The impact of energy consumption on Carbon Intensity of Human Well-Being (CIWB)

Nguyen Thuan, Dang B. Hai. Ho Chi Minh City Open University Journal of Science, 11(1), 19-28

19

The impact of energy consumption on Carbon Intensity of

Human Well-Being (CIWB)

Nguyen Thuan¹, Dang Bac Hai^1,2*

¹Ho Chi Minh City Open University, Vietnam

²University of Natural Resources and Environment, Vietnam

^*Corresponding author: haidb.16ae@ou.edu.vn

ARTICLE INFO

ABSTRACT

DOI:10.46223/HCMCOUJS.

econ.en.11.1.1360.2021

A key concern when constructing sustainable development

policy is reducing the negative impact on environmental systems

and maximizing human welfare. In this study, we assess how

energy consumption effected on Carbon intensity of human well-

being (CIWB). Using two-way fixed effects in panel regression,

this relationship has been investigated during 2000-2018 for 9

lower middle-income countries including Algeria, Bangladesh,

Egypt, India, Morocco, Pakistan, Philippines, Uzbekistan and

Vietnam, while adding GDP and FDI per capita as control

variables. The study reveals that the use of energy for economic

development is ineffective and inconsistent with the overview of

sustainable development due to the result of increasing CIWB.

However, the sign of negative coefficients of GDP and FDI per

capita in control variables have given the striking findings that

these factors will be helpful for lower middle - income countries to

pursue sustainable development by reducing CIWB.

Received: December 22^nd, 2020

Revised: January 27^th, 2021

Accepted: January 28^th, 2021

Keywords:

CIWB, energy consumption,

FDI, GDP, two-way fixed

effects regression

1. Introduction

An assessment of the nexus between economic activity and environmental stressors

offers important policy implications. This could help policymakers move towards economic

sustainability in the future (Bretschger & Smulders, 2007; Narayan & Narayan, 2010). The term

‘sustainability’ refers to the standard behavior with respect to the way humans should act

towards the environment, and how they are responsible towards one another and generations to

come. Baumgartner and Quaas (2010a,b) and Becker (2012) consider that sustainability

economics combines two central concepts: justice (from sustainability) and efficiency (from

economics). Recently, a new research direction has emerged that evaluates the relationship

between various forms of socioeconomic development and what is known as the carbon intensity

of well-being (CIWB) Jorgenson and Givens (2015) measured as a ratio of carbon dioxide

emissions/life expectancy at birth¹. These studies aim to analyze the extent to which

socioeconomic development increases human well-being in countries and reduces anthropogenic

contributions to the environment.

Energy plays an important role in economic development and human life. At the same

¹According to Dietz et al. (2012) suggested that life expectancy at birth, is generally understood to be a good measure of human

well-being (although it is by no means perfect).It is a direct indicator of health and longevity. It captures the overall health

conditions of society since it directly reflects longer life-spans and reductions in infant mortality, and indirectly reflects pre-natal

education, life-long medical services, and levels of literacy and education.

20

Nguyen Thuan, Dang B. Hai. Ho Chi Minh City Open University Journal of Science, 11(1), 19-28

time, the mechanism of development and human life, the use of fossil energy will create threats

to the environment and negatively affect human well-being. However, very few studies have

mentioned the relationship between energy consumption and CIWB. Of the existing studies

exploring this relationship, it was noted that a potentially complicated factor in the assessments

regarding the correlation between energy consumption and CIWB is the change in the energy use

policies in the countries. Therefore, in order to consider whether the energy consumption policy

is geared towards economic sustainability, this article will study the impact of energy

consumption on the CIWB. If energy consumption leads to reductions in CIWB, the energy

consumption policy will have a positive impact on improving sustainability. If energy

consumption increases CIWB, it is necessary to promulgate more relevant policies to reduce

these undesirable environmental impacts.

2. Literature review

2.1. Theoretical background

The Treadmill of Production Theory (TPT) states that environmental degradation and

pollution are an inherent part of the development process (Schnaiberg, 1980; Schnaiberg &

Gould, 1994). Given that the capitalist economy is predicated on the constant pursuit of

expansion and continuous production due to the profit motive of the producers increases energy

use and carbon emission, thereby having a negative effect on the welfare. This is worse when

manufacturers apply new production technology that requires a heavy reliance on energy use and

more carbon emission. In this regard, although per capita income may increase after increased

energy use, it will worsen the well-being of people not only by increasing carbon emissions but

also by making energy less accessible to them (Gould, Pellow, & Schnaiberg, 2004). As a result,

Lewis (2019) applied the Treadmill of Production’s social change model to explain the case of

Ecuador’s changing development trajectory from the 1970s to 2017. Using a Panel Study of the

Ecological Footprints of Nations, 1975-2000, Jorgenson and Clark (2009) indicated that the

treadmill of production in the context of economic development increases per capita footprints

and the treadmill of destruction in the mode of military expenditures per soldier positively

affects per capita footprints.

2.2. Empirical literature

2.2.1. The relationship between energy consumption and environmental pollution

The relationship between energy consumption and environmental emissions is divided

into two investigative directions in country-specific literature and multi-country studies

(Adewuyi & Awodumi, 2017; Alkhathlan & Javid, 2015; Ang, 2008; Saidi & Mbarek, 2016;

Tiba & Omri, 2017). Although, a general conclusion from these studies is that contradictory

results, most generally confirm that energy consumption is a key source of environmental

emissions (carbon emissions) through direct and indirect channels.

Studies in individual country such as France, Ang (2007) suggest that increasing energy

consumption will lead to an increase in carbon emissions. In the case of Turkey, Halicioglu

(2009) found only energy consumption has a relationship with CO2 emissions in the long run.

Similar results were found in China as reported by Zhang and Cheng (2009); Ang (2008) in

Malaysia; Shahbaz, Lean, and Shabbir (2012) in Pakistan and by Kanjilal and Ghosh (2013) in

India.

On the other hand, research by Apergis and Payne (2009) in six Central American

countries shows that there is a one-way relationship from energy consumption to CO2 emissions.

Saboori and Sulaiman (2013) studied in five ASEAN countries and found evidence of a two-way

Nguyen Thuan, Dang B. Hai. Ho Chi Minh City Open University Journal of Science, 11(1), 19-28

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relationship between energy consumption and CO2 emissions in these countries. This also means

that CO2 emissions and energy consumption are highly correlated. Cowan, Chang, Inglesi-Lotz,

and Gupta (2014) studied in the BRICS countries found mixed results for this relationship. In

India, electricity consumption is the cause of CO2 emissions, while Russia, China, Brazil and

South Africa have not found a causal relationship between electricity consumption and CO2

emissions. Chen, Chen, Hsu, and Chen (2016) investigated the relationship between energy

consumption and CO2 emissions in 188 countries, the authors confirm a one-way relationship

from energy consumption to CO2 emissions for both developed and developing country group.

Dogan and Aslan (2017) argue that there is a two-way relationship between energy consumption

and CO2 emissions in European countries.

2.2.2. The relationship between energy consumption and well-being

Several previous studies have examined the link between indicators of living standard

and energy consumption (Martínez & Ebenhack, 2008; Mazur & Rosa, 1974; Pasternak, 2001;

Suarez, 1995) Most studies have found a strong relationship between energy and living standard

at lower energy consumption levels (developing countries) and constant at higher energy

consumption levels (developed countries). The standard of living that did not increase at high

levels of energy consumption was referred to as the “plateau” by Pasternak (2001) or the

“saturation” by Martínez and Ebenhack (2008). In the study of Mazur and Rosa (1974), they

concluded their study of 55 countries by describing this model and stating that “so long as

America’s per capita energy consumption does not go below that of other developed nations, we

can sustain a reduction in energy use without long-term deterioration of our [non-economic]

indicators.” In contrast, Suarez (1995) compared the energy and HDI index in the period 1960-

65 with the period 1991-1992 and found an average improvement in HDI at lower energy levels

in the later data set.

3. Data and methods

3.1. Model and data

This study collects panel data covering the period 2000-2018 for 9 lower middle-income

group (Algeria, Bangladesh, Egypt, India, Morocco, Pakistan, Philippines, Uzbekistan and

Vietnam). Thus, our data sample includes 171 observations. Data sources were collected from

the information of Worldbank, UCSTAD, countryeconomy, ourworldindata. In which, the data

per capita income (GDP) measures the level of development is calculated in thousand USD at

constant prices in 2010 and LE (average life expectancy) is measured by the number of years

taken from Worldbank, foreign investment per capita (FDI) is measured in USD from UCSTAD,

carbon dioxide emissions per capita (CO2) measured in tons from Countryeconomy²and energy

consumption per capita (EPC) in thousand Kwh from Ourworldindata³.

Basing on TPT theory, CIWB_itis a function of the following economic performance

variables:

CIWB_it=F(EPC_it,GDP_it,FDI_it)

(1)

We assume equation (1) is a linear equation. The selected variables are based on not only

TPT but also previous empirical studies on CO2 emissions and human well-being. The subscript

i denotes the unit of our analysis, i.e., country. Subscript t represents the time. CIWB_itdenotes

territorial CIWB, Y_itis the GDP per capita, EPC_itis energy consumption per capita and FDI_itis

foreign investment per capita. Technically, our goal is to consider the impact of EPC_iton CIWB_it,

²https://countryeconomy.com

³https://ourworldindata.org

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Nguyen Thuan, Dang B. Hai. Ho Chi Minh City Open University Journal of Science, 11(1), 19-28

the variables GDP_itand FDI_itserve as a control variable. The method for estimating equation (1),

which is a two-way fixed-effect technique, concentrates on two simultaneous tasks: explaining

the outcomes within-country variation, and reducing the likelihood of biased model estimates

resulting from similar However, to make the estimation results more reasonable, we need to add

two control variables that change over time.

3.2. Methodology

The study applied the time-series cross-sectional Prais-Winsten regression model with

panel-corrected standard errors (PCSE). This methodology has been recommended by Beck and

Katz (1995) because the feasible generalized least squares (FGLS) method produces incorrect

standard errors. However, Cameron and Trivedi (2010) suggested that disturbances are

heteroskedastic and contemporaneously correlated across panels. This paper collects data over a

19-year period for 9 low-income countries in the middle-income group, so the authors corrected

for first-order autocorrelation (AR1) within panels. We estimated our model by the two-way

fixed effect technique. We control for both period-specific and unit-specific disturbances. Our

general model is as follows:

Y_it=휃X_it+u_i+ε_t+e_it

(2)

Where Y_itdenotes the dependent variable for each country at each time period, 휃X_it

represents the vector of coefficients for explanatory variables for each country that vary over

time, u_iis the unit-specific (i.e., country-specific) disturbance term, ε_tis the period-specific

disturbance term that is constant across all countries, and e_itis the disturbance term unique to

each country at each point in time. Throughout our empirical estimations, we use dummy

variables to control for country-specific disturbances (u_i) and time-specific disturbances (et). The

former controls for potential unobserved characteristics that is temporally invariant within

countries (unit specific intercepts), while the latter act as controls for any unobserved features

that is cross-sectionally invariant within periods (period-specific intercepts). More precisely, the

two-way fixed-effect method focuses on two instantaneous missions: (i) clarifying the results

within-country variation, and (ii) dropping the probability of biased model estimates arising from

similar time trends.

The two-way fixed-effect technique of our paper has the following form:

CIWB_it=휃₁ECP_it+ 휃₂ECP_it*year2001+….+휃₁₈ECP_it*year2018+ 휃₁₉year2001+…+

휃₃₇year2018+ 휃₃₈GDP_it+ 휃₃₉FDI_it+u_i+e_it

(3)

where CIWB_it, the dependent variable, is the carbon intensity of wellbeing, and the model

includes ECP per capita (휃₁ECP_it), the interaction for ECP per capita and the dummy variables

for each year (휃₂ECP_it*year2001+….+ 휃₁₈ECP_it*year2018), where 2000 is the reference

category, the period-specific intercepts ( 휃₁₉year2001+…+ 휃₃₇year2018). The model also

includes the gross domestic product per capita (휃₃₈GDP_it), foreign investment per capita

(휃₃₉FDI_it), the country-specific intercepts (u_i) and the disturbance term unique to each country at

each point in time (e_it). The estimated parameter of ECP (휃₁) per capita is the unit change in the

dependent variable (CIWB_it) in 2000 for each unit increase in ECP per capita for the same year.

The overall effect of ECP per capita subsequent years (e.g., 2001, 2002, …, 2017,2018) equals

the sum of the coefficient for ECP per capita (i.e., its effect in 2000) and the appropriate

interaction term if the latter is statistically significant (Allison, 2009).

4. Results

Descriptive statistics for all variables included can be found in Table 1 and Figure 1

Nguyen Thuan, Dang B. Hai. Ho Chi Minh City Open University Journal of Science, 11(1), 19-28

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below. According to Kline (2015) variables with skewness > ± 3 and kurtosis > ± 10, the data

have problems with the normal distribution. The distribution of skewness and kurtosis for all the

variables were within the acceptable ranges, we do not need to convert the data by using the

natural logarithm.

Table 1

Descriptive statistics

STats

Mean

Sd

Skewness

Kurtosis

CIWB

FDI

GDP

ECP

34.09786

1,874719

0.8131247

3.910398

38.22542

36.17273

1.157005

3.730798

1.961481

1.11325

0.9799913

3.208365

7.5651

5.453634

1.217838

3.725438

Source: The reseacher’s data analysis

Figure 1. Carbon intensity of well-being (CIWB) in countries

As for the CIWB_itratio, our data shows that the coefficient of variation of CO2 is 0.64

and ranges from0.2-5.04. Meanwhile, the coefficient of variation of life expectancy (LE) is 0.049

and the range is 62.5-76.69. The coefficient of variation of CO2 (the numerator) is much larger

than that of the lifetime (denominator), because the coefficient of variation of the numerator

affects the variation of this ratio. Therefore, it is necessary to resolve this problem before

considering the CIWB ratio as a dependent variable. We did the same as Dietz, Rosa, and York

(2012) and Jorgenson (2014) did a treatment. That is to add a constant to the numerator. The

constant value calculated from our sample data was 21.98 and added to the value of CO2. So the

adjusted CIWB can be calculated: CIWB = ((CO2 + 21.98) / LE) * 100. The values of CIWB of

countries after adjustment in Figure 1.

Table 2 reports the findings for the estimated model. The R-squared estimate value is

0.99 significantly high and it indicates that the model can account for 99% of all CIWB

variation. Such unusually high R-squared values are mainly the result of year-specific and

country-specific intercepts which are known as fixed effects. Put simply, the R-squared is very

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Nguyen Thuan, Dang B. Hai. Ho Chi Minh City Open University Journal of Science, 11(1), 19-28

high due to the use of the two-way fixed effect. The Rho value is within the acceptable statistical

range that 87% of the CIWB variance is explained by country specific disturbances (u_i). This

implies that the estimation model, after adjusting for AR (1), has no problem with

autocorrelation. Besides, we also checked and did not detect the case of multicollinearity due to

adding more dummy variables to the model. In addition, in order to decide if fixed time effects

(the period specific intercepts) are needed while running the model, we perform a joint test to

check whether the dummy variables for all years are equal to zero. The results show that the null

hypothesis that “all years coefficients are jointly equal to zero” is rejected. This fact shows that

the period-specific intercepts are significant in the four estimated models.

Table 2

The effect of energy consumption per capita (dependent variable is CIWB)

Panel corrected

Independent variables

Coefficients

P>|z|

standards errors

.0102557

.0044939

.0061022

.0076821

.0083684

.0093793

.0112643

.0118724

.0126368

.0133822

.0142494

.0143938

.015168

ECP

.2505281

.0029104

.0105277

.0172942

. 0055789

-.0003515

.0278119

.010079

.0487246

.0216888

.0276134

.0383506

.043654

.0216207

.0130358

.0155259

.0323442

.0280184

.0122181

-.6424431

-.0023558

0.87

0.000

0.517

0.084

0.024

0.505

0.970

0.014

0.396

0.000

0.105

0.053

0.008

0.004

0.187

0.445

0.446

0.168

0.274

0.658

0.000

0.026

ECP_2001

ECP_2002

ECP_2003

ECP_2004

ECP_2005

ECP_2006

ECP_2007

ECP_2008

ECP_2009

ECP_2010

ECP_2011

ECP_2012

ECP_2013

ECP_2014

ECP_2015

ECP_2016

ECP_2017

ECP_2018

GDP

.0163952

.0170706

.0203723

.0234627

.0256274

.0275986

.0691638

.0010593

FDI

Rho

R-squared

N

0.99

171

Source: The reseacher’s data analysis

The results from the regression show the effect of per capita energy consumption in the

reference year 2000 (0.25) and the interaction of per capita energy consumption and the yearly

dummy variables (2002, 2003, 2006, 2008, 2010, 2011 and 2012) were positive and statistically

significant. In the remaining years, this interaction is mostly positive but not statistically

significant. Thus, the relationship between CIWB and energy consumption in these countries is

positive and appears to vary in magnitude through time. According to Jorgenson and Clark

(2012); Dietz et al. (2012); Knight and Rosa (2011); Grossman and Krueger (1995) argue that

the effect of economic performance activity on the environment may change over time. To put it

Nguyen Thuan, Dang B. Hai. Ho Chi Minh City Open University Journal of Science, 11(1), 19-28

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more simply, the energy consumption in low-income countries in the middle-income group is

creating negative pressure on the environment and changing its magnitude over time.

To understand the impact of energy consumption on the CIWB, Figure 2 will plot and

report the coefficients to estimate the influence of energy consumption over the years. The first

co-efficient (0.25) plotted and presented in Figure 2 is the impact of energy consumption on

CIWB in 2000 and remained constant through 2001. In 2002 and 2003, the estimated

coefficients increased by respectively (0.26 and 0.267) and returned to 0.25 in 2004 and 2005.

During the period from 2006-2011, the estimated coefficient of energy consumption fluctuated

strongly and tended to increase in absolute value at a higher level than in the period from 2000-

2005. However, in 2005 the estimated coefficient decreased to 0.254 and decreased to 0.25 in

2013 and unchanged until 2018.

2000 2001 2002

0.25 0.25 0.26

2003

2004 2005

0.25 0.25

2006

2007

0.25

2008

2009

0.25

2010

2011

2012

2013 2014 2015 2016 2017 2018

0.25 0.25 0.25 0.25 0.25 0.25

0.267

0.277

0.298

0.277 0.288 0.254

Figure 2. Estimated coefficients of the effect of yearly energy consumption on CIWB

Regarding the control variables GDP per capita and FDI per capita, there are CIWB

positive effects and statistically significant. Therefore, these two variables contribute to reducing

the stresses on the environment by increasing human well-being.

In summary, our regression results provide a fairly clear picture of the relationship

between energy consumption and CIWB in the low-income group of middle-income countries.

We come to two main findings:

The relationship between energy consumption and CIWB becomes less sustainable. In

other words, the results indicate that energy consumption creates more stress on the environment.

In contrast, GDP per capita and FDI per capita have reduced the pressures exerted on the

environment by increasing human well-being.

5. Conclusions and policy implications

The environmental effects of human well-being are seen as a branch of sustainable

economics. The relationship between economic activity and the environment has attracted the

attention of scientists for a long time and is divided into two branches: First, the impact of

economic development or economic activities on the environment. And the second, the study of

how economic growth can increase human well-being while reducing the pressure on the

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Nguyen Thuan, Dang B. Hai. Ho Chi Minh City Open University Journal of Science, 11(1), 19-28

environment. The studies on carbon intensity on well-being (CIWB) is in the second line of

sustainable development research. Recently, this direction has attained more interest because

almost all countries in the world have prolonged development of economics based on energy

consumption and our world is highly threatened by climate change.

In order to have a good understanding of the second line of sustainable development, this

study investigates the relationship on CIWB with economic growth by choosing energy

consumption as the dependent variable and economic factors like GDP and FDI per capita to be

control variables. The data of 9 lower middle - income countries have been selected during 2000-

2018 because they have a significant change in policies toward sustainable development under

the Kyoto protocol since 1997.

This study reveals the positive effect of energy consumption on CIWB, which means the

more energy consumes, the more CIWB increases. That indicates the economic development based

on energy consumption is ineffective on both environment and human well-being. In the other

hand, the control variables as GDP and FDI per capita have a negatively significant association

with CIWB, thus, economic growth and attracting FDI have increased human well-being.

The study contributes to the academic research on sustainable development by showing that

the withdraw of economic success of those lower middle - income countries when based on energy

consumption to develop, because of the decline of environment and human well-being. Moreover, it

also indicates the possible contribution of economic factors as GDP and FDI per capita to

sustainable development. From the results above, the paper offers the following suggestions:

Firstly, the use of energy for economic development is ineffective and inconsistent with

the view of sustainable development. Therefore, it is necessary to issue additional policies to

limit undesirable outcomes from energy consumption. Besides that, the governments of these

countries can implement various policies to promote renewable energy technologies such as

capital subsidies, feed in tariffs, tradable certificates, and renewable portfolio standards.

Secondly, policies for economic development and FDI attraction are consistent with the

perspective of sustainable development. However, the impact coefficient of FDI is still relatively

low. Therefore, it is necessary to apply strict policies for attracting green FDI which reduces

stress on the environment in the long term.

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