metadata: authors: 0: author: Deichmann,Uwe Klaus
1: author: Reuter,Anna
2: author: Vollmer,Sebastian
3: author: Zhang,Fan
count: World
docna: 0: docna: Relationship between energy intensity and
            economic growth : new evidence from a multi-country
            multi-sector data set
docty: Policy Research Working Paper
subtopic: Industrial Economics,Economic Growth,Economic Theory & Research,Climate Change Mitigation and Green House Gases
teratopic: Macroeconomics and Economic Growth,Industry,Environment
abstracts: cdata!: This paper revisits the relationship
            between energy intensity and economic growth, using a
            flexible piecewise linear regression model. Based on a panel
            data set of 137 economies during 1990-2014, the analysis
            identifies a threshold effect of income growth on energy
            intensity change: although energy intensity is negatively
            correlated with income growth throughout the entire sample
            and study period, the declining rate significantly slows by
            more than 30 percent after the level of per capita income
            reaches $5,000. Based on index decomposition, the analysis
            also finds that although structural change is important for
            intensity levels in all countries, the efficiency effect is
            more important in higher-income countries. The results
            suggest that when countries move beyond lower-middle-income
            levels, energy efficiency policies become far more critical
            for sustaining the rate of improvement in energy efficiency.
repnme: repnme: Relationship between energy intensity and
            economic growth : new evidence from a multi-country
            multi-sector data set
display_title: Relationship between energy
            intensity and economic growth : new evidence from a
            multi-country multi-sector data set
listing_relative_url: /research/2018/01/29547526/relationship-between-energy-intensity-economic-growth-new-evidence-multi-country-multi-sector-data-set
topicv3: Economic Growth m1326241 471,Energy and Climate Change m1327080 1756
publishtoextweb_dt: 2018-01-30T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Environment
no_of_pages: 25
author_keys: 0: author_key: 436115
1: author_key: 1007679
2: author_key: 905148
model_extractions: WPS8322 Policy Research Working Paper 8322 Relationship between Energy Intensity and Economic Growth New Evidence from a Multi‐Country Multi‐Sector Data Set _Uwe Deichmann Anna Reuter Sebastian Vollmer Fan Zhang_ South Asia Region Office of the Chief Economist January 2018
Policy Research Working Paper 8322

# **Abstract**

This paper revisits the relationship between energy intensity and economic growth, using a flexible piecewise linear regression model. Based on a panel data set of 137 economies during 1990–2014, the analysis identifies a threshold effect of income growth on energy intensity change: although energy intensity is negatively correlated with income growth throughout the entire sample and study period, the declining rate significantly slows by more than 30 percent after the level of per capita income reaches $5,000. Based on index decomposition, the analysis also finds that although structural change is important for intensity levels in all countries, the efficiency effect is more important in higher-income countries. The results suggest that when countries move beyond lower-middle-income levels, energy efficiency policies become far more critical for sustaining the rate of improvement in energy efficiency.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at fzhang1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Relationship between Energy Intensity and Economic Growth: New Evidence from a Multi‐Country Multi‐Sector Data Set**<sup>**1**</sup>

**Uwe Deichmann**<sup>**a**</sup> **, Anna Reuter**<sup>**b**</sup> **, Sebastian Vollmer**<sup>**b**</sup> **and Fan Zhang**<sup>**a**</sup>

aWorld Bank, USA bUniversity of Göttingen, Germany JEL: Q43, Q48, O47

> 1 Financial support from the World Bank’s Knowledge for Change Program is gratefully acknowledged. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **I.  Introduction**

The energy sector plays an important role in economic development. But energy consumption is also associated with human‐induced climate change because of the dominant role of fossil fuels in power production. Energy use tends to increase with development as countries shift from labor‐intensive agriculture to capital and energy intensive industries. As the structural transformation proceeds, they subsequently move into information‐intensive services. Energy intensity therefore initially increases with rising incomes and then decreases—a pattern comparable to the Environmental Kuznets Curve (EKC) that describes the relation between per capita GDP and environmental degradation.

The EKC pattern in the income and pollution relationship has been extensively documented (Dasgupta et al., 2002; Carson, 2010). Recently, there is also a growing literature on the validity of EKC in the context of energy use. For example, Luzzati and Orsini (2009) analyze the relationship between per capita GDP and aggregate energy consumption for a sample of 113 countries over 1971 to 2004. Their findings based on different econometric techniques (parametric and semi‐parametric) and across different sample groups (world, cross‐countries, and individual countries) do not support the energy‐ EKC hypothesis. On the other hand, Medlock and Soligo (2001), van Benthem and Romani (2009) and van Benthem (2015) document an S‐shaped relationship between energy intensity and economic growth. Specifically, they find that the income elasticity of energy demand peaks at a GDP per capita level between $5,000 and $10,000, and trends towards zero for high‐income levels. Burke and Csereklyei (2016) present disaggregated analysis of the elasticity of energy demand. Their findings suggest substantial heterogeneity in income elasticity of energy use across sectors: industry and services sectors are most responsive to income growth, followed by residential and agricultural sectors.

Another strand of related literature tests the hypothesis of cross‐country convergence of energy intensity. Specifically, these studies examine whether cross‐country variation in energy intensity is getting smaller (so‐called sigma‐convergence) and whether less efficient countries reduce their energy intensity faster than more efficient ones (beta‐convergence). While the majority of studies find convergence across developing and developed countries (such as Mielnik and Goldemberg, 2000; Markandya et al., 2006; Ezcurra, 2007; Csereklyei et al., 2016; Burke and Csereklyei, 2016), others argue for a more nuanced picture of convergence in energy intensity. For example, Le Pen and Sevi (2010) do not find evidence for global convergence for a group of 97 countries during 1971 – 2003. When looking at subgroups, they find non‐convergence is “less strongly rejected” for the Middle East, OECD and Europe sub‐groups. Ezcurra (2007) show that developing counties tend to converge at a higher level of energy intensity, while for developed countries at least two different levels of convergence are observed. Stern (2012) shows that divergence in energy efficiency is mostly associated with economies that are lacking in economic progress.

A third strand of literature explores the determinants of the growth rate of energy intensity. For example, using data for 51 sectors in 19 OECD countries from 1980 to 2005, Mulder and de Groot (2011) find that reductions in energy intensity have been driven more by within‐sector energy efficiency improvements than by changes in the composition of activities at the economy level or within the manufacturing and service sectors. In another paper, Mulder and de Groot (2012) show that aggregate convergence in energy intensity of 18 OECD countries from 1970 to 2005 is almost entirely driven by convergence of within‐sector energy intensity levels, and not by convergence of the sector composition of economies. By using output distance functions, Wang (2013) decomposes energy 2
intensity change across 100 countries from 1980 to 2010 into five components: technological catch‐ up, technological progress and changes in the capital‐energy ratio, labor‐energy ratio and output structure. Technological progress, capital accumulation and changes in output structure contributed positively to lowering energy intensity while a decrease in the labor‐energy ratio increased intensities.

As this brief survey shows, the existing EKC and convergence literature does not present conclusive evidence on the relationship between energy intensity and economic growth. In this paper we therefore revisit this question using a large data set of 137 economies over the period of 1990‐2014. In our main estimation model, we adopt a novel piece‐wise regression method which imposes less structure on the relationship between the level of economic growth and energy intensity. We find a negative correlation between GDP per capita and energy intensity across the entire sample and study period. However, the downward slope flattens out at higher values of GDP. The inflection point is at a per capita income level of around $5,000. We also find evidence of cross‐country convergence in energy intensity. Finally, based on index decomposition, we investigate the extent to which shifts in economy‐wide energy intensity are affected by structural vs. efficiency changes at the country level. The analysis shows that while structural change is important for intensity levels in all countries, the efficiency effect is more important for higher‐income countries. This finding supports the income threshold effect identified earlier: the relatively automatic efficiency gains (as a result of income gains) are at lower levels of GDP (and thus the steeper slope), while the harder problem (improving efficiency) is more important in higher income countries. The results suggest that when countries move beyond lower‐middle income levels, energy efficiency policies become far more important if they want to continue the rate of improvement in energy efficiency.

# **II. Data**

Energy consumption data are obtained from the International Energy Agency (IEA) World Energy Statistics and Balances data set. We calculate aggregate energy intensity as total final energy consumption divided by total GDP (GDP per capita times population). Sector‐level energy intensity is defined as energy consumption of agriculture, industry and services divided by sector value added.

Data on GDP and value added in agriculture, services, and industry and manufacturing sectors are from the World Bank World Development Indicators (WDI).<sup>2</sup> For comparison across countries and over time, all values are converted into PPP‐based constant 2005 prices.

We also control for other country characteristics, including exports and imports, age structure of the population, and population density. All these factors may lead countries to converge to different steady‐state levels of energy intensity. Data on these variables are also obtained from WDI.

Overall, we have an unbalanced panel of 137 economies from 1970 to 2014, and a balanced panel of 64 countries during the same period. Since the attrition rate is relatively high, it may be difficult to tell whether a change over time is picking up a real development or just a change in the composition of the sample. However, eliminating countries with missing data could cause sample selection bias, if countries with missing data are systematically different from those that have complete observations over the study period. In the following we report results from the unbalanced panel. For robustness

> 2 Last Updated: 05/26/2017.

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check, we also estimate the models using the balanced panel and with data from the Penn World Tables (1980‐2014). Conclusions of the paper are robust to these alternative data sources.

Table 1 reports descriptive statistics for the 137 economies in the unbalanced sample. The variables reported are GDP (PPP) per capita, total final energy consumption and population size. The numbers are period averages. The sample covers the entire spectrum from poor to rich countries.

# **III. Relationship between energy intensity and economic development**

# **A. Descriptive analysis**

We are interested in the relationship between energy intensity and economic development. One hypothesis that can be derived from the literature is that the relationship between energy intensity and economic development follows a Kuznet’s curve (inverted u‐shape). Energy intensity increases in early stages of development (industrialization) and it decreases in later stages of development (transition to a services economy). Structural change of the economy is a long process that is unlikely to be reflected in a panel of only 44 years. However, it is still possible to study this hypothesis. If it were true, we would expect a negative association between energy intensity and GDP per capita for developed countries and a positive association for developing countries (and possibly no association for countries that are in the middle of the transition).

Another hypothesis is that there is a negative association between energy intensity and economic development with no trend shift. The theoretical justification behind this hypothesis is that economic development increases opportunities to make production processes more efficient and therefore reduces energy intensity. If this negative association was preceded by an initial positive one, as the EKC hypothesis suggests, it would have pre‐dated the compilation of reliable cross‐country data. The remaining possibilities are that there is no association between growth and energy intensity or that energy intensity keeps rising with incomes (a positive association). These patterns are not supported by any theories or evidence.

Exploratory data analysis suggests it is appropriate to take logs of both variables. Taking logs does not change the direction of the relationship and therefore does not affect our conclusions regarding the main hypotheses. But the logarithm makes an exponential shape linear and allows correlation and regression analysis of the data.

As a first step we calculate the correlation coefficient between energy consumption per $PPP and GDP per capita PPP for all 137 economies in the data set (Table A1). For about 40 percent of the countries we find a very strong negative linear association between the two variables with a correlation coefficient of ‐0.9 or smaller. For another 35 percent of the countries we find a moderate to strong negative association with correlation coefficients between ‐0.9 and ‐0.5. Of the remaining countries, 16 show a weak to very weak negative association and 15 countries show a weak positive association. The overall impression from the correlation analysis is that there is a fairly strong negative association between energy consumption per $PPP and GDP per capita PPP for most countries. If the relationship followed a Kuznets curve, we would expect a negative association in developed countries and a positive association in developing countries. Even a cursory look at Table A1 contradicts this hypothesis: We find the same strong negative linear association for countries like India, Nigeria and 4
China as for countries like the United States, the United Kingdom and Germany. On the other hand, the relatively rich EU members Spain and Portugal show only a weak negative or even a positive association between the two variables.

# **B. Econometric analysis**

In the econometric analysis, we first specify the following fixed effects model to test the relationship between energy intensity and economic development Where _EIit_ is log energy intensity of country _i_ in year _t_ , _GDPit_ is log GDP per capita. Previous studies suggest the importance of controlling for sector composition of an economy when analyzing the relationship between income and energy intensity (Burke and Csereklyei, 2016; Medlock and Soligo, 2001). We therefore include on the right‐hand‐side of equation (1) a vector ��� , which is a set of control variables for the composition of final energy demand. It includes the percentage of total energy consumption used in industry, transport, residential, services, agriculture and non‐energy use. The reference category is non‐specified other energy consumption, all variables are coded between 0 and 1. ��� is a set of control variables including value added in industry, agriculture, manufacturing and services as well as exports and imports. All variables are measured as a percentage of total GDP and coded between 0 and 100. ��� is a set of demographic variables which could affect energy consumption patterns. It includes population density as well as the percentage of the population aged 14 or younger and 65 and older (the working age population is the reference category). Population density is measured as the number of people per 1,000 square kilometers of area, and the other two variables are coded between 0 and 100. Finally, �� is a country fixed effect capturing time‐invariant country specific characteristics; �� is a time fixed effect controlling for yearly shocks that are common to all countries; and ��� is the error term.

The results of these regressions are shown in Table 2. In the first column we report the results of a simple pooled OLS specification, in the second column we add country fixed effects, and in the third column we add time fixed effects. The coefficient of log GDP per capita is negative and highly significant in all three cases, but its magnitude changes when the country and time fixed effects are included. Adding the control variables for the composition of energy consumption changes the explanatory power of the model, but the coefficient of log GDP per capita remains relatively stable. Adding the control variables for the structure of the economy also increases the explanatory power moderately, but also diminishes the size of the coefficient of log GDP per capita. Adding the demographic controls again increases the explanatory power of the model and enlarges the magnitude of the coefficient of log GDP per capita again. Overall, these regressions are strong evidence for a negative association between GDP per capita and energy intensity. An increase of GDP per capita by 1 percent is associated with a decrease of energy intensity by 0.62 percent.

The scatter plot in Figure 1 and also the country by country correlation analysis in the previous section indicate that the relationship between log GDP per capita and log energy intensity is not perfectly linear, because there are a few countries with no or a positive association. We thus include a squared term of log GDP per capita to equation (1) to allow for a more flexible functional form and also to test for an inverted U‐shaped relationship.

������� ��� ∙�������� ��� ∙��������� ������ ������ ������ ��� ��� ���� (2) 5
<!-- Start of picture text --> = —f,/(2° B2)<br><!-- End of picture text --> <!-- Start of picture text --> logE liz = (a?"® + BP’? - logGDP;,) + Bi"® + (aPst + BP°S - logGDPiz) - Bho + u, + P, + Vit<br>Bitpre<br>Bitpost<br>prre prost<br>prre prost<br>AlogEl;, = a: ABR’ + BPre - BPT® . AlogGDP;, + BP? + BR°* - AlogGDPi, + Ay; + Avit<br>Ax = Xt — Xt-1<br>AlogEli, = @* ABR’? + BPTe - BETS « AlogGDPice-1) + BP » BRO « AlogGDPict-1) + Ae + AVit<br><!-- End of picture text --> <!-- Start of picture text --> Bitpre<br>Bitpost<br><!-- End of picture text -->
The results for the two differences‐in‐differences regressions are reported in Table 4. For both equations, the coefficients are negative and highly significant, both to the right and to the left of the critical value of log GDP per capita. However, the slope is much steeper (we find a more negative coefficient) to the left of the critical value than to the right. These results confirm the previous finding from the fixed effects estimations. The relationship between log GDP per capita and log energy intensity is negative for the entire observation period, but the slope flattens out for higher values of GDP per capita. Specifically, when per capita GDP reaches around $5,000, the declining rate of energy intensity with respect to economic growth significantly slows down by more than 30 percent. This finding suggests that energy productivity increases as income grows. However, the income elasticity of energy demand increases with income. As a result, after exceeding a threshold of around $5,000 GDP per capita, the efficiency dividends of economic growth become much smaller. More aggressive energy efficiency policies are needed to sustain rapid and continued improvement in energy productivity as income levels surpass the threshold.

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# **IV. Convergence of energy intensity**

In an exploratory data analysis (time series plots of energy intensity) we got the impression that energy intensity is converging to the same level across countries. We will now investigate this hypothesis more systematically. Typically, convergence analysis is used in the context of economic growth and goes back to Barro and Sala‐i‐Martin (1991). In order to achieve convergence, or in other words for poor countries to catch up to GDP per capita levels of rich countries over time, two things are necessary: beta convergence and sigma convergence. For beta convergence we study the association between the average growth rate of GDP per capita and the initial level of GDP per capita for a given time period. We speak of beta convergence if the regression coefficient (beta) of the initial level of GDP per capita is negative – as an explanatory variable for the growth rate of GDP per capita. The negative beta coefficient implies that initially poorer countries had higher average growth rates than initially richer countries. Sigma convergence means that the standard deviation of GDP per capita decreases over time. If we find both, we speak of convergence.

How do these concepts translate to convergence of energy intensity? For energy intensity, convergence means that countries that use a lot of energy per dollar of GDP over time catch up to levels of countries that use less energy per dollar of GDP. Thus, we speak of beta convergence in this context if higher rates of reduction in energy consumption per dollar of GDP are associated with higher initial levels of energy consumption per dollar of GDP. Different from convergence of GDP per capita, in this context beta convergence is implied by a positive beta coefficient in the regression. The meaning of sigma convergence is exactly the same as in the GDP per capita context.

We calculate the total change in log energy consumption per dollar of GDP between 1990 and 2014 and divide it by the level in 1990. This is a simple measure for the rate of reduction in energy consumption per dollar of GDP. We then run a regression with the rate of reduction as a dependent variable and the initial level of log energy consumption per dollar of GDP as the explanatory variable according to equation (6):

# ∆��� ��log��������� (6)

∆ _Iit_ is the growth rate of energy intensity of country _i_ during the period from 1990 to 2014. _Ii0_ is the energy intensity of country _i_ in year 1990 and ��� is the error term. The result is shown in the left panel of Figure 3. Indeed, the beta coefficient is positive and highly significant (beta= .029 and p<0.01). Quite remarkably, the variation in the initial level of log energy consumption per dollar of GDP explains about 42 percent of the variation in the rate of reduction across countries. We thus find strong evidence for beta convergence.

In the right panel of Figure 3, we show fitted normal densities for log energy consumption per dollar of GDP for 1990 and 2014. One can clearly see that the density became much narrower over time (and also that the mean shifted to the left). Indeed, the standard deviation decreases from 0.649 in 1990 to 0.511 in 2014. We thus find evidence for sigma convergence. As we find both beta and sigma convergence, we can thus conclude that countries with high initial levels of energy intensity are converging to the levels of countries with low energy intensity.

8
In Figure 4 we show box plots for log energy intensity for 1990, 2000, 2010 and 2014. A box plot displays the median, the first quartile, the third quartile and the largest and smallest adjacent values.<sup>3</sup> The following observations are noteworthy: First quartile, median, third quartile and largest value all decrease over time. The outside values get closer to the largest adjacent observation over time. <mark>The smallest adjacent value also decreases over time, though the largest change occurred between 1990 and 2000. After 2000, the smallest adjacent value stayed fairly constant. The smallest value can</mark> be considered as the “technology frontier” or “minimal energy consumption required per unit of output”. According to this picture, the frontier did not move very much between 2000 and 2014, rather the rest of the distribution moved closer to the frontier. Given that there is a generally increasing trend for per capita income across the sample, this result appears to be consistent with our earlier conclusion that as income rises above a certain threshold, reducing energy intensity becomes more difficult.

To examine whether there is also a difference in the speed of convergence on different sides of the income threshold, we divide the sample into two subgroups based on whether a country‐year observation lies below or above the cutoff point at log per capita GDP of 8.5. We calculate the � conditional convergence rate according to ���� ~~�~~ � ~~�~~ log ���1�� (Barro and Sala‐i‐Martin, 1992; Islam, 1995), where _T_ is the length in the time dimension under consideration (25 years in our case) and � is the coefficient obtained from equation (6). The results are reported in Table 5. The rate of unconditional convergence for groups below and above the income threshold is roughly 0.10 percent and 0.14 percent, respectively. The difference between the two groups is small and the lower‐income sample even has a lower convergence rate. The result could reflect different economic growth rates of the two groups.

# **V. Index Decomposition**

In this section, we investigate how much of the improvement in energy intensity is due to structural change and how much is due to efficiency improvements, and whether the income threshold effects identified earlier play a role in the relative importance of structural vs. efficiency changes. To this end we use the Fisher Ideal Index Decomposition for structural change in energy intensity as described in Boyd and Roop (2004). We consider three sectors: industry, agriculture and services. As described in Boyd and Roop (2004) we first calculate a Laspeyres and a Paasche index of energy intensity for structure and efficiency using the following formulas from Boyd and Roop (2004): S: agriculture, industry and services share of GDP I: energy intensity 0: first year

> 3 Values that are more than 1.5 times the interquartile range (distance between first and third quartile) away from the median are plotted separately.

9
# T: last year

# i: industry, agriculture, service

The Fisher index is the geometric mean of the Laspeyres and the Paasche index. The aggregate Fisher index is the product of the structural part and the intensity part.

Table 6 displays the values of the Fisher index (aggregate, structure and efficiency) for the year 2014 (base year 1990=1) in ascending order of the aggregate index. The structural index ranges between 0.5 and 1.3 while the efficiency intensity ranges from 0.2 to 1.6.<sup>4</sup> To examine whether structural change or efficiency improvement plays a more important role in influencing aggregate energy intensity, we estimate the following country fixed‐effects model.

# _Fagg=_ � _FStr+_ � _Feff+ui+ei_ (7)

_Fagg_ is the aggregate intensity index, _Fstr_ is the structural index and _Feff_ is the efficiency index. _ui_ are time‐invariant country fixed effects. _ei_ is the error term. Similar to the convergence analysis, we conduct both full sample analysis and sub‐sample analysis based on the previously identified income threshold. Table 7 reports the estimation results, where column (1) corresponds to the full sample estimation, and columns (2) and (3) correspond to sub‐samples below and above the income threshold, respectively.

The results suggest that structural change has made a higher contribution to variation in energy intensity for the full sample of observations. However, for country‐year observations that are above the income threshold, efficiency change plays a more important role in driving the change in aggregate intensity.

# **VI. Conclusion**

Reducing the amount of energy required to produce a unit of output is a priority in efforts to slow climate change. This paper contributes to the literature on the relationship between energy use and economic growth by better describing the energy intensity trajectory as incomes grow. Specifically, our results confirm a negative correlation between GDP per capita and energy intensity as well as cross‐country convergence in energy intensity. We also identify a transition from a relatively rapid lowering of energy intensity to slower gains. Using a flexible econometric approach, we show that as countries reach a per capita income of about $5,000 (PPP)—slightly above the level considered by the World Bank as upper‐middle income—the downward sloping curve of energy use per unit of GDP flattens out. Index decomposition identifies the relative contribution of structural change versus energy efficiency to lowering energy intensity. It shows that the former is important at all income levels, while energy efficiency is more important at higher income levels.

The results suggest that we can expect to see relatively rapid improvements in energy intensity as the economies in today’s poor countries grow. However, as they move beyond lower‐middle income, the role for energy efficiency policies then becomes far more critical if a country wants to continue the rate of improvement in energy efficiency (Deichmann and Zhang 2013). There is now a much better

> 4 Malta is dropped as an outlier.

10
understanding of different policy instruments for improving energy efficiency—even if we do not yet sufficiently understand why they are not adopted at a scale that would seem beneficial (Gillingham and Palmer 2014). Energy efficiency gains likely will require numerous sector‐specific instruments such as targeted revenue‐neutral environmental taxation (eco‐taxes) and other price instruments, stricter regulations, and public investments.

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Table 1 Summary statistics (period averages) for the 137 economies

|Country|GDP p.c.|C(Energy)|Population|Country|GDP p.c.|C(Energy)|Population|
|---|---|---|---|---|---|---|---|
|Qatar|120,371|8,363|971|Hungary|19,404|17,949|<br>10,159|
|United Arab||||||||
|Emirates|91,285|29,787|4,593|Slovak Republic|19,142|11,544|<br>5,372|
|Brunei|83,208|860|341|Russian Federation|18,346|454,862|<br>145,585|
|Kuwait|80,735|9,845|2,396|Gabon|18,176|2,406|<br>1,299|
|Luxembourg|78,399|3,446|454|Croatia|18,112|6,937|<br>4,467|
|Norway|57,065|19,581|4,594|Lithuania|17,508|5,784|<br>3,389|
|Singapore|55,826|10,518|4,207|Malaysia|16,782|32,673|<br>24,128|
|Switzerland|50,961|19,507|7,363|Poland|16,114|63,573|<br>38,279|
|United States|45,368|1,465,972|286,382|Chile|15,868|19,762|<br>15,510|
|Bahrain|42,200|3,673|841|Latvia|15,738|4,146|<br>2,317|
|Oman|41,481|6,673|2,542|Argentina|15,601|47,624|<br>37,888|
|Denmark|41,260|14,363|5,378|Venezuela, RB|15,567|36,434|<br>25,364|
|Netherlands|40,688|59,770|16,015|Mexico|14,536|100,630|<br>105,562|
|Saudi Arabia|39,294|79,833|23,095|Kazakhstan|14,408|37,531|<br>15,785|
|Austria|38,803|24,594|8,122|Turkey|14,384|61,553|<br>65,149|
|Ireland|38,762|10,093|4,025|Cuba|13,768|8,324|<br>11,106|
|Hong Kong SAR,||||||||
|<br>China|38,451|7,862|6,610|Uruguay|13,666|2,728|<br>3,298|
|Sweden|37,422|34,295|9,025|Romania|13,640|26,475|<br>21,652|
|Germany|37,219|230,486|81,591|Iran, Islamic Rep.|13,605|113,367|<br>67,204|
|Canada|37,139|186,403|31,501|Lebanon|12,816|3,337|<br>3,699|
|Belgium|37,075|39,658|10,457|Montenegro|12,240|805|<br>613|
|Australia|35,960|69,457|19,867|Panama|12,197|2,190|<br>3,151|
|Italy|35,147|127,407|57,841|Brazil|12,192|164,671|<br>179,867|
|Finland|34,788|24,407|5,217|Mauritius|12,065|665|<br>1,186|
|France|34,390|158,732|62,060|Suriname|11,818|515|<br>480|
|Japan|34,190|312,992|126,642|Bulgaria|11,510|10,647|<br>7,926|
|United Kingdom|33,148|142,358|60,066|Algeria|11,191|19,515|<br>32,155|
|Cyprus|29,805|1,408|973|Botswana|11,075|1,431|<br>1,791|
|Spain|29,434|82,561|42,517|Thailand|10,721|60,940|<br>63,170|
|New Zealand|29,240|12,180|3,975|Costa Rica|10,718|2,608|<br>4,004|
|Israel|25,725|11,519|6,516|South Africa|10,575|60,770|<br>45,775|
|Slovenia|25,406|4,646|2,010|Serbia|10,451|8,941|<br>7,464|
|Greece|25,367|17,934|10,801|Iraq|10,425|18,483|<br>25,292|
|Portugal|24,804|17,508|10,306|Belarus|10,262|20,989|<br>9,847|
|Malta|24,177|352|394|Macedonia|9,491|1,665|<br>2,019|
|Libya|23,898|9,076|5,492|Colombia|9,338|22,091|<br>41,398|
|Czech Republic|23,395|27,003|10,332|Azerbaijan|8,777|8,456|<br>8,264|
|Korea, Rep.|22,810|127,167|47,167|Jordan|8,600|3,799|<br>5,198|
|Trinidad & Tobago|21,923|8,803|1,285|Ecuador|8,458|7,890|<br>13,066|

15
|Estonia|20,317|3,175|1,395|Dominican Rep.|8,446|4,358|8,824|
|---|---|---|---|---|---|---|---|
|Jamaica|8,320|2,178|2,612|Myanmar|2,034|11,930|48,184|
|Tunisia|8,128|5,772|9,804|Senegal|1,974|1,755|10,653|
|Egypt, Arab Rep.|7,956|37,571|71,593|Bangladesh|1,892|17,234|134,500|
|Peru|7,620|11,515|26,521|Cambodia|1,861|3,539|12,447|
|Turkmenistan|7,471|11,636|4,569|Tajikistan|1,855|2,292|6,581|
|Bosnia & Herzeg.|7,148|2,626|3,912|Tanzania|1,729|13,673|36,987|
|Kosovo|7,138|1,076|1,830|Benin|1,653|2,302|7,587|
|Namibia|7,015|1,119|1,921|Nepal|1,638|8,315|24,044|
|Ukraine|6,982|87,717|48,604|Haiti|1,637|2,118|8,836|
|Indonesia|6,807|122,971|217,661|Eritrea|1,516|566|3,755|
|Albania|6,622|1,613|3,064|Togo|1,251|1,391|5,257|
|Paraguay|6,588|3,793|5,452|Niger|813|1,820|12,570|
|El Salvador|6,533|2,700|5,802|Ethiopia|785|27,916|71,227|
|Sri Lanka|6,332|7,331|18,973|Congo, Dem. Rep.|667|15,007|52,737|
|Guatemala|6,125|6,256|12,390|Mozambique|659|7,055|19,728|
|Mongolia|6,094|2,227|2,485|||||
|China|5,526|1,120,968|1,268,418|||||
|Morocco|5,180|9,892|29,466|||||
|Congo, Rep.|5,065|904|3,332|||||
|Georgia|4,940|3,369|4,334|||||
|Angola|4,720|6,949|16,746|||||
|Philippines|4,713|23,033|80,880|||||
|Bolivia|4,688|3,566|8,668|||||
|Armenia|4,460|1,967|3,120|||||
|Yemen, Rep.|3,849|3,846|19,007|||||
|Honduras|3,809|3,264|6,480|||||
|Pakistan|3,767|56,478|144,810|||||
|Nigeria|3,747|86,100|131,933|||||
|Nicaragua|3,593|1,873|5,137|||||
|Moldova|3,250|2,673|3,624|||||
|Uzbekistan|3,199|34,771|25,394|||||
|Vietnam|3,147|31,652|79,062|||||
|India|3,059|359,441|1,087,461|||||
|South Sudan|2,915|537|7,703|||||
|Côte d'Ivoire|2,884|4,652|17,104|||||
|Sudan|2,606|8,388|29,785|||||
|Ghana|2,565|5,282|20,189|||||
|Zambia|2,533|5,605|11,412|||||
|Cameroon|2,486|5,722|17,013|||||
|Kyrgyz Republic|2,468|2,718|5,014|||||
|Kenya|2,336|10,274|33,288|||||

16
Zimbabwe

|2,053|8,415<br>12,729|
|---|---|

17
Table 2: Panel regressions. Dependent variable: log energy intensity, Eq. (1)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|log GDP p.c.|‐0.26***<br>(0.007)|‐0.64***<br>(0.012)|‐0.62***<br>(0.017)|‐0.68***<br>(0.016)|‐0.58***<br>(0.020)|‐0.62***<br>(0.021)|
|E(Industry, %)||||0.78***<br>(0.079)|0.40***<br>(0.095)|0.39***<br>(0.096)|
|E(Transport, %)||||‐0.35***<br>(0.088)|‐0.34***<br>(0.097)|‐0.29***<br>(0.098)|
|E(Residential, %)||||‐0.79***|‐0.70***|‐0.72***|
|||||(0.070)|(0.081)|(0.081)|
|E(Services, %)||||‐0.27**|‐0.15|‐0.11|
|||||(0.135)|(0.148)|(0.148)|
|E(Agriculture, %)||||‐0.05<br>|‐0.25<br>|‐0.24<br>|
|||||(0.189)|(0.191)|(0.190)|
|E(Non‐energy, %)||||1.53***|1.11***|1.21***|
|||||(0.144)|(0.164)|(0.164)|
|VA(Agriculture, %)|||||0.00***<br>(0.001)|0.00***<br>(0.001)|
|VA(Industry, %)|||||0.00**<br>(0.001)|0.00***<br>(0.001)|
|VA(Manufacturing, %)|||||0.01***<br>(0.001)|0.01***<br>(0.001)|
|VA(Services, %)|||||‐0.00***|‐0.00***|
||||||(0.001)|(0.001)|
|Exports, %|||||‐0.00**|‐0.00***|
||||||(0.000)|(0.000)|
|Imports, %|||||0.00*|0.00**|
||||||(0.000)|(0.000)|
|Pop. 0‐14, %||||||‐0.01***<br>(0.002)|
|Pop. 65+, %||||||‐0.00<br>(0.004)|
|Pop. Density||||||0.00***<br>(0.000)|
|Observations|3,238|3,238|3,238|3,238|2,717|2,698|
|R‐squared|0.284|0.487|0.490|0.595|0.582|0.591|
|Country Effects|No|Yes|Yes|Yes|Yes|Yes|
|Year Effects|No|No|Yes|Yes|Yes|Yes|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 18
Table 3: Panel regressions. Dependent variable: log energy intensity, Eq. (2)

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|log GDP p.c.|‐2.06***<br>(0.094)|‐1.08***<br>(0.104)|‐1.14***<br>(0.105)|‐1.54***<br>(0.096)|‐1.54***<br>(0.120)|‐1.57***<br>(0.124)|
|log GDP p.c. sq.|0.10***|0.02***|0.03***|0.05***|0.06***|0.06***|
||(0.005)|(0.006)|(0.006)|(0.006)|(0.007)|(0.007)|
|E(Industry, %)||||0.86***<br>(0.078)|0.46***<br>(0.094)|0.44***<br>(0.095)|
|E(Transport, %)||||‐0.27***<br>(0.088)|‐0.29***<br>(0.096)|‐0.27***<br>(0.097)|
|E(Residential, %)||||‐0.83***|‐0.77***|‐0.80***|
|||||(0.069)|(0.081)|(0.081)|
|E(Services, %)||||‐0.32**|‐0.21|‐0.15|
|||||(0.134)|(0.146)|(0.147)|
|E(Agriculture, %)||||‐0.07|‐0.26|‐0.29|
|||||(0.187)|(0.188)|(0.188)|
|E(Non‐energy, %)||||1.48***|0.99***|1.08***|
|||||(0.142)|(0.162)|(0.163)|
|VA(Agriculture, %)|||||0.00|0.00**|
||||||(0.001)|(0.001)|
|VA(Industry, %)|||||0.00**<br>(0.001)|0.00***<br>(0.001)|
|VA(Manufacturing, %)|||||0.01***<br>(0.001)|0.01***<br>(0.001)|
|VA(Services, %)|||||‐0.00***|‐0.00**|
||||||(0.001)|(0.001)|
|Exports, %|||||‐0.00***|‐0.00***|
||||||(0.000)|(0.000)|
|Imports, %|||||0.00**|0.00**|
||||||(0.000)|(0.000)|
|Pop. 0‐14, %||||||‐0.01***<br>(0.002)|
|Pop. 65+, %||||||‐0.01**<br>(0.004)|
|Pop. Density||||||0.00**<br>(0.000)|
|Observations|3,238|3,238|3,238|3,238|2,717|2,698|
|R‐squared|0.357|0.490|0.494|0.605|0.593|0.601|
|Country Effects|No|Yes|Yes|Yes|Yes|Yes|
|Year Effects|No|No|Yes|Yes|Yes|Yes|
|Standard errors in paren|theses||||||
|*** p<0.01, ** p<0.05, *|p<0.1||||||

19
Table 4: Differences‐in‐Differences Regressions, Eqs. (4) and (5)

||Eq.(4)|Eq.(5)|
|---|---|---|
|�<sup>���</sup>|‐0.64***|‐0.14***|
||(0.029)|(0.033)|
|�<sup>����</sup>|‐0.43***|‐0.09***|
||(0.029)|(0.031)|
|Observations|3,099|2,968|
|R‐squared|0.224|0.073|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Table 5: Beta‐convergence Analysis

|VARIABLES|(1)<br>Full sample|(2)<br>Below|(3)<br>Above|
|---|---|---|---|
|l_base|‐0.0190***|‐0.0166***|‐0.0200***|
||(0.002)|(0.004)|(0.003)|
|Observations|2,748|552|1,692|
|R‐squared|0.028|0.029|0.024|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 20
Table 6: Aggregate, Structural and Efficiency Index in 2014 based on Fisher Ideal Index Decomposition (1990=1)

|**Country**|**Total**|**Struct.**|**Intens.**|**Country**|**Total**|**Struct.**|**Intens.**|
|---|---|---|---|---|---|---|---|
|Azerbaijan|0.185|0.976|0.190|Korea, Rep.|0.813|0.986|0.825|
|Armenia|0.238|0.902|0.264|Cyprus|0.826|0.679|1.216|
|Romania|0.355|0.801|0.444|Nigeria|0.827|1.006|0.822|
|Belarus|0.392|0.944|0.415|Morocco|0.830|0.963|0.862|
|Bulgaria|0.407|0.706|0.576|Costa Rica|0.832|0.867|0.959|
|Georgia|0.412|0.934|0.441|Japan|0.841|0.865|0.973|
|Kyrgyz Republic|0.423|0.859|0.493|Indonesia|0.841|1.028|0.819|
|Cuba|0.479|1.063|0.451|Italy|0.866|0.885|0.978|
|Mongolia|0.485|0.919|0.528|Ethiopia|0.870|1.182|0.736|
|Mauritius|0.520|0.824|0.631|Pakistan|0.874|0.907|0.964|
|China|0.540|0.996|0.542|Finland|0.903|0.868|1.040|
|Albania|0.590|0.708|0.832|Kenya|0.923|1.009|0.915|
|Ukraine|0.601|0.730|0.823|Argentina|0.931|0.919|1.014|
|Honduras|0.603|1.019|0.592|Tanzania|0.945|1.110|0.851|
|Tajikistan|0.656|0.845|0.776|Austria|0.946|0.905|1.045|
|Russian Federation|0.661|0.819|0.807|Venezuela, RB|0.968|0.830|1.166|
|United Kingdom|0.663|0.868|0.763|Chile|0.975|0.917|1.063|
|Zimbabwe|0.678|0.929|0.730|Benin|0.980|1.065|0.920|
|Norway|0.680|1.031|0.660|Panama|1.000|1.191|0.839|
|Sweden|0.692|0.907|0.763|Brazil|1.013|0.766|1.323|
|Cameroon|0.696|1.021|0.681|Congo, Rep.|1.037|1.267|0.819|
|El Salvador|0.698|1.006|0.694|Dominican Rep.|1.038|0.885|1.174|
|Colombia|0.703|0.867|0.812|Trinidad & Tobago|1.051|1.022|1.029|
|Mozambique|0.707|1.071|0.660|Algeria|1.101|0.964|1.143|
|Denmark|0.713|0.891|0.800|Thailand|1.129|0.982|1.150|
|India|0.728|1.045|0.697|Ecuador|1.130|1.130|1.000|
|Sudan|0.731|0.597|1.225|Turkey|1.133|0.878|1.291|
|Ghana|0.745|1.317|0.565|Iran, Islamic Rep.|1.137|1.026|1.108|
|Macedonia|0.746|0.774|0.965|Senegal|1.145|1.021|1.122|
|France|0.753|0.884|0.852|Uruguay|1.153|0.902|1.278|
|Zambia|0.772|0.817|0.944|Congo, Dem. Rep.|1.157|1.094|1.057|
|Australia|0.773|0.914|0.845|Singapore|1.167|0.935|1.248|
|Botswana|0.775|0.809|0.958|Uzbekistan|1.210|0.937|1.292|
|Netherlands|0.787|0.827|0.951|Togo|1.228|0.918|1.337|
|Tunisia|0.796|0.908|0.877|Bahrain|1.317|1.007|1.307|
|Switzerland|0.801|0.949|0.844|Bolivia|1.408|1.001|1.407|
|South Africa|0.806|0.836|0.964|Bangladesh|1.416|1.070|1.324|
|Philippines|0.806|0.985|0.819|Saudi Arabia|1.429|1.082|1.322|
|Egypt, Arab Rep.|0.809|1.137|0.711|Nepal|1.586|1.005|1.578|
|Jordan|0.811|1.004|0.807|Malta|14.383|1.143|12.579|
|Mexico|0.812|1.053|0.770|||||

21
Table 7: Determinants of Aggregate Energy Intensity Index, fixed effects estimation, Eq.(7)

|VARIABLES|(1)<br>Full sample|(2)<br>Below|(3)<br>Above|
|---|---|---|---|
|Fs|1.15***|1.06***|1.11***|
||(0.017)|(0.019)|(0.025)|
|Fi|1.11***|0.95***|1.14***|
||(0.003)|(0.012)|(0.003)|
|Observations|2,007|471|1,136|
|R‐squared|0.988|0.950|0.995|
|Standard errors<br>*** p<0.01, **<br>p<0.1|in parentheses<br>p<0.05, *|||

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