The relationship between biomass energy consumption and human development: Empirical evidence from BRICS countries

Energy 194 (2020) 116906

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The relationship between biomass energy consumption and human development: Empirical evidence from BRICS countries Zhaohua Wang a, b, c, d, Quocviet Bui a, b, Bin Zhang a, b, * a

School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China Center for Energy & Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China c Collaborative Innovation Centre of Electric Vehicles in Beijing, Beijing, 100081, China d Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, 100081, China b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 13 November 2019 Received in revised form 20 December 2019 Accepted 2 January 2020 Available online 3 January 2020

The impact of biomass energy utilization is still a controversial issue, and there is no consensus among researchers. Previous researches paid attention to the effects of biomass energy consumption on economic growth and environmental. While most studies indicate that the usage of biomass energy enhances economic growth and contributes to environmental protection, some studies show the opposite result. Our research wishes to contribute to the existing literature by discovering the effect of biomass energy consumption on human development in BRICS countries in the period 1990e2015. Using econometric methods which can solve the problem of cross-sectional dependence and heterogeneity of slope such as CIPS and CADF unit root tests, LM bootstrap panel cointegration test, Continuously-Updated Fully-Modified (CUP-FM) and Continuously-Updated Bias-Corrected (CUP-BC) estimators, and Dumitrescu-Hurlin panel causality test, our results reveal that biomass energy usage increase human development in BRICS countries and bidirectional causality exists between these two variables. These results may be a suggestion for policymakers to promote the usage of biomass energy. © 2020 Elsevier Ltd. All rights reserved.

Keywords: Biomass energy Human development Economic growth BRICS countries Cross-sectional dependence

1. Introduction The process of industrialization, along with economic and population growth, has led to increasing global energy demand. In the period 1971e2014, global energy consumption increased about 44% [1]. Of which, approximately 80% is provided by fossil energy [2]. The dependence on fossil energy sources of economies has raised concerns about limited supply, energy safety, and environmental degradation [3]. Of these concerns, environmental issues are at the forefront. Fossil energy utilization is considered to be a major factor in increasing the emissions of greenhouse gases that are the culprit of climate change and global warming [4]. Protecting the environment to achieve sustainable development goals has prompted the use of renewable energy sources to replace fossil energy sources. Besides the environmental benefits, the use of renewable energy also helps economies reduce their dependence on foreign resources and contribute to increasing employment [5].

* Corresponding author. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China. E-mail address: [email protected] (B. Zhang). https://doi.org/10.1016/j.energy.2020.116906 0360-5442/© 2020 Elsevier Ltd. All rights reserved.

Renewable energy is the type of energy with the fastest growth rate, and as a result, the share of renewable energy sources in total final energy consumption rises from 10% to 18.1% in the period 2008e2017 [6]. This percentage could increase to 60% by 2050 according to forecasts of IRENA [7]. The type of energy that accounts for the largest share of renewable energy is biomass. In 2017, 12.4% of the total final energy consumption was contributed by bioenergy [6]. The percentage of modern bioenergy (excluding the traditional use of biomass) in renewable energy used in 2017 is 50%. In the near future, namely in the period 2018e2023, bioenergy will still be the energy source with the largest growth rate in renewable energy usage [8]. As the demand increases, the effects of biomass also receive increasing attention from researchers. While one group of researchers focused on the link between biomass and economic growth [3,9e12], another group looked at the effects of biomass on the environment [13e16]. The results obtained from these studies did not reach consensus. Several studies point out that biomass energy consumption enhances economic growth and is environmentally friendly, and several studies provide evidence to the contrary. Therefore, whether biomass should be used more or less remains a controversial issue.

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Z. Wang et al. / Energy 194 (2020) 116906

energy sources. Consequently, five countries in BRICS region contributes up to 38% of the world’s CO2 emissions in 2014 [22] and has faced major environmental challenges in recent years. Finding and using alternative, clean, and renewable energy sources are considered a solution for BRICS countries when concerns about environmental degradation and energy security are increasing. In 2013, among top 10 countries investing in renewable energy, there were 4 BRICS countries (China, Brazil, India, and South Africa) [23]. With great potential, easy access, and being able to start production quickly, it is not difficult to understand why biomass energy is receiving the most attention from BRICS region when compared to other renewable energy types. In 2009, biomass energy accounted for 36.8% of the renewable energy sources used in BRICS countries, and this ratio will be 61% by 2030 if modern bio-energy techniques are applied [12]. The five economies of BRICS also contribute up to 38.6% of total biomass consumed globally [9]. Biomass energy consumption per capita trend in BRICS economies in the period 1990e2015 is described in Fig. 2. Against this background, BRICS countries are the appropriate case to explore whether the use of biomass energy affects human development or not. This study differs from previous studies in the following three points: (i) To the best of our knowledge, our study is the first attempt which examines the linkage between biomass energy use and human development. This relationship was also first analyzed for BRICS region in the period from 1990 to 2015; (ii) In addition to economic growth and biomass energy usage, trade openness, industrialization, and foreign direct investment are added to the function of human development to overcome the issue of specification bias; and (iii) In this study, CADF and CIPS unit root tests, LM bootstrap panel cointegration test, CUP-FM and CUP-BC estimators, and Dumitrescu-Hurlin panel causality test are applied to solve the issue of the dependence between cross-section units and heterogeneity of slope. The remainder of this paper is structured as follows: Section 2 describes the literature review, Section 3 offers the model specification and data sources, Section 4 presents the econometric methodology, and Section 5 introduces empirical results. Finally, conclusions and policy recommendations are summarized in Section 6.

.4

Human development index .5 .6 .7

.8

This study wishes to contribute to the existing literature by analyzing the influences of biomass energy source consumption on human development. The human development concept was developed since the 1970s by the Pakistani economist Mahbub ul Haq and has become one of the important criteria to assess the development of a country [17]. Human development, given by the United Nations Development Program (UNDP), refers to the expansion of human opportunities and choices. For a long time, per capita Gross Domestic Product (GDP) is often used to measure human development. However, economic indicators (like GDP) do not reflect the overall human well-being [18]. Since being first introduced by UNDP in 1990, the Human Development Index (HDI) has gradually replaced GDP to become the primary indicator in evaluating human development. Based on assessing three aspects including long and healthy life, knowledge, and decent living standards, HDI can capture the whole quality of people’s lives, and increased HDI becomes the target of every country. Towards sustainable development, policymakers need to consider all three aspects: economic, environmental, and social. Even so, previous studies focused on the effect of biomass energy use on the environment and economic growth and neglected to assess the effects on human well-being. Examining the relationship between the use of biomass energy and human development would provide more information to policymakers, giving them a comprehensive view of the influences of biomass energy usage. The case of BRICS countries (including five developing countries: Brazil, Russia, India, China, and South Africa) was selected for this study. Along with economic development, BRICS countries have also achieved certain achievements in human development. Fig. 1 shows an uptrend in human development index in all 5 BRICS countries in the 1990e2015 period. The level of human development, however, varies among BRICS countries. In 2017, Russia was classified as a country with a very high human development, Brazil and Russia were in the high human development group, while South Africa and India were ranked into tier of medium human development [19]. Home to 40% of the world’s population and contributes about 23% of global GDP, BRICS countries have consumed an enormous amount of energy [20]. In 2013, 40% of world energy usage belonged to BRICS countries [21]. Even so, the primary source of energy used in BRICS countries comes from fossil

1990

1995

2000 Brazil India South Africa

year

2005

2010

China Russian Federation

Fig. 1. Trend of human development index in BRICS countries in 1990e2015 period.

2015

3

2

Biomass energy use per capita 6 8 10 4

12

Z. Wang et al. / Energy 194 (2020) 116906

1990

1995

2000 Brazil India South Africa

year

2005

2010

2015

China Russian Federation

Fig. 2. Trend of biomass energy use per capita in BRICS countries in 1990e2015 period.

2. Literature review Human development is the ultimate goal of economic development, so every nation strives to improve its human development index. To achieve that goal, it is necessary to answer the question of what factors affect human development? In recent years, determinants of human development have received significant attention from scholars. Different samples, different econometric models, and different explanatory variables were employed [24]. These studies have shown the impact of economic growth [25e30], industrialization [31,32], foreign direct investment [33,34], trade openness [35,36], population [37], urbanization [1,24], terrorism [38], education [17], tourism [39], natural resource rents [40], information and communication technology [41], and carbon emissions [29,37,42] on human development. Besides the above factors, energy consumption is also considered as one of the determinants of human development. For a time, it was believed that consuming more energy led to greater human development. However, when the environmental concerns associated with energy consumption are growing, this view is no longer relevant. Higher energy use does not guarantee higher levels of human development [24]. Some studies which investigate the linkage between energy consumption and human development can be referred to as follows: Martínez and Ebenhack [43] conducted a study for 120 countries, and they found a strong correlation between energy use and human development index. Ouedraogo [44] investigated this linkage in 15 developing countries over the 1988e2008 period. By employing Pedroni panel cointegration, and FMOLS, DOLS estimation, they reported a negative relationship and unidirectional Granger causality between energy consumption and human development. Contrary to the above two studies, Tran et al. [24] examined the association for 93 countries using data in the period 1990e2014 and system GMM approach, and their findings showed that the use of energy does not contribute to the improvement of human development in both developing and developed countries. In addition to these studies, a large number of researchers consider the relationship between electricity consumption and human development. In one study by Niu et al. [45] for 50 countries between 1990 and 2009, long-run bidirectional

causality between the use of electricity and human development is indicated through using the Granger panel causality test. Focusing on Pakistan in the period from 1990 to 2016, Khan et al. [38] used the ARDL bound testing approach and confirmed insignificant impact of electricity utilization on HDI. Compared to energy consumption-human development nexus, the linkage between renewable energy usage and human development is less investigated. We only found studies of Wang et al. [46] and Pîrlogea [47] that mention the effect of renewable energy consumption on human development. Interestingly, these authors pointed to contradictory conclusions. In a study of six European Union countries in the period 1997e2008, Pîrlogea [47] concluded that renewable energy use promotes human development. Meanwhile, using 2SLS regression analysis for the case of Pakistan in the 1990e2014 period, Wang et al. [46] pointed out that the use of renewable energy decreases the level of human development. The conclusion from these studies is ambiguous, and the link between renewable energy use and human development needs further investigation. In the case of BRICS countries, several studies have explored the influence of biomass energy use on GDP and environmental degradation. More specific, using quarterly data from 1991Q1e2015Q4, Shahbaz et al. [12] showed that using biomass energy has significantly positive effects on economic growth. Their result also indicates that the feedback hypothesis exists between biomass energy use and economic growth. Different from the study of Shahbaz et al. [12], Aydin [9] employed AMG estimation and bootstrap panel causality test to check the causal relationship between biomass energy utilization and economic growth for country-specific in BRICS. They reveal that growth hypothesis exists in Brazil and India, and the feedback hypothesis exists in Russia. Meanwhile, the valid hypothesis in China and South Africa is the conservation hypothesis. To our knowledge, the research of Danish and Wang [14] is the only attempt to investigate the effect of biomass energy consumption on carbon emissions in BRICS countries. Their findings confirmed that biomass energy usage helps to protect the environment, more precisely, helps to reduce CO2 emissions into the atmosphere. From the literature, it can be observed that researchers have

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Z. Wang et al. / Energy 194 (2020) 116906

ignored the effect of biomass energy consumption on human development. Also, this linkage in BRICS countries has not been explored. Our research attempts to fill this gap. 3. Model construction and data sources 3.1. Theoretical framework Before describing economic modeling, we present the theoretical framework, which helps us to choose the variables for this study. Economic growth plays an important role in human development. Ranis and Stewart [48] argue that economic upswing offers resources for human development. It can be said that income is highly correlated with education, life expectancy, which affects the HDI [29]. There are also opinions that economic growth is accompanied by environmental degradation and affects the quality of life of current and future generations [49]. In other words, the impact of GDP on the HDI is still a controversial issue. In spite of their interconnectedness, high GDP is not synonymous with high HDI. The fact that some countries can reach high HDI despite facing low GDP and vice versa [48]. Biomass energy, like other renewable energy sources, can influence economic growth, thereby affecting people’s income, living standards, and purchasing power [10,50]. Simultaneously, as the economy develops, it is easier for people to access health care and education services. In developing countries, biomass energy also helps to create job opportunities as well as increase income for rural workers, thus contributing to poverty reduction [11]. Energy gained from biomass sources can be used in electricity generation, residential heating, cooking, and transportation [2], which directly or indirectly impact three dimensions of human development. In recent years, biomass has been given more attention because it is considered to be a source of renewable energy, abundant, and easy to produce [15,51]. Biomass energy helps to meet humanity’s growing energy needs, reduce energy costs, and decrease the dependence on fossil energy [52]. More importantly, biomass energy is considered a “carbon neutral” energy source [53], because planting trees to produce biomass energy helps absorb CO2 from the atmosphere. Therefore, biomass energy usage is considered to help reducing environmental pollution, climate change, which affecting human health and life. However, there are some ideas that due to the effects of land-use changes and carbon leakages, biomass energy use increases CO2 emissions [3]. Even if based on each unit of energy produced, wood-burning biomass is the largest CO2 emitter when compared to other types of energy such as coal, oil, and natural gas. Also, concerns about land competition, food security, resource depletion, deforestation, soil degradation, and biodiversity loss were addressed when producing and using biomass energy [54]. In developing countries, economic growth is closely associated with the process of industrialization. Regarding the effect of industrialization on human development, Qasim and Chaudhary [32] show that industrialization is considered as one of the factors influencing human development from different aspects. Industrial growth has a direct and indirect impact on human well-being. First, the process of industrialization increases the demand for labor, creating employment opportunities [55]. It helps to reduce poverty, increase wealth, and improve living standards (see Refs. [56,57]). Second, industrial development creates the demand for the skilled and trained workforce which in turn rise the demand for education [56]. In addition, as well as GDP, industrialization is also considered one of the culprits causing environmental pollution, leading to environmental degradation, and affecting the human health and quality of life of people [58,59]. Like industrialization, trade openness and foreign direct

investment are said to be the drivers of economic growth in developing countries. While trade openness helps fill a nation’s resource deficit, foreign direct investment helps meet capital needs. Both trade and foreign direct investment create more employment opportunities for society, thereby helping to increase per capita income [34,46]. So, there is reason to believe that trade openness and foreign direct investment have an impact on the process of human development. Fig. 3 depicts the theoretical framework of this study. 3.2. Economic modeling Based on the theoretical framework and the research of Wang et al. [46] and Khan et al. [41], we use the following empirical model to discover the relationship between biomass energy usage and human development while incorporating the effects of economic growth, industrialization, trade openness, and foreign direct investment:

HDIit ¼ f ðGDPit ; BIOit ; INDit ; TROit ; FDIit Þ

(1)

In equation (1), HDI refers to the human development index, GDP denotes the economic growth, BIO indicates biomass energy consumption, IND is industrialization, TRO shows trade openness, while FDI is foreign direct investment. The single multivariate framework is used to explore the linkage between variables of interest. Simultaneously, we also convert variables to natural logarithms form to reduce dispersion and smooth the data [60]. This transformation also helps to minimize the problems of autocorrelation and heteroscedasticity and provide more reliable and consistent results than simple linear form [61]. The log-linear specification of our empirical model is shown in equation (2):

ln HDIit ¼ b0 þ b1 ln GDPit þ b2 ln BIOit þ b3 ln INDit þ b4 ln TROit þ b5 ln FDIit þ εit

(2)

where i shows the number of countries (from 1 to 5), t indicates the period (1990e2016). b0 is the constant term. The coefficients of economic growth, biomass energy use, industrialization, trade openness, and foreign direct investment are denoted by b1, b2, b3, b4, and b5, respectively. The error terms are represented by εit. 3.3. Data sources We have used the database of UNDP [19] to gather data on the human development index. The data on biomass energy use is collected from the Global Material Flows Database [62]. Meanwhile, the data on GDP, foreign direct investment, industrialization, and

Fig. 3. Theoretical framework of the study.

Z. Wang et al. / Energy 194 (2020) 116906

trade openness is provided by World Development Indicators from World Bank [63]. The unit of variables used in this study are as follows: economic growth is calculated as real GDP per capita (in constant 2010 US Dollar); biomass energy usage is measured as tons per capita; industrialization is calculated as percentage of the value-added of industry (including construction) in GDP; the openness of trade is measured as the ratio of total exports and imports to GDP (% of GDP); and FDI is also measured by the percentage of GDP. The time span of 1990e2015 was selected based on the availability of the data. It should be noted here that we have extended the time period of biomass energy consumption data by using interpolation to obtain the longest possible period. A detailed description of variables is presented in Table 1, while correlation matrix and descriptive statistics are shown in Table 2.

Table 2 Descriptive statistics and correlation matrix of variables. Variables

lnHDI

lnGDP

lnBIO

lnIND

lnTRO

lnFDI

Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability Observations lnHDI

0.452 0.435 0.207 0.850 0.149 0.777 3.017 13.105 0.001 130 1.000 e 0.906*** (0.000) 0.706*** (0.000) 0.091 (0.298) 0.389*** (0.000) 0.296*** (0.000)

8.292 8.685 9.385 6.274 0.976 0.765 2.082 17.272 0.000 130

1.202 1.192 2.456 0.418 0.546 0.446 2.222 7.590 0.022 130

3.449 3.397 3.861 2.963 0.234 0.308 2.200 5.521 0.063 130

3.643 3.786 4.705 2.718 0.428 0.429 2.243 7.087 0.028 130

0.217 0.577 1.822 5.993 1.301 1.718 7.384 168.103 0.000 130

1.000 e 0.862*** (0.000) 0.297*** (0.000) 0.198** (0.023) 0.119 (0.174)

1.000 e 0.549*** (0.000) 0.231*** (0.008) 0.082 (0.348)

1.000 e 0.328*** (0.000) 0.035 (0.688)

1.000 e 0.178** (0.041)

1.000 e

lnGDP lnBIO

4. Econometric methodology

lnIND

4.1. Cross-sectional dependence tests

lnTRO

Our study starts analyzing the empirical model by exploring the dependence between cross-section units. Due to the influences of globalization and economic cooperation, the impact of factors on one country can spread to other countries. The connection between nations may lead to the problem of the dependence between crosssection units in panel data. One of the limitations of previous analytical methods is the assumption of cross-sectional independence. Failure to address the presence of cross-sectional dependence in the panel, the results obtained from such methods may be biased [9]. Breusch and Pagan [64] developed an LM test to examine crosssectional dependence. LM statistic can be calculated by using the following equation:

LM ¼ T

N1 X

N X

b r 2ij

(3)

i¼1 j¼iþ1

lnFDI

Notes: p-values are put in parentheses. *** and ** denote statistical significance at the 1% and 5% levels, respectively.

dependence between cross-section units. 4.2. Slope homogeneity tests After checking cross-sectional dependence, we investigated the homogeneity of the slope coefficients by using Pesaran and Yamagata [67] slope homogeneity tests. Previous analytical methods with the homogeneity assumption have overlooked the characteristics of country-specific [42]. Based on the model of Swamy [68], Pesaran and Yamagata [67] proposed a standardized ~ test) to examine slope dispersion test statistic (called D

where T shows the period, N is the number of cross-section units, and b r ij represents the sample estimate of the cross-sectional cor-

homogeneity:

relation of the residuals obtained from individual Ordinary Least Squares (OLS) estimations. The disadvantage of the LM test is that it is only suitable in case T sufficiently large and N relatively small [65]. To overcome this issue, Pesaran [66] proposed a CD test based on Lagrange multiplier statistic as follow:

~¼ D

1 sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi0 N 1 X N X 2T @ b CD ¼ r A NðN  1Þ i¼1 j¼iþ1 ij

(4)

The null hypothesis of both tests assumes that cross-section units are independence, against the alternative hypothesis of the

5

! pffiffiffiffi N1 ~ Sk pffiffiffiffiffiffi N 2k

(5)

where ~ S denotes the modified Swamy test. For the case of small ~ ~ test (called D samples, the following adjusted D adj test) is employed:

! ~ Þ pffiffiffiffi N1 ~ S  EðZ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiiT ffi ~ Þ varðZ iT

~ ¼ N D adj

(6)

~ iT) ¼ k, and var(Z ~ iT) ¼ 2k(T  k  1)/(T þ 1). where E(Z

Table 1 Description of variables and data sources. Variable

Symbol Definition

Measure

Data source

Human development index Economic growth

HDI

HDI is composed of 3 components: health, education, and standard of living

Index

UNDP

GDP BIO

US Dollar per capita Tons per capita

World Bank

Biomass energy use

GDP per capita is measured as the gross domestic product (in constant 2010 US Dollar) divided by midyear population Energy derived from biomass is consumed

Industrialization Trade openness Foreign direct investment

IND TRO FDI

The percentage of industry value-added in GDP The share of the total of import and export in GDP The value of direct investment made by investors from other countries.

% of GDP % of GDP % of GDP

Material Flows World Bank World Bank World Bank

6

Z. Wang et al. / Energy 194 (2020) 116906

In equation (10), MF ¼ IT  T 2 FF’, IT is the identity of matrix dimension T, and F indicates a vector of latent common factors.

4.3. Panel unit root tests In the next step, stationary properties are tested by employing CIPS and CADF tests developed by Pesaran [69]. Unlike Levin et al. [70] LLC test, Im et al. [71] IPS test, Dickey and Fuller [72] ADF test, and Phillips and Perron [73] PP test, both CADF and CIPS tests can control the problem of cross-sectional dependence and slope heterogeneity. Therefore, the results obtained from these methods are more consistent and reliable. Cross-sectional augmented Dickey-Fuller (CADF) regression is expressed as follows:

DYit ¼ ai þ bi Yi;t1 þ ci Y t1 þ ci DY t þ uit

(7)

where D shows difference operator, Y is analyzed variable, Y t ¼ PN PN 1 1 i¼1 Yit , DY t ¼ N i¼1 DYit , and uit denotes the error term. Based N on the CADF statistic, Pesaran [69] introduced cross-sectional augmented IPS (CIPS), which can be calculated by the following equation: N 1 X CIPS ¼ CADFi N i¼1

(8)

(9)

ci denotes the longwhere T shows time period, N is sample size, w run variance of the residuals, sit refers to the partial sum process of error terms. In this test, the null hypothesis assumes that cointegrating relation exists between cross-section units. Meanwhile, the alternative hypothesis is that there is no cointegration among variables. 4.5. Long-run estimates We estimate the long-run coefficient for equation (2) using CUPFM and CUP-BC estimators proposed by Bai et al. [78]. These approaches have some advantages: (i) It takes into consideration the issue of cross-sectional dependence; (ii) The problem of unobserved non-linearity is also controlled by using these method; and (iii) Robust results are obtained even in the case of factors that are a mixture of I(1) and I(0) [79]. The following equation is employed for CUP-FM and CUP-BC estimators: n 1 X ðb b CUP ; bF CUP Þ ¼ argmin 2 ðyi  xi bÞ0 MF ðyi  xi bÞ nT i¼1

Yit ¼ ai þ

K X

gðkÞ Yi;tk þ i

k¼1

K X

bðkÞ Xi;tk þ εi;t i

(11)

k¼1 ðkÞ

where ai denotes constant term, gi ð1Þ ð2Þ ðKÞ ðbi ; bi ;…; bi Þ,

is lag parameter, K indicates ðkÞ

while bi

is the slope coefficient. ðkÞ

Differences between cross-section units are presented through gi ðkÞ

After exploring the unit root properties of all variables, we proceed to the next step by examining the existence of cointegrating relation among variables. At this stage, the LM bootstrap panel cointegration test developed by Westerlund and Edgerton [74] is employed. The reason behind using this method is that it can overcome both slope heterogeneity and cross-sectional dependence problem [15]. The use of this method, therefore, provides more reliable results than using former cointegration tests such as Johansen [75], Kao et al. [76], and Pedroni [77] cointegration test. The bootstrap LM panel cointegration test statistic is determined by equation (9): N X T 1 X _ 2 w s2 2 NT i¼1 t¼1 i it

To provide additional information to policymakers, we examine the causal relations between variables of interest by applying the Dumitrescu and Hurlin [80] panel causality test. This method helps to overcome the problem of cross-sectional dependence and heterogeneity [81]. Also, the use of this method is very flexible as it can be utilized in both N < T and N > T cases as well as unbalanced panels [82]. In this test, the following linear model is used to check the causal associations between variable X and variable Y:

lag length, bi ¼

4.4. Panel cointegration test

LMþ N¼

4.6. Panel causality test

(10)

and bi . The null hypothesis (the so-called homogeneous noncausality hypothesis) and the alternative hypothesis (the socalled heterogeneous non-causality hypothesis) are expressed as follows: The null hypothesis of this test assumes that there is no causal relationship in the panel against the alternative hypothesis, which explains that the causal relationship exists in at least one crosssection unit. To test the null hypothesis, the Wald statistic for all panel is calculated by averaging the values of the individual Wald statistics for each cross-section:

WHnc N;T ¼

N 1 X W N i¼1 i;T

(12)

In the case of T > N (as in this study), Dumitrescu and Hurlin [80] suggested using the statistic test below:

Z Hnc N;T ¼

rffiffiffiffiffiffiffi  N  Hnc WN;T  K 2K

(13)

5. Empirical results and discussion The findings of cross-sectional dependence are reported in Table 3. According to the relevant p-values of CD and LM statistics, we reject the null hypothesis of cross-sectional independence for human development, economic growth, biomass energy use, industrialization, trade openness, and foreign direct investment. In other words, cross-sectional dependence exists in all variables in

Table 3 Cross-sectional dependence test analysis. Variables

LM test

CD test

Statistics

p-values

Statistics

p-values

lnHDI lnGDP lnBIO lnIND lnTRO lnFDI

79.248*** 44.802*** 40.552*** 56.919*** 22.684** 48.797***

0.000 0.000 0.000 0.000 0.012 0.000

12.05*** 14.50*** 2.31** 2.37** 8.04*** 8.89***

0.000 0.000 0.021 0.018 0.000 0.000

Note: *** and ** denote statistical significance at the 1% and 5% levels, respectively.

Z. Wang et al. / Energy 194 (2020) 116906

this study. Besides cross-sectional dependence, the slope heterogeneity problem is also revealed through the results of the homogeneity of ~ and D ~ slope tests in Table 4. P-values of both tests (D tests) are

Table 5 Results of unit root tests. Variable

adj

less than 0.01, so the slope homogeneity hypothesis is rejected. These results from Tables 3 and 4 indicate that slope heterogeneity and cross-sectional dependence issue exist in the panel and need to be considered in the next steps. Table 5 represents the results from panel unit root tests. Both the CIPS and CADF tests give consistent conclusion that biomass energy consumption, foreign direct investment, and trade openness are stationary at level. Meanwhile, human development, economic growth, and industrialization have a unit root at levels and turn into stationary at first difference. The existence of I(1) variables, which are integrated at level 1, in the model suggests for investigating possible cointegration relationship. The results of the LM bootstrap panel cointegration test performed in Table 6 show that the null hypothesis of this test cannot be rejected. Therefore, we have strong evidence to conclude that a long-run relationship exists between human development, economic growth, biomass energy consumption, trade openness, industrialization, and foreign direct investment. Key estimations of this study are stated in Table 7. By using CUPFM and CUP-BC estimators, we obtained the following remarkable results: The main purpose of our study is to explore the effect of biomass energy use on human development, and empirical evidence shows that biomass energy consumption affects human development positively and significantly at the 1% level. Keeping other things constant, a 1% increase in biomass energy use will increase human development by 0.137% (according to CUP-FM) and 0.101% (according to CUP-BC). The production and consumption of biomass energy can cause deforestation, resource depletion, biodiversity loss, and food insecurity. Besides, using traditional forms of biomass such as wood and waste can harm users’ health and the environment. However, the benefit of biomass energy is to help meet the energy demand of humankind in cooking, heating, electricity generation, and transportation. The biomass energy industry also helps rural workers have job opportunities and increase incomes. In addition, economic growth may also be the cause of the transition from the use of traditional biomass energy to modern (such as biofuel and biogas). These forms of biomass energy are considered to be cleaner and more environmentally friendly when compared to fossil energy. Therefore, this transition helps to reduce the negative impacts of energy use on the environment. This may be the reason why biomass energy use increases human development in BRICS countries. This finding, along with conclusions from the research of Shahbaz et al. [12] and Danish and Wang [14], shows that the use of biomass energy not only contributes to economic growth, environmental protection but also enhances human development in BRICS countries. With this conclusion, policymakers can consider biomass energy consumption as one of the tools for achieving sustainable development. The coefficient of GDP is positive and statistically significant at 1% significance level, that means economic growth accelerates human development in the long run. The similar findings are found

Table 4 Homogeneity of slope test analysis. Tests

LM statistics

p-values

~ D

7.639***

0.000

~ D adj

8.876***

0.000

Note: *** denotes statistical significance at the 1% level.

7

lnHDI lnGDP lnBIO lnIND lnTRO lnFDI

CIPS test statistic

CADF test statistic

Level

First difference

Level

First difference

0.532 2.162 2.734*** 1.486 2.699*** 2.502**

2.865*** 2.548** 5.532 *** 3.516*** 4.563*** 4.986***

1.098 1.237 2.735*** 1.808 3.394*** 2.725***

2.452* 3.016*** 4.244*** 2.520** 3.590*** 3.322***

Note: The critical values for CIPS test and CADF test at the 1%, 5%, and 10% levels of significance are 2.57, 2.33, and 2.21, respectively. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

in the studies of Hafner and Mayer-Foulkes [26], Sinha and Sen [36], M. Eren et al. [83], Suri et al. [30], Khan et al. [41], and Arisman [84]. However, this finding differs with the conclusions of Mustafa et al. [35], Wang et al. [46], Khodabakhshi [17], and Khan et al. [38]. Increased GDP per capita helps people have a better living standard. More specifically, they may have better access to health and education services. Moreover, along with economic growth, developing countries like BRICS countries also pay more attention to social welfare and environmental protection. It also contributes to improving the health and quality of life of people. Our study indicates that industrialization has a significant and positive effect on human development. This result is not surprising, given that for emerging countries like BRICS countries, industrialization is often considered to play an important role in economic growth, job creation, productivity growth, and income generation for workers. As a result, the process of industrialization helps to improve human development. Tran et al. [24], Klafke et al. [31], and Qasim and Chaudhary [32] also report the similar findings for ninety countries, China, and Punjab (Pakistan), respectively. The elasticity of human development concerning trade openness is positive. This implies that trade openness increases human development in BRICS countries. Like the industrialization process, the contribution of trade openness to economic growth, employment opportunities creation, and income per capita growth may be plausible reasons for this finding. This result is not supported by the study of Wang et al. [46] and Khan et al. [41]. Considering the case of Pakistan, both studies concluded that trade openness causes to decrease in human development. Regarding the effect of foreign direct investment, the negative sign of coefficients indicates that foreign direct investment reduces human development. The reason may be due to the environmental impacts of FDI. It is undeniable that FDI provides essential resources for industrialization and economic growth. However, lowering environmental standards to attract FDI can turn BRICS countries into pollution havens, where attracts high pollution level industries and outdated technologies from developed countries. Consequently, FDI leads to the environmental degradation and directly affects people’s health and livelihoods. Our conclusions are contrary to those of Reiter and Steensma [34] and Khan et al. [41], who pointed out the positive relationship between foreign direct investment and HDI for developing countries. Along with panel estimation, long-run estimates for individual countries are examined by using fully modified ordinary least square (FMOLS) approach, which is introduced by Phillips and Hansen [85]. Table 8 reports the results of the country-wise analysis. The findings depict a positive and significant relationship between economic growth and human development for all BRICS countries. Regarding the coefficient of biomass energy consumption, the empirical analysis discloses that biomass energy usage positively and significantly impacts human development for Brazil

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Table 6 LM bootstrap panel cointegration test. Tests

Constant

LM bootstrap

Constant and trend

LM statistic

Bootstrap p-value

LM statistic

Bootstrap p-value

5.099

1.000

8.990

1.000

Table 9 Results of Dumitrescu-Hurlin panel causality test.

Table 7 Results of CUP-FM and CUP-BC estimator. Variables

CUP-FM

CUP-BC

Coefficient

t-statistics

Coefficient

t-statistics

lnGDP lnBIO lnIND lnTRO lnFDI

0.060*** 0.137*** 0.107*** 0.039*** 0.046***

83.125 113.689 75.542 57.752 89.839

0.020*** 0.101*** 0.045*** 0.010*** 0.017***

38.532 84.657 41.554 15.577 47.136

Note: The critical value of t statistics at the 1% significance level is 2.845. *** denotes statistical significance at the 1% level.

Variables

lnHDI

lnGDP

lnBIO

lnIND

lnTRO

lnFDI

lnHDI

e

lnGDP

7.391*** (0.000) 6.292*** (0.000) 2.447* (0.073) 0.875 (0.764) 1.972 (0.245)

12.206*** (0.000) e

14.574*** (0.000) 8.703*** (0.000) e

8.553** (0.000) 14.733*** (0.000) 2.717** (0.031) e

5.239*** (0.000) 5.281*** (0.000) 1.504 (0.590) 0.410 (0.359) e

4.290*** (0.000) 5.819*** (0.000) 1.442 (0.648) 0.451 (0.387) 1.238 (0.853) e

lnBIO lnIND lnTRO lnFDI

Table 8 Results from FMOLS long-run analysis. Independent variables lnGDP

lnBIO

lnIND

lnTRO

lnFDI

Brazil

0.147*** (0.006) 0.028** (0.035) 0.246*** (0.000) 0.163*** (0.000) 0.270* (0.056)

0.066** (0.037) 0.138*** (0.000) 0.100*** (0.000) 0.034 (0.310) 0.244* (0.057)

0.069*** (0.000) 0.189*** (0.000) 0.113*** (0.004) 0.072* (0.064) 0.112 (0.563)

0.075*** (0.000) 0.022** (0.024) 0.053*** (0.000) 0.033*** (0.000) 0.198 (0.100)

0.009*** (0.001) 0.001 (0.764) 0.006*** (0.000) 0.010*** (0.009) 0.006 (0.308)

India China South Africa

3.620*** (0.000) 0.897 (0.787) 2.125 (0.172)

6.745*** (0.000) 3.874*** (0.000)

2.141 (0.165)

Note: p-values are put in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

Countries

Russia

9.440*** (0.000) 2.673** (0.036) 1.055 (0.952) 1.844 (0.321)

Note: p-values are shown in parentheses. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

and Russia. While on the other hand, biomass energy use has an adverse effect on human development for other countries like India and South Africa. In the case of China, there is no evidence to determine the relationship between biomass energy consumption and human development. The empirical results also infer that industrialization, which is negative for Brazil, Russia, India and positive for China, has significant impacts on human development. The result regarding trade openness implies that the elasticity of human development with respect to trade openness are positive and significant for Brazil, India, and China. The contrast results were found for countries like Russia and South Africa. Finally, foreign direct investment decreases human development in China but enhances human development in Brazil and India. On the other hand, the remaining countries show an insignificant impact of foreign direct investment on human development. After estimating the long term coefficients, we apply the method of Dumitrescu and Hurlin [80] to investigate the causal relations between variables. The results of the Dumitrescu-Hurlin heterogeneous panel causality test are illustrated in Table 9. The most interesting finding from this test is that the bidirectional causality relation exists between biomass energy usage and human development. That means the changes in biomass energy use lead to the variations in human development, and vice versa. Along with the findings from long-run estimates, this result is a recommendation for policymakers in using biomass energy to promote human development.

Empirical findings also indicate that two-way causality exists between economic growth and human development. A similar result is reported by Sinha and Sen [36] for the case of BRIC countries. However, our results are inconsistent with the conclusions of Rivera [39], Khan et al. [38], and Khan et al. [41]. Also, the bidirectional causality linkage between human development and industrialization was discovered in this study. Contrary to the above results, we only found unidirectional causality running from foreign direct investment and trade openness to human development. Moreover, our findings also provide evidence of the existence of bidirectional causal relationship between biomass energy use and economic growth. This finding is consistent with the result of Shahbaz et al. [12], however, is not in line with the researches of Destek [86] and Aydin [9]. 6. Conclusion The current study aims to explore the relationship between biomass energy utilization and human development in the context of BRICS region for the 1990e2015 period while incorporating economic growth, industrialization, foreign direct investment, and trade openness in a multivariate framework. For this purpose, CADF and CIPS unit root tests, LM bootstrap panel cointegration test, CUPFM and CUP-BC models, and Dumitrescu-Hurlin panel causality test are employed. These techniques help to overcome the issues of slope homogeneity and cross-sectional dependence in panel data. Some crucial findings are observed from empirical analysis: (i) Biomass energy use enhances human development and bidirectional causality between these two variables is detected; (ii) Economic growth, trade openness, and industrialization are significant factors in promoting human development; and (iii) In BRICS countries, foreign direct investment has negative impact on the human development improvement. Based on the findings of empirical analysis, several policy implications are recommended for BRICS countries. The usage of biomass energy benefits the human quality of life, so policies to upsurge the proportion of biomass energy in the energy use should be implemented. BRICS countries need to raise awareness about biomass energy and encourage biomass energy production and consumption. Besides, it is necessary to limit the usage of fossil

Z. Wang et al. / Energy 194 (2020) 116906

energy and partially replace it by using biomass energy. One of the barriers to expanding biomass use is that its energy efficiency is not too high. Governments and businesses should invest in research and development and new technologies to improve energy efficiency of biomass. The governments should also direct research and development to find solutions to lower production costs and increase the competitiveness of biomass energy compared to other energy sources. Expansion of biomass energy production is necessary, however, socio-economic problems such as food insecurity, biodiversity loss, and deforestation also need to be considered when producing biomass on a large scale. Our results reflect that economic growth, industrialization, and trade openness advances human development. Therefore, policies are needed to promote the positive effects of these determinants on human development. For example, governments in BRICS region can offer more budget to health service and education, control income inequality, protect the environment, and encourage the use of clean energy and clean technology in industries. Besides, FDI has been shown to cause a decrease in the human development index. Therefore, in our opinion, BRICS countries need to improve their requirements when attracting foreign direct investment projects, especially environmental requirements. Projects with outdated technology should be restricted to avoid becoming a pollution havens for developed countries. Our research can be replicated for other case studies. Examining the impact of biomass energy consumption on human development in developed countries or other regions will help policymakers to have a more comprehensive view of the effects of biomass energy use. Moreover, in this study, we focus on investigating the impact of biomass energy use on human development without delving into the effects of specific types of biomass energy. This is the limitation of our study and can also be considered as a suggestion for future research. Acknowledgment This study is supported by the National Key Research and Development Program of China (Reference No. 2016YFA0602500), National Natural Science Fund of China (Reference No. 71774014, 91746208, 71573016, 71403021, 71521002), National Science Fund for Distinguished Young Scholars (Reference No. 71625003), Yangtze River Distinguished Professor of MOE, National Social Science Fund of China (Reference No. 17ZDA065), and Joint Development Program of Beijing Municipal Commission of Education. References [1] Eren BM, Taspinar N, Gokmenoglu KK. The impact of financial development and economic growth on renewable energy consumption: empirical analysis of India. Sci Total Environ 2019;663:189e97. https://doi.org/10.1016/ j.scitotenv.2019.01.323. [2] REN21. Renewables 2017 global status report. 2017. Paris. [3] Bilgili F, Koçak E, Bulut Ü, Kus¸kaya S. Can biomass energy be an efficient policy tool for sustainable development? Renew Sustain Energy Rev 2017;71: 830e45. https://doi.org/10.1016/j.rser.2016.12.109. [4] Baek J. Do nuclear and renewable energy improve the environment? Empirical evidence from the United States. Ecol Indicat 2016;66:352e6. https://doi.org/ 10.1016/j.ecolind.2016.01.059. [5] Alper A, Oguz O. The role of renewable energy consumption in economic growth: evidence from asymmetric causality. Renew Sustain Energy Rev 2016;60:953e9. https://doi.org/10.1016/j.rser.2016.01.123. [6] REN21. Renewables 2019 global status report. 2019. Paris. [7] IRENA. Global energy transformation: a roadmap to 2050. 2018. Abu Dhabi. [8] IEA. Renewables 2018. https://www.iea.org/renewables2018/. [Accessed 19 June 2019]. [9] Aydin M. The effect of biomass energy consumption on economic growth in BRICS countries: a country-specific panel data analysis. Renew Energy 2019;138:620e7. https://doi.org/10.1016/j.renene.2019.02.001. € [10] Bildirici M, Ozaksoy F. An analysis of biomass consumption and economic growth in transition countries. Econ Res Istraz 2018;31:386e405. https://

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