Pathways to reduce CO2 emissions as countries proceed through stages of economic development

Energy Policy 129 (2019) 268–278

Contents lists available at ScienceDirect

Energy Policy journal homepage: www.elsevier.com/locate/enpol

Pathways to reduce CO2 emissions as countries proceed through stages of economic development☆

T

Abbas Valadkhani , Jeremy Nguyen, Mark Bowden ⁎

Department of Accounting, Economics and Finance, Swinburne University of Technology, Hawthorn, VIC 3122, Australia

ARTICLE INFO

ABSTRACT

JEL classifications: Q42 Q43 Q53 Q54

We propose a new approach to identify pathways for countries to reduce carbon dioxide emissions (CO2) per capita through possible changes in their energy consumption portfolio. Utilizing data from the last half century (1965–2017) for 79 countries, we investigate how changes in the composition of primary energy consumption (i.e. oil, coal, gas and renewables) can contribute to changes in per capita CO2 emissions, depending on the timevarying level of individual countries’ real per capita income. To this end, threshold panel regressions (with common and fixed effects) are estimated to endogenously determine an unknown number of possible pathways (delineated by break points) to reduce emissions. This study provides important policy insights into the effects of switching from one source of primary energy consumption to another on per capita emissions, as nations progress through stages of economic development. Such relative costs can be compared and contrasted (a) across country groupings, (b) through time, as real per capita income changes, and (c) with those of other country groupings that fall within similar per capita income brackets.

Keywords: Primary energy consumption CO2 emissions Fossil fuels Renewables Threshold regression

1. Introduction Over the last 50 years the world has experienced dramatic growth in GDP. According to the World Bank (2018), world GDP (measured in current US dollars) increased from $1.961 trillion in 1965 to $80.684 trillion in 2017 (World Bank, 2018a). Over the same period, world GDP per capita grew from US$590 to US$10,714 (World Bank, 2018b). There is already a large body of evidence suggesting that the most significant driver of CO2 emissions is GDP growth, followed by population (Andreoni and Galmarini, 2016; Chen et al., 2018; Henriques and Borowiecki, 2017; Li et al., 2018; Tajudeen et al., 2018). Growth in CO2 is a major factor behind global warming (IPCC, 2014). Using regional decomposition analysis, Mundaca et al. (2013) suggest that, since the 2008 global financial crisis, most regions have increased their CO2 emissions above historic trend. Nevertheless, recent evidence supports the view that there are some possible avenues to reduce CO2 emissions independent of changes in levels of GDP; these include improving fuel quality, fuel switching and improvements in energy efficiency (Andreoni and Galmarini, 2016; Henriques and Borowiecki, 2017). There is also convincing evidence, using decomposition analysis, that emissions can be reduced through fuel substitution between fossil fuels (Moutinho et al., 2018). A number of previous studies examine the contribution of fuel

consumption to CO2 emissions at the aggregate level. Recent evidence indicates that disaggregated studies at regional or country levels are more appropriate (Camarero et al., 2014; Li et al., 2018; Mishra and Smyth, 2014; Moreau and Vuille, 2018). Zoundi (2017) employs a panel co-integration approach to estimate short and long run relationships between CO2 emissions with income, renewable energy consumption, other primary energy consumption and population growth, for 25 African countries. Zoundi (2017) finds that renewable energy is negatively related to CO2 emissions in both the short and long run. While the negative effect of renewables is statistically significant, the magnitude is not sizable, as the bulk of the effects on CO2 emissions stems from conventional primary energy consumption. In a panel analysis of 34 OECD countries, Inglesi-Lotz (2016) finds long-run cointegrating relationships between economic growth and renewable energy consumption, reporting that use of renewables is positively and significantly associated with economic growth. In a more recent study on Sub-Saharan Africa, Inglesi-Lotz and Dogan (2018) confirm the negative relationship between renewable energy and CO2 emissions and the positive relationship between non-renewables and emissions, with the latter relationship being the larger. Using a similar method, Dong et al. (2018) examines the relationship between CO2 emissions and natural gas consumption for 14 Asia-Pacific countries. Compared to “dirtier” primary energy sources such as coal and oil, natural gas

We thank two anonymous referees, whose useful feedback considerably improved an earlier version of this article. The usual caveat applies. Corresponding author. E-mail addresses: [email protected] (A. Valadkhani), [email protected] (J. Nguyen), [email protected] (M. Bowden).

☆ ⁎

https://doi.org/10.1016/j.enpol.2019.02.024 Received 19 November 2018; Received in revised form 6 February 2019; Accepted 8 February 2019 0301-4215/ © 2019 Elsevier Ltd. All rights reserved.

Energy Policy 129 (2019) 268–278

A. Valadkhani, et al.

consumption has negative impacts on CO2 emissions in the long run (Dong et al., 2018). In addition to natural gas, the present study will include other forms of energy consumption contributing to CO2 emissions. There is an emerging consensus that the relationship between CO2 emissions and economic indicators also depends on the levels of development of the sample countries (Nguyen and Kakinaka, 2019; Sharma, 2011). We address this very important issue by allowing the effects of different types of energy consumption on CO2 emissions to depend on the level of per capita income across optimally determined income thresholds. Income thresholds are determined endogenously to account for differences among countries due to heterogeneities in the levels of industrialisation, technological advancements and economies of scale. We also estimate the disaggregated marginal effects of different sources of fuel consumption (e.g. oil, gas, coal, and renewables) on CO2 emissions on a per capita basis. Finally, as different countries have different energy portfolios and natural resource endowments, country-specific fixed effect dummy variables are included in the estimated multiple threshold models. The major findings of this paper are as follows. First, the analysis suggests that all countries, regardless of income levels, could reduce CO2 emissions by switching from oil to natural gas. This provides poorer countries with an interim/transitional pathway before eventually switching to renewable energy. Given that, for the period 1995–2009, CO2 emissions grew most rapidly in developing countries (Jiang and Guan, 2016), switching to gas appears to be an effective but interim solution. It should be noted that our analysis does not consider the effects of leaking of natural gas at extraction and transport level (which will be discussed further below in the policy implications). Second, renewable energy consumption does lead to lower CO2 emissions. However, when income thresholds are introduced, we find that this relationship is statistically significant for only the richest countries [defined as those countries with a GDP per capita greater than $16,215, (2011 US dollars, PPP)]. Nevertheless, even for the richest countries the marginal effect of renewables is still small. Finally, with the lowest marginal coefficients, the richest countries are more efficient than other countries in terms of gas and coal consumption. Therefore, there may be avenues for high-income countries to assist both lower and middle

Ln

CO2it Pit

=

it

+

country (i) at a given point in time (t) while we also examine the neutralising effects of consuming renewable energy ( ). To this end, the first starting point is to consider the following non-threshold regressions:

Ln

CO2it Pit

=

+ Ln

Ln

CO2it Pit

=

+ Ln

Oit Pit

i Dit

+ Ln

Rit + Pit

it

+ Ln

Git Pit

+ Ln

Cit Pit

+ Ln

Rit Pit

+

it

(2)

CO2it = carbon dioxide emissions in million tonnes in the ith country in year t, Pit = population in million persons, Oit = consumption of crude oil in million tonnes, Git = consumption of natural gas in million tonnes oil equivalents, Cit = consumption of coal in million tonnes oil equivalents, Rit = total renewable energy consumption (i.e. hydroelectric, solar, wind and geo biomass) in million tonnes oil equivalents, Dit = 1 for the ith country (in all years) and zero otherwise, Eit = total primary energy consumption in million tonnes oil equivalents (Eit= Oit+ Git + Cit+ Rit) Ln = natural logarithm, and εit = the residual term. i = 1,2,…,79, and t = 1965, 1967,…, 2017. We refer to Eqs. (1) and (2) as Models 1 and 2, where all coefficients remain fixed over time and across all countries. However, different countries may have a different energy consumption portfolio depending on their per capita income, natural endowments and ability to afford relevant technology. We use the PPP measure of real per capita income, qit , as an observable threshold variable to allow all of the coefficients to change using the following threshold regression:

Cit Pit

Rit Pit

if0

qit <

1

(2) +

Oit Pit

Git Pit

Cit Pit

Rit Pit

if 1

qit <

2

Oit Pit

Git Pit

Cit Pit

Rit Pit

if

2

qit <

3

if

m 1

Git Pit

Cit Pit

where:

( ) + (1) Ln ( ) + (1) Ln ( ) + (1) Ln ( ) (2) Ln ( ) + (2) Ln ( ) + (2) Ln ( ) + (2) Ln ( ) (3) Ln ( ) + (3) Ln ( ) + (3) Ln ( ) + (3) Ln ( ) ... ... (m) Ln ( ) + (m) Ln ( ) + (m) Ln ( ) + (m) Ln ( ) Oit Pit

+ Ln

i=2

Git Pit

(m) +

Git Pit

79

+

Oit Pit

(3) +

+ Ln

(1)

(1) + (1) Ln

...

Oit Pit

Cit Pit

Rit Pit

qit < +

(3)

The above equation generates m different sets of the coefficients with m-1 breaks at various stages of economic development. Because of the use of relatively smaller sub-samples, the separate estimation of the m piecewise regressions in Eq. (3) by OLS will not yield efficient parameters. A more efficient alternative to capture the multiple shifts in the above system of equations is to use one single discrete threshold regression (Tong and Lim, 1980; Tsay, 1989; Bai and Perron, 2003; Hansen, 2011). An iterative grid search can endogenously determine the m-1 optimal break points (i.e. 1 , 2 ,…, m 1). We use an indicator function 1(·) to re write the m individual piecewise regression equations as follows:

income countries to become more technologically efficient in their use of fossil fuels. 2. Methodology It is important to measure the effects of consuming different types of primary energy sources on per capital CO2 emissions. The marginal effect of oil, gas and coal consumption on CO2 emissions is both positive and instantaneous. Consuming fossil fuel immediately (without any lags) will increase CO2 emissions. However, the use of renewable sources of energy (i.e. hydroelectric, nuclear and other renewables including solar, wind and geo biomass) can reduce CO2 emissions relative to the use of fossil fuels. We expect that non-renewables and renewables to have positive and mitigating effects on CO2 emissions, respectively. The aim of this paper is to measure the effects of fossil fuel consumption on per capita emissions (β, γ and θ coefficients) in a given

Ln

269

CO2it Pit

m

=

1k (qit ,

k ).

+ (k ) Ln

Cit Pit

k=1

(k ) + (k ) Ln +

(k ) Ln

Rit Pit

Oit Pit +

+ (k ) Ln

it

Git Pit (4)

Energy Policy 129 (2019) 268–278

A. Valadkhani, et al.

The expression in parentheses will be true if the indicator function 1k (qit , k ) = 1( k qit < k + 1) is equal to 1, otherwise it will be equal to zero. In the above equation all the coefficients are subject to multiple shifts which will be determined endogenously by the data. At any point of time (t), a country belongs to group (k) if, and only if, ( k qit < k + 1) is satisfied where k = {1,…, m}, 0 = 0 and m = + . In Eq. (4), which is referred to as Model 3, all the coefficients are estimated within each of the sub-samples. Time invariant heterogeneities corresponding to each of 79 countries will be captured by the following fixed effect model (Model 4) in which 78 = 79–1 dummy variables are included to avoid the dummy variable trap, with the US being the reference or benchmark country:

Ln

CO2it Pit

m

1k (qit ,

k ).

it (k )

+

k=1

=

(k ) Ln

i Dit + it

(5)

i=2

(k ) + (k ) Ln +

Variables

Definition

Measurement unit

Mean

Std. Dev.

CV%

( ( ( ( (

Per capita CO2 emissions Per capita crude oil consumption Per capita natural gas consumption Per capita coal consumption Per capita renewable energy consumption

Million tonnes of carbon dioxide Million tonnes oil equivalent Million tonnes oil equivalent Million tones oil equivalent Million tonnes oil equivalent

7.90

7.60

96

1.44

1.49

103

0.89

2.04

229

0.50

0.78

156

0.47

1.11

236

CO2it Pit Oit Pit Git Pit

Cit Pit Rit Pit

) ) ) )

)

79

=

where: it (k )

Table 1 Summary statistics of the data.

( )+ Oit Pit

(k ) Ln

( )+ Git Pit

(k ) Ln

been falling since 2009. The topmost right charts (Figs. 1 and 2) show that oil's share of energy consumption has been in decline for almost half a century. At the peak in 1971, median oil consumption was almost two thirds of total primary energy consumption (66.3%). By 2006, this figure had fallen below 40% reaching 38.6% in 2017. Natural gas has consistently been on an upward trajectory throughout the sample period, rising from a median of less than half of one per cent in 1965 (0.37%) to a median share of 22.4% of total energy consumption in 2017. Reliance on coal exhibited a large drop from the late 1960s to the late 1970s. Since the early 1980s, coal has been stagnant in median terms (as shown in Fig. 1) and gradually falling in terms of the mean (Fig. 2). The semi-resurgence of coal following the steep decline of the 1970s can be attributed to increased coal usage in developing economies (Jiang and Guan, 2016). The combined effects of rising gas consumption and the long-term overall declining reliance on coal was that, by 2017, the mean share of natural gas (28.4%) in the total primary energy consumption portfolio was almost twice that of coal (15.0%). The same inference can be made if we compare the 2017 median share of gas (22.4%) versus coal (9.0%) in Fig. 1. As a component of renewables, hydroelectric sources of energy have exhibited considerable volatility but no clear trend, as can be seen in Figs. 1 and 2; earlier studies have found hydroelectric consumption to be stationary (Lean and Smyth, 2014). In 2017, hydroelectricity made up 8.25% (mean) or 3.30% (median) of total energy consumption. Other renewables have shown a sharp increase during the sample period with the mean rising from 0.1% to 4.2% from 1965 to 2017. The trends with nuclear energy are largely the result of a handful of outlier nations that use proportionately large amounts of nuclear energy, for example France, (24.1% in 2017), Lithuania (22.1%), and Sweden (20.3%). The median share of nuclear energy has fallen since the 1960s, from 1.60 × 10-5 percent in 1965 to 7.17 × 10-6 percent in 2017, whereas the mean has risen drastically from 0.06% to 4.01%. Given the very small share of nuclear sources in total primary energy consumption, and potential undue influence of outlier observations, we exclude it from our analysis.

( ), and Cit Pit

( ) Rit Pit

The estimated i fixed-effect coefficients show the deviation of nonreference countries i = {2,3,…,79} from the reference country (i.e. i = 1 for the US). While the break points { 1, 2, ..., m 1} are unknown a priori, as shown in Eq. (3), they are strictly increasing or 1 < 2 < ... < m 1. Bai and Perron's (2003) sequential approach is utilised to determine the optimal number of breakpoints in the sample with a maximum of breaks set at five. We report breaks if they are statistically significant at the 5% level. The estimation process aims to minimise the overall sum of squared residuals resulting from the various partitions varying in length. 3. Data Annual time series data (1965–2017) for 79 countries on the following variables have been extracted from the British Petroleum (2018) database: total primary energy consumption by source (i.e. crude oil, natural gas, coal, hydroelectric, and other renewables including solar, wind, geo biomass and biofuels) and CO2 emissions. All different types of primary energy consumption are measured in million tonnes oil equivalent and CO2 emissions in million tonnes carbon dioxide. The annual time series data on the purchasing power parity (PPP) measure of real GDP (2011 US dollars) from 1965 to 2014 were sourced from version 9.0 of the Penn World Table (Feenstra, Inklaar and Timmer, 2015). Using the real GDP growth rate (also in PPP measures) published in the World Development Indicators (World Bank, 2018c), we have extracted real GDP data on the remaining three years (i.e. 2015–2017). Population data (million persons) for all 79 countries during the sample period (1965–2017) were also sourced from the World Development Indicators (World Bank, 2018c). We present summary statistics of the five variables in Table 1. As can be seen, during the sample period (1965–2017), on average, crude oil (1.44 million tonnes per person) and gas (0.89 million tonnes equivalent per person) are the most consumed primary energy sources, while coal (0.50 million tonnes equivalent per person) and renewables (0.47 million tonnes equivalent per person) constitute relatively smaller proportions of the world's energy consumption portfolio. In terms of relative variability, the coefficient of variation for per capita consumption of renewables (236%) and natural gas (229%) are substantially higher than those of oil (103%) and coal (56%), suggesting a global tendency to switch to cleaner energy sources. Figs. 1 and 2 present the median and mean paths of CO2 and GDP per capita over time, as well as the proportion of total energy consumption represented by each of the major primary energy sources. As can be seen in the top left chart in Figs. 1 and 2, CO2 per capita shows an overall rising trend over the sample period, although emissions have

4. Results and discussions Using an upper limit of potentially five threshold values, we determine the optimal threshold values by conducting an iterative grid search through both i (countries) and t (time). The Bai and Perron (2003) approach is based on the use of stationary data. We have tested for the stationarity of the five series in Table 2 using four panel-unit root tests. The LLC test allows the existence of a common unit root process whereas the IPS test and the two conventional Fisher type tests (i.e. ADF-Fisher and PP-Fisher tests) examine the existence of possible individual unit root processes. Based on all four tests shown in Table 2, the five variables are stationary at the 2% level or better. Table 3 presents the results of Model 1 with a common effect and Model 2 with fixed effects. All non-threshold coefficients are highly 270

Energy Policy 129 (2019) 268–278

A. Valadkhani, et al.

Fig. 1. The median plots of the panel data (79 countries during 1965–2017). Note: For any given year, the median values for each series is calculated using 79 crosscountry observations in that year.

significant, with the exception of renewable energy consumption in Model 2, which is only significant at 10%. As expected, the estimated signs for fossil fuels are all positive: increased oil, gas, and coal consumption are associated with higher CO2 emissions. The magnitude of the oil coefficient dominates all others: in the common effect model, the coefficient for oil (0.841) is the largest compared with the coefficients for gas (0.042) and coal (0.054). With country-specific fixed effects in

Model 2, the relative magnitude of the oil coefficient (0.784) is again substantially higher than those of gas (0.02) and coal (0.036). In Model 1, renewables have a negative and significant effect on CO2 emissions (-0.043), with a magnitude comparable to the coefficients for coal and gas (0.054 and 0.042). However, when fixed effects are accounted for, renewables are significant only at the 10% level and the magnitude of the coefficient (-0.008) becomes much smaller. One may argue that this 271

Energy Policy 129 (2019) 268–278

A. Valadkhani, et al.

Fig. 2. The mean plots of the panel data (79 countries during 1965–2017). Note: For any given year, the mean values for each series is calculated using 79 crosscountry observations in that year.

fixed effects. The adjusted coefficient of determination (R2 ), Akaike information criterion (AIC), Schwarz criterion (SIC) and Hannan-Quinn information criterion (HQC), all indicate Model 4 as the preferred model. Allowing coefficients to vary as the PPP measure of GDP per capita increases, and allowing for up to five thresholds, we find only two significant breakpoints in both Models 3 and 4. Table 5 shows the results of sequential tests for the optimal number of thresholds based on

inference is drawn due to the fact that the sample countries include many disparate countries with substantial differences in population sizes, real income, technological capabilities, fossil fuel resources and relative prices. Table 4 presents the estimation results for two models with optimally determined threshold effects using a trimming percentage of 15%: Model 3 with a common effect and Model 4 with country-specific 272

Energy Policy 129 (2019) 268–278

A. Valadkhani, et al.

Table 2 Panel unit root test results. Variables

( Ln ( Ln ( Ln ( Ln (

Ln

CO 2it Pit

Oit Pit Git Pit Cit Pit Rit Pit

) ) ) )

)

IPS (2007) test(a)

LLC (2000) test(b)

ADF-Fisher test(c)

PP-Fisher test(d)

W stat.

p-value

t stat.

p-value

χ2 stat.

p-value

χ2 stat.

p-value

− 2.52

0.01

− 9.70

0.00

290.04

0.00

304.88

0.00

− 9.52

0.00

− 15.68

0.00

448.57

0.00

393.83

0.00

− 13.44

0.00

− 26.39

0.00

574.98

0.00

736.18

0.00

− 2.08

0.02

− 38.60

0.00

205.41

0.00

208.31

0.00

− 5.72

0.00

− 10.69

0.00

267.14

0.00

311.40

0.00

Note: (a) Im et al. (2003), (b) Levin et al. (2002), (c) Augmented Dickey-Fuller and Fisher (Maddala and Wu, 1999), and (d) Phillips-Perron-Fisher (Choi, 2001). Table 3 Non-threshold regression results CO O Ln P 2it = + Ln Pit + Ln

( ) it

Explanatory Variables

( ) it

( ) + Ln ( ) + Git Pit

Cit Pit

Ln

( )+ Rit Pit

79 i=2

i Dit

+

it

Model 1: non-threshold with a common effect

Model 2: non-threshold with fixed effects

Coeff.

t ratio

p-value

Coeff.

t ratio

p-value

1.859 0.841 0.042 0.054 − 0.043

74.47 43.19 13.18 10.02 − 7.69

0.00 0.00 0.00 0.00 0.00

2.011 0.784 0.020 0.036 − 0.008 − 0.140 − 0.575 − 0.373

61.21 28.90 8.02 7.89 − 1.76 − 6.79 − 9.95 − 4.48

0.00 0.00 0.00 0.00 0.08 0.00 0.00 0.00

− 0.371 − 0.627 − 0.439 0.977

− 4.83 − 7.85 − 3.75

0.00 0.00 0.00

2 3

4

… … … 77 78 79

R¯ 2 Overall F stat. AIC SIC HQC

0.875

6812 1.056 1.064 1.059

0.00

Model 4. As can be seen, the null hypothesis of no thresholds vs. the alternative hypothesis of one threshold is rejected. In the next sequential step we also reject the null of one threshold against two thresholds. However, we fail to reject the null of 2 thresholds vs. 3 so the iteration testing concludes. We thus present the results for three groups: poorest countries, middle-income countries, and rich countries. Classification into these three groups is performed on each country in each year, i.e. a hypothetical economy may be classified as a poor country in 1970, as a middle-income country in 1990, and have moved to the rich income group by 2010. For our preferred model, Model 4, the thresholds are $6932 per capita (2011 US dollars, PPP), below which a country falls into the poorest income group for a given year, and $16,215 per capita, above which a country falls into the top income group. The middleincome group comprises all countries that fall between these two thresholds at any point of time. For Model 4, almost all coefficients are significant at the 1% level, and are estimated with the expected signs. The exceptions are the coefficients for renewable energy consumption in the poorest (qit ≤ 6932) and middle income (6933 ≤ qit < 16215) economies, neither of which are significant at conventional levels. In contrast, for the highest-income economies, renewables are highly significant with a negative coefficient (-0.016). Our results indicate that consumption of renewables as a primary energy source may be associated with reduced

2035 − 0.626 − 0.492 − 0.578

0.00

CO2 emissions, but only when economies have reached a certain threshold of economic development, namely, an annual $16,216 per capita (2011 PPP US dollars). Fig. 3 presents the individual scatter plots of renewable consumption per capita versus CO2 emissions across the three income groups. As can be seen, the inverse relationship between per capita CO2 emissions and renewable energy consumption is only apparent in the high income group. As is the case in all four models, the estimated oil coefficients in Model 4 are many times greater in magnitude than any other primary energy source coefficient. In poorest economies, the oil coefficient (0.838) is noticeably greater than the coefficient for gas (0.015) and the coefficient for coal (0.047). The estimated coefficients for coal in Model 4 show a similar pattern to the oil coefficients, falling as income rises, from 0.047 for the low income group, to 0.035 for middle income and to 0.020 for the highest income group. Therefore, a quick glance at the estimated coefficients { (1) , (2) , (3) } as well as { (1) , (2) , (3) } in Table 4 indicates that, as real per capita income rises, countries are able to use oil and coal more efficiently in terms of CO2 emissions. Unlike oil and coal, the estimated coefficients for gas consumption do not show a clear upward or downward trend as income rises. For middle income economies, the gas coefficient is estimated to be 0.019, which is greater than that of both the low income group (0.015) and high income group (0.012). As a robustness check for the results presented, we also report 273

Energy Policy 129 (2019) 268–278

A. Valadkhani, et al.

Table 4 Optimal threshold regression results using 15% trimming region Ln it (k )

=

(k ) + (k ) Ln

Explanatory Variables

Poorest countries: (1) (1) (1) (1) (1) Middle income countries: (2) (2) (2) (2) (2) Richest countries: (3) (3) (3) (3) (3)

( ) Oit Pit

+ (k ) Ln

( ) Git Pit

+ (k ) Ln

( ) Cit Pit

+

(k ) Ln

( )

( )= CO2it Pit

+

79 i=2

i Dit

+

Model 3: optimal threshold with common effect

Model 4: optimal threshold with fixed effects

Coeff. t ratio qit≤ 5087,n1 = 696

p-value

Coeff. t ratio qit≤ 6932,n1 = 964

p-value

1.786 12.90 0.780 17.59 0.030 4.89 0.054 5.96 0.021 0.75 5088 ≤ qit < 24196, n2 = 2040 1.904 61.89 0.764 21.85 0.037 11.02 0.057 8.15 − 0.038 − 5.72 qit ≥ 24197, n3 = 1011 1.864 39.46 0.682 13.14 0.045 5.32 0.006 0.62 − 0.031 − 4.03

0.00 0.00 0.00 0.00 0.45

2.266 34.90 0.838 25.00 0.015 4.64 0.047 6.91 0.011 1.37 6933 ≤ qit < 16215, n2 = 1120 2.213 51.77 0.701 21.81 0.019 7.73 0.035 6.54 − 0.001 − 0.30 qit ≥ 16216, n3 = 1663 2.196 56.16 0.635 19.99 0.012 3.43 0.020 4.15 − 0.016 − 3.43 − 0.144 − 7.58 − 0.794 − 13.17 − 0.589 − 8.62 … … … − 0.508 − 5.49 − 0.768 − 12.31 − 0.466 − 4.21 0.983

0.00 0.00 0.00 0.00 0.17

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.53 0.00

2

4

… … … 77 78 79

it (k )

0.894

2248 0.899 0.924 0.908

0.00

threshold regression results assuming a trimming percentage of 20% (Table 6). As can be seen in Table 6, Model 3 returns slightly different results when compared with the results in Table 4. The thresholds separating the income classes, which in Table 4 (with 15% trimming) were $5088 and $24,196 per annum, are now $5701 and $24,196 in Table 6, i.e. the lower threshold is slightly higher. The magnitudes of the estimated coefficients in Model 3 are very similar, when comparing Tables 4 and 6. There are no notable differences with respect to the significance levels of the estimated coefficients. While the change in trimming percentage does not appear to make substantial differences for Model 3, we note that adjusted coefficient of determination, AIC, SIC and HQC all indicate that Model 3 with an assumed 15% trimming region (Table 4) is preferred to 20% (Table 6), albeit the results are highly similar. A comparison of our preferred Model 4 in Table 4 (which uses a 15% trimming percentage) with Model 4 in Table 6 (using a 20% trimming percentage) shows that the estimated income thresholds are identical, leading to the same classifications and sub-sample sizes. Coefficient estimates are also identical to three decimal places for all three income groups, between Model 4 in Tables 4 and 6, as are the associated p-values. In light of both Model 3 and Model 4 results, the findings in this study would thus appear to be robust to assumptions regarding trimming percentage. As a second robustness check, time series data on CO2 emissions from a non-commercial/non-industry source, the World Development Indicators (World Bank, 2018c), were used to estimate Model 1. Observed variations in the coefficients between the two models were small in magnitude, and the estimated coefficients remain significant at the

it

Rit Pit

3

R¯ 2 Overall F stat. AIC SIC HQC

m 1 (q , ). k = 1 k it k

2324 − 0.901 − 0.747 − 0.846

0.00 0.00 0.00 0.00 0.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00

Table 5 Sequential test results for determining the optimal number of thresholds. Null hypothesis

F-stat.

Scaled F-stat.

5% critical value

0 vs. 1a 1 vs. 2a 2 vs. 3

14.71 8.96 1.48

73.53 44.79 7.41

18.23 19.91 20.99

Note: The 5% critical values are obtained from Bai-Perron (2003). a Indicates that the null is rejected at the 5% level.

1% level. In addition, the correlation coefficients during two split periods, and for the full sample, were calculated with the result being (1965–1990: r = 0.999; 1991–2014: r = 0.998) and the whole sample period (1965–2014: r = 0.998), providing further evidence that the results presented using the BP dataset remain robust. The modelling results can be obtained from the lead author upon request. Overall, we find that the highest-income group has lower coefficients for all primary energy sources, and is the only group with a significant (and negative) coefficient for renewables. The richest countries use energy more efficiently in terms of CO2 emissions per unit of energy consumed, and are able to make use of renewable energy to reduce CO2 per capita emissions. Our findings are consistent with existing studies concluding that developing economies can potentially make large reductions in CO2 emissions by utilizing more efficient technologies or increasing human capital (see for example, Ward et al., 2017; Salim et al., 2017). We note, however, that while high-income economies have lower emissions per unit of primary energy 274

Energy Policy 129 (2019) 268–278

A. Valadkhani, et al.

energy (even if they do so more efficiently): see Fig. 4, the time plot of median ln(CO2/P). 5. Conclusion and policy implications This paper examines the threshold effects of various types of primary energy consumption (i.e. oil, coal, gas and renewables) on per capita CO2 emissions utilizing panel data for 79 countries during 1965–2017. We allow these marginal effects to differ depending on the individual countries’ real per capita income. Starting with an upper limit of potentially five threshold values, we find only two optimal threshold break values that are statistically significant. The sequentially estimated threshold break points categorize all countries into three groups at any point of time: the poorest countries, middle-income countries, and the richest countries. The results from both threshold and non-threshold panel regressions appear to be consistent with earlier findings in the literature: an increase in oil, gas, and coal consumption raises CO2 emissions albeit to varying degrees. In general, the marginal contribution of oil appears to be much higher than those of gas and coal. We also find that the use of renewables can reduce CO2 emissions but only among the high income countries with a per capita income in excess of $16,216 per capita (2011 PPP US dollars). The results also suggest that, as real per capita income increases, countries can utilize oil and coal more efficiently in terms of CO2 emissions. However, the use of gas appears to be least efficient among the middle-income economies ($6,933–$16,215). Our proposed approach will enable individual countries at different stages of economic development to evaluate possible pathways and future outcomes based on what other comparable country groupings have achieved in terms of their energy mix portfolios over time. Previous studies suggest that developed countries should focus on optimising their energy portfolio (Ang and Su, 2016). Our findings indicate that switching to renewables is a viable and effective option only for high-income countries. For low- and particularly middle-income countries, natural gas has been proposed as a potential “bridging solution”—an interim feasible option to be used while economies develop towards decarbonised energy systems (Brandt et al., 2014). While our results (for non-rich nations) are consistent with the idea that substitution from oil to gas can reduce CO2 emissions, we note a number of practical complications. First, substantial and variable leakage of methane1 in extraction and transportation imply that natural gas may not necessarily deliver climate benefits, when considering the upper bound estimates of gas-lifetime carbon footprint (Qin et al., 2017). Second, substitution to natural gas risks delaying transitions towards near-zero emission energy systems (Zhang et al., 2016), and may leave postbridge economies with problems of “stranded assets” (McGlade et al., 2018). The net benefits of substitution towards natural gas in the shortterm, as a transitory step, is partially dependent on the effectiveness of leakage detection and repair programs. According to Brandt et al. (2014), such programs can be profitable with existing technology. Methane leakage from both oil and gas consumption over the course of the lifecycle needs to be accounted for (Qin et al., 2018). We note the work of Henriques and Borowiecki (2017) which finds that, for Europe, North America and Japan, technological change and fuel efficiency has become more important in efforts to decrease emissions. Our results are 1

CO2, methane and nitrous oxide are three of the major greenhouse gases. 64% of methane emissions are attributable to human sources, while the remaining 36% are from natural sources (Valadkhani et al., 2016). Human sources of methane emissions include fossil fuels, livestock farming, biomass fuels, landfills, and rice plantations (Bousquet et al., 2006). For nitrous oxide, 38% of emissions are estimated to originate from human sources, versus 62% from natural sources (Valadkhani et al., 2016). Human sources of nitrous oxide emissions include: agriculture, fossil fuels, biomass fuel, atmospheric deposition and human sewerage (Denman et al., 2007). For the purposes of the present study, we focus on per capita CO2 emissions and contributing factors.

Fig. 3. Plot of per capita CO2 emissions and renewable consumption across three income groups.

consumption, their level of CO2 emissions per capita is significantly higher than the other two income groups. This observation is consistent with previous findings that GDP per capita is significant and positively associated with increased CO2 emissions per capita (Sharma, 2011). We explain this by noting that the top income economies consume more 275

Energy Policy 129 (2019) 268–278

A. Valadkhani, et al.

Table 6

Optimal Ln

threshold

( )= CO2it Pit

regression

m 1 (q , ). k = 1 k it k

Explanatory Variables

it (k )

+

results 79 i=2

i Dit

using +

20%

region

it (k )

Model 3: optimal threshold with common effect t ratio

( )+ Oit Pit

(k ) Ln

( )+ Git Pit

(k ) Ln

1.789 13.99 0.810 20.42 0.031 5.43 0.055 6.99 0.000 0.01 5701 ≤ qit < 24196,n2 = 1950 1.905 61.67 0.751 20.71 0.036 10.77 0.056 7.26 − 0.037 − 5.38 qit ≥ 24197, n3 = 1011 1.864 39.46 0.682 13.14 0.045 5.32 0.006 0.62 − 0.031 − 4.03

p-value

Coeff. qit ≤ 6932,n1 = 964

0.00 0.00 0.00 0.00 1.00

2.266 34.90 0.838 25.00 0.015 4.64 0.047 6.91 0.011 1.37 6933 ≤ qit < 16215,n2 = 1120 2.213 51.77 0.701 21.81 0.019 7.73 0.035 6.54 − 0.001 − 0.30 qit ≥ 16216, n3 = 1663 2.196 56.16 0.635 19.99 0.012 3.43 0.020 4.15 − 0.016 − 3.43 − 0.144 − 7.58 − 0.794 − 13.17 − 0.589 − 8.62 … … … − 0.508 − 5.49 − 0.768 − 12.31 − 0.466 − 4.21 0.983

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.53 0.00

2

4

… … … 77 78 79

(k ) + (k ) Ln

( )+ Cit Pit

(k ) Ln

( ) Rit Pit

Model 4: optimal threshold with fixed effects

3

R¯ 2 Overall F stat. AIC SIC HQC

=

it .

Coeff. qit ≤ 570,n1 = 786 Poorest countries: (1) (1) (1) (1) (1) Middle income countries: (2) (2) (2) (2) (2) Richest countries: (3) (3) (3) (3) (3)

trimming

0.893

2242 0.901 0.926 0.910

0.00

2324 − 0.901 − 0.747 − 0.846

Fig. 4. Time plot of the logarithm of per capita CO2 emissions across three country groupings.

276

t ratio

p-value

0.00 0.00 0.00 0.00 0.17 0.00 0.00 0.00 0.00 0.76 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00

Energy Policy 129 (2019) 268–278

A. Valadkhani, et al.

also consistent with the findings of Moutinho et al. (2018) who assert that richer countries are more willing to invest in renewables. Our finding that low- and middle income countries may not benefit from focussing on renewable energy sources until their economies reach higher levels of per capita GDP is consistent with previous studies

(Aliyu et al., 2018; Nguyen and Kakinaka, 2019). For example, Nguyen and Kakinaka (2019) apply panel-data integration analysis to 107 different countries and find a clear distinction between high vs low-income countries. They also support the view that switching to renewables is effective only in high-income countries.

Appendix A (See Table A1) Table A1 List of sample countries and their per capita income (PPP, 2011 US dollars). No.

Country name

1965

1990

2017

No.

Country name

1965

1990

2017

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

United States Canada Mexico Argentina Brazil Chile Colombia Ecuador Peru Trinidad and Tobago Venezuela, RB Austria Belgium Bulgaria Croatia Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Luxembourg Macedonia, FYR Netherlands Norway Poland Portugal Romania Slovak Republic Slovenia Spain Sweden

21363 16300 6843 3480 2565 5318 3735 2875 3160 12102 7823 10736 11186 NA NA 5664 NA 14976 NA 10780 12465 11888 7171 NA 15427 7050 8344 NA NA 16386 NA 13184 13901 NA 5261 2028 NA NA 8212 14968

36870 30040 10313 6102 5920 7192 6784 4817 3346 10652 7552 23924 22942 11955 14118 15296 20829 25493 11758 24775 24687 24896 16320 12571 30152 17572 24836 15672 12642 34322 7099 24765 29157 7779 13920 7104 16494 17150 17113 27215

54404 44378 16338 20222 13711 22051 13403 10769 11583 29159 NA 46695 41268 19232 23249 20325 32735 46053 28350 40017 39568 48088 25441 25272 44219 71773 36046 25398 28473 67802 14172 51146 80165 27769 29039 23645 27664 30828 35927 45322

41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79

Switzerland Turkey United Kingdom Azerbaijan Belarus Kazakhstan Russian Federation Turkmenistan Ukraine Uzbekistan Iran, Islamic Rep. Iraq Israel Kuwait Oman Qatar Saudi Arabia United Arab Emirates Algeria Egypt, Arab Rep. Morocco South Africa Australia Bangladesh China Hong Kong SAR, China India Indonesia Japan Malaysia New Zealand Pakistan Philippines Singapore Korea, Rep. Sri Lanka Taiwan Thailand Vietnam

22648 5148 13558 NA NA NA NA NA NA NA 4769 NA 9962 NA NA NA NA NA 5519 843 1992 6974 16016 1492 1142 6631 1132 1374 7322 2998 15142 1477 2069 3946 1374 2562 3209 1359 NA

35541 10141 24203 9227 13062 11168 19745 10507 10721 5240 3115 4708 20556 19322 12035 32470 18773 109409 8224 2124 4094 8254 26906 1395 2372 25936 1311 3167 27343 8563 21190 2557 3765 25329 12368 3139 19879 5341 1139

63022 22211 40102 15358 19291 24102 23509 22788 9767 9364 17520 12968 31921 63620 38244 134321 47463 73893 13437 11265 7696 11850 45588 3451 15071 47909 6514 10930 37012 23730 35565 5276 7586 70762 37085 11777 NA 14886 6416

der Werf, G.R., Peylin, P., Brunke, E.G., Carouge, C., Langenfelds, R.L., 2006. Contribution of anthropogenic and natural sources to atmospheric methane variability. Nature 443 (7110), 439. Camarero, M., Castillo-Giménez, J., Picazo-Tadeo, A.J., Tamarit, C., 2014. Is eco-efficiency in greenhouse gas emissions converging among European Union countries? Empir. Econ. 47 (1), 143–168. Chen, J., Wang, P., Cui, L., Huang, S., Song, M., 2018. Decomposition and decoupling analysis of CO2 emissions in OECD. Appl. Energy 231, 937–950. Choi, I., 2001. Unit root tests for panel data. J. Int. Money Financ. 20, 249–272. Denman, K.L., et al., 2007. Couplings between changes in the climate system and biogeochemistry. In Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK. Dong, K., Sun, R., Li, H., Liao, H., 2018. Does natural gas consumption mitigate CO2 emissions: testing the environmental Kuznets curve hypothesis for 14 Asia-Pacific countries. Renew. Sustain. Energy Rev. 94, 419–429. Feenstra, R.C., Inklaar, R., Timmer, M.P., 2015. The next generation of the Penn world table. Am. Econ. Rev. 105 (10), 3150–3182. (Accessed 14 December 2017). 〈www. ggdc.net/pwt〉.

References Andreoni, V., Galmarini, S., 2016. Drivers in CO2 emissions variation: a decomposition analysis for 33 world countries. Energy 103, 27–37. Ang, B.W., Su, B., 2016. Carbon emission intensity in electricity production: a global analysis. Energy Policy 94, 56–63. Aliyu, A.K., Modu, B., Tan, C.W., 2018. A review of renewable energy development in Africa: a focus in South Africa, Egypt and Nigeria. Renew. Sustain. Energy Rev. 81, 2502–2518 (Part 2). Bai, J., Perron, P., 2003. Computation and analysis of multiple structural change models. J. Appl. Econ. 18 (1), 1–22. Brandt, A.R., Heath, G.A., Kort, E.A., O'sullivan, F., Pétron, G., Jordaan, S.M., Tans, P., Wilcox, J., Gopstein, A.M., Arent, D., Wofsy, S., 2014. Methane leaks from North American natural gas systems. Science 343 (6172), 733–735. British Petroleum, 2018. Statistical Review of World Energy June 2018. (Accessed 13 November 2018) 〈https://www.bp.com/en/global/corporate/energy-economics/ statistical-review-of-world-energy/downloads.html〉. Bousquet, P., Ciais, P., Miller, J.B., Dlugokencky, E.J., Hauglustaine, D.A., Prigent, C., Van

277

Energy Policy 129 (2019) 268–278

A. Valadkhani, et al. Hansen, B., 2011. Threshold autoregression in economics. Stat. Its Interface 4, 123–127. Henriques, S.T., Borowiecki, K.J., 2017. The drivers of long-run CO2 emissions in Europe, North America and Japan since 1800. Energy Policy 101, 537–549. Im, K.S., Pesaran, M.H., Shin, Y., 2003. Testing for unit roots in heterogeneous panels. J. Econ. 115, 53–74. Inglesi-Lotz, R., 2016. The impact of renewable energy consumption to economic growth: a panel data application. Energy Econ. 53, 58–63. Inglesi-Lotz, R., Dogan, E., 2018. The role of renewable versus non-renewable energy to the level of CO2 emissions a panel analysis of sub-Saharan Africa's Βig 10 electricity generators. Renew. Energy 123, 36–43. Intergovernmental Panel on Climate Change (IPCC), 2014. Climate Change 2014: Mitigation of Climate Change. 〈https://www.ipcc.ch/report/ar5/wg3/〉. Jiang, X., Guan, D., 2016. Determinants of global CO2 emissions growth. Appl. Energy 184, 1132–1141. Lean, H.H., Smyth, R., 2014. Will initiatives to promote hydroelectricity consumption be effective? Evidence from univariate and panel LM unit root tests with structural breaks. Energy Policy 68, 102–115. Levin, A., Lin, C.F., Chu, C., 2002. Unit root tests in panel data: asymptotic and finitesample properties. J. Econ. 108, 1–24. Li, X., Liao, H., Du, Y.F., Wang, C., Wang, J.W., Liu, Y., 2018. Carbon dioxide emissions from the electricity sector in major countries: a decomposition analysis. Environ. Sci. Pollut. Res. 25 (7), 6814–6825. Maddala, G.S., Wu, S., 1999. A comparative study of unit root tests with panel data and a new simple test. Oxf. Bull. Econ. Stat. 61, 631–652. McGlade, C., Pye, S., Ekins, P., Bradshaw, M., Watson, J., 2018. The future role of natural gas in the UK: a bridge to nowhere? Energy Policy 113, 454–465. Mishra, V., Smyth, R., 2014. Convergence in energy consumption per capita among ASEAN countries. Energy Policy 73, 180–185. Moreau, V., Vuille, F., 2018. Decoupling energy use and economic growth: counter evidence from structural effects and embodied energy in trade. Appl. Energy 215, 54–62. Moutinho, V., Madaleno, M., Inglesi-Lotz, R., Dogan, E., 2018. Factors affecting CO2 emissions in top countries on renewable energies: a LMDI decomposition application. Renew. Sustain. Energy Rev. 90, 605–622. Mundaca, L.T., Markandya, A., Nørgaard, J., 2013. Walking away from a low-carbon economy? Recent and historical trends using a regional decomposition analysis.

Energy Policy 61, 1471–1480. Nguyen, K.H., Kakinaka, M., 2019. Renewable energy consumption, carbon emissions, and development stages: some evidence from panel cointegration analysis. Renew. Energy 132, 1049–1057. Qin, Y., Edwards, R., Tong, F., Mauzerall, D.L., 2017. Can switching from coal to shale gas bring net carbon reductions to China? Environ. Sci. Technol. 51 (5), 2554–2562. Qin, Y., Tong, F., Yang, G., Mauzerall, D.L., 2018. Challenges of using natural gas as a carbon mitigation option in China. Energy Policy 117, 457–462. Salim, R., Yao, Y., Chen, G., 2017. Does human capital matter for energy consumption in China? Energy Econ. 67, 45–59. Sharma, S.S., 2011. Determinants of carbon dioxide emissions: empirical evidence from 69 countries. Appl. Energy 88, 376–382. Tajudeen, I.A., Wossink, A., Banerjee, P., 2018. How significant is energy efficiency to mitigate CO2 emissions? Evidence from OECD countries. Energy Econ. 72, 200–221. Tong, H., Lim, K.S., 1980. Threshold autoregression, limit cycles and cyclical data. J. R. Stat. Soc. 42, 245–292. Tsay, R.S., 1989. Testing and modeling threshold autoregressive processes. J. Am. Stat. Assoc. 84, 231–240. Valadkhani, A., Roshdi, I., Smyth, R., 2016. A multiplicative environmental DEA approach to measure efficiency changes in the world's major polluters. Energy Econ. 54, 363–375. Ward, H., Radebach, A., Vierhaus, I., Fügenschuh, A., Steckel, J.C., 2017. Reducing global CO2 emissions with the technologies we have. Resour. Energy Econ. 49, 201–217. World Bank, 2018a. World Bank Open Data. (Accessed 15 October 2018) 〈https://data. worldbank.org/indicator/NY.GDP.MKTP.CD〉. World Bank, 2018b. World Bank Open Data. (Accessed 15 October 2018) 〈https://data. worldbank.org/indicator/NY.GDP.PCAP.CD〉. World Bank, 2018c. World Development Indicators 2018. World Bank, Washington, DC. https://doi.org/10.1596/978-1-4648-0683-4. (License: Creative Commons Attribution CC BY3.0 IGO). Zhang, X., Myhrvold, N.P., Hausfather, Z., Caldeira, K., 2016. Climate benefits of natural gas as a bridge fuel and potential delay of near-zero energy systems. Appl. Energy 167, 317–322. Zoundi, Z., 2017. CO2 emissions, renewable energy and the Environmental Kuznets Curve, a panel cointegration approach. Renew. Sustain. Energy Rev. 72, 1067–1075.

278