhong, T., Ding, M., Chuai, X., 2015. Carbon emission of regional land use and its decomposition analysis: case study of Nanjing City, China. Chinese geographical science 25(2), 198-212. 50

Journal Pre-proof Zoundi, Z., 2017. CO2 emissions, renewable energy and the Environmental Kuznets Curve, a panel cointegration approach. Renewable and Sustainable Energy Reviews 72, 1067-1075.

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Author Contribution Statement Qiang Wang: Conceptualization, Methodology, Software, Data curation, Writing- Original draft preparation, Supervision, WritingReviewing and Editing. Fuyu Zhang: Methodology, Software, Data curation, Investigation Writing- Original draft, Writing- Reviewing and Editing,

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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

Journal Pre-proof Highlights

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The decoupling ability of economic growth from carbon emission in BRICS.

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Research and development investment can help economic growth decoupling from carbon emission.

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Combine time-series data and panel data FMOLS regression analysis of carbon emission factors.

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Spatial ditributions of influencing factors of carbon emission reduction in BRICS.