.


International Journal of Energy Economics and Policy | Vol 10 • Issue 3 • 2020296

International Journal of Energy Economics and 
Policy

ISSN: 2146-4553

available at http: www.econjournals.com

International Journal of Energy Economics and Policy, 2020, 10(3), 296-302.

Information and Communication Technology and Electricity 
Consumption in Transitional Economies

Bester Chimbo*

Department of Information Systems, School of Computing, University of South Africa, South Africa. *Email: chimbb@unisa.ac.za

Received: 17 May 2019 Accepted: 15 November 2019 DOI: https://doi.org/10.32479/ijeep.8143

ABSTRACT

The study investigated the impact of information and communication technology (ICT) on electricity consumption in transitional economies using 
panel data analysis methods (dynamic generalized methods of moments [GMM], pooled ordinary least squares, fixed effects, random effects) with 
annual secondary data ranging from 1995 to 2014. Majority of prior studies on the subject matter had not focused on the impact of ICT on electricity 
consumption but on energy consumption, which is a broader area. They also did not focus exclusively on transitional economies and they ignored both 
the dynamic characteristics of electricity consumption data and endogeneity issues. The study revealed that electricity consumption is positively and 
significantly influenced by its own lag, in line with theoretical literature (Nayan et al., 2013). However, the impact of ICT on electricity consumption 
was found to be mixed. For example, the influence of ICT on electricity consumption was found to be negative and non-significant under the dynamic 
GMM and pooled OLS. Fixed and random effects observed that ICT had a significant positive impact on electricity consumption in emerging markets. 
It is against this backdrop that the current study urges transitional economies to develop and implement policies that ensures that ICT gadgets being 
used reduces the quantity of electricity consumption. In other words, transitional economies should focus on developing or importing energy efficient 
ICT gadgets in order to meet the required energy saving threshold levels. Future studies should investigate channels through which ICT influences 
electricity consumption, in line with Shahbaz et al. (2014) whose study noted that the relationship between ICT and electricity consumption is non-linear.

Keywords: Information and Communication Technology, Electricity Consumption, Panel Data, Transitional Economies 
JEL Classifications: L17, Z32

1. INTRODUCTION

Several studies have attempted to investigate the impact of 
information and communication technology (ICT) on electricity 
consumption but they have so far produced divergent, mixed 
and unclear results. Four set of views emerged from the study 
on ICT-led electricity consumption hypothesis and these are: 
(1) ICT-led positive impact on electricity consumption, (2) ICT-led 
negative influence on electricity consumption, (3) the non-linear 
argument, (4) the neutrality hypothesis. The ICT-led positive 
impact on electricity consumption hypothesis argued that 
increased investment in ICT infrastructure leads to more electricity 
consumption in the economy, a view which was supported by 
Zhang and Liu (2015), Sadorsky (2012), Salahuddin and Alam 

(2016), Tunali (2016), Afzal and Gow (2016), Collard et al. (2005), 
Pothitou et al. (2017) and Choo et al. (2007), among others.

The ICT-led negative impact on electricity consumption view says 
that ICT has got a deleterious effect on electricity consumption. 
The view was supported by Lee and Brahmasrene (2014), Lu 
(2018), Han et al. (2016), Gelenbe and Caseau (2015), Wang 
and Han (2016), Horner et al. (2016), Schulte et al. (2016), Pano 
(2017), Choo et al. (2007) and Bernstein and Madlener (2010). 
The non-linear argument says that the impact of ICT on electricity 
consumption is non-linear in the sense that ICT influences 
electricity consumption through some channels and not in a 
direct manner. A study done by Shahbaz et al. (2014) produced 
findings which supports the non-linear argument. The neutrality 

This Journal is licensed under a Creative Commons Attribution 4.0 International License



Chimbo: Information and Communication Technology and Electricity Consumption in Transitional Economies

International Journal of Energy Economics and Policy | Vol 10 • Issue 3 • 2020 297

hypothesis which was supported by Inani and Tripathi (2017) is 
of the view that there is no relationship at all between ICT and 
electricity consumption. Although the first two views are the 
most supported in the literature, the relationship between ICT and 
electricity consumption is still not clear and inclusive. It is against 
this backdrop that the current study performed further empirical 
tests to find out the impact of ICT on electricity consumption in 
transitional economies.

Majority of the empirical studies on the impact of ICT on electricity 
consumption shied away from focusing on transitional economies 
except two of them (Sadorsky, 2012; Afzal and Gow, 2016). The 
similarities between these prior studies and the current study 
is that they all used dynamic generalized methods of moments 
(GMM) which takes into account the dynamic nature of electricity 
consumption data and effectively deal with the endogeneity 
problem. The current study deviates from these two similar prior 
studies in the following ways: (1) The current study used other 
panel data analysis methods (fixed effects, random effects, pooled 
OLS) apart from the dynamic GMM for comparative analysis 
purposes, (2) the current study used more up to date dataset, (3) the 
current study used a different proxy of ICT (individuals using 
internet as a ratio of the population), which is a better measure of 
ICT investment and development in the country.

The rest of the paper is organised as follows: Section 2 is literature 
review, section 3 explains the other factors that influence electricity 
consumption whilst section 3 describes the methodology used 
in this study. Pre-estimation diagnostics is section 4, main data 
analysis, interpretation and discussion of results is done in 
section 5. Section 6 is the conclusion.

2. LITERATURE REVIEW

The ICT led electricity consumption view according to Zhang and 
Liu (2015) argues that ICT increases the amount of electricity 
consumption because more electrical gadgets are used which 
consumes a lot of energy. The optimistic view says that the 
use of ICT gadgets saves the overall energy consumption 
but not necessarily the quantity of electricity used (Lee and 
Brahmasrene, 2014). In line with Houghton’s (2009) proposition, 
the relationship between ICT and electricity (energy consumption) 
consumption is quite unclear as it could be positive, negative or 
non-existent at all.

On the empirical front, quite a number studies investigated the 
relationship between ICT and electricity or energy consumption 
(Table 1).

Table 1 shows that the relationship between ICT and electricity 
consumption can be categorized into four: (1) ICT led positive 
impact on electricity consumption, (2) ICT led negative effect 
on electricity consumption, (3) there is no relationship between 
ICT and electricity consumption and (4) the impact of ICT on 
electricity consumption is non-linear. Clearly, both theoretical and 
empirical literature shows that the influence of ICT on electricity 
consumption is mixed and debate on the relationship between the 
two variables is inconclusive and still far from being over.

Other factors that influence electricity consumption are presented 
in Table 2.

Individuals using internet (% of population) is the proxy of 
ICT that was used in this study, consistent with Tsaurai and 
Chimbo (2019).

3. METHODOLOGY DESCRIPTION

3.1. Data
The study used annual panel data ranging from 1995 to 2014 
for transitional economies (Argentina, Brazil, China, Colombia, 
Czech Republic, Greece, Hong Kong, Indonesia, India, Mexico, 
Malaysia, Peru, Philippines, Poland, Portugal, Republic of Korea, 
Russia, Thailand, Turkey, Singapore, South Africa). The sample 
of countries is in line with International Monetary Fund (2015) 
and data availability considerations. The data was obtained from 
World Development Indicators, African Development Indicators, 
International Financial Statistics and International Monetary Fund 
databases.

3.2. Empirical and Econometric Model Specification
In line with both theoretical and empirical literature, the general 
model specification of the electricity consumption function is 
shown in equation 1.

 ELECTR = f(ICT, GDPPC, URBAN, ACCESS,  
  RESOURCE, FDI, FIN, OPEN, HCD) (1)

Where ELECTR, ICT, GDPPC, URBAN, ACCESS, RESOURCE, 
FDI, FIN, OPEN and HCD stands for electricity consumption, 
ICT, gross domestic product per capita, urban population, access 
to electricity, resource endowment, foreign direct investment, 
financial development, trade openness and human capital 
development.

Equation 1 is transformed into equation 2 when presented as an 
econometric estimation model.

 ELECTRi,t=β0+β1ICTi,t+β2GDPPCi,t+β3URBANi,t 
 +β4CCESS+β5RESOURCEi,t+β6FDIi,t+β7FINi, 
  +β8OPENi,t+β9HCDi,t+µ+ε  (2)

ε is the error term. i and t stands respectively stands for country 
and time. µi is the time invariant and unobserved country 
specific effect, β0 represents the intercept term, β1 up to β9 are the 
co-efficients of the respective variables used. Fixed effects, pooled 
OLS and the random effects were the three panel data analysis 
methods which were used to estimate equation 2.

Following Nayan et al. (2013) whose study argued that electricity 
consumption is affected by its own lag, the current study took into 
account the dynamic characteristics of the electricity consumption 
data (equation 3).

ELECTRi,t=β0+β1ELECTRi,t−1+β2ICTi,t+β3GDPPCi,t 
+β4URBANi,t+β5ACCESS+β6RESOURCEi,t 

 +β7FDIi,t+β8FINi,t+β9OPENi,t+β10HCDi,t+µ+ε (3)



Chimbo: Information and Communication Technology and Electricity Consumption in Transitional Economies

International Journal of Energy Economics and Policy | Vol 10 • Issue 3 • 2020298

Where ELECTRit−1is the lag of electricity consumption. Equation 
3 was estimated using Arellano and Bond (1991)’s dynamic panel 
GMM approach.

4. PRE-ESTIMATION DIAGNOSTICS

This section includes correlation analysis and descriptive statistics. 
Table 3 shows that variables which were individually positively 
and significantly correlated with electricity consumption are 

ICT, economic growth (GDPPC), urban population (URBAN), 
foreign direct investment (FDI), financial development (FIN), 
trade openness (OPEN) and human capital development (HCD).

Access to electricity was found to have been negatively but 
non-significantly related with electricity consumption whilst 
a significant negative relationship between human capital 
development and electricity consumption was detected. The results 
are supports by the literature. The problem of multi-collinearity 

Table 1: A summary of empirical studies on ICT-electricity consumption nexus
Author Focal unit of analysis Methodology Research findings
Sadorsky (2012) Emerging economies Dynamic panel 

data analysis
When ICT is measured using mobile phones, number 
of computers and internet connections, ICT was 
found to have had a significant positive influence on 
electricity consumption

Salahuddin and 
Alam (2016)

OECD countries Panel data analysis A significant positive relationship running from ICT 
towards electricity consumption was detected both in 
the long and short run

Lu (2018) Asian countries Panel data analysis ICT was found to have reduced (significant negative 
influence) carbon dioxide emissions in Asian 
countries

Han et al. (2016) China ARDL and ECM ICT had a negative impact on energy consumption in 
the short run. In the long run, the influence of ICT on 
energy consumption was found to be U-shaped

Tunali (2016) European union countries ARDL ICT led to an increase in electricity consumption in 
European Union countries in the long run

Yan et al. (2018) 50 economies Panel data analysis ICT had a significant positive effect on energy 
productivity

Gelenbe and 
Caseau (2015)

World-wide Panel data analysis ICT had a deleterious impact on energy consumption 
and carbon emissions

Afzal and Gow (2016) Emerging economies Dynamic panel 
data analysis and 
system GMM

ICT as measured by mobile phones, internet 
connections and import percentage of ICT goods 
of total imports was found to have had a significant 
positive effect on electricity consumption in emerging 
economies studied

Wang and Han (2016) China Panel ECM ICT reduced energy intensity in the long run in China
Shahbaz et al. (2014) United Arab Emirates VECM The non-linear relationship between ICT and 

electricity consumption was found to be an inverted 
U-shape

Inani and 
Tripathi (2017)

India VECM and ARDL The relationship between ICT and electricity 
consumption was found to be non-existent in India 
both in the short and long run

Solarin et al. (2019) Malaysia Toda-Yamamoto 
granger causality 
approach

A feedback effect between ICT and electricity 
consumption was observed

Collard et al. (2005) France VECM The use of computers and software led to the increase 
in electricity consumption in France

Pothitou et al. (2017) European Union Descriptive 
statistics

ICT led to an increase in electricity consumption

Horner et al. (2016) World-wide Literature review ICT was found to have had a negative impact on 
energy consumption

Schulte et al. (2016) OECD countries Difference-GMM ICT was found to have a reduction effect on energy 
demand in the OECD countries

Pano (2017) Albania Descriptive 
statistics

The study found out that ICT reduced energy usage in 
Albania

Choo et al. (2007) South Korea Descriptive 
statistics

ICT investment in the manufacturing sector increased 
electricity consumption whilst ICT investment in the 
services sector was found to have had a deleterious 
impact on electricity consumption

Bernstein and 
Madlener (2010)

European union countries (UK, Sweden, 
Slovenia, Portugal, Italy, Germany, 
Finland, Denmark)

Panel econometric 
approach

ICT was found to have had an electricity consumption 
reduction effect in the sectors studied

Source: Author compilation. ICT: Information and communication technology, ECM: Error correction model, ARDL: Autoregressive distributed lag, VECM: Vector error correction 
model, GMM: Generalized methods of moments



Chimbo: Information and Communication Technology and Electricity Consumption in Transitional Economies

International Journal of Energy Economics and Policy | Vol 10 • Issue 3 • 2020 299

Table 2: Variables, a priori expectation and theory intuition
Variable Proxy used Theory intuition Expected sign
Lag of electricity consumption Electric power 

consumption (kWh 
per capita)

Consistent with Nayan et al. (2013), the electricity consumption 
level follows a similar pattern of the previous electricity 
consumption period. In other words, the electricity consumption 
interdepends on each other across periods

+

Economic growth (GROWTH) GDP per capita Aye and Edoja (2017) noted that higher levels of economic 
growth increases the number of economic activities which uses 
a lot of electricity

+

Urbanization (URBAN) Urban population 
(% of total)

The expansion of urban areas is associated with increased 
activities such as construction and the maintenance of roads and 
other related infrastructure, all of which leads to more electricity 
consumption (Zhao and Zhang, 2018; Sadorsky, 2014). On the 
contrary, Ye et al. (2013) revealed that urbanization is associated 
with high levels of technological advances which could lead to 
more energy use efficiency and lower electricity consumption

±

Access to electricity (ACCESS) Access to 
electricity (% of 
population)

The author is of the view that more access to electricity reduces 
the cost per unit of electricity and consequently increases the 
overall quantity of electricity usage in the economy

±

Resource endowment (RESOURCE) Total natural 
resources rents (% 
of GDP)

Consistent with Kwakwa et al. (2018), heavy machinery which 
requires the use of more electricity energy is employed in the 
process of extracting natural resources. However, a country uses 
other forms of energy apart from electricity if it is endowed 
with diverse type of natural resources

±

Foreign direct investment Net FDI inflows 
(% of GDP)

FDI increases the number and level of manufacturing 
activities in the economy which require the use of more 
electricity (Blanco et al., 2013). However, Cheng and 
Yang (2016) observed that foreign investors bring the host 
country some advanced and smart technology which is energy 
use efficient

±

Financial development Stock market 
capitalisation (% 
of GDP)

Sadorsky (2010) argued that financial sector development 
enables consumers and firms to borrow money in order to 
purchase more electricity consuming items such refrigerators, 
houses washing machines, among others. On the other hand, the 
author is of the view that higher levels of financial development 
allows domestic firms and individuals to borrow money and 
invest in state of the art and electricity saving gadgets

±

Trade openness (OPEN) Total trade (% of 
GDP)

Consistent with Tsaurai (2019a), trade openness multiplies the 
number of energy use linked manufacturing activities in the 
economy. Grossman and Krueger (1991) however noted that 
trade openness allows companies to import new technology that 
is energy use efficient

±

Human capital development Human capital 
development index

A study by Inglesi-Lotz and Morales (2017) noted that higher 
levels of education had a significant positive impact on energy 
consumption in developing countries

+

Source: Author compilation

Table 3: Correlation analysis
ELECTR ICT GDPPC URBAN ACCESS RESOURCE FDI FIN OPEN HCD

ELECTR 1.00
ICT 0.61*** 1.00
GDPPC 0.78*** 0.68*** 1.00
URBAN 0.49*** 0.43*** 0.61*** 1.00
ACCESS −0.05 0.03 0.12** 0.36*** 1.00
RESOURCE −0.17*** −0.07 −0.36*** −0.06 0.16*** 1.00
FDI 0.39*** 0.35*** 0.63*** 0.50*** 0.23*** −0.19*** 1.00
FIN 0.33*** 0.32*** 0.50*** 0.36*** 0.08* −0.10** 0.79*** 1.00
OPEN 0.54*** 0.40*** 0.70*** 0.46*** 0.18*** −0.19*** 0.81*** 0.72*** 1.00
HCD 0.68*** 0.46*** 0.67*** 0.61*** 0.13*** −0.33*** 0.36*** 0.23*** 0.44*** 1.00
Source: Author’s compilation from E-views. ***, **and *denote 1%, 5% and 10% levels of significance, respectively

does not exist, consistent with Tsaurai (2019b. p. 171) because 
the maximum absolute correlation value is 81% (between trade 
openness and FDI). This is understandable because both FDI and 
trade openness are measure of how open an economy is to the 
outside world.

The probabilities of the Jarque-Bera criterion equal to zero across 
all the variables studied, an indication that the data is not normally 
distributed (Odhiambo, 2008; Tsaurai and Ndou, 2019). Standard 
deviation values (>1000) show that electricity consumption and 
economic growth (GDPPC) data has abnormal values (Table 4). 



Chimbo: Information and Communication Technology and Electricity Consumption in Transitional Economies

International Journal of Energy Economics and Policy | Vol 10 • Issue 3 • 2020300

Abel and Le Roux (2016) transformed all the data sets into natural 
logarithms in order to effectively dealt with the issues of abnormal 
values and data that is not following a normal distribution. The 
current study used the same approach.

5. RESULTS AND DISCUSSION

Panel unit root tests (results presented in Table 5) show that the 
variables are integrated of order 1 (Odhiambo, 2008).

Table 6 shows that the null hypothesis which says that there is no 
long run relationship between and among the variables studied 
is rejected, thus paving way for main data analysis whose results 
are presented in Table 7.

The lag of electricity consumption had a significant positive 
impact on electricity consumption under the dynamic GMM 
approach, consistent with Nayan et al. (2013) whose study 
noted that electricity consumption level follows a similar 
pattern of the previous electricity consumption period. Under 
the dynamic GMM and pooled OLS, a non-significant negative 
relationship running from ICT towards electricity consumption 
was observed, in line with the optimistic view propagated by 
Lee and Brahmasrene (2014) which argues that the use of ICT 
gadgets reduces energy consumption. The finding also resonates 
with other related prior empirical studies (Han et al., 2016; Horner 
et al., 2016; Gelenbe and Caseau, 2015). On the other hand, ICT 
was found to have had a significant positive effect on electricity 
consumption under both the fixed and random effects methods, 

a finding which supports Zhang and Liu’s (2015) view that ICT 
leads to increased electricity consumption because it triggers the 
use of more electrical gadgets.

Under the dynamic GMM approach, economic growth had a 
non-significant positive influence on electricity consumption 
whilst pooled OLS, fixed and random effects shows a significant 
positive relationship running from economic growth towards 
electricity consumption. The results resonate with Aye and Edoja 
(2017) whose study revealed that increased levels of economic 
growth boost the magnitude of economic activities which rely 
more on electricity consumption. Whilst urbanization had a 
significant negative impact on electrical consumption under the 
dynamic GMM, pooled OLS shows a non-significant negative 
relationship running from urbanization towards electricity 
consumption. The results support Ye et al. (2013) whose study 
argued that urbanization triggers the use of more technologically 
advanced equipment and machinery that is more energy efficient. 
In line with Sadorsky (2014) whose study argued that urbanization 
expands economic activities that uses a lot of electricity such as 
construction and infrastructure maintenance, the current study 
observed that urbanization had a significant positive effect on 
electricity consumption under both fixed and random effects.

Table 4: Descriptive statistics
Descriptive statistics ELECTR ICT GDPPC URBAN ACCESS RESOURCE FDI FIN OPEN HCD
Mean 3274.7 26.3 9796.6 66.2 89.4 3.64 4.12 88.4 94.4 0.77
Median 2702.6 18.1 6239.9 70.5 97.5 2.17 2.56 39.5 58.0 0.77
Maximum 10497 90.4 56284 100.0 100.0 21.7 39.9 1254 455.3 0.94
Minimum 263.6 0.00 381.5 26.6 2.98 0.0003 0.03 3.27 15.64 0.48
Std. Dev. 2361 25.07 9940.4 19.01 19.0 4.29 5.85 160.8 95.9 0.09
Skewness 0.71 0.72 1.82 −0.13 −2.68 1.62 3.56 4.93 2.28 −0.41
Kurtosis 2.77 2.28 6.87 2.48 10.3 5.51 17.0 30.6 7.36 2.78
Jarque-Bera 36.6 45.3 493.1 5.8 1447 294.8 4309.3 14998.7 695.6 12.5
Probability 0.00 0.00 0.00 0.05 0.00 0.00 0.00 0.00 0.00 0.00
Observations 420 420 420 420 420 420 420 420 420 420
Source: Author’s compilation from E-views

Table 5: Panel stationarity tests –individual intercept
Variables Level First difference

LLC IPS ADF PP LLC IPS ADF PP
LELECTR −1.90** 2.19 38.82 103.31 −9.01*** −8.26*** 150.41*** 270.50***
LICT −18.48*** −16.58*** 412.15*** 1622.71*** −12.43*** −6.02*** 130.62*** 122.80***
LGDPPC 1.13 4.80 10.27 16.71 −6.96*** −5.32*** 98.90*** 145.50***
LURBAN −4.43*** 1.43 40.42 74.18*** −3.14*** −5.95*** 103.91*** 473.57***
LACCESS −4.29*** −1.00 38.77* 89.13*** −7.31*** −12.64*** 193.47*** 810.23***
LRESOURCE −2.35*** −0.33 36.92 44.10 −12.67*** −10.63*** 186.38*** 290.29***
LFDI −6.02*** −5.65*** 105.42*** 152.99*** −11.65*** −13.47*** 236.69*** 1565.71***
LFIN −4.74*** −3.48*** 87.67*** 111.69*** −15.87*** −15.40*** 273.93*** 751.67***
LOPEN −2.53*** 0.30 36.69 39.49 −15.87*** −15.40*** 273.93*** 751.67***
LHCD −10.40*** −7.25*** 128.39*** 180.02*** −17.77*** −15.71*** 276.20*** 2596.02***
Source: Author’s compilation from E-views. LLC, IPS, ADF and PP stands for Levin et al. (2002); Im et al. (2003); ADF fisher Chi-square and PP fisher Chi-square tests respectively.  
*, **and *** denote 1%, 5% and 10% levels of significance, respectively

Table 6: Kao residual co-integration test - individual 
intercept
Statistical description T-statistic Probability
Augmented Dickey-Fuller −2.127601 0.0167
Source: Author’s compilation from E-views



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International Journal of Energy Economics and Policy | Vol 10 • Issue 3 • 2020 301

Access to electricity was found to have had a significant negative 
impact on electricity consumption under the dynamic GMM 
and pooled OLS. On the other hand, a significant positive 
impact of access to electricity on electricity consumption was 
detected under the fixed and random effects methods. Resource 
endowment was found to have had a non-significant impact on 
electricity consumption under the dynamic GMM, fixed and 
random effects whilst pooled OLS shows a significant positive 
relationship running from resource endowment towards electricity 
consumption. The results generally resonate with Kwakwa et al. 
(2018) whose study argued that heavy machinery required to 
extract natural resources uses a lot of electricity energy.

The dynamic GMM shows that FDI had a non-significant positive 
effect on electricity consumption, in line with theoretical literature 
(Blanco et al., 2013). Pooled OLS shows a significant negative 
relationship running from FDI towards electricity consumption 
whilst fixed and random effects show that the impact of FDI on 
electricity consumption was negative but non-significant. The 
results imply that FDI reduced the levels of electricity consumption 
in line with Cheng and Yang’s (2016) observation that foreign 
investors bring into the host country some advanced and smart 
technology which is energy efficient. A significant positive impact 
of financial development on electricity consumption was observed 
under the dynamic GMM yet pooled OLS shows that financial 
development had a non-significant positive effect on electricity 
consumption, findings which resonate with Sadorsky (2010) whose 
study noted that consumers and are able to purchase high electricity 
usage equipment through borrowing from financial markets if they 
are developed. On the contrary, fixed and random effects show that 
the impact of financial development on electricity consumption 
was negative and significant, in line with an argument that says 
developed financial markets enable firms and consumers to borrow 
money in order to purchase advanced technology which overall 
contributes to a reduction in energy consumption levels.

Under the dynamic GMM, trade openness had a non-significant 
negative effect on electricity consumption, in support of Grossman 
and Krueger’s (1991) argument. Pooled OLS show a significant 
positive relationship running from trade openness towards electricity 

consumption yet trade openness was found to have had a non-
significant positive impact on electricity consumption under the fixed 
and random effects, results which resonate with Tsaurai (2019a) whose 
study noted that the number of energy usage linked manufacturing 
activities multiplies if trade openness of a country is high. Last but not 
least, human capital development was found to have had a significant 
positive effect on electricity consumption across all the panel data 
analysis methods used, in support of Inglesi-Lotz and Morales’s 
(2017) findings in the case of developing countries.

6. CONCLUSION

The study investigated the impact of ICT on electricity consumption 
in transitional economies using panel data analysis methods 
(dynamic GMM, pooled OLS, fixed effects, random effects) with 
annual secondary data ranging from 1995 to 2014. Majority of prior 
studies on the subject matter had not focused on the impact of ICT 
on electricity consumption but on energy consumption, which is 
a broader area. They also did not focus exclusively on transitional 
economies and they ignored both the dynamic characteristics of 
electricity consumption data and endogeneity issues.

The study revealed that electricity consumption is positively and 
significantly influenced by its own lag, in line with theoretical 
literature (Nayan et al., 2013). However, the impact of ICT on 
electricity consumption was found to be mixed. For example, 
the influence of ICT on electricity consumption was found to 
be negative and non-significant under the dynamic GMM and 
pooled OLS. Fixed and random effects observed that ICT had a 
significant positive impact on electricity consumption in emerging 
markets. It is against this backdrop that the current study urges 
transitional economies to develop and implement policies that 
ensures that ICT gadgets being used reduces the quantity of 
electricity consumption. In other words, transitional economies 
should focus on developing or importing energy efficient ICT 
gadgets in order to meet the required energy saving threshold 
levels. Future studies should investigate channels through which 
ICT influences electricity consumption, in line with Shahbaz et al. 
(2014) whose study noted that the relationship between ICT and 
electricity consumption is non-linear.

Table 7: Panel data analysis results
Variables Dynamic GMM Pooled OLS Fixed effects Random 

effects
ELECTRi, t−1 0.9822*** - - -
ICT −0.0018 −0.0265 0.0313*** 0.0314***
GDPPC 0.0079 0.6771*** 0.1556*** 0.1615***
URBAN −0.0299*** −0.1150 1.3181*** 1.2902***
ACCESS −0.0158*** −0.1357** 0.2982*** 0.2894***
RESOURCE 0.0018 0.0586*** 0.0052 0.0002
FDI 0.0007 −0.0680** −0.0068 −0.0073
FIN 0.0048* 0.0489 −0.0167* −0.0167*
OPEN −0.0007 0.1929*** 0.0231 0.0374
HCD 0.0784*** 1.3530*** 0.2555*** 0.2583***
Number of countries 21 21 21 21
Number of observations 420 420 420 420
Adjusted R-squared 0.8123 0.7420 0.6514 0.8684
F-statistic J-static=409.00 134.92 1829.43 308.13
Prob (F-statistic) Prob (J-statistic)=0.00 0.00 0.00 0.00
Source: Author’s compilation from E-views. ***, **and * denote 1%, 5% and 10% levels of significance, respectively



Chimbo: Information and Communication Technology and Electricity Consumption in Transitional Economies

International Journal of Energy Economics and Policy | Vol 10 • Issue 3 • 2020302

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