- Email: [email protected]
An investigation of renewable and non-renewable energy consumption and economic growth nexus using industrial and residential energy consumption
An investigation of Renewable and Non-renewable Energy Consumption and Economic Growth Nexus using Industrial and Residential Energy Consumption Seema Narayan, Nadia Doytch PII: DOI: Reference:
S0140-9883(17)30308-0 doi:10.1016/j.eneco.2017.09.005 ENEECO 3753
To appear in:
Energy Economics
Received date: Revised date: Accepted date:
4 September 2016 6 September 2017 11 September 2017
Please cite this article as: Narayan, Seema, Doytch, Nadia, An investigation of Renewable and Non-renewable Energy Consumption and Economic Growth Nexus using Industrial and Residential Energy Consumption, Energy Economics (2017), doi:10.1016/j.eneco.2017.09.005
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT An investigation of Renewable and Non-renewable Energy Consumption and Economic Growth Nexus using Industrial and Residential Energy Consumption
PT
Seema Narayana* and Nadia Doytchb
NU
Corresponding author Phone: +61 3 99255890; Email: [email protected]
MA
*
SC
Koppelman School of Business, CUNY-Brooklyn College b
RI
School of Economics, Finance and Marketing, RMIT University a
Abstract
TE
D
Our study shows that links between economic growth and energy consumption (both expressed in per capita terms) differed for renewables and non-renewables for income panels over the
AC CE P
period 1971 to 2011. Renewables are mainly found to support the neutrality hypothesis. Only renewable totals in low and lower middle income (LLMI) countries are found to drive economic growth. The feedback, growth and conservative hypotheses strongly feature with non-renewables (total and industrial). Our results are derived by linking different definitions of energy consumption with economic growth for 89 countries divided into LLMI; upper middle income (UMI); and high income (HI) panels. JEL: Q42, Q43 Keywords: Residential energy consumption; Industrial energy consumption; Nonrenewables; Renewables; Income groups
1. Introduction
1
ACCEPTED MANUSCRIPT The study of the economic growth and energy consumption nexus allows for testing of four hypotheses with strong implications for growth and energy policy: (1) the feedback hypothesis (which indicates a bidirectional causation between the two variables); (2) the growth hypothesis
PT
(or a unidirectional causation flowing from energy to economic growth); (3) the conservative
RI
hypothesis (or a unidirectional causation flowing from economic growth to energy); or (4) the
SC
neutrality hypothesis (no causation). Hypothesis (2) advances the view that energy consumption promotes economic growth. It is also widely believed that demand for energy is propelled by
NU
economic growth, giving rise to hypothesis (3). The complementarity between energy and economic growth, as portrayed in (2) and (3), has given rise to hypothesis (1). Hypotheses (1)
MA
and (2) posit a connection between energy policy and economic growth. Acceptance of these hypotheses discourages restrictive energy policies, but calls for policies that promote energy
TE
D
efficiency. The prevalence of hypotheses (3) or (4) emphasises a disconnection between energy
2016).1
AC CE P
policies and economic growth and gives a green light to energy conservation policies (Narayan,
While a plethora of studies examine this nexus for total energy consumption, there is an emerging strand that examines the four hypotheses explicitly for total renewables and/or nonrenewables for one or more individual countries, or a panel of countries, by their level of economic development or region (see, Tiba et al., 2016; Bhattacharya et al., 2016; Chang et al., 2015; Salim, et al., 2014; Pao et al., 2014; Marques and Fuinhas, 2012; Apergis and Payne 2011a; Aperis and Payne, 2011b; Apergis and Payne 2010a; Apergis et al. 2010; and Sadorsky, 2009). Some also examine the traditional energy consumption and economic growth nexus for different energy mixes of renewables and non-renewables (see Bloch et al., 2015; Long et al.,
1
For further details and an exhaustive survey of this literature see Tiba and Omri (2017) and Payne (2010).
2
ACCEPTED MANUSCRIPT 2015; Pao and Fu, 2013; Marques and Fuinhas, 2012; and Yildirim et al., 2012). We summarise these studies in Table 1.
PT
By mainly using cointegration/VECM/VAR/Granger causality tests for developing and developed nations, this group of studies clarifies policy directions along the lines of long-term
RI
growth and energy by renewable and non-renewable energy. While this literature shows
SC
evidence that is mixed, for both renewables and non-renewables, majority tend to find support
NU
for the feedback hypothesis (see Table 1).
MA
Of the 29 previous studies surveyed, for renewables, the majority (55%) show evidence
D
in support of the feedback effect in the long run (Apergis and Payne 2010a and b, 2011a and b;
TE
Apergis et al., 2010; Apergis and Payne, 2012; Apergis and Danuletiu, 2014; Chang et al., 2015; Salim et al. 2014; Tiba et al., 2014; Tugcu et al., 2012; Pao and Fu, 2013; Lin and Moubarak,
AC CE P
2014; Sebri and Ben-Salha, 2014; Bloch et al., 2015; Omri et al., 2015). Only nine out of 31 studies consider short-term causality and all nine show evidence in favour of the feedback effect in the short-run (Sadorsky, 2009; Apergis and Payne 2010a and b, 2011a and b; Apergis and Payne, 2012; Pao et al., 2014; Sebri and Ben-Salha, 2014; and Shahbaz et al., 2015). Evidence that renewable energy leads economic growth is found in 21% of the studies in the long-run (Pao et al., 2014; Bhattacharya et al., 2016; Yildirim et al., 2012; Chang et al., 2015; Shahbaz et al., 2015; Omri et al., 2015). Evidence that economic growth causes higher demand for renewable energy is found in 14% of the studies (Sadorsky, 2009; Menyah and Wolde-Rufael, 2010; Long et al., 2015; Omri et al., 2015). Neutrality between renewable energy consumption and economic growth is supported in 10% of the studies (Marques and Fuinhas, 2012; Pao and Fu, 2015; Chang et al., 2015).
3
ACCEPTED MANUSCRIPT Of the 12 studies that examine the link between non-renewable energy consumption and economic growth, majority (67%) show the same feedback effect in the long run (Apergis and Payne, 2011a; Pao et al., 2014; Salim et al. 2014; Pao and Fu, 2013; Pao and Fu, 2015; Shahbaz
PT
et al., 2015; Bloch et al., 2015; Long et al., 2015). The evidence of the growth hypothesis is
RI
prevalent in 25% of the studies (Tugcu et al., 2012; Bhattacharya et al., 2016) in the long run and
SC
one study, Pao et al., 2014, in the short run. Evidence of the conservative hypothesis is not found, but the neutrality hypothesis is found in Tugcu et al. (2012).
NU
In this study, we re-examine the four hypotheses outlined above for renewable and non-
MA
renewable energy consumption per capita.2 The point of difference between this study and the extant literature is that we distinguish between residential and industrial users of renewable and
D
non-renewable energy. This distinction recognises the varying energy source mix which we
TE
explain below. We find that the disparity in the energy type mix between the two user groups
AC CE P
becomes more apparent when we classify the users of energy by their level of development. It can therefore be argued that the link between energy and economic growth will depend on the level of development, the users, and the energy mix. The four hypotheses are tested for countries categorised into four income groups: high income (HI), upper middle income (UMI), and low and lower middle income (LLMI). Several of the abovementioned panel-based studies examine the four hypotheses on the basis of the level of development of the country, although most of these studies only examine a developed country panel (Apergis and Payne 2010a; Salim, et al, 2014; and Chang et al, 2015) or developed and developing panels (see Apergis and Payne 2011a; Apergis et al. 2010; and Omri et al, 2015). Tiba et al. (2016) is the only study that examines the energy-growth nexus for two out of four
2
Energy consumption and economic growth are examined in per capita terms, although we mostly refer these simply as energy consumption or economic growth.
4
ACCEPTED MANUSCRIPT income groups (see Table 3 below) normally used to classify countries, i.e. high income and middle income countries. Such a division is important as across these income groups the nature of changes occurring with respect to the use of renewables and non-renewables, is significantly
PT
different. The International Energy Agency (IEA, 2015) notes that in the United States and the
RI
European Union, expansion of renewable energy occurs largely at the expense of aging fossil-
SC
fuelled capacity. In China, India, and many other developing countries, expansion of renewable capacity goes hand-in-hand with efforts to expand energy supply from other sources, to keep up
NU
with rapidly increasing demand.
MA
So far, only one study has shed some light on the association between economic growth and renewable and non-renewable industrial energy consumption (see Doytch and Narayan,
D
2016).3 Doytch and Narayan (2016) examine the one-way link going from economic growth to
TE
total industrial energy consumption for high, middle and low and lower middle income groups –
AC CE P
in effect only testing the conservative hypothesis. Their GMM estimations, which also factor in for sectoral FDI, suggest that real GDP per capita growth positively and significantly influences industrial energy consumption (renewable and non-renewable). Our study follows the same testing procedure, but focuses on the bilateral relationship for this category of energy consumption (industrial) as well as residential renewables and non-renewables. In doing so, our study is one of the few to explore the short-term association between renewable (and nonrenewable) energy consumption and economic growth. The link between industrial energy consumption and economic growth is one that can be contemplated without much difficulty using the growth theory. For instance, industrial energy use by both labour and capital produces goods and services. However, the theoretical
3
See footnote 4 for a study that uses industrial energy consumption without differentiating between renewables and non-renewables.
5
ACCEPTED MANUSCRIPT underpinning for the relationship between residential energy consumption and economic growth is unknown. Empirical evidence on hypotheses (1) to (4) for total residential (and industrial) energy consumption is also rather thin. 4 Nonetheless residential energy users are seen by
PT
policymakers as key target groups for energy conservation policies. “Residential energy
RI
conservation is a key component of contemporary energy and climate change policy in the US
SC
and elsewhere” (Suter and Shammin, 2013; 551). Pablo-Romero et al. (2017; 342) in their article “Global changes in residential energy consumption” reinforce this view, “(t)he residential energy
NU
sector is crucial to achieving CO2 emission reductions as it has an important energy-saving
MA
potential and its environmental controls are difficult to displace to other countries”. On the basis of an analysis of the residential energy consumption trends for the period
D
1993-2013, Pablo-Romero et al. (2017) recommend different energy policies across regions.
TE
They suggest that East Asian, Southern Asian, EU15, and other developed countries should
AC CE P
reduce residential energy use. However, since energy is generally seen as a vital component of growth, it is important to identify whether or not conserving residential energy use hurts economic growth. Further, should this policy of conserving residential energy use be enforced on all countries? Knowledge about the link between residential energy and economic growth, or whether residential energy is a viable target for conservation, is lacking. Our study attempts to provide more evidence for understanding these important questions. We focus on the effects of residential energy on short-term economic growth for income group panels of countries. In this study, using data from 89 countries, we uncover additional compelling differences in the consumption patterns of residential and industrial – renewable and non-renewable – 4
Yang et al. (2010) is the only study that examines the link between energy and economic growth in Taiwan by distinguishing between industrial and residential energy consumption. Their linear Granger causality test shows that economic growth has a bidirectional link with industrial energy consumption but an insignificant relationship with residential energy consumption, hence implying a disconnection between energy conservation policy and economic growth.
6
ACCEPTED MANUSCRIPT energy consumption, by income group, over the period 1971 to 2011. We explain these in detail in Section 2 (also see Table 2). Here we briefly explain some of the patterns and trends relating to the energy mix of the income groups from our preliminary analysis to illustrate the importance
PT
of the energy mix, users and income groups in the examination of the linkage between energy
RI
consumption and economic growth.
SC
First, total industrial energy consumption takes a larger share of final energy consumption than total residential energy usage in high income and upper middle income
NU
countries (Table2, I7 and R7). Nonetheless, between these two groups, over the period 1971 to
MA
2011, the average growth of industrial share (of final consumption) has been falling in the high income countries, but increasing strongly in the case of the upper middle income countries
D
(Table 2, I10). Of the low and lower middle income countries, industrial share of final
TE
consumption has been on average increasing (at a slower rate than the upper middle income) and
AC CE P
for this income group (unlike other groups), on average residential energy users are bigger than industrial energy users (Table 2, I10). From these preliminary observations, given the difference in the key energy users by income group, we expect that: (a) residential energy consumption is a significant contributor to economic growth in the low and lower middle income countries and it seems that for this set of countries conservation of energy will hurt economic growth; and (b) industrial energy consumption is a significant contributor to economic growth in upper middle income countries, and possibly the high income group as well. Second, in terms of the distribution of renewable and non-renewable, we notice that, across all income panels, residential users are more important than industrial users for renewable energy; and for non-renewable energy, industries are more substantial users than residential users. The residential renewable energy consumption share of total final renewable consumption is, on average, greater than the industrial renewable energy consumption share of total final 7
ACCEPTED MANUSCRIPT renewable energy consumption for the period 1971 to 2011 (Table 2, I9 and R9). However, the opposite is true for non-renewable consumption share of total final non-renewable consumption for all panels (Table 2, I8 and R8). From this observation we can state that: (c) residential
PT
renewable energy, not industrial renewable energy, is a driver of economic growth and/or vice
RI
versa. However, with biomass being the most dominant source of energy for residences in low
SC
income countries, the negative effects of biomass energy on health, environment, and productivity is also widely known and documented (see our discussion in Section 3.3). As a
NU
result, it is likely that: (d) for low and middle income countries the positive effects of residential
MA
renewable energy use on economic growth will be nullified, or will become negative.
D
Third, growth of residential renewable energy consumption share of total final
TE
renewable consumption averaged over 1971 to 2011 has been strongest in high income countries,
AC CE P
but growth of industrial renewable energy consumption share of total final renewable consumption is the strongest for the upper middle income countries (Table 2, I12, R12). Since several studies (mentioned above) suggest economic growth encourages demand for renewable energy consumption, it becomes important to investigate whether: (e) economic growth is a trigger for residential renewable energy consumption in high income countries; and (f) economic growth is a trigger of industrial renewable energy consumption in the upper middle income countries. Fourth, over the same period the average growth of renewable energy consumption is stronger for the residential sector than for the industrial sector in high income and low and lower middle income countries, suggesting higher adoption of renewables in the residential sector (see Table 1, I6 and R6). In the upper middle income countries, we observe the opposite effect, with
8
ACCEPTED MANUSCRIPT industries adopting renewable energy more than the residential sector. This preliminary finding matches with our statement in (a) that residential energy consumption may be seen as a
PT
contributor of economic growth in low and lower middle income countries. Overall, given the difference between the levels, proportions, and growth of the share of
RI
residential and. industrial renewables and non-renewables across the income groups, we believe
SC
that the short-run effects of economic growth on energy (and/or vice versa) – and therefore the policy implications – cannot be homogenous for different energy users and energy sources.
NU
Hence, when testing the four hypotheses related to the economic growth-energy consumption
MA
nexus, we apply the different energy categories and income panels mentioned previously. For completeness, we also examine energy consumption as total residential; total industrial; total
D
renewables; and total non-renewables. We apply the GMM approach as our main estimation
TE
method, but also provide results derived using the fixed effect method.
AC CE P
To foreshadow our key GMM results, our study shows significant differences in results amongst residential and industrial users by income panel. In fact we find that the effect of economic growth is multi-facetted. In other words the effects of economic growth are felt by more than one energy user group, although not all user groups contribute to economic growth. Our findings suggest the presence of a feedback effect, with total non-renewable for both the low and lower middle income, and upper middle income panels. Interestingly, for the upper middle income panel, economic growth is also found to positively impact on non-renewable industrial energy. However, this panel’s non-renewable industrial energy consumption is not a significant driver of growth in the short-term. The high income panel also shows feedback for total non-renewable after allowing for investment. Further we find evidence of feedback for non-renewable industrial energy of the 9
ACCEPTED MANUSCRIPT high income, as well as the low and lower middle income group, implying that non-renewable industrial energy consumption is the significant source of the feedback effect for total non-
PT
renewable energy use. Further, total residential energy consumption encourages economic growth in low and
RI
lower middle income (LLMI) countries. Additionally, economic growth in the low and lower
SC
middle income panel boosts total industrial, non-renewable residential and total renewable energy consumption , a sign of the multi-facet effect of economic growth in the case of LLMI
NU
countries.
MA
The models with economic growth-energy consumption variables find neutrality for renewable industrial and residential (for all panels). The neutrality hypothesis is also present
TE
D
with renewables (except LLMI); total residential (UMI, HI) and industrial (HI). We augmented the per capita based models with investment, following Apergis and
AC CE P
Payne (2010a, 2010b, 2011a, 2011b). From this exercise, we find that most of our results are robust. After accounting for capital investment, we were able to find the presence of feedback for high income total non-renewables. We are also able to show feedback in non-renewable industrial use for the LLMI group. However, non-renewable residential use for low and lower middle income countries, which originally supported the conservative hypothesis, moves to neutrality after capital investment is captured. The balance of the paper is organised as follows. Section 2 continues the discussion of the average trend of renewable and non-renewable – residential and industrial – energy consumption, in terms of levels, share, and growth. These are explained by paying attention to all 89 nations and each of the three income groups studied here. Section 3 presents different case studies on groups (EU, developing countries) or specific countries (the US, India) to explain 10
ACCEPTED MANUSCRIPT some of the trends that were presented in the previous section. This section explains the composition of residential and industrial energy consumption as well as some of the social and economic issues relating to renewable and non-renewable energy consumption by residential and
PT
industrial users. Section 4 lays out the methodology applied in the paper to test the hypotheses.
RI
Section 5 presents the results. We also provide a summary of the results. Section 6 provides the
NU
SC
concluding remarks.
2. Residential and industrial energy consumption (renewable and non-renewable) by
MA
income group
D
The average industrial and residential energy consumption, in terms of total, non-renewable, and
TE
renewable, for the period 1971 to 2011 is presented in Table 2 in levels, shares, and growth terms according to three income groups: low and lower middle income, upper middle income, and high
AC CE P
income countries.
Table 2 displays some of the facts which are readily known about energy consumption across income groups. For instance, high income countries are the largest consumers of industrial and residential energy. It is also known that high density industrial sectors in upper middle income groups are the second biggest users. And with relatively lower industrial density, the low and lower middle income groups have the least amount of industrial energy consumption.5 What is probably less known is that this pattern is also prevalent in the case of non-renewable energy consumption – for industrial and residential – but is not the case for renewables.
5
These facts are based on a sample of nations chosen on the basis of data availability. These statistics indicate that each group is sufficiently represented and hence the other features of the energy mix representative of the income groups.
11
ACCEPTED MANUSCRIPT There are many interesting facts that can be observed from Table 2. First, total industrial energy consumption per capita is greater than those of residential consumption for the upper middle income and high income groups (see Table 2, rows I1 and R1). However, in the case of
PT
the low and lower middle income group, residential energy consumption is far greater than
RI
industrial consumption. In terms of growth, residential energy consumption is higher than
SC
industrial for high income countries. This is consistent with industrial and residential energy consumption as a share of total final consumption for these nations (Table 2, rows I7 and R7).
NU
Second, in terms of growth of total industrial consumption, the upper middle income
MA
takes the lead followed by low and lower middle income countries and high income countries (see Table 2, row I7). The pattern is different for growth of non-renewable industrial energy –
D
the low and lower middle income group shows the strongest average growth rates, followed by
TE
the upper middle income countries (Table 2, row I8). The high income countries show a negative
AC CE P
growth rate for non-renewable energy. While this is being compensated for by renewable energy, row I9 of Table 2 also reveals that growth rates for renewable energy consumption by industries in high income countries has in fact, on average, been lower than those of upper middle income countries (but higher than the lower income countries). Table 2 (row I11) reveals that the share of non-renewable energy consumption by industry to total non-renewable energy consumption is indeed declining for high income countries. This trend is also present in the upper middle income countries. At the same time, the share of industrial renewable energy to total renewable energy consumption is increasing for both these panels, although growth is much higher for upper middle income countries. Further, in the total renewable energy use by industries, the high income panel takes the lead, followed by the other two income groups (see Table 2, R3). Together these indicate that efficient use of
12
ACCEPTED MANUSCRIPT energy and adoption of renewable energy is mainly driving the negative growth for nonrenewable energy use in our panels of high income countries and upper middle income countries.
PT
Third, for residential energy consumption, the narrative is different (from industrial energy consumption) across income groups and energy mix. While total energy consumption by
RI
the residential sector is indeed highest in the high income countries and lowest in the low and
SC
lower middle income countries, the share of residential energy consumption to final consumption
NU
is greatest for the low and lower middle income group.
Total renewable energy consumption by residents is, on average, greatest for low and
MA
lower middle income countries, with the upper middle income and high income countries lagging behind (see Table 2, row R3). Similarly, the average share of renewable residential energy
D
consumption to total renewables is strongest for our low and lower middle income group and
TE
weakest for the high income group. However, the mean growth of this share is highest among the
AC CE P
high income nations and lowest among poor nations. As well, the average share of nonrenewable residential energy consumption to total non-renewables is highest for the high income group and lowest for the low and lower middle income group. But the mean growth of this share is highest among the low and lower middle income countries and lowest among rich nations. Fourth, in terms of growth, residential energy is strongest in the high income group, followed by upper middle income and low and lower middle income groups (see row Table 2, row R9). Non-renewable energy consumption, on the other hand, grew fastest in the low and lower middle income countries – the energy deficit countries (see Table 2, row R8). This was followed by upper middle income and high income countries. Fifth, if we compare renewable and non-renewable energy consumption between industrial and residential users, we find that for all three income panels the share of growth in 13
ACCEPTED MANUSCRIPT non-renewable energy consumption is higher in residential than industrial (Table 2, rows I8 and R8). However, growth in renewable energy consumption by residence is much stronger in high income and low and lower middle income countries (Table 2, I9 and R9). In the upper middle
PT
income countries, we observe the opposite effect – industries are adopting more renewable
RI
energy than residences.
SC
Sixth, we find that over the study period, mean renewable energy consumption total as a percentage of total final consumption is highest for the low income countries. This is followed by
NU
middle income countries, and finally high income countries (Figure 1). This is same for
MA
renewable industrial (or residential) energy consumption as a percentage of total industrial (or residential) energy consumption (Figures 2 and 3). We observe a recent resurgence of renewable
D
energy use in LLMI countries mainly due to residential energy consumption.6
TE
3. Residential and industrial energy consumption (renewable and non-renewable)
AC CE P
In this section, we look behind the trends described in the previous section. 3.1 Renewable energy - residential and industrial Our preliminary analysis revealed an increase in the share of renewable energy for high income and upper middle income countries. There is strong evidence that the share of renewable energy has been increasing for the European Union (EU), which comprises high and upper middle income nations, reaching 16% in gross final energy consumption, and is on track to meet its target, a 20% share of renewable energy in its gross final energy consumption by 2020 (European Environment Agency, EEA, 2015). Between 2005 and 2013, renewable energy use grew by an average 0.8% every year.
6
We thank an anonymous referee for suggesting the addition of the three figures.
14
ACCEPTED MANUSCRIPT In the EU countries, residences use renewable energy in power generation in the heating and cooling sector. The renewable energy share in electricity consumed in the EU grew at an average of 1.3% per year between 2005 and 2013. In 2013, in the EU 25.4% of electricity
PT
consumed was generated from renewables, with around 38.0% contributed by variable renewable
RI
electricity (wind and solar) (EEA, 2015). Bioenergy (for electricity and heating) make the largest
SC
source of renewable energy in the EU, followed by hydropower, and then wind power. In the EU, renewable energy support comes in the form of surcharges on the energy bill
NU
of the end users (this prevails in the case of electricity) and governmental support. Countries with
MA
a higher effective tax rate on carbon dioxide generally have a higher rate of patent applications in renewable technologies. The 2014 EEA report identifies that in 2012, 32 countries have a total of
D
236 support measures associated with renewables, which represents 40.5% of the identified
AC CE P
fuels and nuclear energy
TE
support measures. The rest of the energy support measures (59.5%) were in support of fossil
Innovations in, and deployment of, renewable energy may be viewed as an important driver in shaping the future of our economic systems (EEA, 2014). In Europe, the renewable energy industry provides more than 2.2 million full-time equivalent jobs (EEA, 2014). There is evidence to suggest that investment in renewables is also growing in nonEuropean countries. The International Energy Agency (IEA, 2016) reports that global investment in renewable-based power generation was $270 billion in 2014, and positive policy moves in many countries are encouraging growth of the renewable sector. In many countries, renewable technologies are becoming increasingly cost-competitive, although public support schemes are still required for their deployment in many others. In 2014, renewable-based power generation capacity amounted to 45% of world power generation capacity additions and the International
15
ACCEPTED MANUSCRIPT Energy Agency (IEA, 2015) also reports that China (a lower middle income country) remains the largest wind power market, with increasing capacity (20 GW) more than the wind power market of Germany (5GW) and US (less than 5GW). Solar power has also expanded strongly in Asia,
PT
particularly in China and Japan. Nonetheless, the power sector continues to be the world’s
RI
dominant fossil-fuel consumer, accounting for 40% of the energy sector total in 2013 (IEA,
NU
SC
2013).
MA
3.2 Residential energy consumption in a high income country It was reported above that residential energy consumption per capita is the strongest in high
D
income countries. In the US, in 2012, the residential sector accounted for 21% of total primary
TE
energy consumption and about 20% of carbon dioxide emissions in the (Energy Information Administration (EIA), 2015). While household energy usage increased in the 1980-2009 period
AC CE P
by 8.9%- an average annual growth of 0.3%, aggregate energy intensity per household and per square foot declined by 24.2% and 43.1%, respectively (see EIA, 2015). In the US, single-family units form the major type of household. In 2009 single-family households in the US accounted for about 69.1% of the housing, 85.9% of floor space and 80.0% of site energy consumption (EIA, 2015). The rest was apartments (24.8% of total housing) and mobile homes (6.1%). There is, however, a movement from detached single-family housing to attached ones in larger buildings, using less energy. The size of housing, on the other hand, has increased, on average, in the US. While this varies by the type of housing, single-family detached houses have grown faster in size than other types of housing. Nonetheless, they have used less energy on heating due to improved energy efficiency of heating equipment, along with better window design and insulation to more effectively seal homes (EIA, 2013). 16
ACCEPTED MANUSCRIPT The US Energy Information Administration (EIA, 2015) reports that part of the decline in residential energy consumption, particularly space heating, is due to the household shifting from the Northeast and the Midwest to the warmer regions of the South and West, where demand is
PT
lower for space heating than for space cooling, and where energy consumption (per household
RI
and per square foot) is also lower.
SC
Appliances (dishwashers, clothes washers, clothes dryers, two or more TVs, computers, digital video recorders, and video game systems) and refrigeration account for the largest share
NU
of household electricity consumption. Other key end uses are space heating, water heating, and
MA
air conditioning. In fact an EIA report released in 2011 suggested that in US homes the share of residential electricity used by appliances and electronics has doubled in three decades. The
D
energy consumption share of space heating has fallen from 53.1% in 1993 to 41.5% in 2009;
TE
while the share of appliances, electronics and lighting has climbed from 24.0% to 34.9%
AC CE P
between 1993 and 2009, offsetting some of the efficiency gains (EIA, 2013). While natural gas remains as the main source of energy in the US over the 29 year period from 1980 to 2009, its share has fallen from 53.3% in 1980 to 46.1% in 2009, while the share of electricity increased from 26.6% to 43.1%. The fall in natural gas intensity was mainly a result of improvements in the efficiency of household space heating units and building codes as well as increases in natural gas prices (EIA, 2015:12). More expensive natural gas has led to its replacement with heating equipment using other fuels, as well as the purchase of gas heating equipment more efficient than the 78% Annual Fuel Utilization Efficiency (AFUE) standard – currently units are as high as 97% AFUE (EIA, 2015).7
7
A AFUE of 78% means that 22% of the heating gas was lost in the combustion process and did not contribute to warming homes.
17
ACCEPTED MANUSCRIPT According to the EIA (2015), the post-1990s period reflects the impact of many forms of energy efficiency programs, including the initial US Federal mandatory energy efficiency standards, enacted between 1988 and 1994, for all major appliances, demand-side management,
PT
and ENERGY STAR rating systems.
RI
3.3 Residential energy mix in India and other low and lower middle income nations
SC
Historically, in India the industrial sector was the highest electricity user, but the residential
NU
sector has become equal to, or slightly greater than, industrial demand. The EIA reports (EIA, 2014) that while electricity consumption is growing exponentially in India, the country’s
MA
electricity system is maturing, with difficulties (outages, shortages, issues with reliability and
D
quality) that are characteristic of a developing country.
TE
Like many aspects of a developing country, there are clear distinctions between urban and rural centres in energy consumption. In terms of energy mix, electricity is the least used
AC CE P
energy source (EIA, 2014). Indian urban centres use LPG most, followed by electricity, biomass (wood), and kerosene, while rural centres use wood, followed by kerosene, electricity, and LPG (EIA, 2014). Lighting is provided by both electricity and kerosene (EIA, 2014). Furthermore, in rural centres of developing countries in tropical/temperate zones (Asia and Africa), energy is mostly used for cooking. While a precise breakdown is difficult, the main use of energy in households in developing countries is for cooking, followed by heating and lighting (IMF, 2006). In its World Economic Outlook the IMF (2006) reported that 2.5 billion people, or 52 % of the populations in developing countries (mostly in rural centres) use biomass (wood, agricultural waste and animal dung) to meet their energy needs for cooking, accounting for over 90% of household energy consumption in many countries. Over half of these people live in India, China, and Indonesia. The proportion of the population relying on biomass is highest in 18
ACCEPTED MANUSCRIPT sub-Saharan Africa. The IMF (2006) report argues that unsustainable harvesting practices and inefficient energy conversion technologies have serious adverse consequences for health, the environment and economic development. Every year about 1.3 million people– mostly women
PT
and children – die prematurely because of exposure to indoor air pollution from biomass fuels.
RI
Valuable time and effort is devoted to fuel collection instead of education or income generation
SC
(IMF, 2006:416). Land degradation and regional air pollution can also result (World Energy Council, 1999).
NU
In contrast, in the urban centres, the trends are different. For instance, Zhao et al. (2012)
MA
examines China’s urban renewable energy consumption during the period 1998 to 2007 and finds evidence of an intensive structural change towards high quality and cleaner energy, such as
D
electricity, oil, and natural gas. The authors point out that this trend, “…reflects a changing
TE
lifestyle and consumption mode in pursuit of a higher level of comfort, convenience and
AC CE P
environmental protection” (2012; 644). 4. Model, methodology and data. 4.1 Methodology
To examine the four hypotheses associated with energy consumption and economic growth, our empirical models are:
ln( EnConsitk ) 0 1 ln( EnConsik,t 1 ) 2 (Growth _ yit ) 5 t i1 it1
(1)
ln( y it ) 0 1 ln( y i ,t 1 ) 2 (Growth _ EnCons itk ) 5 t i 2 it 2
(2)
with in ~ i.i.d (0, in ) , itn ~ i.i.d.(0, ), E[in itn ] 0, 𝑛 = 1,2. Here EnConsitk is a measure of energy consumption divided by the population; 𝑦𝑡 is the per capita GDP in constant 2005 19
ACCEPTED MANUSCRIPT prices, PPP; and is a time (annual) dummy and 𝜇𝑖 is an idiosyncratic country-specific effect. 8 t
Model (1) tests the conservative hypothesis while model (2) tests the growth hypothesis. If 2 in models (1) and (2) is found to be different from zero, then we accept the feedback hypothesis.
PT
However, insignificance of 2 in both models (1) and (2) suggests the prevalence of the
SC
RI
neutrality hypothesis.
We examine the two models for several measures of energy consumption. The subscript "k"
NU
[k=(1)...(8)] captures measures of different types of energy consumption: (1) total residential; (2) total industrial; (3) total non-renewables; (4) total renewables; (5) residential non-renewables; (6)
MA
industrial non-renewables; (7) residential renewables; and (8) industrial renewables.
D
System GMM is a method superior to fixed effects, when there is an endogeneity problem in
TE
the data. The correlation between lagged dependent variables and the unobserved residual is precisely the reason why panel data is to be preferred to cross-sectional when analysing change
AC CE P
in the dependent variable. Cross-section estimates produce a bias, caused by the correlation between EnCons
k
i ,t 1
and i1 in model 1 and yi ,t 1 and i 2 in model 2, which disappears in
samples with a large time-dimension, but does not disappear with time-averaging. Thus, if such a correlation exists, the true underlying structure has a dynamic nature and time-averaging crosssection techniques introduce a bias that cannot be removed by controlling for fixed-effects. Therefore, to avoid these pitfalls, we adopt the GMM methodology.
8
The model combines equations:
ln( EnCons itk ) 0 1 ln( EnCons ik,t 1 ) 2 (Growth _ y it ) 5 t u it1 , and
ln( yit ) 0 1 ln( yik,t 1 ) 2 ln(Growth _ EnConsitk ) 5 t i 2 uit 2 and
the unobserved country-specific effects and
itn
u1it = i1 it1 ,
and
uit 2 = i 2 it 2 where in are
are the errors, 𝑛 = 1,2.
20
ACCEPTED MANUSCRIPT A potential problem of the Arellano-Bond difference GMM estimator is that, under certain conditions, the variance of the estimates may increase asymptotically and create considerable bias if: (i) the dependent variable follows a random walk, which makes the first lag a poor
PT
instrument for its difference, (ii) the explanatory variables are persistent over time, which makes
RI
the lagged levels weak instruments for their differences, (iii) the time dimension of the sample is
SC
small (Alonso-Borrego and Arellano, 1999 and Blundell and Bond, 1998). These sets of conditions, therefore, apply: (i) no second order autocorrelation in the error
NU
term: E[Growth _ EnConsik,t s ( i1t i1,t 1 )] 0 ; E[Growth _ yi ,t s ( i 2t i 2,t 1 )] 0 ; for s
MA
≥ 2 and t=3,….T, where the growth rate of GDP and energy consumption, respectively, are instrumented with GMM-style instruments in each of the models and (ii) No correlation of the country-specific
D
unobserved
effect
with
their
difference
E[(Growth _ EnConsik,t 1 Growth _ EnConsik,t 2 )(i i1t )] 0
TE
;
AC CE P
E[(Growth _ yi ,t 1 Growth _ yi ,t 2 )(i i 2t )] 0 .
In addition to the GMM results, we also report robustness tests (see, Section 5.6 “Robustness tests”) with respective fixed effects models9.
4.2 Data The data are annual, multi-country (up to 89 countries) and span a forty-year period, from 1971 to 2011.10 Table 3 displays the list of countries in the sample under four categories: 1- low income; 2- lower middle income; 3- upper middle income; and 4- high income. This grouping is
9
In the recent literature there is a trend for dynamic panel studies utilising small cross-country samples to report simultaneously GMM and some static panel results as a robustness check (See, Bhattacharya et al., 2017 and Seven and Coskun, 2016). 10 The country panels are unbalanced.
21
ACCEPTED MANUSCRIPT based on the World Bank classification. In this study, we combine categories 1 and 2 as "low and lower middle income" countries. We use time-varying stratification of income level categories to
methodology of the World Bank.
RI
PT
capture shifts in development levels of countries 11 . The time-variation is also based on the
SC
According to the International Energy Agency, (IEA, 2013), the Residential and Industrial
NU
Energy breakdown is: Non-renewable Energy, including: coal, peat, crude oil and oil, natural gas; and Renewable Energy, including: nuclear, hydro, geothermal, solar, wind, and biofuel.
MA
Energy is measured in thousands of tonnes. The data source is the International Energy Agency
D
(IEA), World Energy Balances, Ed. 2013, extracted data set.12
TE
Real GDP per capita is measured in constant 2005 international dollars. We use the growth
(IEA).
AC CE P
rate of real GDP per capita. The source for this variable is the International Energy Agency
5. Empirical Results
In this section we highlight the empirical results in terms of the four different sets of measures on energy consumption. 5.1 Industrial and Residential total energy consumption and economic growth
11
For example, Albania transitioned from “lower middle income” to “upper middle income” in 2008 (please see Table 3). Thus, Albania was included as “lower middle income” for the period 1971 to 2007 and as “upper middle income” for the period 2008 to 2011. We thank an anonymous referee for indicating the need for this clarification. 12 The renewable and non-renewable energy consumption data are compiled from the proprietary data source "World Energy Balances", Edition 2013, available through subscription at International Energy Agency (IEA): http://www.iea.org/t&c/termsandconditions/. For more information on data definitions, please see Table 5.
22
ACCEPTED MANUSCRIPT Results relating to the nexus between industrial and residential total energy consumption and economic growth are presented in Tables 4 and 5, respectively. Panels 1 and 2 report estimated
PT
coefficients corresponding to equations 1 and 2. Economic growth has a positive and significant impact on industrial total energy
RI
consumption for all countries; the low and lower middle income; and upper middle income
SC
panels (Table 4, panel 1). Industries in low and middle income nations are increasingly becoming manufacturing based while high income countries’ key industries are service driven.
NU
Manufacturing demands more energy than service industries. Furthermore, high income
MA
countries are at the forefront when it comes to funding, developing, and using energy efficient technologies and appliances, which reduces energy intensities (see, for example, discussion in
D
case studies in Section 3). Hence, economic growth driving industrial energy consumption of the
TE
low and lower middle income and upper middle income panels, and not high income, at this level
AC CE P
of aggregation seems reasonable.
However, economic growth does not have a significant effect on residential total energy consumption (Table 5, panel 1). This result resonates best with upper middle to high income residential energy users. Where energy is mostly used for space heating and cooling, these households are equipped with standard heating, cooling and electrical appliances. For these groups, if income is a contributor to increased energy use, this would be insignificant. In comparison, for the low to lower middle income groups, because users are energy poor, economic growth should increase energy demand due to an improved ability to buy electrical appliances that will increase demand for electricity. However, we don’t find any evidence of this. It seems that not enough is earned by the low and lower middle income group, on a per capita basis, to have any significant impact on demand for residential energy.
23
ACCEPTED MANUSCRIPT Further improvements in energy efficiency reduce demand for energy and slow down the growth of energy use. We see from Table 2 that growth of residential energy consumption is the weakest for high income countries and strongest for the low and lower middle income group. In
PT
the EU for instance, energy efficiency has increased strongly in the residential sector. The
RI
Odyssee energy efficiency index for the EU27 increased by 24.0% over the 1990 to 2009 period
SC
with an annual average rate of 1.4%, driven by the diffusion of more efficient buildings, space heating technologies and electrical appliances (EEA, 2015).13 This is true for other industrialised
NU
nations. It is likely that this has weakened the link between economic growth and energy
MA
consumption.
In the EU nations, residential renewable energy is used in power generation in the heating
D
and cooling sector. The renewable energy share of electricity consumed in the EU grew at an
TE
average of 1.3% per year between 2005 and 2013. In 2013, in the EU 25.4% of electricity
AC CE P
consumed was generated from renewables, with around 38.0% contributed by variable renewable electricity sources (wind and solar) (EEA, 2015). Bioenergy (for electricity and heating) make the largest source of renewable energy in the EU, followed by hydropower and then wind power. However, in middle to lower income countries, relatively fewer households have access to electrical appliances; hence a tendency for residential energy consumption to be sensitive to economic growth is highly likely. Here, energy consumption increases are linked to rising personal income which permits higher standards of living, with increases in comfort levels and ownership of domestic appliances. Section 3.3 suggests that for these users, there are critical problems associated with the supply of energy.
13
http://www.eea.europa.eu/data-and-maps/figures/odyssee-energy-efficiency-index-odex-2
24
ACCEPTED MANUSCRIPT Hence, at the aggregated level, we are not seeing any significant effect (see Tables 4 and 5).
PT
RI
Both residential and industrial total energy consumption are, however, found to contribute to economic growth. For residential total, their effects are significant at the five
SC
percent level or better, only in the case of low and lower middle income countries.
NU
For industrial total, their effects on economic growth are also positive, but this effect is
MA
found to be at best significant at the 10 percent level in the case of all countries panel and upper middle income panel. Being heavily dependent on heavy industrial type manufacturing, an
D
increase in energy use is linked to increased industrial productivity in the upper middle countries.
TE
From Table 1, we find that this result is consistent with several studies.
AC CE P
5.2 Non-renewable and renewable total energy consumption and economic growth As noted in Section 1, various studies have examined the link between economic growth and total energy (renewable and non-renewable) consumption. Our results relating to the nexus between non-renewable and renewable total energy consumption and economic growth are presented in Tables 6 and 7, respectively. Panels 1 and 2 in each of these tables report estimated coefficients corresponding to equations 1 and 2. Economic growth leads to a higher consumption of non-renewables total for all panels: all countries; low and lower middle income; upper middle income; and high income. The marginal effect is more than proportional for the all countries and low and lower middle income panels, but less than proportional for upper middle and high income countries (Table 6, panel 1).
25
ACCEPTED MANUSCRIPT Economic growth has a positive and significant effect on the consumption of renewables total only for the low and lower middle income panel (Table 7, panel 1).
PT
Non-renewable total energy consumption also boosts economic growth – this is true for all, except the high income countries (Table 6, panel 2). The effect is strongest for the upper
RI
middle income countries. Renewable total, on the other hand, does not have any significant
SC
effect on income growth for any of the four panels.
NU
Overall, for non-renewables total energy consumption, our results confirm the feedback hypothesis for three out of four panels: all countries; low and lower middle income; and upper
MA
middle income countries. These findings imply a strong connection between energy policy on non-renewables and economic growth, with conservative energy policies directed at non-
D
renewables hurting economic growth in the short-term. In this respect, our findings are consistent
TE
with a few studies which consider groupings similar to ours, or cover nations that fall into one of
AC CE P
our income groups (see Apergis and Payne 2011a; Apergis and Payne, 2012; Pao and Fu, 2013; Pao and Fu, 2015; Salim, et al, 2014). Still, in regard to total non-renewables, the result for the high income panel confirms the growth hypothesis, which suggests that conservative energy policies relating to non-renewables will reduce economic growth. This is consistent with two studies that also examine total non-renewables as we have (see Bhattacharya et al, 2016; Marques and Fuinhas, 2012). With total energy consumption of renewables, we only find evidence to support of the conservation hypothesis for the low and lower middle income panel, and not for the other groups. This difference in results by income groups is consistent with the fact that total renewable energy consumption, in per capita terms in high and upper middle income countries, is still far less when compared to low and lower middle income countries (see Table 2A, I3 and Table 3B, R3). 26
ACCEPTED MANUSCRIPT Our finding implies that low and lower middle income countries can activate conservation policies with respect to renewable energy consumption without affecting economic growth. This finding is consistent with previous studies that examine individual countries in the
PT
low and lower middle income group (see, Long et al, 2015 on China; and Omri et al, 2015 on
RI
Argentina).
SC
The acceptance of the neutrality hypothesis for the high and upper middle income countries suggests that renewables and economic growth are unrelated. Nonetheless, our finding
MA
and economic growth for these countries.
NU
is at odds with Tiba et al. (2016) who suggest bidirectional relations between renewable energy
D
5.3 Non-renewable industrial and residential energy consumption and economic growth
TE
Results relating to the nexus between non-renewable industrial and residential energy consumption and economic growth are presented in Tables 8 and 9, respectively. Panels 1 and 2
AC CE P
of each table report estimated coefficients corresponding to equations 1 and 2. Economic growth positively and significantly impacts consumption of non-renewable industrial energy for all, except the low and lower middle income countries (Table 8, panel 1). The labour-intensive production processes in low and lower middle income countries, versus capital intensive processes in the upper middle income and high income countries, is likely to be the key driving force behind this finding. It is also worth noting that high income countries’ economic growth was not found to affect total industrial energy use, but the effect of economic growth became significant against non-renewable industrial energy. This suggests that there is strong potential for energy intensive industry to switch from non-renewables to renewables.
27
ACCEPTED MANUSCRIPT That economic growth affects residential total energy consumption was rejected previously (see Table 5, panel 1). Our results here show that non-renewable residential energy consumption has been positively impacted by economic growth, and, as expected, this is only
PT
visible for low and lower middle income countries (Table 9, panel 1). This finding suggests
RI
economic growth has improved access of non-renewables energy (whose average growth per
SC
capita (3.7%) has been stronger than that of total residential energy use per capita (0.02%)) for residential users in energy-poor nations (see Table 2, R5 and R4, i.e. the low and lower middle
NU
income countries).
MA
Non-renewable industrial energy consumption has a positive and significant influence on the income of high income countries only (Table 8, panel 2). Results in Panel 1 and 2 in Table
D
suggest that a switch to renewable would be a better policy direction than the conservative
TE
approach for non-renewable energy usage in industries in high income countries. In contrast,
AC CE P
residential non-renewable energy consumption has no significant effect on any of the income groups (Table 9, panel 2).
5.4 Renewable industrial and residential energy consumption and economic growth Results relating to the nexus between renewable and non-renewable total energy consumption and economic growth are presented in Tables 10 and 11, respectively. In both tables, panels 1 and 2 report estimated coefficients corresponding to equations 1 and 2. Results suggest that economic growth has a positive, but insignificant, influence on renewable industrial (Table 10) and renewable residential (Table 11) energy consumption. Similarly, renewable energy consumption – residential or industrial – has no significant impact on income. One conclusion that can be made from this is that while the upper middle income and higher income nations are putting increasingly more effort into making the switch to renewable 28
ACCEPTED MANUSCRIPT energy (as shown in Table 2), the major industries which are seen as the growth engines in these nations are lagging behind in switching from non-renewables to renewables. The low and lower middle income countries, whose per capita renewable energy consumption is greater than other
PT
income groups, are still using more of the renewable energy sources for household use. In poor
RI
countries the renewable energy mix largely comprises biomass fuels that have proven negative
SC
health and economic effects, hence this result of neutrality meets the expectation expressed in point (d) (Section 1).
NU
5.5 Summary of results by hypothesis and income groups and some additional discussion
MA
In Table 12, we present a summary of the above results according to four relevant hypotheses: the feedback hypothesis; the growth hypothesis; the conservative hypothesis; and the neutrality
TE
D
hypothesis.
The feedback hypothesis supports a bidirectional relationship between energy and
AC CE P
economic growth, suggesting complementarity between the two such that an increase in economic growth boosts energy consumption and a rise in energy consumption will stimulate economic growth.
We found support for this hypothesis in industries’ usage of total energy and/or nonrenewable energy. Upper middle income countries show bidirectional links with industrial total and non-renewable total use. Low and lower middle income countries show the feedback effect when dealing with non-renewable consumption totals, while high income countries support the feedback effect in the case of non-renewable industrial energy use. In all, for these groups policies directed towards either economic growth or energy consumption (of industrial total, non-renewable industrial, or/and total non-renewable) seem to be sufficient for increasing both economic growth and energy consumption. 29
ACCEPTED MANUSCRIPT Given that there is significant government support around the world for providing more efficient usage of non-renewables, this suggests that energy policies that lead to efficient use of
economic activity as well.
RI
PT
energy (of industrial total, non-renewable industrial, or/and total non-renewable) are stimulating
SC
An important question here is why our results do not show any support for the feedback
NU
effect in case of renewables. This may be a symptom of government policies, as well as the
MA
private sector still being biased towards non-renewable energy sources. The growth hypothesis, which suggests that energy consumption leads to economic
D
growth or that inadequate provision of energy limits economic growth, is found to be true for
TE
energy-deficient countries – the low and lower middle income countries. This unidirectional argument supports residential total energy consumption of poor nations and suggests that energy
AC CE P
is not only an input in the production of goods and services but also complements labour inputs. The conservation hypothesis takes the view that economic growth influences energy consumption. This view also suggests that the economy is less energy dependent and has the capacity for energy conservation which, if implemented, will have little to no effect on economic growth. For the low and lower middle income countries this is true when dealing with total industrial energy consumption and total renewables. For the upper middle income and high income panels, this is true when using non-renewables as energy sources. The case of neutrality, or no causality between these variables, appears to be another scenario that favours energy conservation policies; such policies will not harm economic growth. Evidence in support of the neutrality hypothesis is very strong and found in the use of renewable
30
ACCEPTED MANUSCRIPT industrial and residential, total renewables, non-renewable residential, total residential, and total industrial energy consumption.
PT
The link between economic growth and energy consumption is neutral for high income countries in the case of all, except non-renewables total and non-renewables industrial. For the
RI
upper middle income panel, this hypothesis could not be rejected, except for total industrial, total
SC
non-renewables and non-renewables industrial. In contrast, for the low and lower middle income countries, the neutrality hypothesis is only accepted in the case of non-renewables industrial and
MA
5.6 Robustness test
NU
renewables industrial and residential.
D
To ascertain the robustness of our results, we first follow Apergis and Payne (2010a, 2010b,
TE
2011a, 2011b), and introduce investment as a share of GDP in the models estimated above. Capital investment is a crucial determinant of economic growth hence it has strong theoretical
AC CE P
implications for equation (2) and inclusion of the variable capital investment in equation (1) highlights the investment in energy-related technology. These models are estimated using the GMM approach. We summarise the results in Table 12.14 We find that all, or most, of the results with and without gross investment are consistent. The differences are as follows. In the case of total non-renewables we find that models with investment favour the feedback hypothesis for the high income panel when models without investment showed the presence of the conservative hypothesis. For non-renewable industrial energy consumption, we find that models with investment favour the feedback hypothesis for all countries and the low and lower middle income panel. However, models without investment favoured the conservative
14
Detailed results are displayed as supplementary results.
31
ACCEPTED MANUSCRIPT hypothesis only. Non-renewable residential consumption supports the conservative hypothesis without investment but the neutrality hypothesis with investment. There is a suggested weakness of instruments in some of our models, as the Sargan
PT
statistic shows.15 In models with relatively small samples, or a relatively large set of excluded
RI
instruments, the test of the orthogonality of the instruments becomes less powerful (Roodman,
SC
2009). However, as Roodman (2009, p. 142) points out further, “there is no precise guidance on what is a relatively safe number of instruments”. As a result, we complement the GMM results
NU
with the fixed effect (FE) results.16
MA
The FE models point to an overall robustness of the GMM regression coefficients. They do not require special moment conditions, but they also do not incorporate dynamics or solve the
D
endogeneity problem. For these reasons, we recommend that they are read as complementary to
TE
the GMM results and not in their substitution. The two respective fixed effects models are:
AC CE P
(ln EnConsitk ) 0 1 (Growth _ yit ) 5 t i1 it 3
(3)
(ln yit ) 0 1 (Growth _ EnConsitk ) 5 t i 2 it 4
where in ~ i.i.d (0, in ) , itn ~ i.i.d.(0, ), E[in itn ] 0, 𝑛 = 3,4.
(4)
t
represents a vector of
annual dummy variables. All other variables in models (3) and (4) are as in models (1) and (2), respectively. These results are presented next to the GMM results in Tables 4-11 in columns 5-8. We also summarise the FE results in Table 12. The FE coefficients confirm three out of four of our results regarding the feedback hypothesis (the bidirectional relationship) and 50% of our neutrality hypotheses. The FE coefficients are less helpful for confirming our results relating to
15
We thank an anonymous referee for pointing this out. There is a recent literature trend for dynamic panel studies utilizing small cross-country samples to report simultaneously GMM and some static panel results as a robustness check (See, Bhattacharya et al., 2017 and Seven and Coskun, 2016). 16
32
ACCEPTED MANUSCRIPT the conservative hypothesis (unidirectional from growth to energy consumption) and the growth hypothesis (unidirectional from energy consumption to growth) results (see Table 12). To reemphasize, the FE results are to be treated with caution as they do not control for endogeneity
PT
and do not capture the dynamics of the processes.
RI
Concluding Remarks
SC
Our study takes an extensive look at the four hypotheses relating to the energy consumption and
NU
economic growth nexus. While there is a large body of literature that differentiates between renewable and non-renewable energy, this paper suggests further dividing such studies by
MA
residential and industrial users of energy. There are distinct and compelling features among the consumption patterns of these user groups, particularly across panels of countries by income. We
D
test the four hypotheses across different energy categories by income panel and show
AC CE P
situation.
TE
differences. We use the GMM approach to estimate the growth model to highlight the current
One of our key results relates to low and lower middle income countries where the residential energy consumption total shows growth effects for low and lower middle income countries (see point (a) in Section 1). This is the first large scale study that shows the importance of residential energy for the economic development of low income countries. There is an urgent need for both the public and private sectors to work harder to promote better access to electricity for households in these nations. Further efforts to conserve residential energy in these nations would hurt their economic growth. We show that the feedback effect is associated with the matured energy sources nonrenewables – total and industrial – in all income groups (point (b), Section 1). Neutrality is still associated with renewables, except in the case of upper middle income countries that support the 33
ACCEPTED MANUSCRIPT presence of the conservative hypothesis. Together, both findings suggest the global depth of dependence on non-renewables.
PT
We failed to find any evidence: that residential renewable energy is a driver of economic growth and/or vice versa (point c); that economic growth is a trigger of residential renewable
RI
energy consumption in high income countries (point e); and that economic growth is a trigger of
SC
industrial renewable energy consumption in the upper middle income countries (point f).
NU
Overall, we believe this study provides an important dynamic panel data perspective on the bidirectional relationships between economic growth and energy consumption, with useful
MA
insight on renewables versus non-renewables and industrial versus residential energy
D
consumption.
TE
References
AC CE P
Apergis, N., Payne, J. E., Menyah, K., Wolde-Rufael, Y., 2010. On the casual dynamics between emissions, nuclear energy, renewable energy consumption and economic growth, Ecological Economics, 69(1), 2255-2260. Apergis, N., Payne, J. E., 2010a. Renewable energy consumption and economic growth: Evidence from a panel of OECD countries, Energy Policy, 38(1), 656-660. Apergis, N., Payne, J. E., 2010b. Renewable energy consumption and economic growth in Eurasia, Energy Economics, 32(6), 1362-1397. Apergis, N., Payne, J. E., 2011a. On the casual dynamics between renewable and nonrenewable energy consumption and economic growth in developed and developing countries, Energy Systems, 2(3), 299-312. Apergis, N., Payne, J. E., 2011b. The renewable energy consumption-growth nexus in Central America, Applied Energy, 88(1), 343-347. Apergis, N., Payne, J. E., 2012. Renewable and non-renewable energy consumption nexus: Evidence from a panel error correction model, Energy Economics, 34(3), 733-738.
34
ACCEPTED MANUSCRIPT Apergis, N., Danuletiu, D., 2014. Renewable energy and economic growth: Evidence from the sign of panel long-run causality, International Journal of Energy Economics and Policy, 4(4), 578-587.
PT
Bloch, H., Rafiq, S., Salim, R., 2015. Economic growth with coal, oil and renewable energy consumption in China: Prospects for fuel substitution, Economic Modelling, 44, 104-115.
RI
Bhattacharya, M., Churchill, S. A., & Paramati, S. R. (2017). The dynamic impact of renewable energy and institutions on economic output and CO 2 emissions across regions. Renewable Energy.
NU
SC
Bhattacharya, M., Paramati, S. R., Ozturk, I., Bhattacharya, S., 2016. The effect of renewable energy consumption on economic growth: Evidence from top 38 countries, Applied Energy, 162, 733-741.
MA
Seven, U., & Coskun, Y. (2016). Does financial development reduce income inequality and poverty? Evidence from emerging countries. Emerging Markets Review, 26, 34-63.
D
Chang, T., Gupta, R., Inglesi-Lots, R., Simo-Kengne, B., Smithers, D., Trembling, A., 2015. Renewable energy and growth: Evidence from heterogeneous panel of G7 countries using Granger causality, Renewable and Sustainable Energy Reviews, 52, 1405-1412.
AC CE P
TE
Dyotch, N., and Narayan, S (2016) Does FDI Influence Renewable Energy Consumption? An Analysis of Sectoral FDI Impact on Renewable and Non-renewable Industrial Energy Consumption, Energy Economics, 54, 291–301 European Environment Agency (EEA), 2015. Trends and projections in Europe 2015. Tracking progress towards Europe’s climate and energy targets, EEA Report, 4/2015. ISSN 1977-8449. European Environment Agency (EEA), 2014. Energy support measures and their impact on innovation in the renewable energy sector in Europe, EEA Technical report, 21/2014. ISSN 1725-2237. International Monetary Fund (IMF), 2006. World Economic Outlook. International Energy Agency, 2015. Energy for Cooking in Developing Countries, World Energy Outlook, 2006, 419-445. International Energy Agency, 2015. World Energy Outlook, 2015. https://www.iea.org/Textbase/npsum/WEO2015SUM.pdf International Energy Agency, 2016. World Energy Outlook, 2016. Kraft, J., and Kraft, A., (1978) On the relationship between Energy and GNP, Journal of Energy and Development, 3: 401-403.
35
ACCEPTED MANUSCRIPT Lin, B., Moubarak, M., 2014. Renewable energy consumption – Economic growth nexus for China, Renewable and Sustainable Energy Reviews, 40, 111-117.
PT
Long, X., Naminse, E. Y., Du, J., Zhuang, J., 2015. Nonrenewable energy, renewable energy, carbon dioxide emissions and economic growth in China from 1952 to 2012. Renewable and sustainable Energy Reviews, 52, 680-609.
RI
Marques, A.C., Fuinhas, J. A., 2012. Is renewable energy effective in promoting growth? Energy policy, 46, 434-442.
SC
Menyah, K., Wolde-Rufael, Y., 2010 CO2 emissions, nuclear energy, renewable energy and economic growth in the US, Energy Policy, 38, 2911-2915.
NU
Narayan, S (2016) Predictability within the Economic Growth and Energy Consumption Nexus: Some evidence from Income and regional panels, Economic Modelling, 54, 515-521.
MA
Ocal, O., Aslan, A., 2013. Renewable energy consumption-economic growth nexus in Turkey, Renewable and Sustainable Energy Reviews, 28, 494-499.
D
Pablo-Romero, M. D., Pozo-Barajas, R., Yniguez, R., 2017. Global changes in residential energy consumption, Energy Policy, 101, 342-352.
TE
Pao, H-T., Fu, H-C., 2013. The casual relationship between energy resources and economic growth in Brazil, Energy Policy, 61, 793-801.
AC CE P
Pao, H-T., Li, Y-Y., Fu, H-C., 2014. Clean energy, non-clean energy, and economic growth in the MIST countries, Energy policy, 67, 932-942. Pao, H-T., Fu, H-C., 2015. Competition and stability analyses among emissions, energy, and economy: Application from Mexico, 82, 98-107. Payne, J. E. (2010). Survey of the international evidence on the causal relationship between energy consumption and growth, Journal of Economic Studies, 2010, 37(1), 53-95. Roodman, D. (2009). A note on the theme of too many instruments. Oxford Bulletin of Economics and statistics, 71(1), 135-158. Sardosky, P., 2009. Renewable energy consumption and income in emerging economies, Energy Policy, 37(10), 4021-4028. Salim, R. A., Hassan, K., Shafiei, S., 2014. Renewable and non-renewable energy consumption and economic activities: Further evidence from OECD countries, Energy Economics, 44, 350-360. Sebri, M., Ben-Salha, O., 2014. On the casual dynamic between economic growth, renewable energy consumption, CO2 emissions and trade openness: Fresh evidence from BRICS countries, Renewable and Sustainable Energy Reviews, 39, 14-23. 36
ACCEPTED MANUSCRIPT Shahbaz, M., Loganathan, N., Zeshan, M., Zaman, K., Does renewable energy consumption add in economic growth? As application of auto-regressive distributed lag model in Pakistan, Renewable and sustainable energy reviews, 44, 576-585.
PT
Shahbaz, M., Zeshan, M., Afza, T., Is energy consumption effective to spur economic growth in Pakistan? New evidence from bounds test to level relationships and Granger causality tests, 29(6), 2310-2319.
RI
Suter, J. F., Shammin. M. R., Returns to residential energy efficiency and conservation measures: A field experiment, Energy policy, 59, 551-561.
SC
Tiba, S., Omri, A., 2017. Literature survey on the relationship between energy environment and economic growth, Renewable and Sustainable Energy Reviews, 69, 1129-1146.
MA
NU
Tiba, S., Omri, A., Frikha, M., 2016. The four-way linkages between renewable energy, environment quality, trade and economic growth: a comparative analysis between high and middle income countries, Energy Systems, 7(1), 103-144.
D
Tugcu, C.T., Ozturk, I, Aslan, A., 2012. Renewable and non-renewable energy consumption and economic growth relationship revisited: Evidence from G7 countries, Energy Economics, 34(6), 1942-1950.
TE
US Energy Information Administration (EIA), 2015. Divers of US Household Energy Consumption, 1980-2009. US Department of Energy, Washington, DC.
AC CE P
US Energy Information Administration (EIA), 2014. Issues in International Energy Consumption Analysis: Electricity Usage in India’s Housing Sector, US Department of Energy, Washington, DC. US Energy Information Administration (EIA), 2011. Share of energy used by appliances and consumer electronics increases in US homes, US Department of Energy, Washington, DC.March 20. U.S. Energy Information Administration, 2013. Heating and cooling no longer majority of home energy use, US Department of Energy, Washington, DC. March 7. U.S. Energy Information Administration, 2013. Newer US homes are 30% larger but consume about as much energy as older homes, US Department of Energy, Washington, DC. February, 2013. Yang, C-L., Lin, H-P., Chang, C-H., 2010. Linear and nonlinear causality between sectoral electricity consumption and economic growth: Evidence from Taiwan, Energy Policy, 38(11), 6570-6573. Yildirim, E., Sarac, S., Aslan, A., 2012. Energy consumption and economic growth in the USA: Evidence from renewable energy, Renewable and Sustainable Energy Reviews, 16(9), 6770-6774.
37
ACCEPTED MANUSCRIPT Zeb, R., Salar, L., Awan, U., Zaman, K., Shahbaz, M. 2014. Causal links between renewable energy, environmental degradation and economic growth in selected SAARC countries: Progress towards green economy, Renewable Energy, 17, 123-132.
AC CE P
TE
D
MA
NU
SC
RI
PT
Zhao, X., Li, N., Ma, C., (2012). Residential energy consumption in urban China: A decomposition analysis, Energy Policy, 41, 644-653.
38
ACCEPTED MANUSCRIPT
1980
1990 year
MA
1970
NU
0
SC
20
RI
%
40
PT
60
Figure 1: Mean renewable energy as a percentage of total final consumption
All Countries Middle Income Countries
2000
2010
Low Income Countries High Income Countries
AC CE P
TE
D
Source: Authors' Compilations
39
ACCEPTED MANUSCRIPT
1980
1990 year
MA
1970
NU
10
20
SC
%30
RI
40
PT
50
Figure 2: Mean renewable industrial energy consumption as a percentage of total industrial energy consumption
Low Income Countries High Income Countries
AC CE P
TE
Source: Authors' Compilations
2010
D
All Countries Middle Income Countries
2000
40
ACCEPTED MANUSCRIPT
1980
1990 year
MA
1970
NU
10
20
SC
%30
RI
40
PT
50
Figure 3: Mean renewable residential energy consumption as a percentage of total residential energy consumption
Low Income Countries High Income Countries
AC CE P
TE
Source: Authors' Compilations
2010
D
All Countries Middle Income Countries
2000
41
ACCEPTED MANUSCRIPT Table 1: Summary of the literature: Relationship between energy consumption (E) and economic growth (Y) This table summarizes surveyed panel and time series studies that have examined the EC-G nexus for: renewable; nonrenewable, residential, industrial, and residential and industrial renewable and non-renewable energy consumption. Study
Panel
Timeframe
Method
Other variables
Renewable
Multivariate panel data framework Panel error correction models
Real gross capital formation; Labor force Real gross capital formation; Labor force Real gross capital formation; Labor force
E↔Y (short-run & long-run) E↔Y (short-run & long-run) E↔Y (short-run & long-run)
Heterogeneous panel cointegration Panel cointegration; VECM
Real gross capital formation; Labor force Real gross capital formation; Labor force
E↔Y (short-run & long-run) E↔Y (short-run & long-run)
OECD
1985-2005
Granger causality
13 Eurasian countries Developed Developing
1992-2007
Apergis and Payne, 2011b Apergis and Payne, 2012
6 Central America
1980-2006
80 countries
1990-2007
Apergis and Danuletiu, 2014 Apergis et al. 2010
1990-2012
Long-run causality
1984-2007
Panel error correction model
Bhattacharya et al, 2016 Bloch et al, 2015
80 countries – regional panels 19 Developed and Developing countries Top 38 renewable energy consumers China
1991-2012
Chang et al, 2015
G7
1977-2013 1965-2011 1990-2011
Panel cointegration DOLS/FMOLS Cointegration
Lin and Moubarak, 2014 Long et al, 2015
China
1977-2011
China
1952-2012
ARDL and Johansen technique Granger Causality
Marques and Fuinhas, 2012 Mbarek et al, 2015 Menyah and Wolde-Rufael, 2010 Ocal and Aslan, 2013 Omri et al, 2015
24 European countries France
1990-2007
Panel estimations
Carbon emissions; Labor CO2 emissions; Nonrenewable energy (coal, gas, and oil); Labor; Capital; Renewable energy (hydro) Coal; Oil
2001:12012:3 1960-2007
Granger Causality
Nuclear energy
Granger Causality test
Carbon emissions; Nuclear energy
Turkey
1990-2010
17 Developed and Developing
1990-2011
Toda-Yamamoto causality Granger Causality
Real gross capital formation; Labor force Nuclear energy
Pao and Fu, 2013
Brazil
1980-2009
Cointegration and error correction model
Real gross capital formation; Labor force
RI
SC
NU
MA
Nuclear energy; Carbon emissions Real gross capital formation; Labor force Coal; Oil; Labor; Capital
D
Granger Causality test
TE
AC CE P
US
1990-2007
PT
Apergis and Payne 2010a Apergis and Payne, 2010b Apergis and Payne 2011a
Non-renewable
E↔Y (short-run &longrun)
E↔Y (short-run & long-run)
LR: E↔Y E↔Y Positive short-run impact LR: E→Y
LR: E→Y
LR:E↔Y
LR:E↔Y
E≠Y Canada; Italy & US Y→E France & UK E→Y Germany E↔Y (long-run) Y→E
LR:E↔Y
E≠Y
E→Y Coal(-); Oil(+)
Y→E Nuc→E Y→E
Y→E E→Y for Hungary, India, Japan, Sweden & Netherlands Y→E for Argentina, Spain, & Switzerland E↔Y for US, Belgium, Bulgaria, Canada, France, & Pakistan E≠Y for Brazil, Finland & Switzerland E↔Y (wind, biomass, waste)
E↔Y (fossil fuel)
42
ACCEPTED MANUSCRIPT Table 1 continued: Summary of the literature: Relationship between energy consumption (E) and economic growth (Y) Method
Other Variables
Lotka-Voterra model for Sustainable Development Panel cointegration
Nuclear; Fossil fuel; Total energy; CO2
Pao et al, 2014
Mexico, Indonesia, S. Korea, Turkey 18 Emerging economies
1990-2010
Salim, et al, 2014
29 OECD countries
1980-2011
Sebri and BenSalha, 2014 Shahbaz et al, 2015 Tiba et al., 2016
BRICS
1971-2010
Pakistan
1972-2011
24 High income (HI) and Middle Income (MI) countries
1990-2011
Tugcu et al., 2012
G7
1980-2009
Yildirim et al, 2012
US
Zeb et al, 2014
5 SAARC nations: Bangladesh, India, Nepal, Pakistan, Sri Lanka
Cointegration FMOLS, OLS, DOLS Panel cointegration with structural breaks ARDL and VECM ARDL; VECM Granger causality Simultaneous equations
ARDL Cointe; causality test
E≠Y
LR: E→Y SR: E↔Y
Non-renewable
E↔Y (fossil fuel) LR:E↔Y SR: E→Y
LR:Y→E SR: E↔Y LR:E↔Y SR: Y→E
LR:E↔Y SR: E↔Y
Trade openness; Carbon emissions Capital; Labor
E↔Y (short-run) E↔Y
E↔Y
CO2 emissions; Renewable energy; Foreign trade
HI: E↔Y; E↔CO2 MI: E↔Y; Y↔CO2; trade↔CO2; E→ CO2 E↔Y for England and Japan E≠Y for France, Italy, Canada, US Y→E for Germany
D
1994-2003
Classical PF with Labor force; Augmented PF – with human capital; R&D
1960-2010
Toda-Yamamoto causality
Employment; Investment
1975-2010
Granger Causality – LR and SR
CO2emissions; Resource depletion; Poverty
TE
Sadorsky, 2009
Real gross capital formation; Labor force
RI
1980-2011
SC
Mexico
NU
Pao and Fu, 2015
Renewable
PT
Time frame
MA
Panel
AC CE P
Study
E→Y Japan E≠Y for England, France, Italy, Canada, US, & Germany
E→Y (only biomass-waste derived E) -electricity production from renewable resources -bidirectional link between energy production from renewables & poverty reduction in Pakistan
43
ACCEPTED MANUSCRIPT
Table 2A: Non-renewable and renewable industrial energy consumption Obs 5214
Mean 0.0006
SD 0.0009
I2. Non-renewable energy industrial consumption per capita
3093
0.0004
0.0006
1431
0.0002
I3. Renewable energy industrial consumption per capita
3093
0.0001
0.0001
1431
I4. Growth of total industrial energy consumption per capita I5. Growth of non-renewable energy industrial consumption per capita I6. Growth of renewable energy industrial consumption
5078
0.0297
0.4248
2990
0.3096
2991
0.0790
All countries
PT
I1. Total industrial energy consumption per capita
Low and lower middle income Obs Mean SD 2460 0.0002 0.0008
Industrial energy consumption level*, share and growth
Upper middle income
High income
Mean 0.0004
SD 0.0003
Obs 1727
Mean 0.0011
SD 0.0011
0.0006
740
0.0004
0.0003
922
0.0009
0.0005
0.0000
0.0001
740
0.0000
0.0000
922
0.0001
0.0001
2389
0.0255
0.2595
1000
0.0483
0.7096
1689
0.0245
0.3863
15.7578
1379
0.6656
23.2024
717
0.0118
0.1518
894
-0.0008
0.0712
1.0186
1380
0.0284
0.6250
717
0.1624
1.7433
894
0.0905
0.6514
17. Industrial energy consumption share of total final consumption 5217 0.2665 0.1361 2460 0.2069 I8. Industrial energy non-renewable consumption share of total 3093 0.2999 0.1108 1431 0.2947 final nonrenewable consumption I9. Industrial energy renewable consumption share of total final 3093 0.2795 0.2456 1431 0.1827 renewable consumption I10. Growth of industrial energy consumption share of total final 5082 0.0115 0.3046 2389 0.0164 consumption I11. Growth of industrial energy non-renewable consumption share 2990 0.2831 15.3730 1379 0.6219 of total final nonrenewable consumption I12. Growth industrial energy renewable consumption share of 2991 0.0587 0.8598 1380 0.0378 total final renewable consumption Note: *Energy Consumption is measured in thousands of tonnes of oil equivalent (ktoe)
0.1242
1027
0.3030
0.1110
1730
0.3296
0.1296
0.1281
740
0.3262
0.1140
922
0.2868
0.0670
0.1562
740
0.2992
0.2633
922
0.4140
0.2757
0.2268
1000
0.0223
0.5450
1693
-0.0019
0.1742
22.6362
717
0.0048
0.1093
894
-0.0087
0.0492
0.6800
717
0.1304
1.4243
894
0.0334
0.3596
SC
NU
MA
PT ED
AC
CE
RI
Obs 1027
44
ACCEPTED MANUSCRIPT
Table 2B: Non-renewable and renewable residential energy consumption
Mean
R1. Total residential energy consumption per capita
5237
0.0004
R2. Non-renewable energy residential consumption per capita
4209
0.0003
R3. Renewable energy residential consumption
4209
0.0002
R4. Growth of total residential energy consumption per capita
5102
0.0262
R5. Growth of non-renewable energy residential consumption per capita
4061
1.9361
R6. Growth of renewable energy residential consumption
4083
0.1196
R7. Residential energy consumption share of total final consumption
5240 4212
R10. Growth of residential energy consumption share of total final consumption
R11. Growth of residential energy nonrenewable consumption share of total final nonrenewable consumption R12. Growth residential energy renewable consumption share of total final renewable consumption
High income
Obs
Mean
Std. Dev.
Obs
Mean
2466
0.0004
0.0012
1024
0.0003
0.0006
2172
0.0001
0.0007
945
0.0002
0.0002
1092
0.0005
0.0003
0.0007
2172
0.0003
0.0009
945
0.0001
0.0001
1092
0.0001
0.0003
0.3864
2396
0.0166
0.3612
997
0.0236
0.1678
1709
0.0412
0.4962
121.2835
2093
3.7392
168.9400
912
0.0260
0.1675
1056
0.0119
0.1039
3.7330
2109
0.0354
0.9250
918
0.0908
2.7142
1056
0.3127
6.7641
0.3361
0.2203
2466
0.4680
0.2377
1024
0.2559
0.1271
1750
0.1973
0.0937
0.1703
0.0761
2172
0.1582
0.0755
945
0.1775
0.0777
1095
0.1880
0.0713
4212
0.7436
0.2530
2172
0.8254
0.1862
945
0.7306
0.2597
1095
0.5927
0.2885
5106
0.0163
0.4163
2396
0.0132
0.3976
997
0.0105
0.1775
1713
0.0242
0.5264
4065
1.9596
123.7276
2093
3.7999
172.4295
912
0.0106
0.1408
1060
0.0027
0.1089
4087
0.0306
1.4778
2109
0.0079
0.2470
918
0.0250
0.8174
1060
0.0808
2.7790
NU
PT ED
R8. Residential energy nonrenewable consumption share of total final nonrenewable consumption R9. Residential energy renewable consumption share of total final renewable consumption
Upper middle income Std. Dev. 0.0002
RI
Obs
Std. Dev. 0.0009
PT
Low and lower middle income
SC
All countries
MA
Residential energy consumption levels*, shares and growth
Obs
Mean
1747
0.0006
Std. Dev. 0.0004
AC
CE
Note: *Energy Consumption is measured in thousands of tonnes of oil equivalent (ktoe)
45
ACCEPTED MANUSCRIPT
Table 3: Income groups This table categorizes the countries covered in this study by four income groups based on the World Bank classification: 1- Low income; 2- Lower middle income; 3- Upper middle income; and 4- High income countries. Data coverage
Income group
Country
Data coverage
Income group
Country
Data coverage
Income group
Albania Albania Argentina Armenia Austria Australia Azerbaijan Azerbaijan Cambodia Bangladesh Belgium Bolivia Bosnia & Herzegovina Bosnia & Herzegovina Brazil
1971-2007 2008-2011 1971-2012 1991-2011 1971-2012 1971-2012 1991-2008 2009-2011 1995-2011 1971-2011 1971-2011 1971-2011 1991-2007 2008-2011 1971-2011
2 3 3 2 4 4 2 3 1 1 4 2 2 3 3
Greece Guatemala Honduras Honduras Hong Kong Hungary Hungary Iceland India India Indonesia Ireland Israel Italy Jamaica
1971-2011 1971-2001 1971-2001 2002-2011 1971-2011 1971-2007 2008-2011 1971-2011 1971-2008 2009-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2004
4 2 1 2 4 3 4 4 1 2 2 4 4 4 2
Pakistan Pakistan Panama Paraguay Peru Peru Philippines Poland Portugal Romania Russian Fed. Saudi Arabia Serbia Singapore Slovak Rep
1971-2008 2009-2011 1971-2011 1971-2011 1971-2009 2010-2011 1971-2011 1971-2011 1971-2011 1971-2011 1991-2011 1971-2011 1991-2011 1971-2011 1971-2005
1 2 3 2 2 3 2 3 4 3 3 4 3 4 3
Brunei Bulgaria Canada Chile China China Colombia Colombia Costa Rica Croatia Croatia Cyprus Czech Rep. Denmark Dominican Rep. Dominican Rep. Ecuador Ecuador El Salvador Estonia Finland France Germany
1971-2011 1971-2012 1971-2011 1971-2011 1971-2009 2010-2011 1971-2006 2007-2011 1971-2011 1991-2007 2008-2011 1971-2011 1971-2011 1971-2011 1971-2007 2008-2011 1971-2009 2010-2011 1971-2011 1991-2011 1971-2011 1971-2011 1971-2011
4 3 4 3 2 3 2 3 3 3 4 4 4 4 2 3 2 3 2 4 4 4 4
Jamaica Japan Kazakhstan Korea, Rep. Kyrgyz Rep Latvia Lithuania Lithuania Luxembourg Macedonia, FYR Macedonia, FYR Malaysia Mexico Moldova Morocco Mozambique Myanmar Netherlands New Zealand Nicaragua Norway Oman Oman
2005-2011 1971-2011 1991-2011 1971-2011 1991-2011 1991-2011 1991-2007 2008-2011 1971-2011 1991-2007 2008-2011 1971-2011 1971-2011 1971-2008 1971-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2006 2007-2011
3 4 3 4 1 3 3 4 4 2 3 3 3 2 2 1 1 4 4 2 4 3 4
Slovak Rep Slovenia Sri Lanka Spain Sweden Switzerland Syrian Arab Rep. Tanzania Thailand Thailand Tunisia Tunisia Turkey UA Emirates United Kingdom United States Uruguay Venezuela Vietnam Vietnam
2006-2011 1991-2011 1971-2011 1971-2011 1971-2011 1993-2012 1971-2011 1971-2011 1971-2009 2010-2011 1971-2008 2009-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2008 2009-2011
4 4 2 4 4 4 2 1 2 3 2 3 3 4 4 4 3 3 1 2
RI
SC
NU
MA
D
TE
AC CE P
PT
Country
Source: Authors’ calculations based on the income level methodology of the World Bank
46
ACCEPTED MANUSCRIPT Table 4: Economic growth and total industrial energy consumption FE (4) High income
(6) Low and lower middle
(7) Upper middle
(8) High income
0.860***
0.332*
PT
0.971***
(0.0144) 0.433
0.714***
(0.275)
(0.109)
(0.122)
(0.0979)
(0.197)
1,553 40
3,538 89
1,177 34
808 34
1,553 40
0.103
0.110
0.180
0.109
0.00174
0.0157
0.0823***
0.0227*
0.00337
(0.00315) 1,553 40
(0.0100) 3,538 89 0.147
(0.0159) 1,177 34 0.167
(0.0130) 808 34 0.220
(0.00275) 1,553 40 0.29
0.837***
RI
SC
0.0125 135.9 0.00102
0.996***
MA
D
TE
(5) All
NU
GMM (2) (3) Low and Upper lower middle middle Panel 1: GDP growth on industrial total energy consumption Log of lagged 0.995*** 0.991*** 0.980*** total industrial energy per capita (0.0170) (0.0231) (0.00795) GDP per capita, 1.132*** 1.228*** 0.848*** 2005 USD, PPP, (0.187) (0.353) (0.278) growth Observations 3,538 1,177 808 Number of 89 34 34 countries R-squared AR(2) pval 0.712 0.477 0.273 Sargan test chi2 120.2 65.18 110.6 Sargan test pval 0.0155 0.188 0.0523 Panel 2: Industrial total energy consumption on GDP growth Log of lagged 1.003*** 1.015*** 0.990*** GDP, 2005 USD, PPP per (0.00416) (0.00554) (0.00512) capita Growth of 0.0160* 0.0420 0.0185* industrial total energy per capita (0.00853) (0.0343) (0.0100) Observations 3,538 1,177 808 Number of count 89 34 34 R-squared AR(2) pval 0.00387 0.170 0.0104 Sargan test chi2 55.68 124.7 136.3 Sargan test pval 0.998 1.31e-06 0.0005 (1) All
(0.00439)
0.0269 351 0.0000
AC CE P
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. b The Sargan test is a commonly used test for instrument strength with the GMM method. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
47
ACCEPTED MANUSCRIPT Table 5: Economic growth and total residential energy consumption FE (6) Low and lower middle
(7) Upper middle
(8) High income
0.199**
0.0795
(0.133) 1,177 34
(0.0760) 805 34
(0.132) 1,553 40
0.029
0.081
0.159
0.00413
0.0243**
0.00518
(0.0369)
(0.00967)
(0.0213)
1,177 34
805 34
1,553 40
0.108
0.196
0.290
PT
GMM (2) (3) (4) (5) Low and Upper High All lower middle income middle Panel 1: GDP per capita (2005 USD, PPP) growth on residential total energy consumption Log of lagged 0.976*** 0.985*** 0.905*** 0.963*** residential total energy per (0.0130) (0.0155) (0.0253) (0.0120) capita GDP per capita, 0.0901 -0.104 -0.134 -0.0959 0.124* 2005 USD, PPP (0.222) (0.237) (0.177) (0.231) (0.0657) Observations 3,535 1,177 805 1,553 3,535 Number of count 89 34 34 40 89
0.0761
0.408 129.2
0.381 102.2
0.799 72.78
0.058
Sargan test pval
0.00344
0.000159
0.879
0.560 164.6
NU
R-squared AR(2) pval Sargan test chi2
SC
RI
(1) All
1.97e-06
Sargan test pval
0.136
0.000423 96.06
0.502 92.60
0.313 2.848
0.00999 307.3
0.286
0.00340
1
0
AC CE P
R-squared AR(2) pval Sargan test chi2
TE
D
MA
Panel 2: Residential total energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 1.001*** 1.019*** 0.989*** 0.996*** GDP, 2005 USD, PPP (0.00474) (0.00850) (0.0130) (0.00321) per capita Growth of 0.103* 0.156*** -0.0259 0.0360 0.0147 residential total energy per (0.0526) (0.0479) (0.130) (0.0490) (0.0120) capita Observations 3,535 1,177 805 1,553 3,535 Number of count 89 34 34 40 89
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. bThe Sargan test is a commonly used test for instrument strength with the GMM method. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
48
ACCEPTED MANUSCRIPT Table 6: Economic growth and total non-renewable energy consumption GMM (2) (3) (4) (5) Low and Upper High All lower middle income middle Panel 1: GDP per capita (2005 USD, PPP) growth on total non-renewable energy consumption Log of lagged 0.999*** 0.970*** 0.976*** 0.952*** total nonrenewable energy per (0.00841) (0.0171) (0.00594) (0.0154) capita GDP per 1.029*** 1.021*** 0.676*** 0.301** 0.694*** capita, 2005 USD, PPP, (0.195) (0.290) (0.130) (0.141) (0.0651) growth Observations 3,117 1,175 773 1,169 3,117 Number of 88 34 33 39 88 count R-squared 0.226 AR(2) pval 0.177 0.257 0.513 0.228 Sargan test 80.24 119.5 40.19 248.2 chi2 Sargan test 0.735 1.69e-06 1 0 pval Panel 2: Total non-renewable energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 1.003*** 1.006*** 0.999*** 1.002*** GDP, 2005 USD PPP per (0.00292) (0.00359) (0.00473) (0.00305) capita Growth of 0.373*** 0.343*** 0.452*** 0.0911 0.235*** total nonrenewable energy per (0.0798) (0.105) (0.0890) (0.0580) (0.0268) capita Observations 3,117 1,175 773 1,169 3,117 Number of 88 34 33 39 88 count R-squared 0.271 AR(2) pval 0.862 0.882 0.265 0.0542 Sargan test 124.5 133 27.11 30.32 chi2 Sargan test 0.00778 1.22e-07 1 1 pval
FE (6) Low and lower middle
(7) Upper middle
(8) High income
0.701***
0.486***
(0.107)
(0.0782)
(0.128)
1,175 34
773 33
1,169 39
0.213
0.386
0.246
0.211***
0.361***
0.140**
(0.0333)
(0.0439)
(0.0523)
1,175 34
773 33
1,169 39
0.234
0.416
0.373
PT
(1) All
AC CE P
TE
D
MA
NU
SC
RI
0.742***
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. bThe Sargan test is a commonly used test for instrument strength with the GMM method. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
49
ACCEPTED MANUSCRIPT
Table 7: Economic growth and total renewable energy consumption FE (6) Low and lower middle
(7) Upper middle
(8) High income
0.0139
0.0943
0.0286
(0.176)
(0.123)
(0.206)
1,175 34
773 33
1,169 39
0.035
0.064
0.050
0.00929
0.0120
2.24e-07
(0.0180) 1,175 34
(0.00791) 773 33
(6.95e-05) 1,169 39
0.107
0.235
0.329
AC CE P
TE
D
MA
NU
SC
RI
PT
GMM (2) (3) (4) (5) Low and Upper High All lower middle income middle Panel 1: GDP per capita (2005 USD, PPP) growth on total renewable energy consumption Log of lagged 1.008*** 1.002*** 1.006*** 1.003*** total renew. energy per (0.00681) (0.00623) (0.0175) (0.00714) capita GDP per capita, 0.241 0.455* 0.0293 0.293 0.0594 2005 USD, PPP, (0.210) (0.253) (0.225) (0.351) (0.0994) growth Observations 3,117 1,175 773 1,169 3,117 Number of 88 34 33 39 88 count R-squared 0.018 AR(2) pval 0.372 0.209 0.849 0.242 Sargan test chi2 86.25 166.3 47.99 110.4 Sargan test pval 0.563 0 1 0.0617 Panel 2: Total renewable energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 0.992*** 1.006*** 0.994*** 0.994*** GDP, 2005 USD PPP per (0.00362) (0.00619) (0.00703) (0.00263) capita Growth of total -0.00670 0.00104 -0.0265 -0.0116 0.000186 renew. energy per capita (0.0123) (0.0155) (0.0322) (0.0107) (0.000148) Observations 3,117 1,175 773 1,169 3,117 Number of 88 34 33 39 88 count R-squared 0.139 AR(2) pval 0.0663 0.337 0.0588 0.587 Sargan test chi2 42.87 114.6 13.74 48.98 Sargan test pval 1 1.99e-05 1 1 (1) All
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. bThe Sargan test is a commonly used test for instrument strength with the method of GMM. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
50
ACCEPTED MANUSCRIPT Table 8: Economic growth and non-renewable industrial energy consumption GMM FE (2) (3) (4) (5) (6) Low and Upper High All Low and lower middle income lower middle middle Panel 1: GDP per capita (2005 USD, PPP) growth on non-renewable industrial energy consumption Log of lagged 0.972*** 0.953*** 0.977*** 0.989*** non-renewable industrial Energy per (0.0234) (0.0341) (0.0134) (0.0111) capita GDP per 0.765** 0.345 1.066*** 0.815*** 0.887*** 0.822*** capita, 2005 USD, PPP, (0.333) (0.434) (0.174) (0.181) (0.106) (0.180) growth Observations 2,354 821 639 894 2,356 823 Number of 74 27 30 33 74 27 count R-squared 0.063 0.055 AR(2) pval 0.341 0.275 0.331 0.133 Sargan test chi2 52.70 50.67 45.77 156.5 Sargan test pval 0.999 0.676 0.998 1.32e-05 Panel 2: Non-renewable industrial energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 0.996*** 0.998*** 0.996*** 1.001*** GDP, 2005 USD PPP per (0.0425) (0.00727) (0.00254) (0.00171) capita Growth of total -5.83e-05 -5.12e-05 0.0132 0.173*** -0.000101 -0.000104 non-renewable industrial Energy per (0.000492) (5.27e-05) (0.0106) (0.0330) (7.91e-06) (1.43e-05) capita Observations 2,355 822 639 894 2,355 822 Number of 74 27 30 33 74 27 count R-squared 0.164 0.136 AR(2) pval 0.296 0.266 0.00598 0.336 Sargan test chi2 5311 137.5 74.28 179 Sargan test pval 0 3.26e-08 0.534 5.23e-08
(7) Upper middle
(8) High income
0.850***
0.847***
(0.158)
(0.175)
639 30
894 33
0.229
0.424
0.0801**
0.139***
(0.0367)
(0.0373)
639 30
894 33
0.251
0.493
AC CE P
TE
D
MA
NU
SC
RI
PT
(1) All
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. bThe Sargan test is a commonly used test for instrument strength with the GMM method. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
51
ACCEPTED MANUSCRIPT Table 9: Economic growth and non-renewable residential energy consumption GMM FE (2) (3) (4) (5) (6) (7) Low and Upper High All Low and Upper lower middle income lower middle middle middle Panel 1: GDP per capita (2005 USD, PPP) growth on non-renewable residential energy consumption Log of lagged 0.976*** 0.966*** 0.972*** 0.955*** non-renewable residential energy per (0.0122) (0.0203) (0.00965) (0.0214) capita GDP per 0.141 0.394** 0.0164 -0.350 0.145** 0.256** 0.127* capita, 2005 USD, PPP, (0.175) (0.191) (0.167) (0.313) (0.0643) (0.110) (0.0654) growth Observations 2,815 1,072 729 1,014 2,815 1,072 729 Number of 82 30 33 37 82 30 33 count R-squared 0.039 0.056 0.097 AR(2) pval 0.451 0.701 0.895 0.0991 Sargan test 107.2 53.76 64.45 155.3 chi2 Sargan test 0.0922 0.560 0.972 1.72e-05 pval Panel 2: Non-renewable residential energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 0.995*** 1.009*** 0.995*** 0.994*** GDP, 2005 USD PPP per (0.00437) (0.00633) (0.00546) (0.00244) capita Growth of 0.0188 0.0204 0.0774 0.0306 0.00371 0.00426 0.0273 total nonrenewable residential energy per (0.0276) (0.0275) (0.0994) (0.0361) (0.0108) (0.0138) (0.0253) capita Observations 2,815 1,072 729 1,014 2,815 1,072 729 Number of 82 30 33 37 82 30 33 count R-squared 0.139 0.090 0.221 AR(2) pval 0.00967 0.0341 0.320 0.00251 Sargan test 56.61 206 55.22 130.8 chi2 Sargan test 0.997 0 0.996 0.00263 pval
(8) High income
-0.0753 (0.123) 1,014 37 0.144
-0.0154
AC CE P
TE
D
MA
NU
SC
RI
PT
(1) All
(0.0174) 1,014 37 0.356
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. b The Sargan test is a commonly used test for instrument strength with the method of GMM. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
52
ACCEPTED MANUSCRIPT Table 10: Economic growth and renewable industrial energy consumption GMM FE (2) (3) (4) (5) (6) (7) Low and Upper High All Low and Upper lower middle income lower middle middle middle Panel 1: GDP per capita (2005 USD, PPP) growth on renewable industrial energy consumption Log of lagged 0.964*** 0.999*** 0.968*** 0.973*** renewable industrial energy per (0.0237) (0.00972) (0.0270) (0.0181) capita GDP per -0.533 1.060 -0.834 -0.487 0.203 0.480** -0.183 capita, 2005 USD, PPP, (0.372) (0.804) (0.652) (0.373) (0.144) (0.197) (0.225) growth Observations 2,356 823 639 894 2,356 823 639 Number of 74 27 30 33 74 27 30 count R-squared 0.027 0.059 0.054 AR(2) pval 0.934 0.874 0.115 0.541 Sargan test 102.2 75.05 34.85 129.6 chi2 Sargan test 0.161 0.0455 1 0.00323 pval Panel 2: Renewable industrial energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 0.995*** 1.003*** 0.998*** 0.997*** GDP, 2005 USD PPP per (0.00293) (0.00539) (0.00506) (0.00111) capita Growth of 0.00159 0.0169 -0.00149 -0.00289 0.00103*** 0.0195*** -0.000383 total renewable industrial energy per (0.00314) (0.0129) (0.00266) (0.00256) (0.000338) (0.00582) (0.000761) capita Observations 2,356 823 639 894 2,356 823 639 Number of 74 27 30 33 74 27 30 count R-squared 0.163 0.139 0.203 AR(2) pval 0.0287 0.0927 0.588 0.239 Sargan test 48.96 89.88 4.561 11.68 chi2 Sargan test 1 0.00591 1 1 pval
(8) High income
-0.468 (0.293) 894 33 0.052
AC CE P
TE
D
MA
NU
SC
RI
PT
(1) All
-0.000144
(0.000902) 894 33 0.430
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. b The Sargan test is a commonly used test for instrument strength with the method of GMM. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
53
ACCEPTED MANUSCRIPT Table 11: Economic growth and renewable residential energy consumption GMM FE (2) (3) (4) (5) (6) Low and Upper High All Low and lower middle income lower middle middle Panel 1: GDP per capita (2005 USD, PPP) growth on renewable residential energy consumption Log of lagged 1.000*** 0.985*** 0.995*** 0.990*** renewable residential energy per (0.0103) (0.0149) (0.0115) (0.00891) capita GDP per capita, 0.313 0.164 0.228 -0.294 0.0814 0.0903 2005 USD, PPP, (0.229) (0.305) (0.233) (0.542) (0.0881) (0.149) growth Observations 2,815 1,072 729 1,014 2,815 1,072 Number of 82 30 33 37 82 30 count R-squared 0.022 0.030 AR(2) pval 0.730 0.217 0.904 0.493 Sargan test chi2 53.94 108.8 50.94 80.83 Sargan test pval 0.999 3.02e-05 0.999 0.720 Panel 2: Renewable residential energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 0.992*** 1.002*** 0.996*** 0.993*** GDP, 2005 USD PPP per (0.0208) (0.00548) (0.0119) (0.00326) capita Growth of total -0.00139 0.0227* -0.000279 -0.000967 0.000130*** 0.0163 renewable residential energy per (0.00404) (0.0119) (0.00111) (0.00118) (3.33e-05) (0.0185) capita Observations 2,815 1,072 729 1,014 2,815 1,072 Number of 82 30 33 37 82 30 count R-squared 0.139 0.093 AR(2) pval 0.709 0.0674 0.711 0.0514 Sargan test chi2 1148 55.71 463.8 18.62 Sargan test pval 0 0.598 0 1
(7) Upper middle
(8) High income
0.123
-0.155
(0.114)
(0.194)
729 33
1,014 37
0.052
0.076
7.75e-05
6.36e-05**
(0.000118)
(2.97e-05)
729 33
1,014 37
0.219
0.354
AC CE P
TE
D
MA
NU
SC
RI
PT
(1) All
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. b The Sargan test is a commonly used test for instrument strength with the method of GMM. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
54
ACCEPTED MANUSCRIPT Table 12: Summary of results by income panels and energy consumption
Panels
Feedback Growth Conservative Neutral LLM UM LLM UM LLM UM LL UM ALL I I HI ALL I I HI ALL I I HI ALL MI I HI *
Total residential
*
Total non-renewable
*!
*
*! *!
*!
#!
#
SC
#
*
*!
Non-renewable residential
NU
Renewable industrial
* *!
* *!
*!
*
*
Total renewable Non-renewable industrial
*
*
RI
Total industrial
PT
Hypothesis
*!
*!
* *
#
*
*!
*
*
*!
*!
AC CE P
TE
D
MA
* *! *! * Renewable residential Notes: ALL refers to all countries panel; LLMI: low and lower middle income panel; UMI: upper middle income panel; and HI: high income panel. * capture significant GMM results; ! captures every GMM that is confirmed by FE; and # captures additional results after the inclusion of investment. Detailed results are presented as supplementary results.
55
ACCEPTED MANUSCRIPT Highlights: 1. The feedback effect features in non-renewable and non-renewable industrial energy. 2. There is sign of feedback in residential energy in LLMICs.
PT
3. Economic growth in LLMICs encourages industrial energy. 4. Economic growth boosts non-renewables in HIs and non-renewable industrial in UMICs.
AC CE P
TE
D
MA
NU
SC
RI
5. Neutrality is supported by renewable industrial and residential (for all panels).
56
S0140-9883(17)30308-0 doi:10.1016/j.eneco.2017.09.005 ENEECO 3753
To appear in:
Energy Economics
Received date: Revised date: Accepted date:
4 September 2016 6 September 2017 11 September 2017
Please cite this article as: Narayan, Seema, Doytch, Nadia, An investigation of Renewable and Non-renewable Energy Consumption and Economic Growth Nexus using Industrial and Residential Energy Consumption, Energy Economics (2017), doi:10.1016/j.eneco.2017.09.005
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT An investigation of Renewable and Non-renewable Energy Consumption and Economic Growth Nexus using Industrial and Residential Energy Consumption
PT
Seema Narayana* and Nadia Doytchb
NU
Corresponding author Phone: +61 3 99255890; Email: [email protected]
MA
*
SC
Koppelman School of Business, CUNY-Brooklyn College b
RI
School of Economics, Finance and Marketing, RMIT University a
Abstract
TE
D
Our study shows that links between economic growth and energy consumption (both expressed in per capita terms) differed for renewables and non-renewables for income panels over the
AC CE P
period 1971 to 2011. Renewables are mainly found to support the neutrality hypothesis. Only renewable totals in low and lower middle income (LLMI) countries are found to drive economic growth. The feedback, growth and conservative hypotheses strongly feature with non-renewables (total and industrial). Our results are derived by linking different definitions of energy consumption with economic growth for 89 countries divided into LLMI; upper middle income (UMI); and high income (HI) panels. JEL: Q42, Q43 Keywords: Residential energy consumption; Industrial energy consumption; Nonrenewables; Renewables; Income groups
1. Introduction
1
ACCEPTED MANUSCRIPT The study of the economic growth and energy consumption nexus allows for testing of four hypotheses with strong implications for growth and energy policy: (1) the feedback hypothesis (which indicates a bidirectional causation between the two variables); (2) the growth hypothesis
PT
(or a unidirectional causation flowing from energy to economic growth); (3) the conservative
RI
hypothesis (or a unidirectional causation flowing from economic growth to energy); or (4) the
SC
neutrality hypothesis (no causation). Hypothesis (2) advances the view that energy consumption promotes economic growth. It is also widely believed that demand for energy is propelled by
NU
economic growth, giving rise to hypothesis (3). The complementarity between energy and economic growth, as portrayed in (2) and (3), has given rise to hypothesis (1). Hypotheses (1)
MA
and (2) posit a connection between energy policy and economic growth. Acceptance of these hypotheses discourages restrictive energy policies, but calls for policies that promote energy
TE
D
efficiency. The prevalence of hypotheses (3) or (4) emphasises a disconnection between energy
2016).1
AC CE P
policies and economic growth and gives a green light to energy conservation policies (Narayan,
While a plethora of studies examine this nexus for total energy consumption, there is an emerging strand that examines the four hypotheses explicitly for total renewables and/or nonrenewables for one or more individual countries, or a panel of countries, by their level of economic development or region (see, Tiba et al., 2016; Bhattacharya et al., 2016; Chang et al., 2015; Salim, et al., 2014; Pao et al., 2014; Marques and Fuinhas, 2012; Apergis and Payne 2011a; Aperis and Payne, 2011b; Apergis and Payne 2010a; Apergis et al. 2010; and Sadorsky, 2009). Some also examine the traditional energy consumption and economic growth nexus for different energy mixes of renewables and non-renewables (see Bloch et al., 2015; Long et al.,
1
For further details and an exhaustive survey of this literature see Tiba and Omri (2017) and Payne (2010).
2
ACCEPTED MANUSCRIPT 2015; Pao and Fu, 2013; Marques and Fuinhas, 2012; and Yildirim et al., 2012). We summarise these studies in Table 1.
PT
By mainly using cointegration/VECM/VAR/Granger causality tests for developing and developed nations, this group of studies clarifies policy directions along the lines of long-term
RI
growth and energy by renewable and non-renewable energy. While this literature shows
SC
evidence that is mixed, for both renewables and non-renewables, majority tend to find support
NU
for the feedback hypothesis (see Table 1).
MA
D
in support of the feedback effect in the long run (Apergis and Payne 2010a and b, 2011a and b;
TE
Apergis et al., 2010; Apergis and Payne, 2012; Apergis and Danuletiu, 2014; Chang et al., 2015; Salim et al. 2014; Tiba et al., 2014; Tugcu et al., 2012; Pao and Fu, 2013; Lin and Moubarak,
AC CE P
2014; Sebri and Ben-Salha, 2014; Bloch et al., 2015; Omri et al., 2015). Only nine out of 31 studies consider short-term causality and all nine show evidence in favour of the feedback effect in the short-run (Sadorsky, 2009; Apergis and Payne 2010a and b, 2011a and b; Apergis and Payne, 2012; Pao et al., 2014; Sebri and Ben-Salha, 2014; and Shahbaz et al., 2015). Evidence that renewable energy leads economic growth is found in 21% of the studies in the long-run (Pao et al., 2014; Bhattacharya et al., 2016; Yildirim et al., 2012; Chang et al., 2015; Shahbaz et al., 2015; Omri et al., 2015). Evidence that economic growth causes higher demand for renewable energy is found in 14% of the studies (Sadorsky, 2009; Menyah and Wolde-Rufael, 2010; Long et al., 2015; Omri et al., 2015). Neutrality between renewable energy consumption and economic growth is supported in 10% of the studies (Marques and Fuinhas, 2012; Pao and Fu, 2015; Chang et al., 2015).
3
ACCEPTED MANUSCRIPT Of the 12 studies that examine the link between non-renewable energy consumption and economic growth, majority (67%) show the same feedback effect in the long run (Apergis and Payne, 2011a; Pao et al., 2014; Salim et al. 2014; Pao and Fu, 2013; Pao and Fu, 2015; Shahbaz
PT
et al., 2015; Bloch et al., 2015; Long et al., 2015). The evidence of the growth hypothesis is
RI
prevalent in 25% of the studies (Tugcu et al., 2012; Bhattacharya et al., 2016) in the long run and
SC
one study, Pao et al., 2014, in the short run. Evidence of the conservative hypothesis is not found, but the neutrality hypothesis is found in Tugcu et al. (2012).
NU
In this study, we re-examine the four hypotheses outlined above for renewable and non-
MA
renewable energy consumption per capita.2 The point of difference between this study and the extant literature is that we distinguish between residential and industrial users of renewable and
D
non-renewable energy. This distinction recognises the varying energy source mix which we
TE
explain below. We find that the disparity in the energy type mix between the two user groups
AC CE P
becomes more apparent when we classify the users of energy by their level of development. It can therefore be argued that the link between energy and economic growth will depend on the level of development, the users, and the energy mix. The four hypotheses are tested for countries categorised into four income groups: high income (HI), upper middle income (UMI), and low and lower middle income (LLMI). Several of the abovementioned panel-based studies examine the four hypotheses on the basis of the level of development of the country, although most of these studies only examine a developed country panel (Apergis and Payne 2010a; Salim, et al, 2014; and Chang et al, 2015) or developed and developing panels (see Apergis and Payne 2011a; Apergis et al. 2010; and Omri et al, 2015). Tiba et al. (2016) is the only study that examines the energy-growth nexus for two out of four
2
Energy consumption and economic growth are examined in per capita terms, although we mostly refer these simply as energy consumption or economic growth.
4
ACCEPTED MANUSCRIPT income groups (see Table 3 below) normally used to classify countries, i.e. high income and middle income countries. Such a division is important as across these income groups the nature of changes occurring with respect to the use of renewables and non-renewables, is significantly
PT
different. The International Energy Agency (IEA, 2015) notes that in the United States and the
RI
European Union, expansion of renewable energy occurs largely at the expense of aging fossil-
SC
fuelled capacity. In China, India, and many other developing countries, expansion of renewable capacity goes hand-in-hand with efforts to expand energy supply from other sources, to keep up
NU
with rapidly increasing demand.
MA
So far, only one study has shed some light on the association between economic growth and renewable and non-renewable industrial energy consumption (see Doytch and Narayan,
D
2016).3 Doytch and Narayan (2016) examine the one-way link going from economic growth to
TE
total industrial energy consumption for high, middle and low and lower middle income groups –
AC CE P
in effect only testing the conservative hypothesis. Their GMM estimations, which also factor in for sectoral FDI, suggest that real GDP per capita growth positively and significantly influences industrial energy consumption (renewable and non-renewable). Our study follows the same testing procedure, but focuses on the bilateral relationship for this category of energy consumption (industrial) as well as residential renewables and non-renewables. In doing so, our study is one of the few to explore the short-term association between renewable (and nonrenewable) energy consumption and economic growth. The link between industrial energy consumption and economic growth is one that can be contemplated without much difficulty using the growth theory. For instance, industrial energy use by both labour and capital produces goods and services. However, the theoretical
3
See footnote 4 for a study that uses industrial energy consumption without differentiating between renewables and non-renewables.
5
ACCEPTED MANUSCRIPT underpinning for the relationship between residential energy consumption and economic growth is unknown. Empirical evidence on hypotheses (1) to (4) for total residential (and industrial) energy consumption is also rather thin. 4 Nonetheless residential energy users are seen by
PT
policymakers as key target groups for energy conservation policies. “Residential energy
RI
conservation is a key component of contemporary energy and climate change policy in the US
SC
and elsewhere” (Suter and Shammin, 2013; 551). Pablo-Romero et al. (2017; 342) in their article “Global changes in residential energy consumption” reinforce this view, “(t)he residential energy
NU
sector is crucial to achieving CO2 emission reductions as it has an important energy-saving
MA
potential and its environmental controls are difficult to displace to other countries”. On the basis of an analysis of the residential energy consumption trends for the period
D
1993-2013, Pablo-Romero et al. (2017) recommend different energy policies across regions.
TE
They suggest that East Asian, Southern Asian, EU15, and other developed countries should
AC CE P
reduce residential energy use. However, since energy is generally seen as a vital component of growth, it is important to identify whether or not conserving residential energy use hurts economic growth. Further, should this policy of conserving residential energy use be enforced on all countries? Knowledge about the link between residential energy and economic growth, or whether residential energy is a viable target for conservation, is lacking. Our study attempts to provide more evidence for understanding these important questions. We focus on the effects of residential energy on short-term economic growth for income group panels of countries. In this study, using data from 89 countries, we uncover additional compelling differences in the consumption patterns of residential and industrial – renewable and non-renewable – 4
Yang et al. (2010) is the only study that examines the link between energy and economic growth in Taiwan by distinguishing between industrial and residential energy consumption. Their linear Granger causality test shows that economic growth has a bidirectional link with industrial energy consumption but an insignificant relationship with residential energy consumption, hence implying a disconnection between energy conservation policy and economic growth.
6
ACCEPTED MANUSCRIPT energy consumption, by income group, over the period 1971 to 2011. We explain these in detail in Section 2 (also see Table 2). Here we briefly explain some of the patterns and trends relating to the energy mix of the income groups from our preliminary analysis to illustrate the importance
PT
of the energy mix, users and income groups in the examination of the linkage between energy
RI
consumption and economic growth.
SC
First, total industrial energy consumption takes a larger share of final energy consumption than total residential energy usage in high income and upper middle income
NU
countries (Table2, I7 and R7). Nonetheless, between these two groups, over the period 1971 to
MA
2011, the average growth of industrial share (of final consumption) has been falling in the high income countries, but increasing strongly in the case of the upper middle income countries
D
(Table 2, I10). Of the low and lower middle income countries, industrial share of final
TE
consumption has been on average increasing (at a slower rate than the upper middle income) and
AC CE P
for this income group (unlike other groups), on average residential energy users are bigger than industrial energy users (Table 2, I10). From these preliminary observations, given the difference in the key energy users by income group, we expect that: (a) residential energy consumption is a significant contributor to economic growth in the low and lower middle income countries and it seems that for this set of countries conservation of energy will hurt economic growth; and (b) industrial energy consumption is a significant contributor to economic growth in upper middle income countries, and possibly the high income group as well. Second, in terms of the distribution of renewable and non-renewable, we notice that, across all income panels, residential users are more important than industrial users for renewable energy; and for non-renewable energy, industries are more substantial users than residential users. The residential renewable energy consumption share of total final renewable consumption is, on average, greater than the industrial renewable energy consumption share of total final 7
ACCEPTED MANUSCRIPT renewable energy consumption for the period 1971 to 2011 (Table 2, I9 and R9). However, the opposite is true for non-renewable consumption share of total final non-renewable consumption for all panels (Table 2, I8 and R8). From this observation we can state that: (c) residential
PT
renewable energy, not industrial renewable energy, is a driver of economic growth and/or vice
RI
versa. However, with biomass being the most dominant source of energy for residences in low
SC
income countries, the negative effects of biomass energy on health, environment, and productivity is also widely known and documented (see our discussion in Section 3.3). As a
NU
result, it is likely that: (d) for low and middle income countries the positive effects of residential
MA
renewable energy use on economic growth will be nullified, or will become negative.
D
Third, growth of residential renewable energy consumption share of total final
TE
renewable consumption averaged over 1971 to 2011 has been strongest in high income countries,
AC CE P
but growth of industrial renewable energy consumption share of total final renewable consumption is the strongest for the upper middle income countries (Table 2, I12, R12). Since several studies (mentioned above) suggest economic growth encourages demand for renewable energy consumption, it becomes important to investigate whether: (e) economic growth is a trigger for residential renewable energy consumption in high income countries; and (f) economic growth is a trigger of industrial renewable energy consumption in the upper middle income countries. Fourth, over the same period the average growth of renewable energy consumption is stronger for the residential sector than for the industrial sector in high income and low and lower middle income countries, suggesting higher adoption of renewables in the residential sector (see Table 1, I6 and R6). In the upper middle income countries, we observe the opposite effect, with
8
ACCEPTED MANUSCRIPT industries adopting renewable energy more than the residential sector. This preliminary finding matches with our statement in (a) that residential energy consumption may be seen as a
PT
contributor of economic growth in low and lower middle income countries. Overall, given the difference between the levels, proportions, and growth of the share of
RI
residential and. industrial renewables and non-renewables across the income groups, we believe
SC
that the short-run effects of economic growth on energy (and/or vice versa) – and therefore the policy implications – cannot be homogenous for different energy users and energy sources.
NU
Hence, when testing the four hypotheses related to the economic growth-energy consumption
MA
nexus, we apply the different energy categories and income panels mentioned previously. For completeness, we also examine energy consumption as total residential; total industrial; total
D
renewables; and total non-renewables. We apply the GMM approach as our main estimation
TE
method, but also provide results derived using the fixed effect method.
AC CE P
To foreshadow our key GMM results, our study shows significant differences in results amongst residential and industrial users by income panel. In fact we find that the effect of economic growth is multi-facetted. In other words the effects of economic growth are felt by more than one energy user group, although not all user groups contribute to economic growth. Our findings suggest the presence of a feedback effect, with total non-renewable for both the low and lower middle income, and upper middle income panels. Interestingly, for the upper middle income panel, economic growth is also found to positively impact on non-renewable industrial energy. However, this panel’s non-renewable industrial energy consumption is not a significant driver of growth in the short-term. The high income panel also shows feedback for total non-renewable after allowing for investment. Further we find evidence of feedback for non-renewable industrial energy of the 9
ACCEPTED MANUSCRIPT high income, as well as the low and lower middle income group, implying that non-renewable industrial energy consumption is the significant source of the feedback effect for total non-
PT
renewable energy use. Further, total residential energy consumption encourages economic growth in low and
RI
lower middle income (LLMI) countries. Additionally, economic growth in the low and lower
SC
middle income panel boosts total industrial, non-renewable residential and total renewable energy consumption , a sign of the multi-facet effect of economic growth in the case of LLMI
NU
countries.
MA
The models with economic growth-energy consumption variables find neutrality for renewable industrial and residential (for all panels). The neutrality hypothesis is also present
TE
D
with renewables (except LLMI); total residential (UMI, HI) and industrial (HI). We augmented the per capita based models with investment, following Apergis and
AC CE P
Payne (2010a, 2010b, 2011a, 2011b). From this exercise, we find that most of our results are robust. After accounting for capital investment, we were able to find the presence of feedback for high income total non-renewables. We are also able to show feedback in non-renewable industrial use for the LLMI group. However, non-renewable residential use for low and lower middle income countries, which originally supported the conservative hypothesis, moves to neutrality after capital investment is captured. The balance of the paper is organised as follows. Section 2 continues the discussion of the average trend of renewable and non-renewable – residential and industrial – energy consumption, in terms of levels, share, and growth. These are explained by paying attention to all 89 nations and each of the three income groups studied here. Section 3 presents different case studies on groups (EU, developing countries) or specific countries (the US, India) to explain 10
ACCEPTED MANUSCRIPT some of the trends that were presented in the previous section. This section explains the composition of residential and industrial energy consumption as well as some of the social and economic issues relating to renewable and non-renewable energy consumption by residential and
PT
industrial users. Section 4 lays out the methodology applied in the paper to test the hypotheses.
RI
Section 5 presents the results. We also provide a summary of the results. Section 6 provides the
NU
SC
concluding remarks.
2. Residential and industrial energy consumption (renewable and non-renewable) by
MA
income group
D
The average industrial and residential energy consumption, in terms of total, non-renewable, and
TE
renewable, for the period 1971 to 2011 is presented in Table 2 in levels, shares, and growth terms according to three income groups: low and lower middle income, upper middle income, and high
AC CE P
income countries.
Table 2 displays some of the facts which are readily known about energy consumption across income groups. For instance, high income countries are the largest consumers of industrial and residential energy. It is also known that high density industrial sectors in upper middle income groups are the second biggest users. And with relatively lower industrial density, the low and lower middle income groups have the least amount of industrial energy consumption.5 What is probably less known is that this pattern is also prevalent in the case of non-renewable energy consumption – for industrial and residential – but is not the case for renewables.
5
These facts are based on a sample of nations chosen on the basis of data availability. These statistics indicate that each group is sufficiently represented and hence the other features of the energy mix representative of the income groups.
11
ACCEPTED MANUSCRIPT There are many interesting facts that can be observed from Table 2. First, total industrial energy consumption per capita is greater than those of residential consumption for the upper middle income and high income groups (see Table 2, rows I1 and R1). However, in the case of
PT
the low and lower middle income group, residential energy consumption is far greater than
RI
industrial consumption. In terms of growth, residential energy consumption is higher than
SC
industrial for high income countries. This is consistent with industrial and residential energy consumption as a share of total final consumption for these nations (Table 2, rows I7 and R7).
NU
Second, in terms of growth of total industrial consumption, the upper middle income
MA
takes the lead followed by low and lower middle income countries and high income countries (see Table 2, row I7). The pattern is different for growth of non-renewable industrial energy –
D
the low and lower middle income group shows the strongest average growth rates, followed by
TE
the upper middle income countries (Table 2, row I8). The high income countries show a negative
AC CE P
growth rate for non-renewable energy. While this is being compensated for by renewable energy, row I9 of Table 2 also reveals that growth rates for renewable energy consumption by industries in high income countries has in fact, on average, been lower than those of upper middle income countries (but higher than the lower income countries). Table 2 (row I11) reveals that the share of non-renewable energy consumption by industry to total non-renewable energy consumption is indeed declining for high income countries. This trend is also present in the upper middle income countries. At the same time, the share of industrial renewable energy to total renewable energy consumption is increasing for both these panels, although growth is much higher for upper middle income countries. Further, in the total renewable energy use by industries, the high income panel takes the lead, followed by the other two income groups (see Table 2, R3). Together these indicate that efficient use of
12
ACCEPTED MANUSCRIPT energy and adoption of renewable energy is mainly driving the negative growth for nonrenewable energy use in our panels of high income countries and upper middle income countries.
PT
Third, for residential energy consumption, the narrative is different (from industrial energy consumption) across income groups and energy mix. While total energy consumption by
RI
the residential sector is indeed highest in the high income countries and lowest in the low and
SC
lower middle income countries, the share of residential energy consumption to final consumption
NU
is greatest for the low and lower middle income group.
Total renewable energy consumption by residents is, on average, greatest for low and
MA
lower middle income countries, with the upper middle income and high income countries lagging behind (see Table 2, row R3). Similarly, the average share of renewable residential energy
D
consumption to total renewables is strongest for our low and lower middle income group and
TE
weakest for the high income group. However, the mean growth of this share is highest among the
AC CE P
high income nations and lowest among poor nations. As well, the average share of nonrenewable residential energy consumption to total non-renewables is highest for the high income group and lowest for the low and lower middle income group. But the mean growth of this share is highest among the low and lower middle income countries and lowest among rich nations. Fourth, in terms of growth, residential energy is strongest in the high income group, followed by upper middle income and low and lower middle income groups (see row Table 2, row R9). Non-renewable energy consumption, on the other hand, grew fastest in the low and lower middle income countries – the energy deficit countries (see Table 2, row R8). This was followed by upper middle income and high income countries. Fifth, if we compare renewable and non-renewable energy consumption between industrial and residential users, we find that for all three income panels the share of growth in 13
ACCEPTED MANUSCRIPT non-renewable energy consumption is higher in residential than industrial (Table 2, rows I8 and R8). However, growth in renewable energy consumption by residence is much stronger in high income and low and lower middle income countries (Table 2, I9 and R9). In the upper middle
PT
income countries, we observe the opposite effect – industries are adopting more renewable
RI
energy than residences.
SC
Sixth, we find that over the study period, mean renewable energy consumption total as a percentage of total final consumption is highest for the low income countries. This is followed by
NU
middle income countries, and finally high income countries (Figure 1). This is same for
MA
renewable industrial (or residential) energy consumption as a percentage of total industrial (or residential) energy consumption (Figures 2 and 3). We observe a recent resurgence of renewable
D
energy use in LLMI countries mainly due to residential energy consumption.6
TE
3. Residential and industrial energy consumption (renewable and non-renewable)
AC CE P
In this section, we look behind the trends described in the previous section. 3.1 Renewable energy - residential and industrial Our preliminary analysis revealed an increase in the share of renewable energy for high income and upper middle income countries. There is strong evidence that the share of renewable energy has been increasing for the European Union (EU), which comprises high and upper middle income nations, reaching 16% in gross final energy consumption, and is on track to meet its target, a 20% share of renewable energy in its gross final energy consumption by 2020 (European Environment Agency, EEA, 2015). Between 2005 and 2013, renewable energy use grew by an average 0.8% every year.
6
We thank an anonymous referee for suggesting the addition of the three figures.
14
ACCEPTED MANUSCRIPT In the EU countries, residences use renewable energy in power generation in the heating and cooling sector. The renewable energy share in electricity consumed in the EU grew at an average of 1.3% per year between 2005 and 2013. In 2013, in the EU 25.4% of electricity
PT
consumed was generated from renewables, with around 38.0% contributed by variable renewable
RI
electricity (wind and solar) (EEA, 2015). Bioenergy (for electricity and heating) make the largest
SC
source of renewable energy in the EU, followed by hydropower, and then wind power. In the EU, renewable energy support comes in the form of surcharges on the energy bill
NU
of the end users (this prevails in the case of electricity) and governmental support. Countries with
MA
a higher effective tax rate on carbon dioxide generally have a higher rate of patent applications in renewable technologies. The 2014 EEA report identifies that in 2012, 32 countries have a total of
D
236 support measures associated with renewables, which represents 40.5% of the identified
AC CE P
fuels and nuclear energy
TE
support measures. The rest of the energy support measures (59.5%) were in support of fossil
Innovations in, and deployment of, renewable energy may be viewed as an important driver in shaping the future of our economic systems (EEA, 2014). In Europe, the renewable energy industry provides more than 2.2 million full-time equivalent jobs (EEA, 2014). There is evidence to suggest that investment in renewables is also growing in nonEuropean countries. The International Energy Agency (IEA, 2016) reports that global investment in renewable-based power generation was $270 billion in 2014, and positive policy moves in many countries are encouraging growth of the renewable sector. In many countries, renewable technologies are becoming increasingly cost-competitive, although public support schemes are still required for their deployment in many others. In 2014, renewable-based power generation capacity amounted to 45% of world power generation capacity additions and the International
15
ACCEPTED MANUSCRIPT Energy Agency (IEA, 2015) also reports that China (a lower middle income country) remains the largest wind power market, with increasing capacity (20 GW) more than the wind power market of Germany (5GW) and US (less than 5GW). Solar power has also expanded strongly in Asia,
PT
particularly in China and Japan. Nonetheless, the power sector continues to be the world’s
RI
dominant fossil-fuel consumer, accounting for 40% of the energy sector total in 2013 (IEA,
NU
SC
2013).
MA
3.2 Residential energy consumption in a high income country It was reported above that residential energy consumption per capita is the strongest in high
D
income countries. In the US, in 2012, the residential sector accounted for 21% of total primary
TE
energy consumption and about 20% of carbon dioxide emissions in the (Energy Information Administration (EIA), 2015). While household energy usage increased in the 1980-2009 period
AC CE P
by 8.9%- an average annual growth of 0.3%, aggregate energy intensity per household and per square foot declined by 24.2% and 43.1%, respectively (see EIA, 2015). In the US, single-family units form the major type of household. In 2009 single-family households in the US accounted for about 69.1% of the housing, 85.9% of floor space and 80.0% of site energy consumption (EIA, 2015). The rest was apartments (24.8% of total housing) and mobile homes (6.1%). There is, however, a movement from detached single-family housing to attached ones in larger buildings, using less energy. The size of housing, on the other hand, has increased, on average, in the US. While this varies by the type of housing, single-family detached houses have grown faster in size than other types of housing. Nonetheless, they have used less energy on heating due to improved energy efficiency of heating equipment, along with better window design and insulation to more effectively seal homes (EIA, 2013). 16
ACCEPTED MANUSCRIPT The US Energy Information Administration (EIA, 2015) reports that part of the decline in residential energy consumption, particularly space heating, is due to the household shifting from the Northeast and the Midwest to the warmer regions of the South and West, where demand is
PT
lower for space heating than for space cooling, and where energy consumption (per household
RI
and per square foot) is also lower.
SC
Appliances (dishwashers, clothes washers, clothes dryers, two or more TVs, computers, digital video recorders, and video game systems) and refrigeration account for the largest share
NU
of household electricity consumption. Other key end uses are space heating, water heating, and
MA
air conditioning. In fact an EIA report released in 2011 suggested that in US homes the share of residential electricity used by appliances and electronics has doubled in three decades. The
D
energy consumption share of space heating has fallen from 53.1% in 1993 to 41.5% in 2009;
TE
while the share of appliances, electronics and lighting has climbed from 24.0% to 34.9%
AC CE P
between 1993 and 2009, offsetting some of the efficiency gains (EIA, 2013). While natural gas remains as the main source of energy in the US over the 29 year period from 1980 to 2009, its share has fallen from 53.3% in 1980 to 46.1% in 2009, while the share of electricity increased from 26.6% to 43.1%. The fall in natural gas intensity was mainly a result of improvements in the efficiency of household space heating units and building codes as well as increases in natural gas prices (EIA, 2015:12). More expensive natural gas has led to its replacement with heating equipment using other fuels, as well as the purchase of gas heating equipment more efficient than the 78% Annual Fuel Utilization Efficiency (AFUE) standard – currently units are as high as 97% AFUE (EIA, 2015).7
7
A AFUE of 78% means that 22% of the heating gas was lost in the combustion process and did not contribute to warming homes.
17
ACCEPTED MANUSCRIPT According to the EIA (2015), the post-1990s period reflects the impact of many forms of energy efficiency programs, including the initial US Federal mandatory energy efficiency standards, enacted between 1988 and 1994, for all major appliances, demand-side management,
PT
and ENERGY STAR rating systems.
RI
3.3 Residential energy mix in India and other low and lower middle income nations
SC
Historically, in India the industrial sector was the highest electricity user, but the residential
NU
sector has become equal to, or slightly greater than, industrial demand. The EIA reports (EIA, 2014) that while electricity consumption is growing exponentially in India, the country’s
MA
electricity system is maturing, with difficulties (outages, shortages, issues with reliability and
D
quality) that are characteristic of a developing country.
TE
Like many aspects of a developing country, there are clear distinctions between urban and rural centres in energy consumption. In terms of energy mix, electricity is the least used
AC CE P
energy source (EIA, 2014). Indian urban centres use LPG most, followed by electricity, biomass (wood), and kerosene, while rural centres use wood, followed by kerosene, electricity, and LPG (EIA, 2014). Lighting is provided by both electricity and kerosene (EIA, 2014). Furthermore, in rural centres of developing countries in tropical/temperate zones (Asia and Africa), energy is mostly used for cooking. While a precise breakdown is difficult, the main use of energy in households in developing countries is for cooking, followed by heating and lighting (IMF, 2006). In its World Economic Outlook the IMF (2006) reported that 2.5 billion people, or 52 % of the populations in developing countries (mostly in rural centres) use biomass (wood, agricultural waste and animal dung) to meet their energy needs for cooking, accounting for over 90% of household energy consumption in many countries. Over half of these people live in India, China, and Indonesia. The proportion of the population relying on biomass is highest in 18
ACCEPTED MANUSCRIPT sub-Saharan Africa. The IMF (2006) report argues that unsustainable harvesting practices and inefficient energy conversion technologies have serious adverse consequences for health, the environment and economic development. Every year about 1.3 million people– mostly women
PT
and children – die prematurely because of exposure to indoor air pollution from biomass fuels.
RI
Valuable time and effort is devoted to fuel collection instead of education or income generation
SC
(IMF, 2006:416). Land degradation and regional air pollution can also result (World Energy Council, 1999).
NU
In contrast, in the urban centres, the trends are different. For instance, Zhao et al. (2012)
MA
examines China’s urban renewable energy consumption during the period 1998 to 2007 and finds evidence of an intensive structural change towards high quality and cleaner energy, such as
D
electricity, oil, and natural gas. The authors point out that this trend, “…reflects a changing
TE
lifestyle and consumption mode in pursuit of a higher level of comfort, convenience and
AC CE P
environmental protection” (2012; 644). 4. Model, methodology and data. 4.1 Methodology
To examine the four hypotheses associated with energy consumption and economic growth, our empirical models are:
ln( EnConsitk ) 0 1 ln( EnConsik,t 1 ) 2 (Growth _ yit ) 5 t i1 it1
(1)
ln( y it ) 0 1 ln( y i ,t 1 ) 2 (Growth _ EnCons itk ) 5 t i 2 it 2
(2)
with in ~ i.i.d (0, in ) , itn ~ i.i.d.(0, ), E[in itn ] 0, 𝑛 = 1,2. Here EnConsitk is a measure of energy consumption divided by the population; 𝑦𝑡 is the per capita GDP in constant 2005 19
ACCEPTED MANUSCRIPT prices, PPP; and is a time (annual) dummy and 𝜇𝑖 is an idiosyncratic country-specific effect. 8 t
Model (1) tests the conservative hypothesis while model (2) tests the growth hypothesis. If 2 in models (1) and (2) is found to be different from zero, then we accept the feedback hypothesis.
PT
However, insignificance of 2 in both models (1) and (2) suggests the prevalence of the
SC
RI
neutrality hypothesis.
We examine the two models for several measures of energy consumption. The subscript "k"
NU
[k=(1)...(8)] captures measures of different types of energy consumption: (1) total residential; (2) total industrial; (3) total non-renewables; (4) total renewables; (5) residential non-renewables; (6)
MA
industrial non-renewables; (7) residential renewables; and (8) industrial renewables.
D
System GMM is a method superior to fixed effects, when there is an endogeneity problem in
TE
the data. The correlation between lagged dependent variables and the unobserved residual is precisely the reason why panel data is to be preferred to cross-sectional when analysing change
AC CE P
in the dependent variable. Cross-section estimates produce a bias, caused by the correlation between EnCons
k
i ,t 1
and i1 in model 1 and yi ,t 1 and i 2 in model 2, which disappears in
samples with a large time-dimension, but does not disappear with time-averaging. Thus, if such a correlation exists, the true underlying structure has a dynamic nature and time-averaging crosssection techniques introduce a bias that cannot be removed by controlling for fixed-effects. Therefore, to avoid these pitfalls, we adopt the GMM methodology.
8
The model combines equations:
ln( EnCons itk ) 0 1 ln( EnCons ik,t 1 ) 2 (Growth _ y it ) 5 t u it1 , and
ln( yit ) 0 1 ln( yik,t 1 ) 2 ln(Growth _ EnConsitk ) 5 t i 2 uit 2 and
the unobserved country-specific effects and
itn
u1it = i1 it1 ,
and
uit 2 = i 2 it 2 where in are
are the errors, 𝑛 = 1,2.
20
ACCEPTED MANUSCRIPT A potential problem of the Arellano-Bond difference GMM estimator is that, under certain conditions, the variance of the estimates may increase asymptotically and create considerable bias if: (i) the dependent variable follows a random walk, which makes the first lag a poor
PT
instrument for its difference, (ii) the explanatory variables are persistent over time, which makes
RI
the lagged levels weak instruments for their differences, (iii) the time dimension of the sample is
SC
small (Alonso-Borrego and Arellano, 1999 and Blundell and Bond, 1998). These sets of conditions, therefore, apply: (i) no second order autocorrelation in the error
NU
term: E[Growth _ EnConsik,t s ( i1t i1,t 1 )] 0 ; E[Growth _ yi ,t s ( i 2t i 2,t 1 )] 0 ; for s
MA
≥ 2 and t=3,….T, where the growth rate of GDP and energy consumption, respectively, are instrumented with GMM-style instruments in each of the models and (ii) No correlation of the country-specific
D
unobserved
effect
with
their
difference
E[(Growth _ EnConsik,t 1 Growth _ EnConsik,t 2 )(i i1t )] 0
TE
;
AC CE P
E[(Growth _ yi ,t 1 Growth _ yi ,t 2 )(i i 2t )] 0 .
In addition to the GMM results, we also report robustness tests (see, Section 5.6 “Robustness tests”) with respective fixed effects models9.
4.2 Data The data are annual, multi-country (up to 89 countries) and span a forty-year period, from 1971 to 2011.10 Table 3 displays the list of countries in the sample under four categories: 1- low income; 2- lower middle income; 3- upper middle income; and 4- high income. This grouping is
9
In the recent literature there is a trend for dynamic panel studies utilising small cross-country samples to report simultaneously GMM and some static panel results as a robustness check (See, Bhattacharya et al., 2017 and Seven and Coskun, 2016). 10 The country panels are unbalanced.
21
ACCEPTED MANUSCRIPT based on the World Bank classification. In this study, we combine categories 1 and 2 as "low and lower middle income" countries. We use time-varying stratification of income level categories to
methodology of the World Bank.
RI
PT
capture shifts in development levels of countries 11 . The time-variation is also based on the
SC
According to the International Energy Agency, (IEA, 2013), the Residential and Industrial
NU
Energy breakdown is: Non-renewable Energy, including: coal, peat, crude oil and oil, natural gas; and Renewable Energy, including: nuclear, hydro, geothermal, solar, wind, and biofuel.
MA
Energy is measured in thousands of tonnes. The data source is the International Energy Agency
D
(IEA), World Energy Balances, Ed. 2013, extracted data set.12
TE
Real GDP per capita is measured in constant 2005 international dollars. We use the growth
(IEA).
AC CE P
rate of real GDP per capita. The source for this variable is the International Energy Agency
5. Empirical Results
In this section we highlight the empirical results in terms of the four different sets of measures on energy consumption. 5.1 Industrial and Residential total energy consumption and economic growth
11
For example, Albania transitioned from “lower middle income” to “upper middle income” in 2008 (please see Table 3). Thus, Albania was included as “lower middle income” for the period 1971 to 2007 and as “upper middle income” for the period 2008 to 2011. We thank an anonymous referee for indicating the need for this clarification. 12 The renewable and non-renewable energy consumption data are compiled from the proprietary data source "World Energy Balances", Edition 2013, available through subscription at International Energy Agency (IEA): http://www.iea.org/t&c/termsandconditions/. For more information on data definitions, please see Table 5.
22
ACCEPTED MANUSCRIPT Results relating to the nexus between industrial and residential total energy consumption and economic growth are presented in Tables 4 and 5, respectively. Panels 1 and 2 report estimated
PT
coefficients corresponding to equations 1 and 2. Economic growth has a positive and significant impact on industrial total energy
RI
consumption for all countries; the low and lower middle income; and upper middle income
SC
panels (Table 4, panel 1). Industries in low and middle income nations are increasingly becoming manufacturing based while high income countries’ key industries are service driven.
NU
Manufacturing demands more energy than service industries. Furthermore, high income
MA
countries are at the forefront when it comes to funding, developing, and using energy efficient technologies and appliances, which reduces energy intensities (see, for example, discussion in
D
case studies in Section 3). Hence, economic growth driving industrial energy consumption of the
TE
low and lower middle income and upper middle income panels, and not high income, at this level
AC CE P
of aggregation seems reasonable.
However, economic growth does not have a significant effect on residential total energy consumption (Table 5, panel 1). This result resonates best with upper middle to high income residential energy users. Where energy is mostly used for space heating and cooling, these households are equipped with standard heating, cooling and electrical appliances. For these groups, if income is a contributor to increased energy use, this would be insignificant. In comparison, for the low to lower middle income groups, because users are energy poor, economic growth should increase energy demand due to an improved ability to buy electrical appliances that will increase demand for electricity. However, we don’t find any evidence of this. It seems that not enough is earned by the low and lower middle income group, on a per capita basis, to have any significant impact on demand for residential energy.
23
ACCEPTED MANUSCRIPT Further improvements in energy efficiency reduce demand for energy and slow down the growth of energy use. We see from Table 2 that growth of residential energy consumption is the weakest for high income countries and strongest for the low and lower middle income group. In
PT
the EU for instance, energy efficiency has increased strongly in the residential sector. The
RI
Odyssee energy efficiency index for the EU27 increased by 24.0% over the 1990 to 2009 period
SC
with an annual average rate of 1.4%, driven by the diffusion of more efficient buildings, space heating technologies and electrical appliances (EEA, 2015).13 This is true for other industrialised
NU
nations. It is likely that this has weakened the link between economic growth and energy
MA
consumption.
In the EU nations, residential renewable energy is used in power generation in the heating
D
and cooling sector. The renewable energy share of electricity consumed in the EU grew at an
TE
average of 1.3% per year between 2005 and 2013. In 2013, in the EU 25.4% of electricity
AC CE P
consumed was generated from renewables, with around 38.0% contributed by variable renewable electricity sources (wind and solar) (EEA, 2015). Bioenergy (for electricity and heating) make the largest source of renewable energy in the EU, followed by hydropower and then wind power. However, in middle to lower income countries, relatively fewer households have access to electrical appliances; hence a tendency for residential energy consumption to be sensitive to economic growth is highly likely. Here, energy consumption increases are linked to rising personal income which permits higher standards of living, with increases in comfort levels and ownership of domestic appliances. Section 3.3 suggests that for these users, there are critical problems associated with the supply of energy.
13
http://www.eea.europa.eu/data-and-maps/figures/odyssee-energy-efficiency-index-odex-2
24
ACCEPTED MANUSCRIPT Hence, at the aggregated level, we are not seeing any significant effect (see Tables 4 and 5).
PT
RI
Both residential and industrial total energy consumption are, however, found to contribute to economic growth. For residential total, their effects are significant at the five
SC
percent level or better, only in the case of low and lower middle income countries.
NU
For industrial total, their effects on economic growth are also positive, but this effect is
MA
found to be at best significant at the 10 percent level in the case of all countries panel and upper middle income panel. Being heavily dependent on heavy industrial type manufacturing, an
D
increase in energy use is linked to increased industrial productivity in the upper middle countries.
TE
From Table 1, we find that this result is consistent with several studies.
AC CE P
5.2 Non-renewable and renewable total energy consumption and economic growth As noted in Section 1, various studies have examined the link between economic growth and total energy (renewable and non-renewable) consumption. Our results relating to the nexus between non-renewable and renewable total energy consumption and economic growth are presented in Tables 6 and 7, respectively. Panels 1 and 2 in each of these tables report estimated coefficients corresponding to equations 1 and 2. Economic growth leads to a higher consumption of non-renewables total for all panels: all countries; low and lower middle income; upper middle income; and high income. The marginal effect is more than proportional for the all countries and low and lower middle income panels, but less than proportional for upper middle and high income countries (Table 6, panel 1).
25
ACCEPTED MANUSCRIPT Economic growth has a positive and significant effect on the consumption of renewables total only for the low and lower middle income panel (Table 7, panel 1).
PT
Non-renewable total energy consumption also boosts economic growth – this is true for all, except the high income countries (Table 6, panel 2). The effect is strongest for the upper
RI
middle income countries. Renewable total, on the other hand, does not have any significant
SC
effect on income growth for any of the four panels.
NU
Overall, for non-renewables total energy consumption, our results confirm the feedback hypothesis for three out of four panels: all countries; low and lower middle income; and upper
MA
middle income countries. These findings imply a strong connection between energy policy on non-renewables and economic growth, with conservative energy policies directed at non-
D
renewables hurting economic growth in the short-term. In this respect, our findings are consistent
TE
with a few studies which consider groupings similar to ours, or cover nations that fall into one of
AC CE P
our income groups (see Apergis and Payne 2011a; Apergis and Payne, 2012; Pao and Fu, 2013; Pao and Fu, 2015; Salim, et al, 2014). Still, in regard to total non-renewables, the result for the high income panel confirms the growth hypothesis, which suggests that conservative energy policies relating to non-renewables will reduce economic growth. This is consistent with two studies that also examine total non-renewables as we have (see Bhattacharya et al, 2016; Marques and Fuinhas, 2012). With total energy consumption of renewables, we only find evidence to support of the conservation hypothesis for the low and lower middle income panel, and not for the other groups. This difference in results by income groups is consistent with the fact that total renewable energy consumption, in per capita terms in high and upper middle income countries, is still far less when compared to low and lower middle income countries (see Table 2A, I3 and Table 3B, R3). 26
ACCEPTED MANUSCRIPT Our finding implies that low and lower middle income countries can activate conservation policies with respect to renewable energy consumption without affecting economic growth. This finding is consistent with previous studies that examine individual countries in the
PT
low and lower middle income group (see, Long et al, 2015 on China; and Omri et al, 2015 on
RI
Argentina).
SC
The acceptance of the neutrality hypothesis for the high and upper middle income countries suggests that renewables and economic growth are unrelated. Nonetheless, our finding
MA
and economic growth for these countries.
NU
is at odds with Tiba et al. (2016) who suggest bidirectional relations between renewable energy
D
5.3 Non-renewable industrial and residential energy consumption and economic growth
TE
Results relating to the nexus between non-renewable industrial and residential energy consumption and economic growth are presented in Tables 8 and 9, respectively. Panels 1 and 2
AC CE P
of each table report estimated coefficients corresponding to equations 1 and 2. Economic growth positively and significantly impacts consumption of non-renewable industrial energy for all, except the low and lower middle income countries (Table 8, panel 1). The labour-intensive production processes in low and lower middle income countries, versus capital intensive processes in the upper middle income and high income countries, is likely to be the key driving force behind this finding. It is also worth noting that high income countries’ economic growth was not found to affect total industrial energy use, but the effect of economic growth became significant against non-renewable industrial energy. This suggests that there is strong potential for energy intensive industry to switch from non-renewables to renewables.
27
ACCEPTED MANUSCRIPT That economic growth affects residential total energy consumption was rejected previously (see Table 5, panel 1). Our results here show that non-renewable residential energy consumption has been positively impacted by economic growth, and, as expected, this is only
PT
visible for low and lower middle income countries (Table 9, panel 1). This finding suggests
RI
economic growth has improved access of non-renewables energy (whose average growth per
SC
capita (3.7%) has been stronger than that of total residential energy use per capita (0.02%)) for residential users in energy-poor nations (see Table 2, R5 and R4, i.e. the low and lower middle
NU
income countries).
MA
Non-renewable industrial energy consumption has a positive and significant influence on the income of high income countries only (Table 8, panel 2). Results in Panel 1 and 2 in Table
D
suggest that a switch to renewable would be a better policy direction than the conservative
TE
approach for non-renewable energy usage in industries in high income countries. In contrast,
AC CE P
residential non-renewable energy consumption has no significant effect on any of the income groups (Table 9, panel 2).
5.4 Renewable industrial and residential energy consumption and economic growth Results relating to the nexus between renewable and non-renewable total energy consumption and economic growth are presented in Tables 10 and 11, respectively. In both tables, panels 1 and 2 report estimated coefficients corresponding to equations 1 and 2. Results suggest that economic growth has a positive, but insignificant, influence on renewable industrial (Table 10) and renewable residential (Table 11) energy consumption. Similarly, renewable energy consumption – residential or industrial – has no significant impact on income. One conclusion that can be made from this is that while the upper middle income and higher income nations are putting increasingly more effort into making the switch to renewable 28
ACCEPTED MANUSCRIPT energy (as shown in Table 2), the major industries which are seen as the growth engines in these nations are lagging behind in switching from non-renewables to renewables. The low and lower middle income countries, whose per capita renewable energy consumption is greater than other
PT
income groups, are still using more of the renewable energy sources for household use. In poor
RI
countries the renewable energy mix largely comprises biomass fuels that have proven negative
SC
health and economic effects, hence this result of neutrality meets the expectation expressed in point (d) (Section 1).
NU
5.5 Summary of results by hypothesis and income groups and some additional discussion
MA
In Table 12, we present a summary of the above results according to four relevant hypotheses: the feedback hypothesis; the growth hypothesis; the conservative hypothesis; and the neutrality
TE
D
hypothesis.
The feedback hypothesis supports a bidirectional relationship between energy and
AC CE P
economic growth, suggesting complementarity between the two such that an increase in economic growth boosts energy consumption and a rise in energy consumption will stimulate economic growth.
We found support for this hypothesis in industries’ usage of total energy and/or nonrenewable energy. Upper middle income countries show bidirectional links with industrial total and non-renewable total use. Low and lower middle income countries show the feedback effect when dealing with non-renewable consumption totals, while high income countries support the feedback effect in the case of non-renewable industrial energy use. In all, for these groups policies directed towards either economic growth or energy consumption (of industrial total, non-renewable industrial, or/and total non-renewable) seem to be sufficient for increasing both economic growth and energy consumption. 29
ACCEPTED MANUSCRIPT Given that there is significant government support around the world for providing more efficient usage of non-renewables, this suggests that energy policies that lead to efficient use of
economic activity as well.
RI
PT
energy (of industrial total, non-renewable industrial, or/and total non-renewable) are stimulating
SC
An important question here is why our results do not show any support for the feedback
NU
effect in case of renewables. This may be a symptom of government policies, as well as the
MA
private sector still being biased towards non-renewable energy sources. The growth hypothesis, which suggests that energy consumption leads to economic
D
growth or that inadequate provision of energy limits economic growth, is found to be true for
TE
energy-deficient countries – the low and lower middle income countries. This unidirectional argument supports residential total energy consumption of poor nations and suggests that energy
AC CE P
is not only an input in the production of goods and services but also complements labour inputs. The conservation hypothesis takes the view that economic growth influences energy consumption. This view also suggests that the economy is less energy dependent and has the capacity for energy conservation which, if implemented, will have little to no effect on economic growth. For the low and lower middle income countries this is true when dealing with total industrial energy consumption and total renewables. For the upper middle income and high income panels, this is true when using non-renewables as energy sources. The case of neutrality, or no causality between these variables, appears to be another scenario that favours energy conservation policies; such policies will not harm economic growth. Evidence in support of the neutrality hypothesis is very strong and found in the use of renewable
30
ACCEPTED MANUSCRIPT industrial and residential, total renewables, non-renewable residential, total residential, and total industrial energy consumption.
PT
The link between economic growth and energy consumption is neutral for high income countries in the case of all, except non-renewables total and non-renewables industrial. For the
RI
upper middle income panel, this hypothesis could not be rejected, except for total industrial, total
SC
non-renewables and non-renewables industrial. In contrast, for the low and lower middle income countries, the neutrality hypothesis is only accepted in the case of non-renewables industrial and
MA
5.6 Robustness test
NU
renewables industrial and residential.
D
To ascertain the robustness of our results, we first follow Apergis and Payne (2010a, 2010b,
TE
2011a, 2011b), and introduce investment as a share of GDP in the models estimated above. Capital investment is a crucial determinant of economic growth hence it has strong theoretical
AC CE P
implications for equation (2) and inclusion of the variable capital investment in equation (1) highlights the investment in energy-related technology. These models are estimated using the GMM approach. We summarise the results in Table 12.14 We find that all, or most, of the results with and without gross investment are consistent. The differences are as follows. In the case of total non-renewables we find that models with investment favour the feedback hypothesis for the high income panel when models without investment showed the presence of the conservative hypothesis. For non-renewable industrial energy consumption, we find that models with investment favour the feedback hypothesis for all countries and the low and lower middle income panel. However, models without investment favoured the conservative
14
Detailed results are displayed as supplementary results.
31
ACCEPTED MANUSCRIPT hypothesis only. Non-renewable residential consumption supports the conservative hypothesis without investment but the neutrality hypothesis with investment. There is a suggested weakness of instruments in some of our models, as the Sargan
PT
statistic shows.15 In models with relatively small samples, or a relatively large set of excluded
RI
instruments, the test of the orthogonality of the instruments becomes less powerful (Roodman,
SC
2009). However, as Roodman (2009, p. 142) points out further, “there is no precise guidance on what is a relatively safe number of instruments”. As a result, we complement the GMM results
NU
with the fixed effect (FE) results.16
MA
The FE models point to an overall robustness of the GMM regression coefficients. They do not require special moment conditions, but they also do not incorporate dynamics or solve the
D
endogeneity problem. For these reasons, we recommend that they are read as complementary to
TE
the GMM results and not in their substitution. The two respective fixed effects models are:
AC CE P
(ln EnConsitk ) 0 1 (Growth _ yit ) 5 t i1 it 3
(3)
(ln yit ) 0 1 (Growth _ EnConsitk ) 5 t i 2 it 4
where in ~ i.i.d (0, in ) , itn ~ i.i.d.(0, ), E[in itn ] 0, 𝑛 = 3,4.
(4)
t
represents a vector of
annual dummy variables. All other variables in models (3) and (4) are as in models (1) and (2), respectively. These results are presented next to the GMM results in Tables 4-11 in columns 5-8. We also summarise the FE results in Table 12. The FE coefficients confirm three out of four of our results regarding the feedback hypothesis (the bidirectional relationship) and 50% of our neutrality hypotheses. The FE coefficients are less helpful for confirming our results relating to
15
We thank an anonymous referee for pointing this out. There is a recent literature trend for dynamic panel studies utilizing small cross-country samples to report simultaneously GMM and some static panel results as a robustness check (See, Bhattacharya et al., 2017 and Seven and Coskun, 2016). 16
32
ACCEPTED MANUSCRIPT the conservative hypothesis (unidirectional from growth to energy consumption) and the growth hypothesis (unidirectional from energy consumption to growth) results (see Table 12). To reemphasize, the FE results are to be treated with caution as they do not control for endogeneity
PT
and do not capture the dynamics of the processes.
RI
Concluding Remarks
SC
Our study takes an extensive look at the four hypotheses relating to the energy consumption and
NU
economic growth nexus. While there is a large body of literature that differentiates between renewable and non-renewable energy, this paper suggests further dividing such studies by
MA
residential and industrial users of energy. There are distinct and compelling features among the consumption patterns of these user groups, particularly across panels of countries by income. We
D
test the four hypotheses across different energy categories by income panel and show
AC CE P
situation.
TE
differences. We use the GMM approach to estimate the growth model to highlight the current
One of our key results relates to low and lower middle income countries where the residential energy consumption total shows growth effects for low and lower middle income countries (see point (a) in Section 1). This is the first large scale study that shows the importance of residential energy for the economic development of low income countries. There is an urgent need for both the public and private sectors to work harder to promote better access to electricity for households in these nations. Further efforts to conserve residential energy in these nations would hurt their economic growth. We show that the feedback effect is associated with the matured energy sources nonrenewables – total and industrial – in all income groups (point (b), Section 1). Neutrality is still associated with renewables, except in the case of upper middle income countries that support the 33
ACCEPTED MANUSCRIPT presence of the conservative hypothesis. Together, both findings suggest the global depth of dependence on non-renewables.
PT
We failed to find any evidence: that residential renewable energy is a driver of economic growth and/or vice versa (point c); that economic growth is a trigger of residential renewable
RI
energy consumption in high income countries (point e); and that economic growth is a trigger of
SC
industrial renewable energy consumption in the upper middle income countries (point f).
NU
Overall, we believe this study provides an important dynamic panel data perspective on the bidirectional relationships between economic growth and energy consumption, with useful
MA
insight on renewables versus non-renewables and industrial versus residential energy
D
consumption.
TE
References
AC CE P
Apergis, N., Payne, J. E., Menyah, K., Wolde-Rufael, Y., 2010. On the casual dynamics between emissions, nuclear energy, renewable energy consumption and economic growth, Ecological Economics, 69(1), 2255-2260. Apergis, N., Payne, J. E., 2010a. Renewable energy consumption and economic growth: Evidence from a panel of OECD countries, Energy Policy, 38(1), 656-660. Apergis, N., Payne, J. E., 2010b. Renewable energy consumption and economic growth in Eurasia, Energy Economics, 32(6), 1362-1397. Apergis, N., Payne, J. E., 2011a. On the casual dynamics between renewable and nonrenewable energy consumption and economic growth in developed and developing countries, Energy Systems, 2(3), 299-312. Apergis, N., Payne, J. E., 2011b. The renewable energy consumption-growth nexus in Central America, Applied Energy, 88(1), 343-347. Apergis, N., Payne, J. E., 2012. Renewable and non-renewable energy consumption nexus: Evidence from a panel error correction model, Energy Economics, 34(3), 733-738.
34
ACCEPTED MANUSCRIPT Apergis, N., Danuletiu, D., 2014. Renewable energy and economic growth: Evidence from the sign of panel long-run causality, International Journal of Energy Economics and Policy, 4(4), 578-587.
PT
Bloch, H., Rafiq, S., Salim, R., 2015. Economic growth with coal, oil and renewable energy consumption in China: Prospects for fuel substitution, Economic Modelling, 44, 104-115.
RI
Bhattacharya, M., Churchill, S. A., & Paramati, S. R. (2017). The dynamic impact of renewable energy and institutions on economic output and CO 2 emissions across regions. Renewable Energy.
NU
SC
Bhattacharya, M., Paramati, S. R., Ozturk, I., Bhattacharya, S., 2016. The effect of renewable energy consumption on economic growth: Evidence from top 38 countries, Applied Energy, 162, 733-741.
MA
Seven, U., & Coskun, Y. (2016). Does financial development reduce income inequality and poverty? Evidence from emerging countries. Emerging Markets Review, 26, 34-63.
D
Chang, T., Gupta, R., Inglesi-Lots, R., Simo-Kengne, B., Smithers, D., Trembling, A., 2015. Renewable energy and growth: Evidence from heterogeneous panel of G7 countries using Granger causality, Renewable and Sustainable Energy Reviews, 52, 1405-1412.
AC CE P
TE
Dyotch, N., and Narayan, S (2016) Does FDI Influence Renewable Energy Consumption? An Analysis of Sectoral FDI Impact on Renewable and Non-renewable Industrial Energy Consumption, Energy Economics, 54, 291–301 European Environment Agency (EEA), 2015. Trends and projections in Europe 2015. Tracking progress towards Europe’s climate and energy targets, EEA Report, 4/2015. ISSN 1977-8449. European Environment Agency (EEA), 2014. Energy support measures and their impact on innovation in the renewable energy sector in Europe, EEA Technical report, 21/2014. ISSN 1725-2237. International Monetary Fund (IMF), 2006. World Economic Outlook. International Energy Agency, 2015. Energy for Cooking in Developing Countries, World Energy Outlook, 2006, 419-445. International Energy Agency, 2015. World Energy Outlook, 2015. https://www.iea.org/Textbase/npsum/WEO2015SUM.pdf International Energy Agency, 2016. World Energy Outlook, 2016. Kraft, J., and Kraft, A., (1978) On the relationship between Energy and GNP, Journal of Energy and Development, 3: 401-403.
35
ACCEPTED MANUSCRIPT Lin, B., Moubarak, M., 2014. Renewable energy consumption – Economic growth nexus for China, Renewable and Sustainable Energy Reviews, 40, 111-117.
PT
Long, X., Naminse, E. Y., Du, J., Zhuang, J., 2015. Nonrenewable energy, renewable energy, carbon dioxide emissions and economic growth in China from 1952 to 2012. Renewable and sustainable Energy Reviews, 52, 680-609.
RI
Marques, A.C., Fuinhas, J. A., 2012. Is renewable energy effective in promoting growth? Energy policy, 46, 434-442.
SC
Menyah, K., Wolde-Rufael, Y., 2010 CO2 emissions, nuclear energy, renewable energy and economic growth in the US, Energy Policy, 38, 2911-2915.
NU
Narayan, S (2016) Predictability within the Economic Growth and Energy Consumption Nexus: Some evidence from Income and regional panels, Economic Modelling, 54, 515-521.
MA
Ocal, O., Aslan, A., 2013. Renewable energy consumption-economic growth nexus in Turkey, Renewable and Sustainable Energy Reviews, 28, 494-499.
D
Pablo-Romero, M. D., Pozo-Barajas, R., Yniguez, R., 2017. Global changes in residential energy consumption, Energy Policy, 101, 342-352.
TE
Pao, H-T., Fu, H-C., 2013. The casual relationship between energy resources and economic growth in Brazil, Energy Policy, 61, 793-801.
AC CE P
Pao, H-T., Li, Y-Y., Fu, H-C., 2014. Clean energy, non-clean energy, and economic growth in the MIST countries, Energy policy, 67, 932-942. Pao, H-T., Fu, H-C., 2015. Competition and stability analyses among emissions, energy, and economy: Application from Mexico, 82, 98-107. Payne, J. E. (2010). Survey of the international evidence on the causal relationship between energy consumption and growth, Journal of Economic Studies, 2010, 37(1), 53-95. Roodman, D. (2009). A note on the theme of too many instruments. Oxford Bulletin of Economics and statistics, 71(1), 135-158. Sardosky, P., 2009. Renewable energy consumption and income in emerging economies, Energy Policy, 37(10), 4021-4028. Salim, R. A., Hassan, K., Shafiei, S., 2014. Renewable and non-renewable energy consumption and economic activities: Further evidence from OECD countries, Energy Economics, 44, 350-360. Sebri, M., Ben-Salha, O., 2014. On the casual dynamic between economic growth, renewable energy consumption, CO2 emissions and trade openness: Fresh evidence from BRICS countries, Renewable and Sustainable Energy Reviews, 39, 14-23. 36
ACCEPTED MANUSCRIPT Shahbaz, M., Loganathan, N., Zeshan, M., Zaman, K., Does renewable energy consumption add in economic growth? As application of auto-regressive distributed lag model in Pakistan, Renewable and sustainable energy reviews, 44, 576-585.
PT
Shahbaz, M., Zeshan, M., Afza, T., Is energy consumption effective to spur economic growth in Pakistan? New evidence from bounds test to level relationships and Granger causality tests, 29(6), 2310-2319.
RI
Suter, J. F., Shammin. M. R., Returns to residential energy efficiency and conservation measures: A field experiment, Energy policy, 59, 551-561.
SC
Tiba, S., Omri, A., 2017. Literature survey on the relationship between energy environment and economic growth, Renewable and Sustainable Energy Reviews, 69, 1129-1146.
MA
NU
Tiba, S., Omri, A., Frikha, M., 2016. The four-way linkages between renewable energy, environment quality, trade and economic growth: a comparative analysis between high and middle income countries, Energy Systems, 7(1), 103-144.
D
Tugcu, C.T., Ozturk, I, Aslan, A., 2012. Renewable and non-renewable energy consumption and economic growth relationship revisited: Evidence from G7 countries, Energy Economics, 34(6), 1942-1950.
TE
US Energy Information Administration (EIA), 2015. Divers of US Household Energy Consumption, 1980-2009. US Department of Energy, Washington, DC.
AC CE P
US Energy Information Administration (EIA), 2014. Issues in International Energy Consumption Analysis: Electricity Usage in India’s Housing Sector, US Department of Energy, Washington, DC. US Energy Information Administration (EIA), 2011. Share of energy used by appliances and consumer electronics increases in US homes, US Department of Energy, Washington, DC.March 20. U.S. Energy Information Administration, 2013. Heating and cooling no longer majority of home energy use, US Department of Energy, Washington, DC. March 7. U.S. Energy Information Administration, 2013. Newer US homes are 30% larger but consume about as much energy as older homes, US Department of Energy, Washington, DC. February, 2013. Yang, C-L., Lin, H-P., Chang, C-H., 2010. Linear and nonlinear causality between sectoral electricity consumption and economic growth: Evidence from Taiwan, Energy Policy, 38(11), 6570-6573. Yildirim, E., Sarac, S., Aslan, A., 2012. Energy consumption and economic growth in the USA: Evidence from renewable energy, Renewable and Sustainable Energy Reviews, 16(9), 6770-6774.
37
ACCEPTED MANUSCRIPT Zeb, R., Salar, L., Awan, U., Zaman, K., Shahbaz, M. 2014. Causal links between renewable energy, environmental degradation and economic growth in selected SAARC countries: Progress towards green economy, Renewable Energy, 17, 123-132.
AC CE P
TE
D
MA
NU
SC
RI
PT
Zhao, X., Li, N., Ma, C., (2012). Residential energy consumption in urban China: A decomposition analysis, Energy Policy, 41, 644-653.
38
ACCEPTED MANUSCRIPT
1980
1990 year
MA
1970
NU
0
SC
20
RI
%
40
PT
60
Figure 1: Mean renewable energy as a percentage of total final consumption
All Countries Middle Income Countries
2000
2010
Low Income Countries High Income Countries
AC CE P
TE
D
Source: Authors' Compilations
39
ACCEPTED MANUSCRIPT
1980
1990 year
MA
1970
NU
10
20
SC
%30
RI
40
PT
50
Figure 2: Mean renewable industrial energy consumption as a percentage of total industrial energy consumption
Low Income Countries High Income Countries
AC CE P
TE
Source: Authors' Compilations
2010
D
All Countries Middle Income Countries
2000
40
ACCEPTED MANUSCRIPT
1980
1990 year
MA
1970
NU
10
20
SC
%30
RI
40
PT
50
Figure 3: Mean renewable residential energy consumption as a percentage of total residential energy consumption
Low Income Countries High Income Countries
AC CE P
TE
Source: Authors' Compilations
2010
D
All Countries Middle Income Countries
2000
41
ACCEPTED MANUSCRIPT Table 1: Summary of the literature: Relationship between energy consumption (E) and economic growth (Y) This table summarizes surveyed panel and time series studies that have examined the EC-G nexus for: renewable; nonrenewable, residential, industrial, and residential and industrial renewable and non-renewable energy consumption. Study
Panel
Timeframe
Method
Other variables
Renewable
Multivariate panel data framework Panel error correction models
Real gross capital formation; Labor force Real gross capital formation; Labor force Real gross capital formation; Labor force
E↔Y (short-run & long-run) E↔Y (short-run & long-run) E↔Y (short-run & long-run)
Heterogeneous panel cointegration Panel cointegration; VECM
Real gross capital formation; Labor force Real gross capital formation; Labor force
E↔Y (short-run & long-run) E↔Y (short-run & long-run)
OECD
1985-2005
Granger causality
13 Eurasian countries Developed Developing
1992-2007
Apergis and Payne, 2011b Apergis and Payne, 2012
6 Central America
1980-2006
80 countries
1990-2007
Apergis and Danuletiu, 2014 Apergis et al. 2010
1990-2012
Long-run causality
1984-2007
Panel error correction model
Bhattacharya et al, 2016 Bloch et al, 2015
80 countries – regional panels 19 Developed and Developing countries Top 38 renewable energy consumers China
1991-2012
Chang et al, 2015
G7
1977-2013 1965-2011 1990-2011
Panel cointegration DOLS/FMOLS Cointegration
Lin and Moubarak, 2014 Long et al, 2015
China
1977-2011
China
1952-2012
ARDL and Johansen technique Granger Causality
Marques and Fuinhas, 2012 Mbarek et al, 2015 Menyah and Wolde-Rufael, 2010 Ocal and Aslan, 2013 Omri et al, 2015
24 European countries France
1990-2007
Panel estimations
Carbon emissions; Labor CO2 emissions; Nonrenewable energy (coal, gas, and oil); Labor; Capital; Renewable energy (hydro) Coal; Oil
2001:12012:3 1960-2007
Granger Causality
Nuclear energy
Granger Causality test
Carbon emissions; Nuclear energy
Turkey
1990-2010
17 Developed and Developing
1990-2011
Toda-Yamamoto causality Granger Causality
Real gross capital formation; Labor force Nuclear energy
Pao and Fu, 2013
Brazil
1980-2009
Cointegration and error correction model
Real gross capital formation; Labor force
RI
SC
NU
MA
Nuclear energy; Carbon emissions Real gross capital formation; Labor force Coal; Oil; Labor; Capital
D
Granger Causality test
TE
AC CE P
US
1990-2007
PT
Apergis and Payne 2010a Apergis and Payne, 2010b Apergis and Payne 2011a
Non-renewable
E↔Y (short-run &longrun)
E↔Y (short-run & long-run)
LR: E↔Y E↔Y Positive short-run impact LR: E→Y
LR: E→Y
LR:E↔Y
LR:E↔Y
E≠Y Canada; Italy & US Y→E France & UK E→Y Germany E↔Y (long-run) Y→E
LR:E↔Y
E≠Y
E→Y Coal(-); Oil(+)
Y→E Nuc→E Y→E
Y→E E→Y for Hungary, India, Japan, Sweden & Netherlands Y→E for Argentina, Spain, & Switzerland E↔Y for US, Belgium, Bulgaria, Canada, France, & Pakistan E≠Y for Brazil, Finland & Switzerland E↔Y (wind, biomass, waste)
E↔Y (fossil fuel)
42
ACCEPTED MANUSCRIPT Table 1 continued: Summary of the literature: Relationship between energy consumption (E) and economic growth (Y) Method
Other Variables
Lotka-Voterra model for Sustainable Development Panel cointegration
Nuclear; Fossil fuel; Total energy; CO2
Pao et al, 2014
Mexico, Indonesia, S. Korea, Turkey 18 Emerging economies
1990-2010
Salim, et al, 2014
29 OECD countries
1980-2011
Sebri and BenSalha, 2014 Shahbaz et al, 2015 Tiba et al., 2016
BRICS
1971-2010
Pakistan
1972-2011
24 High income (HI) and Middle Income (MI) countries
1990-2011
Tugcu et al., 2012
G7
1980-2009
Yildirim et al, 2012
US
Zeb et al, 2014
5 SAARC nations: Bangladesh, India, Nepal, Pakistan, Sri Lanka
Cointegration FMOLS, OLS, DOLS Panel cointegration with structural breaks ARDL and VECM ARDL; VECM Granger causality Simultaneous equations
ARDL Cointe; causality test
E≠Y
LR: E→Y SR: E↔Y
Non-renewable
E↔Y (fossil fuel) LR:E↔Y SR: E→Y
LR:Y→E SR: E↔Y LR:E↔Y SR: Y→E
LR:E↔Y SR: E↔Y
Trade openness; Carbon emissions Capital; Labor
E↔Y (short-run) E↔Y
E↔Y
CO2 emissions; Renewable energy; Foreign trade
HI: E↔Y; E↔CO2 MI: E↔Y; Y↔CO2; trade↔CO2; E→ CO2 E↔Y for England and Japan E≠Y for France, Italy, Canada, US Y→E for Germany
D
1994-2003
Classical PF with Labor force; Augmented PF – with human capital; R&D
1960-2010
Toda-Yamamoto causality
Employment; Investment
1975-2010
Granger Causality – LR and SR
CO2emissions; Resource depletion; Poverty
TE
Sadorsky, 2009
Real gross capital formation; Labor force
RI
1980-2011
SC
Mexico
NU
Pao and Fu, 2015
Renewable
PT
Time frame
MA
Panel
AC CE P
Study
E→Y Japan E≠Y for England, France, Italy, Canada, US, & Germany
E→Y (only biomass-waste derived E) -electricity production from renewable resources -bidirectional link between energy production from renewables & poverty reduction in Pakistan
43
ACCEPTED MANUSCRIPT
Table 2A: Non-renewable and renewable industrial energy consumption Obs 5214
Mean 0.0006
SD 0.0009
I2. Non-renewable energy industrial consumption per capita
3093
0.0004
0.0006
1431
0.0002
I3. Renewable energy industrial consumption per capita
3093
0.0001
0.0001
1431
I4. Growth of total industrial energy consumption per capita I5. Growth of non-renewable energy industrial consumption per capita I6. Growth of renewable energy industrial consumption
5078
0.0297
0.4248
2990
0.3096
2991
0.0790
All countries
PT
I1. Total industrial energy consumption per capita
Low and lower middle income Obs Mean SD 2460 0.0002 0.0008
Industrial energy consumption level*, share and growth
Upper middle income
High income
Mean 0.0004
SD 0.0003
Obs 1727
Mean 0.0011
SD 0.0011
0.0006
740
0.0004
0.0003
922
0.0009
0.0005
0.0000
0.0001
740
0.0000
0.0000
922
0.0001
0.0001
2389
0.0255
0.2595
1000
0.0483
0.7096
1689
0.0245
0.3863
15.7578
1379
0.6656
23.2024
717
0.0118
0.1518
894
-0.0008
0.0712
1.0186
1380
0.0284
0.6250
717
0.1624
1.7433
894
0.0905
0.6514
17. Industrial energy consumption share of total final consumption 5217 0.2665 0.1361 2460 0.2069 I8. Industrial energy non-renewable consumption share of total 3093 0.2999 0.1108 1431 0.2947 final nonrenewable consumption I9. Industrial energy renewable consumption share of total final 3093 0.2795 0.2456 1431 0.1827 renewable consumption I10. Growth of industrial energy consumption share of total final 5082 0.0115 0.3046 2389 0.0164 consumption I11. Growth of industrial energy non-renewable consumption share 2990 0.2831 15.3730 1379 0.6219 of total final nonrenewable consumption I12. Growth industrial energy renewable consumption share of 2991 0.0587 0.8598 1380 0.0378 total final renewable consumption Note: *Energy Consumption is measured in thousands of tonnes of oil equivalent (ktoe)
0.1242
1027
0.3030
0.1110
1730
0.3296
0.1296
0.1281
740
0.3262
0.1140
922
0.2868
0.0670
0.1562
740
0.2992
0.2633
922
0.4140
0.2757
0.2268
1000
0.0223
0.5450
1693
-0.0019
0.1742
22.6362
717
0.0048
0.1093
894
-0.0087
0.0492
0.6800
717
0.1304
1.4243
894
0.0334
0.3596
SC
NU
MA
PT ED
AC
CE
RI
Obs 1027
44
ACCEPTED MANUSCRIPT
Table 2B: Non-renewable and renewable residential energy consumption
Mean
R1. Total residential energy consumption per capita
5237
0.0004
R2. Non-renewable energy residential consumption per capita
4209
0.0003
R3. Renewable energy residential consumption
4209
0.0002
R4. Growth of total residential energy consumption per capita
5102
0.0262
R5. Growth of non-renewable energy residential consumption per capita
4061
1.9361
R6. Growth of renewable energy residential consumption
4083
0.1196
R7. Residential energy consumption share of total final consumption
5240 4212
R10. Growth of residential energy consumption share of total final consumption
R11. Growth of residential energy nonrenewable consumption share of total final nonrenewable consumption R12. Growth residential energy renewable consumption share of total final renewable consumption
High income
Obs
Mean
Std. Dev.
Obs
Mean
2466
0.0004
0.0012
1024
0.0003
0.0006
2172
0.0001
0.0007
945
0.0002
0.0002
1092
0.0005
0.0003
0.0007
2172
0.0003
0.0009
945
0.0001
0.0001
1092
0.0001
0.0003
0.3864
2396
0.0166
0.3612
997
0.0236
0.1678
1709
0.0412
0.4962
121.2835
2093
3.7392
168.9400
912
0.0260
0.1675
1056
0.0119
0.1039
3.7330
2109
0.0354
0.9250
918
0.0908
2.7142
1056
0.3127
6.7641
0.3361
0.2203
2466
0.4680
0.2377
1024
0.2559
0.1271
1750
0.1973
0.0937
0.1703
0.0761
2172
0.1582
0.0755
945
0.1775
0.0777
1095
0.1880
0.0713
4212
0.7436
0.2530
2172
0.8254
0.1862
945
0.7306
0.2597
1095
0.5927
0.2885
5106
0.0163
0.4163
2396
0.0132
0.3976
997
0.0105
0.1775
1713
0.0242
0.5264
4065
1.9596
123.7276
2093
3.7999
172.4295
912
0.0106
0.1408
1060
0.0027
0.1089
4087
0.0306
1.4778
2109
0.0079
0.2470
918
0.0250
0.8174
1060
0.0808
2.7790
NU
PT ED
R8. Residential energy nonrenewable consumption share of total final nonrenewable consumption R9. Residential energy renewable consumption share of total final renewable consumption
Upper middle income Std. Dev. 0.0002
RI
Obs
Std. Dev. 0.0009
PT
Low and lower middle income
SC
All countries
MA
Residential energy consumption levels*, shares and growth
Obs
Mean
1747
0.0006
Std. Dev. 0.0004
AC
CE
Note: *Energy Consumption is measured in thousands of tonnes of oil equivalent (ktoe)
45
ACCEPTED MANUSCRIPT
Table 3: Income groups This table categorizes the countries covered in this study by four income groups based on the World Bank classification: 1- Low income; 2- Lower middle income; 3- Upper middle income; and 4- High income countries. Data coverage
Income group
Country
Data coverage
Income group
Country
Data coverage
Income group
Albania Albania Argentina Armenia Austria Australia Azerbaijan Azerbaijan Cambodia Bangladesh Belgium Bolivia Bosnia & Herzegovina Bosnia & Herzegovina Brazil
1971-2007 2008-2011 1971-2012 1991-2011 1971-2012 1971-2012 1991-2008 2009-2011 1995-2011 1971-2011 1971-2011 1971-2011 1991-2007 2008-2011 1971-2011
2 3 3 2 4 4 2 3 1 1 4 2 2 3 3
Greece Guatemala Honduras Honduras Hong Kong Hungary Hungary Iceland India India Indonesia Ireland Israel Italy Jamaica
1971-2011 1971-2001 1971-2001 2002-2011 1971-2011 1971-2007 2008-2011 1971-2011 1971-2008 2009-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2004
4 2 1 2 4 3 4 4 1 2 2 4 4 4 2
Pakistan Pakistan Panama Paraguay Peru Peru Philippines Poland Portugal Romania Russian Fed. Saudi Arabia Serbia Singapore Slovak Rep
1971-2008 2009-2011 1971-2011 1971-2011 1971-2009 2010-2011 1971-2011 1971-2011 1971-2011 1971-2011 1991-2011 1971-2011 1991-2011 1971-2011 1971-2005
1 2 3 2 2 3 2 3 4 3 3 4 3 4 3
Brunei Bulgaria Canada Chile China China Colombia Colombia Costa Rica Croatia Croatia Cyprus Czech Rep. Denmark Dominican Rep. Dominican Rep. Ecuador Ecuador El Salvador Estonia Finland France Germany
1971-2011 1971-2012 1971-2011 1971-2011 1971-2009 2010-2011 1971-2006 2007-2011 1971-2011 1991-2007 2008-2011 1971-2011 1971-2011 1971-2011 1971-2007 2008-2011 1971-2009 2010-2011 1971-2011 1991-2011 1971-2011 1971-2011 1971-2011
4 3 4 3 2 3 2 3 3 3 4 4 4 4 2 3 2 3 2 4 4 4 4
Jamaica Japan Kazakhstan Korea, Rep. Kyrgyz Rep Latvia Lithuania Lithuania Luxembourg Macedonia, FYR Macedonia, FYR Malaysia Mexico Moldova Morocco Mozambique Myanmar Netherlands New Zealand Nicaragua Norway Oman Oman
2005-2011 1971-2011 1991-2011 1971-2011 1991-2011 1991-2011 1991-2007 2008-2011 1971-2011 1991-2007 2008-2011 1971-2011 1971-2011 1971-2008 1971-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2006 2007-2011
3 4 3 4 1 3 3 4 4 2 3 3 3 2 2 1 1 4 4 2 4 3 4
Slovak Rep Slovenia Sri Lanka Spain Sweden Switzerland Syrian Arab Rep. Tanzania Thailand Thailand Tunisia Tunisia Turkey UA Emirates United Kingdom United States Uruguay Venezuela Vietnam Vietnam
2006-2011 1991-2011 1971-2011 1971-2011 1971-2011 1993-2012 1971-2011 1971-2011 1971-2009 2010-2011 1971-2008 2009-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2011 1971-2008 2009-2011
4 4 2 4 4 4 2 1 2 3 2 3 3 4 4 4 3 3 1 2
RI
SC
NU
MA
D
TE
AC CE P
PT
Country
Source: Authors’ calculations based on the income level methodology of the World Bank
46
ACCEPTED MANUSCRIPT Table 4: Economic growth and total industrial energy consumption FE (4) High income
(6) Low and lower middle
(7) Upper middle
(8) High income
0.860***
0.332*
PT
0.971***
(0.0144) 0.433
0.714***
(0.275)
(0.109)
(0.122)
(0.0979)
(0.197)
1,553 40
3,538 89
1,177 34
808 34
1,553 40
0.103
0.110
0.180
0.109
0.00174
0.0157
0.0823***
0.0227*
0.00337
(0.00315) 1,553 40
(0.0100) 3,538 89 0.147
(0.0159) 1,177 34 0.167
(0.0130) 808 34 0.220
(0.00275) 1,553 40 0.29
0.837***
RI
SC
0.0125 135.9 0.00102
0.996***
MA
D
TE
(5) All
NU
GMM (2) (3) Low and Upper lower middle middle Panel 1: GDP growth on industrial total energy consumption Log of lagged 0.995*** 0.991*** 0.980*** total industrial energy per capita (0.0170) (0.0231) (0.00795) GDP per capita, 1.132*** 1.228*** 0.848*** 2005 USD, PPP, (0.187) (0.353) (0.278) growth Observations 3,538 1,177 808 Number of 89 34 34 countries R-squared AR(2) pval 0.712 0.477 0.273 Sargan test chi2 120.2 65.18 110.6 Sargan test pval 0.0155 0.188 0.0523 Panel 2: Industrial total energy consumption on GDP growth Log of lagged 1.003*** 1.015*** 0.990*** GDP, 2005 USD, PPP per (0.00416) (0.00554) (0.00512) capita Growth of 0.0160* 0.0420 0.0185* industrial total energy per capita (0.00853) (0.0343) (0.0100) Observations 3,538 1,177 808 Number of count 89 34 34 R-squared AR(2) pval 0.00387 0.170 0.0104 Sargan test chi2 55.68 124.7 136.3 Sargan test pval 0.998 1.31e-06 0.0005 (1) All
(0.00439)
0.0269 351 0.0000
AC CE P
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. b The Sargan test is a commonly used test for instrument strength with the GMM method. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
47
ACCEPTED MANUSCRIPT Table 5: Economic growth and total residential energy consumption FE (6) Low and lower middle
(7) Upper middle
(8) High income
0.199**
0.0795
(0.133) 1,177 34
(0.0760) 805 34
(0.132) 1,553 40
0.029
0.081
0.159
0.00413
0.0243**
0.00518
(0.0369)
(0.00967)
(0.0213)
1,177 34
805 34
1,553 40
0.108
0.196
0.290
PT
GMM (2) (3) (4) (5) Low and Upper High All lower middle income middle Panel 1: GDP per capita (2005 USD, PPP) growth on residential total energy consumption Log of lagged 0.976*** 0.985*** 0.905*** 0.963*** residential total energy per (0.0130) (0.0155) (0.0253) (0.0120) capita GDP per capita, 0.0901 -0.104 -0.134 -0.0959 0.124* 2005 USD, PPP (0.222) (0.237) (0.177) (0.231) (0.0657) Observations 3,535 1,177 805 1,553 3,535 Number of count 89 34 34 40 89
0.0761
0.408 129.2
0.381 102.2
0.799 72.78
0.058
Sargan test pval
0.00344
0.000159
0.879
0.560 164.6
NU
R-squared AR(2) pval Sargan test chi2
SC
RI
(1) All
1.97e-06
Sargan test pval
0.136
0.000423 96.06
0.502 92.60
0.313 2.848
0.00999 307.3
0.286
0.00340
1
0
AC CE P
R-squared AR(2) pval Sargan test chi2
TE
D
MA
Panel 2: Residential total energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 1.001*** 1.019*** 0.989*** 0.996*** GDP, 2005 USD, PPP (0.00474) (0.00850) (0.0130) (0.00321) per capita Growth of 0.103* 0.156*** -0.0259 0.0360 0.0147 residential total energy per (0.0526) (0.0479) (0.130) (0.0490) (0.0120) capita Observations 3,535 1,177 805 1,553 3,535 Number of count 89 34 34 40 89
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. bThe Sargan test is a commonly used test for instrument strength with the GMM method. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
48
ACCEPTED MANUSCRIPT Table 6: Economic growth and total non-renewable energy consumption GMM (2) (3) (4) (5) Low and Upper High All lower middle income middle Panel 1: GDP per capita (2005 USD, PPP) growth on total non-renewable energy consumption Log of lagged 0.999*** 0.970*** 0.976*** 0.952*** total nonrenewable energy per (0.00841) (0.0171) (0.00594) (0.0154) capita GDP per 1.029*** 1.021*** 0.676*** 0.301** 0.694*** capita, 2005 USD, PPP, (0.195) (0.290) (0.130) (0.141) (0.0651) growth Observations 3,117 1,175 773 1,169 3,117 Number of 88 34 33 39 88 count R-squared 0.226 AR(2) pval 0.177 0.257 0.513 0.228 Sargan test 80.24 119.5 40.19 248.2 chi2 Sargan test 0.735 1.69e-06 1 0 pval Panel 2: Total non-renewable energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 1.003*** 1.006*** 0.999*** 1.002*** GDP, 2005 USD PPP per (0.00292) (0.00359) (0.00473) (0.00305) capita Growth of 0.373*** 0.343*** 0.452*** 0.0911 0.235*** total nonrenewable energy per (0.0798) (0.105) (0.0890) (0.0580) (0.0268) capita Observations 3,117 1,175 773 1,169 3,117 Number of 88 34 33 39 88 count R-squared 0.271 AR(2) pval 0.862 0.882 0.265 0.0542 Sargan test 124.5 133 27.11 30.32 chi2 Sargan test 0.00778 1.22e-07 1 1 pval
FE (6) Low and lower middle
(7) Upper middle
(8) High income
0.701***
0.486***
(0.107)
(0.0782)
(0.128)
1,175 34
773 33
1,169 39
0.213
0.386
0.246
0.211***
0.361***
0.140**
(0.0333)
(0.0439)
(0.0523)
1,175 34
773 33
1,169 39
0.234
0.416
0.373
PT
(1) All
AC CE P
TE
D
MA
NU
SC
RI
0.742***
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. bThe Sargan test is a commonly used test for instrument strength with the GMM method. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
49
ACCEPTED MANUSCRIPT
Table 7: Economic growth and total renewable energy consumption FE (6) Low and lower middle
(7) Upper middle
(8) High income
0.0139
0.0943
0.0286
(0.176)
(0.123)
(0.206)
1,175 34
773 33
1,169 39
0.035
0.064
0.050
0.00929
0.0120
2.24e-07
(0.0180) 1,175 34
(0.00791) 773 33
(6.95e-05) 1,169 39
0.107
0.235
0.329
AC CE P
TE
D
MA
NU
SC
RI
PT
GMM (2) (3) (4) (5) Low and Upper High All lower middle income middle Panel 1: GDP per capita (2005 USD, PPP) growth on total renewable energy consumption Log of lagged 1.008*** 1.002*** 1.006*** 1.003*** total renew. energy per (0.00681) (0.00623) (0.0175) (0.00714) capita GDP per capita, 0.241 0.455* 0.0293 0.293 0.0594 2005 USD, PPP, (0.210) (0.253) (0.225) (0.351) (0.0994) growth Observations 3,117 1,175 773 1,169 3,117 Number of 88 34 33 39 88 count R-squared 0.018 AR(2) pval 0.372 0.209 0.849 0.242 Sargan test chi2 86.25 166.3 47.99 110.4 Sargan test pval 0.563 0 1 0.0617 Panel 2: Total renewable energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 0.992*** 1.006*** 0.994*** 0.994*** GDP, 2005 USD PPP per (0.00362) (0.00619) (0.00703) (0.00263) capita Growth of total -0.00670 0.00104 -0.0265 -0.0116 0.000186 renew. energy per capita (0.0123) (0.0155) (0.0322) (0.0107) (0.000148) Observations 3,117 1,175 773 1,169 3,117 Number of 88 34 33 39 88 count R-squared 0.139 AR(2) pval 0.0663 0.337 0.0588 0.587 Sargan test chi2 42.87 114.6 13.74 48.98 Sargan test pval 1 1.99e-05 1 1 (1) All
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. bThe Sargan test is a commonly used test for instrument strength with the method of GMM. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
50
ACCEPTED MANUSCRIPT Table 8: Economic growth and non-renewable industrial energy consumption GMM FE (2) (3) (4) (5) (6) Low and Upper High All Low and lower middle income lower middle middle Panel 1: GDP per capita (2005 USD, PPP) growth on non-renewable industrial energy consumption Log of lagged 0.972*** 0.953*** 0.977*** 0.989*** non-renewable industrial Energy per (0.0234) (0.0341) (0.0134) (0.0111) capita GDP per 0.765** 0.345 1.066*** 0.815*** 0.887*** 0.822*** capita, 2005 USD, PPP, (0.333) (0.434) (0.174) (0.181) (0.106) (0.180) growth Observations 2,354 821 639 894 2,356 823 Number of 74 27 30 33 74 27 count R-squared 0.063 0.055 AR(2) pval 0.341 0.275 0.331 0.133 Sargan test chi2 52.70 50.67 45.77 156.5 Sargan test pval 0.999 0.676 0.998 1.32e-05 Panel 2: Non-renewable industrial energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 0.996*** 0.998*** 0.996*** 1.001*** GDP, 2005 USD PPP per (0.0425) (0.00727) (0.00254) (0.00171) capita Growth of total -5.83e-05 -5.12e-05 0.0132 0.173*** -0.000101 -0.000104 non-renewable industrial Energy per (0.000492) (5.27e-05) (0.0106) (0.0330) (7.91e-06) (1.43e-05) capita Observations 2,355 822 639 894 2,355 822 Number of 74 27 30 33 74 27 count R-squared 0.164 0.136 AR(2) pval 0.296 0.266 0.00598 0.336 Sargan test chi2 5311 137.5 74.28 179 Sargan test pval 0 3.26e-08 0.534 5.23e-08
(7) Upper middle
(8) High income
0.850***
0.847***
(0.158)
(0.175)
639 30
894 33
0.229
0.424
0.0801**
0.139***
(0.0367)
(0.0373)
639 30
894 33
0.251
0.493
AC CE P
TE
D
MA
NU
SC
RI
PT
(1) All
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. bThe Sargan test is a commonly used test for instrument strength with the GMM method. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
51
ACCEPTED MANUSCRIPT Table 9: Economic growth and non-renewable residential energy consumption GMM FE (2) (3) (4) (5) (6) (7) Low and Upper High All Low and Upper lower middle income lower middle middle middle Panel 1: GDP per capita (2005 USD, PPP) growth on non-renewable residential energy consumption Log of lagged 0.976*** 0.966*** 0.972*** 0.955*** non-renewable residential energy per (0.0122) (0.0203) (0.00965) (0.0214) capita GDP per 0.141 0.394** 0.0164 -0.350 0.145** 0.256** 0.127* capita, 2005 USD, PPP, (0.175) (0.191) (0.167) (0.313) (0.0643) (0.110) (0.0654) growth Observations 2,815 1,072 729 1,014 2,815 1,072 729 Number of 82 30 33 37 82 30 33 count R-squared 0.039 0.056 0.097 AR(2) pval 0.451 0.701 0.895 0.0991 Sargan test 107.2 53.76 64.45 155.3 chi2 Sargan test 0.0922 0.560 0.972 1.72e-05 pval Panel 2: Non-renewable residential energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 0.995*** 1.009*** 0.995*** 0.994*** GDP, 2005 USD PPP per (0.00437) (0.00633) (0.00546) (0.00244) capita Growth of 0.0188 0.0204 0.0774 0.0306 0.00371 0.00426 0.0273 total nonrenewable residential energy per (0.0276) (0.0275) (0.0994) (0.0361) (0.0108) (0.0138) (0.0253) capita Observations 2,815 1,072 729 1,014 2,815 1,072 729 Number of 82 30 33 37 82 30 33 count R-squared 0.139 0.090 0.221 AR(2) pval 0.00967 0.0341 0.320 0.00251 Sargan test 56.61 206 55.22 130.8 chi2 Sargan test 0.997 0 0.996 0.00263 pval
(8) High income
-0.0753 (0.123) 1,014 37 0.144
-0.0154
AC CE P
TE
D
MA
NU
SC
RI
PT
(1) All
(0.0174) 1,014 37 0.356
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. b The Sargan test is a commonly used test for instrument strength with the method of GMM. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
52
ACCEPTED MANUSCRIPT Table 10: Economic growth and renewable industrial energy consumption GMM FE (2) (3) (4) (5) (6) (7) Low and Upper High All Low and Upper lower middle income lower middle middle middle Panel 1: GDP per capita (2005 USD, PPP) growth on renewable industrial energy consumption Log of lagged 0.964*** 0.999*** 0.968*** 0.973*** renewable industrial energy per (0.0237) (0.00972) (0.0270) (0.0181) capita GDP per -0.533 1.060 -0.834 -0.487 0.203 0.480** -0.183 capita, 2005 USD, PPP, (0.372) (0.804) (0.652) (0.373) (0.144) (0.197) (0.225) growth Observations 2,356 823 639 894 2,356 823 639 Number of 74 27 30 33 74 27 30 count R-squared 0.027 0.059 0.054 AR(2) pval 0.934 0.874 0.115 0.541 Sargan test 102.2 75.05 34.85 129.6 chi2 Sargan test 0.161 0.0455 1 0.00323 pval Panel 2: Renewable industrial energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 0.995*** 1.003*** 0.998*** 0.997*** GDP, 2005 USD PPP per (0.00293) (0.00539) (0.00506) (0.00111) capita Growth of 0.00159 0.0169 -0.00149 -0.00289 0.00103*** 0.0195*** -0.000383 total renewable industrial energy per (0.00314) (0.0129) (0.00266) (0.00256) (0.000338) (0.00582) (0.000761) capita Observations 2,356 823 639 894 2,356 823 639 Number of 74 27 30 33 74 27 30 count R-squared 0.163 0.139 0.203 AR(2) pval 0.0287 0.0927 0.588 0.239 Sargan test 48.96 89.88 4.561 11.68 chi2 Sargan test 1 0.00591 1 1 pval
(8) High income
-0.468 (0.293) 894 33 0.052
AC CE P
TE
D
MA
NU
SC
RI
PT
(1) All
-0.000144
(0.000902) 894 33 0.430
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. b The Sargan test is a commonly used test for instrument strength with the method of GMM. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
53
ACCEPTED MANUSCRIPT Table 11: Economic growth and renewable residential energy consumption GMM FE (2) (3) (4) (5) (6) Low and Upper High All Low and lower middle income lower middle middle Panel 1: GDP per capita (2005 USD, PPP) growth on renewable residential energy consumption Log of lagged 1.000*** 0.985*** 0.995*** 0.990*** renewable residential energy per (0.0103) (0.0149) (0.0115) (0.00891) capita GDP per capita, 0.313 0.164 0.228 -0.294 0.0814 0.0903 2005 USD, PPP, (0.229) (0.305) (0.233) (0.542) (0.0881) (0.149) growth Observations 2,815 1,072 729 1,014 2,815 1,072 Number of 82 30 33 37 82 30 count R-squared 0.022 0.030 AR(2) pval 0.730 0.217 0.904 0.493 Sargan test chi2 53.94 108.8 50.94 80.83 Sargan test pval 0.999 3.02e-05 0.999 0.720 Panel 2: Renewable residential energy consumption on GDP per capita (2005 USD, PPP) growth Log of lagged 0.992*** 1.002*** 0.996*** 0.993*** GDP, 2005 USD PPP per (0.0208) (0.00548) (0.0119) (0.00326) capita Growth of total -0.00139 0.0227* -0.000279 -0.000967 0.000130*** 0.0163 renewable residential energy per (0.00404) (0.0119) (0.00111) (0.00118) (3.33e-05) (0.0185) capita Observations 2,815 1,072 729 1,014 2,815 1,072 Number of 82 30 33 37 82 30 count R-squared 0.139 0.093 AR(2) pval 0.709 0.0674 0.711 0.0514 Sargan test chi2 1148 55.71 463.8 18.62 Sargan test pval 0 0.598 0 1
(7) Upper middle
(8) High income
0.123
-0.155
(0.114)
(0.194)
729 33
1,014 37
0.052
0.076
7.75e-05
6.36e-05**
(0.000118)
(2.97e-05)
729 33
1,014 37
0.219
0.354
AC CE P
TE
D
MA
NU
SC
RI
PT
(1) All
Note: *,**, and *** denote statistical significance at 10, 5 and 1 percent levels. Standard errors are reported in parentheses. a The AR(2) test is standard for the method of GMM. It refers to the GMM conditions described in the methodology section. b The Sargan test is a commonly used test for instrument strength with the method of GMM. The Sargan statistic relies on the assumption of sphericity of errors and it is not weakened by too many instruments.
54
ACCEPTED MANUSCRIPT Table 12: Summary of results by income panels and energy consumption
Panels
Feedback Growth Conservative Neutral LLM UM LLM UM LLM UM LL UM ALL I I HI ALL I I HI ALL I I HI ALL MI I HI *
Total residential
*
Total non-renewable
*!
*
*! *!
*!
#!
#
SC
#
*
*!
Non-renewable residential
NU
Renewable industrial
* *!
* *!
*!
*
*
Total renewable Non-renewable industrial
*
*
RI
Total industrial
PT
Hypothesis
*!
*!
* *
#
*
*!
*
*
*!
*!
AC CE P
TE
D
MA
* *! *! * Renewable residential Notes: ALL refers to all countries panel; LLMI: low and lower middle income panel; UMI: upper middle income panel; and HI: high income panel. * capture significant GMM results; ! captures every GMM that is confirmed by FE; and # captures additional results after the inclusion of investment. Detailed results are presented as supplementary results.
55
ACCEPTED MANUSCRIPT Highlights: 1. The feedback effect features in non-renewable and non-renewable industrial energy. 2. There is sign of feedback in residential energy in LLMICs.
PT
3. Economic growth in LLMICs encourages industrial energy. 4. Economic growth boosts non-renewables in HIs and non-renewable industrial in UMICs.
AC CE P
TE
D
MA
NU
SC
RI
5. Neutrality is supported by renewable industrial and residential (for all panels).
56