metadata: authors: 0: author: Foster,Vivien
1: author: Dim,Jennifer Uju
2: author: Vollmer,Sebastian
3: author: Zhang,Fan
count: World
docna: 0: docna: Understanding Drivers of Decoupling of
            Global Transport CO2 Emissions from Economic Growth :
            Evidence from 145 Countries
docty: Policy Research Working Paper
subtopic: Transport Services,Energy Demand,Energy and Mining,Energy and Environment,Industrial Economics,Economic Growth,Economic Theory & Research
teratopic: Macroeconomics and Economic Growth,Industry,Transport,Energy
abstracts: cdata!: This paper examines the extent to which
            countries have succeeded in decoupling transport emissions
            from economic growth, and how changes in emissions
            intensity, economic growth, and population growth have
            contributed to changes in transportation-related emissions.
            The paper employs a modified version of the Tapio decoupling
            model, and demonstrates that over the 1990&#x2013;2018
            study period only 12 of 145 countries achieved
            &#x201c;absolute decoupling,&#x201d; defined as
            reducing emissions while growing gross domestic product. The
            majority of the top emitters remain in a
            &#x201c;relative decoupling&#x201d; state, with
            emissions growing more slowly than gross domestic product.
            Many of the middle- and low-income countries have not
            achieved decoupling; their emissions are growing as fast as
            or faster than gross domestic product. To understand the
            driving factors of transport-related carbon emissions, the
            paper conducts index-decomposition and an econometric
            analysis. The results reveal that while transportation
            emission intensity has declined in most countries, economic
            growth and population growth have offset these declines. If
            these patterns continue, achieving the goals of the Paris
            Agreement with improvements in efficiency alone seems
            unrealistic. The paper also shows evidence that higher
            energy prices are associated with strong emissions reduction.
repnme: repnme: Understanding Drivers of Decoupling of
            Global Transport CO2 Emissions from Economic Growth :
            Evidence from 145 Countries
display_title: Understanding Drivers of Decoupling
            of Global Transport CO2 Emissions from Economic Growth :
            Evidence from 145 Countries
listing_relative_url: /research/2021/10/33494963/understanding-drivers-decoupling-global-transport-co2-emissions-economic-growth-evidence-145-countries
topicv3: Economic Growth m1326241 471,Energy and Extractives m1326595 507,Transport m1326661 135,Environment, Natural Resources and Blue
            Economy m1327391 566,Population, Aging and Health m1327736 640
publishtoextweb_dt: 2021-10-18T00:00:00Z
dois: 10.1596/1813-9450-9809
historic_topic: Macroeconomics and Economic Growth,Industry,Transport,Energy
origu: Chief Economist Infrastructure (INFCE)
no_of_pages: 40
author_keys: 0: author_key: 452278
1: author_key: 1724425
2: author_key: 1007679
3: author_key: 905148
model_extractions: Policy Research Working Paper 9809 Understanding Drivers of Decoupling of Global Transport CO2 Emissions from Economic Growth Evidence from 145 Countries _Vivien Foster Jennifer Uju Dim Sebastian Vollmer Fan Zhang_ Infrastructure Chief Economist Office October 2021
Policy Research Working Paper 9809

# **Abstract**

This paper examines the extent to which countries have succeeded in decoupling transport emissions from economic growth, and how changes in emissions intensity, economic growth, and population growth have contributed to changes in transportation-related emissions. The paper employs a modified version of the Tapio decoupling model, and demonstrates that over the 1990–2018 study period only 12 of 145 countries achieved “absolute decoupling,” defined as reducing emissions while growing gross domestic product. The majority of the top emitters remain in a “relative decoupling” state, with emissions growing more slowly than gross domestic product. Many of the middle- and low-income countries have not achieved decoupling; their emissions are growing as fast as or faster than gross domestic product. To understand the driving factors of transport-related carbon emissions, the paper conducts index-decomposition and an econometric analysis. The results reveal that while transportation emission intensity has declined in most countries, economic growth and population growth have offset these declines. If these patterns continue, achieving the goals of the Paris Agreement with improvements in efficiency alone seems unrealistic. The paper also shows evidence that higher energy prices are associated with strong emissions reduction.

This paper is a product of the Infrastructure Chief Economist Office. 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://www.worldbank.org/prwp. 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
**Understanding Drivers of Decoupling of Global Transport CO2 Emissions from Economic Growth: Evidence from 145 Countries**

# **Vivien Foster, Jennifer Uju Dim, Sebastian Vollmer, Fan Zhang**

**Keywords** : Decoupling, Transport carbon emissions, Economic growth, Population

**JEL Classification** : O18, O44, Q54
# **1. Introduction**

The transportation sector, which relies heavily on fossil fuels, is a major contributor to total global greenhouse gas emissions. In 2018, emissions from transport activities reached a level equivalent to 8.2 gigatons of CO2, representing roughly 24 percent of all global CO2 emissions. Carbon emissions from transport have more than doubled since 1970. This rapid increase in emissions from transportation is expected to continue, largely because the demand for transportation is likely to increase as global population grows and incomes rise. According to the International Energy Agency (IEA) Energy Technology Perspectives report, between 2019 and 2070, global transport demand is expected to double, and car ownership rates are projected to grow by 60 percent (IEA, 2020). According to the International Panel on Climate Change (IPCC), emissions linked to transportation will likely increase faster than those from any other sector unless the link between transportation and economic growth can be severed (IPCC et al., 2014).

Reducing transport-related carbon emissions without undermining economic growth is pivotal to combating climate change and maintaining living standards. An effective transport emissions-reduction strategy requires a deep understanding of the degree to which countries have broken or kept the connection between the growth of transportation emissions and the growth of their economies. Such strategy also demands a clear understanding of the drivers of transportation related emissions of CO2. Thus, this paper aims to answer the following questions: To what extent have countries succeeded in decoupling economic growth from transport carbon emissions? What policies can facilitate decoupling?

To answer these questions, first, we explore the issue of decoupling of economic growth from transportrelated carbon emissions at the country level. We use a modified version of the Tapio decoupling model (Tapio, 2005). We define three decoupling states: absolute decoupling, relative decoupling, and no decoupling. “Absolute decoupling” occurs when CO2 emissions are decreasing or stabilized even though GDP is increasing. “Relative decoupling” occurs when both GDP and CO2 emissions are increasing, but CO2 emissions are increasing at a slower rate. “No decoupling” occurs when a country is in neither of these two states.

Only 12 countries achieved absolute decoupling over the study period. The remaining countries experienced either relative decoupling or no decoupling. This implies that transport emissions in most countries, including the top-emitting countries, are still increasing, and that the growth of their emissions 2
remains strongly linked to their economic growth. Hence, to achieve decarbonization in the transport sector without prejudice to economic growth, there is a dire need to break this link.

The long-term decoupling results show considerable differences in the decoupling status among countries by income levels. While many advanced economies have managed to achieve absolute or relative decoupling, in many developing countries per capita transport emissions are increasing at a much faster rate than per capita GDP growth. Increases in transport CO2 emissions were particularly pronounced in the two largest middle-income countries: China and India. Both are still contributing less to global transport CO2 emissions than their population shares, unlike the United States or the EU countries, but they are rapidly catching up and are to a large extent driving current increases in global transport CO2 emissions.

Next, to explore the underlying drivers of CO2 emissions, we conduct both index decomposition and econometric analyses. For the decomposition analysis we specify two identities. The first decomposition breaks down emissions into three factors: emission intensity measured by transportation emissions per unit of GDP, economic growth (in terms of GDP per capita), and population growth. In many developed countries, declining emission intensity contributes to the decoupling of transportation emissions from economic growth. In most developing countries, although emission intensity also fell, population and GDP growth outstripped these efficiency-related transport emission savings.

In the second decomposition analysis, we further break down emission intensity (measured as CO2 emissions per unit of GDP) into carbon intensity of fuel consumption, energy intensity of transportation, and transport demand. We focus on the inland transportation sector, including rail and road sectors for which disaggregated data are available on travel volume measured by kilometers traveled by passengers and freight. Inland transportation is the largest source of global transport emissions, <mark>accounting for 84 percent of total emissions in 2018. The results again show that efficiency improvements hav</mark> e played an important role in flattening the emissions curve. Most countries have managed to reduce the energy intensity of inland transport. By contrast, there has so far been minimal progress in reducing the carbon intensity of energy consumption, and that concentrated in a handful of countries. Gasoline and diesel are still the dominant fuels for transportation; renewable fuels and electricity accounted for about 10 percent of the energy mix as of 2018.

To further explore the economic drivers of transport carbon emissions, we conduct country-level regression analysis to understand the associations between CO2 emissions and GDP, urbanization, 3
structural changes, energy prices and public transit. Urbanization is first associated with an increase then a decrease in emissions – possibly because urbanization initially coincides with a rise in vehicle ownership and then later allows for economies of scale through improved public transit. The results also show that per capita CO2 emissions are negatively correlated with agriculture value-added share. Higher energy prices and the presence of transportation policies (such as fuel economy standards and tailpipe emissions control) are both significantly correlated with lower per capita CO2 emissions.

The remainder of the paper is structured as follows: Section 2 reviews the existing literature. Section 3 describes the methodology. Section 4 presents the data. Section 5 discusses results from index decomposition and the econometric analyses. Section 6 concludes with a summary of the main findings and a discussion of policy implications.

# **2. Related literature**

This study contributes to two strands of the literature: the decoupling literature and the literature on the drivers of carbon emissions from the transportation sector. Decoupling analyses have been conducted in different contexts – at the global level (Shuai et al., 2019), national level (Chen et al., 2018; de Freitas & Kaneko, 2011; Jiang et al., 2016; Q. Wang et al., 2018; Zhang et al., 2020), provincial level (FY Fan, 2016; JJ Jiang, 2017; A. Li et al., 2017; Q. Lu et al., 2015; Wu et al., 2019), and sectoral levels, such as agriculture, construction, and transportation (Bai et al., 2019; Hang et al., 2019; Huo et al., 2021; M & C, 2019).

Decoupling analysis examines the relationship between environmental pressure and economic prosperity. The relationship between economic growth and carbon emissions has been widely studied using the decoupling indices created by the Organisation of Economic Development and Co-operation (OECD) and Tapio (OECD, 2002; Tapio, 2005). Although the OECD decoupling index is easier to calculate, it is sensitive to the choice of the base period, leading to poor stability in calculated results. In addition, the OECD decoupling index is associated with emission intensity reductions only. It does not specifically address the percentage change of emissions in relation to GDP growth (Grand 2016). The Tapio decoupling index addresses these shortcomings. In the Tapio model, decoupling states are divided into three categories and eight subcategories. We adopt the Tapio model but slightly modify and simplify the categorization of the decoupling states.

Many studies have explored the determinants and drivers of carbon emissions from the transportation sector. For instance, (Lakshmanan & Han, 1997) employ a decomposition analysis to identify the magnitude and the relative effects of the various factors in U.S. transportation energy use and carbon 4
emissions between 1970 and 1991. They show that the growth in the propensity to travel, population, and GDP were the three most important factors driving U.S. transportation energy use and CO2 emissions. Timilsina & Shrestha (2009a, 2009b) analyze the factors influencing the growth of carbon dioxide (CO2) emissions from the transportation sector in selected Asian and Latin American and Caribbean countries. Using the Log Mean Divisia Index (LMDI) approach, they decomposed annual emissions growth into several factors; they find that changes in GDP per capita and transportation energy intensity are the main factors driving transport-sector CO2 emission growth.

Several decomposition methods can be used to understand the drivers of the changes in transport carbon emissions including index decomposition, variance decomposition and structural decomposition. However, the index decomposition method is the most commonly used approach in the literature, with the Laspeyres and Divisia being the most popular ￼(Ang, 2004; F. Li et al., 2019; & Shrestha, 2009b; Yasmeen et al., 2020)￼. The index decomposition approach allows the change in emissions to be decomposed into several factors, including GDP per capita, population, energy intensity, fuel mix, fuel carbon intensity, modal structure, and car ownership. The approach (W. W. Wang et al., 2011)￼. We employ this approach to identify the driving forces of the past changes in transport CO2 emissions.

Some papers have explored the determinants of transport emissions using regression techniques including, dynamic panel quantile regressions, dynamic nonparametric additive regressions, and fully modified ordinary least squares regressions (Huang et al., 2020; Saboori et al., 2014; Xu & Lin, 2015). Typically, these papers have employed either an index decomposition analysis or a regression approach, but not both. We use both methods to obtain a robust understanding of the determinants of global transport emissions. The fixed effects and generalized method of moments regressions expand the results of the index decomposition by including additional economic and policy factors that influence the change in global transport emissions.

Furthermore, previous decomposition analyses were conducted at either the country level (Georgatzi et al., 2020; Kwon, 2005; Liang et al., 2017; I. J. Lu et al., 2007; Rasool et al., 2019; Q. Wang et al., 2018), regional level (Amin et al., 2020; Andreoni & Galmarini, 2012; Timilsina & Shrestha, 2009a, 2009b), or city level (Fan & Lei, 2016; Li et al., 2019; Wang et al., 2011). There have been mixed results in the literature. While some studies find that population growth decreases carbon emissions (I. J. Lu et al., 2007; Xie et al., 2017), others find a positive relationship between population growth and carbon emissions (Fan & Lei, 2016; Kim, 2019; Timilsina & Shrestha, 2009b). In addition, the majority of the studies focused on road transportation, specifically passenger cars (González et al., 2019; Papagiannaki & Diakoulaki, 2009; Shiraki 5
et al., 2020). None of the existing papers has conducted a comprehensive study that looks at the main factors affecting the growth of all types of transport emissions on a global scale.

In sum, our paper contributes to the literature in three ways: First, we provide the literature’s first comprehensive study of transport-related emissions globally; our data allow us to examine the situation in 145 countries over an extensive period, from 1990 to 2018. Such in-depth analysis of the trends, patterns, and drivers of transport emissions of countries in different development stages is important for the design of effective decarbonization policies for the transportation sector. Second, the study provides evidence of the degree of decoupling between economic growth and emissions in the transport sector that has taken place – or failed to occur – in a large number of countries with widely differing economic conditions and income levels. Third, we estimate associations of transport carbon emissions with various country level indicators. These analyses provide helpful insights for the design and implementation of effective emission-reduction policies in the transportation sector, which are urgently needed to address its role as a driver of climate change.

# **3. Methodology**

In this section, we present the methodology used to conduct the decoupling analysis and to examine the sources and drivers of CO2 emissions in the transport sector. First, we employ the Tapio decoupling model to identify the decoupling status of countries. Second, we conduct an index decomposition analysis using two identities. The first identity investigates how changes in CO2 emissions are driven by changes in population, economic growth, and transportation carbon intensity (measured by transportation emissions per unit of GDP). The second identity further examines how changes in transport emission intensity are driven by changes in carbon intensity of transport energy consumption, transport energy intensity, and transport demand. Third, we explore the economic determinants of CO2 emissions and emission intensity based on a panel regression analysis.

# **3.1. Tapio decoupling model**

The formula for the Tapio decoupling model is given by: 6
status is divided into eight categories depending on the magnitude and sign of the elasticity ( , we define the decoupling status into three categories – absolute decoupling, relative decoupling, and no decoupling. ββ)

1. **_Absolute decoupling_** : occurs when carbon emissions are decreasing, and GDP is increasing, i.e. ∆C 2 < 0 and ∆G . This follows that .

2. **_Relative decoupling_** : occurs when both carbon emissions and GDP are increasing, but GDP is GG> 0 ββ< 0 2. **_Relative decoupling_** : occurs when both carbon emissions and GDP are increasing, but GDP is increasing at a faster rate. Here ∆C 2 > 0 and ∆G . This follows that .

3. **_No decoupling_** : is defined as residual category where no absolute or relative decoupling occurs. GG> 0 0 < ββ< 1

# **3.2. Index decomposition analysis**

To conduct the decomposition analysis of the driving forces of CO2 emissions, we specify two identities starting from an IPAT equation, one in which environmental impact (I) is a product of population (P), affluence (A), and technology (T) (Commoner et al., 1971; Ehrlich & Holdren, 1971). According to Kwon (2005), the IPAT formula is a valuable starting point to understand the determinants of past changes in an aggregate environmental indicator to inform future environmental policies. Our first IDA identity is given by: where _j_ and _t_ represent transportation modes (road, rail, domestic navigation, domestic aviation, and pipeline transport) and year, respectively. _CO2_ are transport-related carbon emissions; _GDP_ represents gross domestic output in million dollars based on 2015 PPP rates; and _POP_ refers to the population size. Equation (1) can be rewritten as: where _CO2_ represents the environmental-impact (I) variable in the IPAT identity. The first term on the right-hand side of equation (2) measures the population effect (P). The second term is GDP per capita, a measure of economic growth and a proxy for affluence (A). The third term is the emissions intensity per unit of GDP, which is a proxy for technology (T).

In addition to the first decomposition equation, we specify another identity to further examine the factors affecting the changes in carbon intensity measured by CO2 emissions per unit of GDP in inland transportation for which more disaggregated data on travel volumes are available. The identity is given by equations (3) below: 7
~~—~~ > ~~»—— a~~ ~~00~~ ] 5» ~~—]~~ en] ~~—|~~ x onl ~~— (oo)~~

(4)
# **3.3 Panel regression analysis**

Lastly, we estimate a dynamic model to understand the effects of various economic factors on the level of transport-related CO2 emissions. We assume current level of per capita emissions depends on the level of per capita emissions in the previous year, that is, transport activities respond to changes in economic factors with some lags. The model is specified as follows: C 0 1C 2G G 3G G 4 5 + 6 iitt 7B ii,tt−1 iitt (5) iitt iitt iitt 2GGCC = ββ + ββ 2GGCC + ββ CC + ββ CC2 + ββ GGDDeeDDeeTT + ββ GGTTDDT DDllee iitt iitt iitt ii tt iitt Where ββ UUffUUTTllC + + ββ is the natural logarithm of per capita CO2 emissions in country TT + γγXX + αα + λλ + εε _i_ in year _t_ . _GDPPCit_ is per capita GDP. _GDPPC2_ iitt is the square term of _GDPPC_ . _Diesel_ and _Gasoline_ are diesel and gasoline prices, 2GGCC respectively. _Urban_ is the percentage of the total population living in urban areas. _BRT_ represents the number of Bus Rapid Transit (BRT) systems a country has. is a vector of variables, including the valueadded share of agriculture, manufacturing, and service sectors. iitt is an unobserved country fixed effect, XX which includes country-specific characteristics that are fixed over time, such as culture, climate zone, and ii αα government regulation. We also include year fixed effects, , to control for common cyclical components such as a common technology shock. is an idiosyncratic error term. tt 0 7 are parameters to λλ be estimated. We opted for a relatively modest set of explanatory variables because due to limited data iitt εε ββ − ββ TTllaa γγ availability the inclusion of some further explanatory variables would have substantially cut the sample size.

# **4. Data**

Data used in this study come from several sources. The CO2 emissions and energy consumption data are from International Energy Agency (IEA) Fuel Combustion Statistics database and World Energy Statistics and Balances database, respectively. The databases report CO2 emissions and final energy consumption due to fuel combustion of transportation activities at the country level. The transportation sector is further divided into road, rail, domestic navigation, domestic aviation, and pipeline transport. The study period is from 1990 to 2018. The full sample contains 145 countries for which data are available. In 2018, the full sample of countries contributed to 84 percent of global transport carbon emissions. International aviation and marine bunkers account for the remaining 16 percent. The list of countries included in the study is presented in Table A1 in the appendix. We obtain PPP-based GDP (based on 2015 PPP rates) and population data from IEA.

9
For the decomposition analysis of emissions intensity, we obtain annual country-level total passenger and freight transport traveled by road and rail from the World Road Statistics and the International Transport Forum databases. Total passenger transport, measured in million passenger-kilometers, refers to the total movement of passengers using inland transportation. It represents the transport of one passenger for one kilometer. Total freight transport, measured in million tons-kilometer, refers to the total movement of goods using inland transport. It represents the transport of one ton over one kilometer. The total kilometer variable is available for 65 countries during the period from 2000 to 2018. These countries are listed in Table A2 in the appendix.

The level of urbanization, and the value added from agriculture, manufacturing, and service sectors are all obtained from the World Bank World Development Indicators database. Data on country-level BRT systems are obtained from global BRT database. Diesel and gasoline prices are compiled from fuel price documentation from the <mark>Deutsche Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ).</mark> The summary statistics of variables are presented in Table 3.

# **5. Results**

In this section, we describe the results of the decoupling analyses, the general pattern of transport-related CO2 emissions, and the results of the regression analyses. The descriptive statistics reveal that CO2 emissions of the transport sector account for 24 percent of global CO2 emissions, making the transport sector the second-largest contributor to global CO2 emissions after the energy sector, which accounts for 42 percent of such emissions. Within the transport sector, road transportation is the predominant driver, accounting for 74 percent of all CO2 emissions; international sea transport accounts for 9 percent, and international aviation accounts for 7 percent (Figure 1). The composition of global transport CO2 emissions by mode has been fairly stable over time; the shares in 1990 were about the same as those in 2018 (Figure A1). There is interesting variation between income groups and regions; the CO2 emissions from road transportation are slightly below the global average in high-income countries, and above average in lower-middle and low-income countries (Figure A3).

Figures 2a and 2b present the distribution of global transport CO2 emissions in terms of GDP and population by income group and major emitting countries in 2018. The area of each rectangle represents the contributions of a country/income group to total global transport emissions. The rectangle's width represents the share of global GDP (population), and the height represents transport emissions per unit of GDP (population). Figure 2a suggests that the countries with the most carbon-intensive transport 10
sectors are the Russian Federation, Brazil, Mexico, and India. Excluding China, the transport sectors in middle-income countries are more carbon intensive than those of high-income countries. In terms of the fuel mix, Russia has a high fraction of “other” fuels, composed of mainly natural gas, compared to other countries and aggregates. The high share of biofuels in Brazil’s transportation-sector fuels also stands out.

Figure 2b provides additional insights on the global carbon footprint of the transport sector. In 2018, the global average per capita emissions from the transport sector were 1.09 ton of CO2. Per capita emissions in the United States were five times the world average. Per capita emissions of other high-income countries were three times the world average, and those from the EU countries were double the world average. Although middle-income countries at that time had relatively carbon-intensive transport sectors, their per capita emissions were significantly lower than those of high-income countries.

In absolute levels, the findings show that high-income countries originated as many transport CO2 emissions as all low- and middle-income countries combined (Figure 3). However, this is rapidly changing due to high emissions growth in middle-income countries. Lower-middle-income countries have more than tripled their transport CO2 emissions, and upper-middle-income countries have more than doubled their transport CO2 emissions since 1990. In 1990, high-income countries still generated about twice the level of transport-related CO2 emissions as those created by low- and middle-income countries combined. By 2018, levels generated by high-income countries, and by the combination of low- and middle-income countries were about even. In per capita terms, the gap between high- and middle-income countries is still wide. A person in a high-income country is generating around three times as much in the way of transport CO2 emissions as a person in a middle-income country. But this gap is also rapidly decreasing. Per capita transport CO2 emissions in middle-income countries have doubled since 1990, while they remained relatively stable in high-income countries. Since 1990, transport-related CO2 emissions per unit of GDP have declined for all income groups, but obviously not enough to make up for the large increases in GDP that we have observed over the same period. We show these trends by world regions in the appendix (Figure A2).

The United States is by far the leading contributor to global transport CO2 emissions; it generates 21 percent of such emissions, while accounting for less than 5 percent of the world’s population. The United States is followed by China and the EU-28 countries (still including the United Kingdom), which both contribute around 11 percent to global transport CO2 emissions (Figure 4). The EU-28 countries contribute slightly more than their share of the world population and China contributes less than its world population share. However, transport CO2 emissions in China are rapidly increasing, experiencing a tenfold increase 11
since 1990 (Figure 5). Transport CO2 emissions have also increased by around 20 percent in the United States. Among the lower-middle-income countries, India is growing rapidly, and has increased its transport CO2 emissions by a factor of five since 1990; it now contributes around 4 percent of global transport CO2 emissions, still much less than its population share. There is not a single low-income country that accounts for more than 1 percent of the transport CO2 emissions compared to the United States.

Looking at the fuel mix, we see that motor gasoline and gas/diesel oil account for the vast majority of the transport fuel mix, and today both account for around 35 percent of fuels used. Since 1990, the share of motor gasoline has steadily declined, and the share of gas/diesel oil has steadily increased. The share of the remaining fuel types has remained relatively stable (Figure 6). Consequently, motor gasoline and gas/diesel oil also account for the majority of transport CO2 emissions.

Only a dozen countries achieved absolute decoupling over the study period. The remaining countries experienced either relative decoupling or no decoupling. This implies that transport emissions in most countries, including the top-emitting countries, are still increasing and strongly linked to economic growth. Hence, in order to achieve decarbonization in the transport sector, there is an urgent need to break this link. This is especially the case in middle- and low-income countries. While about 72 percent of highincome countries are in the absolute or relative decoupling states, only 29 percent of middle- and lowerincome countries have achieved some level of decoupling.

Moving onto the decomposition analysis, the first decomposition results show that since 1990, transport related CO2 emissions have increased by around 80 percent, despite sizeable improvements in efficiency. The results also reveal that these efficiency improvements did not fully compensate for the increase in emissions due to population growth. The net increase in transport CO2 emissions is almost in sync with the increase that is due to GDP growth (Figure 7). If these patterns continue, achieving the goals of the Paris Agreement with improvements in efficiency alone seems unrealistic. According to the UN population projections, the world population will continue to grow rapidly and reach about 10 billion people by 2050. Then population growth will gradually slow down, and the world population is projected to peak at around 11 billion people in 2100. Notwithstanding the uncertainty surrounding these projections, the slowdown in population growth will likely come too late to allow further efficiency improvements to outweigh the increases in emissions due to economic growth.

If we look at the results of the index decomposition by income group, we see that in high-income countries the improvements in efficiency fully compensate for the effect of population growth, and even partially 12
outweigh the effect of economic growth. The efficiency improvements in upper-middle income countries are almost as large as in high-income countries, but there they are only just enough to outweigh the effects of population growth such that total transport-generated CO2 emissions continue to rise with growth of GDP. Efficiency improvements are much less in lower-middle-income countries, such that in these countries increases in transport-related CO2 emissions are driven by both sizeable population growth and GDP growth (Figure 8).

The first decomposition results for top-emitting countries reveal several interesting points (Figure 9). All countries shown, except Japan and Russia, reveal increases in transport emissions since 1990. In line with the descriptive analysis, the decomposition results suggest that China experienced the most significant increase in transport-related CO2 emissions since 1990. This increase was largely driven by economic growth, which is not surprising, given that the Chinese economy is among the fastest-growing economies in the world, with an average annual GDP growth rate of 9.5 percent between 1990 and 2018. Transportation emissions intensity in China declined slightly, but population growth remained relatively stable. In contrast to China, in India, the country with the second-largest increases in transportation emissions, the increases have been driven by both population growth and economic growth.

Among the largest emitters, Japan is the only country that achieved absolute decoupling in the transportation sector over the last three decades (i.e., it experienced economic growth while transport CO2 emissions fell absolutely). Efficiency improvements and stable population growth in Japan were sufficient to decrease transport-related emissions. On the other hand, the United States and the EU countries achieved relative decoupling of transport emissions, meaning that GDP grew faster than emissions.

To better understand the drivers of the changes in transportation emissions intensity, we further decompose the emissions intensity of transportation (i.e., transport-generated CO2 emissions divided by GDP) by carbon intensity of fuel, energy consumption of kilometers traveled, and total kilometers traveled (Figure 10). Due to limited data and time coverage for total kilometers traveled, the base year for the second decomposition is 2000. Nevertheless, we observe a similar decreasing trend of transport emissions intensity as in the first decomposition. Transport emissions intensity in India and Mexico have remained at the same level where they were in 2000. In India this happened because kilometers traveled per unit of GDP increased, and energy consumption per kilometer decreased, balancing each other out. In Mexico all indicators are at levels from 2000, with some movements in between. China also increased kilometers 13
traveled per unit of GDP, but its increase was more modest than that of India, and the reduction in energy consumption per kilometer was sufficient to outweigh this increase.

The significant decrease in transport carbon intensity that contributed to the absolute decoupling of CO2 emissions in Japan was driven by improvements in the energy efficiency of the transport sector and a decrease in transport demand. The carbon intensity of transport fuel consumption was relatively stable in both countries. Results for Ukraine and Kazakhstan are similar to those of Japan and Russia. The United States results reveal that the decrease in transport demand was the main contributing factor to the decline in transport carbon intensity. Conversely, the main contributing factor for the decline of transport carbon intensity in EU countries was the improvements in transport energy efficiency. This implies that energy consumption per kilometer traveled in EU countries has declined since 2000.

We now turn to a regression analysis of the determinants of per capita transport CO2 emissions measured by transport-generated CO2 emissions per unit of GDP. The first three columns of Table 2 report results from static panel regression via OLS, the random-effects and fixed-effects methods.. The last column report results identified in equation (5) using the generalized methods of moments (GMM) technique outlined in Arellano and Bond (1991). Because governments may respond to increasing pollutions and growing traffic congestion by raising energy prices, energy price could be endogenous to carbon emissions. To address the potential endogeneity concern, we use observations on energy price lagged two to four periods as instruments in the GMM model. The coefficient associated with the lagged CO2 emissions is statistically significant, suggesting that the current level of emissions is indeed strongly correlated with the level of emissions in the previous period. The dynamic panel regression reported in column (4) of Table 2 is therefore our preferred specification.

Controlling for country and year fixed effects, the parameter estimates suggest that per capita GDP, energy price, urbanization, and structural transformation are strongly correlated with transport related emissions. The squared term of GDP per capita is negative and statistically significant, but if we calculate the maximum of the inverted U, the point where the slope changes from positive to negative, we find that it is out of sample, and that all observations are on the increasing part of the parabola.

The estimates of economic structure measured by the relative share of the value added of agriculture, manufacturing, and services show that as countries move away from agriculture, transport-related emissions decreases. Another interesting finding that emerged from the results is that CO2 emissions first increase and then decrease with the increase of the percentage of people living in urban area. The turning 14
point occurs at 58 percent of urbanization. Finally, higher diesel prices is associated with lower per capita carbon emissions. The coefficients associated with gasoline prices are of the expected sign but are not statistically significant. Because gasoline prices are highly correlated with diesel prices, including the diesel price in the regression could absorb most of the explanatory power.

In the above analysis, we do not control for the existence of transportation policies aimed at reducing transport-related emissions. As a robustness check, we collect data from IEA transportation policy database and control for the existence of various transportation policies in the estimation. We use the principal component analysis method to create one comprehensive transport decarbonization policy variable that summarizes several transportation policies: fuel efficiency standards, tailpipe emissions regulations, driving restrictions, promotion of electric vehicles, promotion of biofuels, R&D and vehicle emission standards. Including transportation policy variable in the regression reduces the sample size by half due to limited data availability for many developing countries. The results are reported in Table 3. The existence of transportation policies has a strong impact, sharply decreasing per capita emissions. The coefficients of the other variables have the expected sign, but the statistical significance reduces due to the smaller sample.

# **6. Conclusions**

Emissions from the transportation sector – which includes travel of people and goods by road, rail, aircraft, and marine vessels; and the pipeline transportation of many fuels – represented more than 24 percent of global CO2 emissions in 2018. Worldwide, emissions from transportation are increasing faster than those from any other sector. Decarbonization of the transport sector therefore plays a crucial role in achieving the Paris Agreement target of limiting global temperature rise to 2 degrees Celsius – and preferably to below 1.5 degree Celsius – compared to pre-industrial levels.

This paper examines the status of the decoupling of transport-generated carbon emissions and economic growth of 145 countries. In addition, it studies the patterns and drivers of transport-related CO2 emissions of 145 countries during the period from 1990 to 2018. We observe considerable differences in the decoupling status of countries according to their income levels. Most high-income countries are in the relative decoupling states while most of the countries with low incomes are in no decoupling states. This implies that there is considerable room for breaking the linkage between economic growth and carbon emissions in low- and middle-income countries.

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The empirical results in this paper point to several opportunities for developing countries to decouple CO2 emissions from transportation. First, transport’s reliance on fossil fuels needs to shift dramatically. Renewables and electricity still account for a negligible share of transportation fuel. Thus, there is large, unexploited potential to reduce CO2 emissions from mobility by leveraging technological advances (in batteries, for example) that have made “green” vehicles possible and more practical transportation options than previously has been thought. Nevertheless, the impact of electrification on transport-related CO2 emissions will be much higher once the power system itself has been decarbonized. Second, continued energy price reforms to phase out subsidies for fossil fuel could lead to a sizable reduction in emissions. Third, rapid urbanization in developing countries could provide both challenges and opportunities for the decarbonization of transportation. On the one hand, the high concentration of people and activities in cities could lead to a rise in vehicle ownership and traffic congestion. On the other hand, urbanization allows for the development of complex public transportation systems and other economies of scale to facilitate emissions reductions. It is important for policy makers to integrate landuse planning and transportation options to encourage low-emission mobility so as to avoid urbanization being associated with a phase of extremely high carbon intensity of transportation.

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# **Figure 1: Sectoral CO2 emissions and global transport emissions by mode, 2018**

<!-- Start of picture text --> (a) CO2 emissions by sector<br><!-- End of picture text --> <!-- Start of picture text --> Others<br>8% Industry<br>18%<br>Energy<br>42% Transport<br>25%<br>Agriculture<br>Residential /forestry<br>6% 1%<br><!-- End of picture text --> <!-- Start of picture text --> (b) Transport emissions by mode<br>Pipeline<br>2%<br>Rail<br>Domestic<br>1%<br>aviation<br>5%<br>Road<br>Intl. marine<br>74%<br>9%<br>Intl.<br>aviation<br>7%<br>Domestic<br>navigation<br>2%<br><!-- End of picture text --> 21
<!-- Start of picture text --> @=m Motor Gasoline |= Gas/diesel oil =m Aviation fuels @mm Biofuels mm other<br>x<br>0.200 @<br>)<br>i<br>0.175<br>0.150 Other MICs q 8<br>a5 EEme x<br>© 0.125 ee ee =fg 2<br>g | —_2<br>8 [—o<br>| oa<br>5 0.100 =<br>a sss” Other HiCs<br>&5£ 0.075 POO—_—EE>E>:a Chinai<br>——— EU<br>~0.000 | | | zaPS|<br>Share of global GDP (%)<br><!-- End of picture text -->

<!-- Start of picture text --> 6000 USA<br>|_|<br>5 2<br>q@ 4000 e)<br>FS =<br>°LS 3000 oO.2P=<br>ac =<br>© g<br>©<br>eve5 =I38<br>2—_@<br>1000 | | im =<br>a India<br>; iea—es |S<br>Share of global population (%)<br><!-- End of picture text -->
<!-- Start of picture text --> (a) Absolute transport emissions (b) Change in transport emissions<br>3500<br>300<br>3000 =<br>2500 =8 250<br>a©Q 2000 83"200<br>= 15001000 2s3 150<br>500 fp 100<br>————E—E—EE<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>(c) Absolute transport emissions per capita (d) Change in transport emissions per capita<br>80 oe ee ld is ae 200180<br>25 s<br>= 160<br>fe}w 2.0 gu 140<br>Qis 2<br>= % 120<br>051.0 rn a ee 3= 108 0<br>0.0 —$—$—_—__—_——_—_—————————————— 60<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>4e-5 (e) Absolute transport emissions per GDP (f) Change in transport emissions per GDP<br>9<br>8 = 100<br>S<br>a? "<br>=65 x3=o 80<br>4<br>70<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>— Highincome —— Lowincome —— Lower middle income | —— Upper middle income<br><!-- End of picture text -->
<!-- Start of picture text --> (a) Top High Income Transport CO2 Emitters (b) Top Upper Middle Income Transport CO2 Emitters<br>1750<br>4500 800<br>1250 600<br>Nn© 1000 Nnfo}<br>gg<br>=750 = 400<br>500<br>200<br>2500 SSS 0<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>— Canada — Japan — United Kingdom — Brazil — Iran —— Russia<br>—— France —— SaudiArabia —— United States —— China —— Mexico —— Turkey<br>—— Germany — Indonesia<br>(c) Top Lower Middle Income Transport CO2 Emitters (d) Top Low Income Transport CO2 Emitters<br>300 16<br>14<br>250<br>12<br>N 200 10<br>fe} lo}Nn<br>Q 150 Qo 8<br>= =<br>6<br>100<br>4<br>50<br>2<br>0 0<br>1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015<br>— Algeria —— Nigeria —— Ukraine — DRCongo — NorthKorea —— Syria<br>— Egypt — Pakistan —— VietNam — Ethiopia — Sudan — Yemen<br>— India —— Mozambique<br><!-- End of picture text --> <!-- Start of picture text --> Global Transportation Fuel Mix<br>0.450.400.35 ee<br>S©D 030 pf pf<br>2 0.25<br>:<br>a 0.20<br>0.15<br>0.10 ——<br>ee S——<br>0.05<br>1990 1995 2000 2005 2010 2015<br>—— Motor gasoline —— Gas/dieseloil —— Other fuels<br>—— Fuel oil — Aviation fuels<br><!-- End of picture text -->
<!-- Start of picture text --> World<br>1.6<br>= 1.4<br>"<br>Oo<br>a<br>@<br>modaP<br>3<br>£<br>1.0<br>0.8<br>1990 1995 2000 2005 2010 2015<br>—— Enmission intensity effect —— population effect<br>—— Economic growth effect — co2<br><!-- End of picture text --> <!-- Start of picture text --> 3.5 High income 3.5 Upper middle income 35 Lower middle income 3.5 Low income<br>3.0 3.0 3.0 3.0<br>= 2.5 = 25 = 25 = 25<br>ul iT Il tH<br>& 2.0 & 2.0 S 2.0 8 2.0<br>= 2 2 2<br>Zz 15 Z15 Z15 15<br>oO a oD oO<br>= 1.0 2 10 Z 1.0 2 1.0<br>0.5 0.5 05 0.5<br>0.0 0.0 0.0 0.0<br>1990 2000 2010 1990 2000 2010 1990 2000 2010 1990 #2000 2010<br>—— Emission intensity effect _—— population effect —— CQ2<br>— Economic growth effect<br><!-- End of picture text -->
<!-- Start of picture text --> China India Japan Mexico Russia<br>10 1.3<br>1.8 12<br>= 8 =~4 ~ 12 _ _<br>V V V 1 16 V<br>2 ¢ oS a 141 o o 10<br>3 34 2 io a 14 3<br>5 4 5 2 5 oo 6 12 § 88<br>a=]&2a=]&3”&a = ) a=]&<br>108 1.0 06<br>199006020000 a0 789006 2000 2010 i800 2000 a 1990 = 2000 2010 1980 2000 = 2010<br>United States EU-28 Other HICs Other MICs Uts<br>2.00 25 200<br>2“ ='4 24175 = = 175<br>Bg3" 12 3a" 28a" 1.50 3&20 §3" 1.50<br>=3 410 = = 1.25 = =<br>£08 5£ 10 5= 1.00 gt£ F= 1.251.00<br>06 1.0<br>06 ors O75<br>1990 2000 2010 1990 2000 2010 1990 2000 2010 71990 2000 2010 1990 2000 2010<br>—— Emission intensity effect —— population effect — coz<br>— Economic growth effect<br>China India Japan Mexico Russia<br>1.5 1.5 1.5 1.5 1.5<br>Qa Qa a Qo Qa<br>a a a Eo a<br>a % & x x<br>Bos Bos B05 Bos Bos<br>=£ =£ =£ =£ =<br>0.0 a0 0.0 ‘0.0 0.0<br>2000 2005 2010 2015 2000 2005 2070 2015 2000 2005 2070 2015 2000 2005 2070 2015 2000 2005 2070 2015<br>United States Eu Kazakhstan Ukraine CIS countries<br>1.6 16 1.6 1.5 1.5<br>v v v 1 0<br>oa oa a ao ao<br>g2gg<br>xx x x x<br>ou O5 B05 O05 3 O05 B05<br>= — — = =<br>0.0 a0 0.0 ‘0.0 0.0<br>2000 2005 2010 2015 2000 2005 2070 2015 2000 2005 2070 2015 2000 2005 2070 2015 2000 2005 2070 2015<br>—— Emission intensity (CO2/GDP) —— energy consumption pertotalkm effect —— total km per gdp effect<br>— ©02 per energy consumption effect<br><!-- End of picture text -->
# **Table 1: Status of the level of decoupling of the link between transport emissions and GDP**

|country|90-<br>91<br>9<br>9|1-<br>2|92-<br>93<br>93-<br>94|94-<br>95<br>95-<br>96|96-<br>97|97-<br>98|98-<br>99|99-<br>00<br>00-<br>01|01-<br>02<br>02-<br>03|03-<br>04<br>04-<br>05|05-<br>06<br>06-<br>07<br>07-<br>08|08-<br>09<br>09-<br>10<br>10-<br>11<br>11-<br>12<br>12-<br>13<br>1<br>1|3-<br>4|14-<br>15<br>15-<br>16<br>16-<br>17<br>17-<br>18|1990-2018|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_High-income cou_**|**_ntries_**|||||||||||||||
|Australia|AD<br>|RD|RD<br>RD|ND<br>RD|RD|RD|RD|ND<br>AD|RD<br>RD|ND<br>RD|RD<br>RD<br>ND|AD<br>ND<br>RD<br>RD<br>RD<br>|RD|<br>RD<br>RD<br>RD<br>ND|RD|
|Austria|ND<br>|RD|ND<br>RD|RD<br>ND|AD|ND|AD|ND<br>ND|ND<br>ND|RD<br>ND|AD<br>RD<br>AD|ND<br>ND<br>AD<br>AD<br>ND<br>|AD|<br>ND<br>ND<br>RD<br>RD|ND|
|Bahrain|RD<br>|ND|RD<br>ND|ND<br>RD|ND|ND|RD|ND<br>ND|ND<br>ND|ND<br>ND|RD<br>ND<br>ND|AD<br>RD<br>AD<br>ND<br>RD<br>|RD|<br>ND<br>RD<br>RD<br>ND|ND|
|Belgium<br>|RD<br>|ND|ND<br>RD|RD<br>ND|RD|ND|RD|RD<br>ND|AD<br>ND|ND<br>AD|RD<br>RD<br>RD|ND<br>AD<br>AD<br>AD<br>AD<br>|RD|<br>ND<br>AD<br>AD<br>RD|RD|
|Brunei||||||||||||||||
|Darussalam|ND<br>|ND|ND<br>ND|ND<br>ND|ND|ND|AD|AD<br>ND|ND<br>ND|ND<br>AD|ND<br>ND<br>ND|ND<br>ND<br>ND<br>ND<br>ND<br>|ND|<br>ND<br>ND<br>AD<br>ND|ND|
|Canada|ND<br>|ND|RD<br>ND|ND<br>ND|RD|RD|RD|AD<br>AD|ND<br>RD|RD<br>RD|AD<br>ND<br>AD|ND<br>RD<br>RD<br>RD<br>ND<br>|AD|<br>AD<br>RD<br>RD<br>ND|RD|
|Chile|RD<br>|RD|ND<br>ND|RD<br>ND|RD|ND|ND|RD<br>AD|ND<br>AD|RD<br>ND|AD<br>ND<br>ND|ND<br>RD<br>RD<br>RD<br>ND<br>|AD|<br>ND<br>ND<br>ND<br>RD|RD|
|Chinese Taipei|RD<br>|ND|ND<br>RD|RD<br>RD|RD|ND|RD|RD<br>ND|RD<br>AD|RD<br>RD|AD<br>AD<br>AD|ND<br>RD<br>RD<br>AD<br>AD<br>|RD|<br>ND<br>ND<br>AD<br>AD|RD|
|Croatia|ND<br>|ND|ND<br>ND|RD<br>ND|ND|ND|ND|RD<br>RD|ND<br>ND|RD<br>RD|ND<br>ND<br>AD|ND<br>ND<br>ND<br>ND<br>ND<br>|ND|<br>ND<br>ND<br>ND<br>AD|ND|
|Curacao|ND<br>|ND|ND<br>ND|ND<br>RD|RD|ND|RD|RD<br>ND|ND<br>RD|ND<br>ND|RD<br>RD<br>RD|ND<br>ND<br>ND<br>ND<br>AD<br>|ND|<br>ND<br>ND<br>ND<br>ND|ND|
|Cyprus|AD<br>|ND|ND<br>RD|RD<br>ND|ND|RD|RD|RD<br>RD|AD<br>ND|ND<br>RD|AD<br>ND<br>RD|ND<br>ND<br>AD<br>ND<br>ND<br>|ND|<br>ND<br>RD<br>RD<br>RD|RD|
|Czech Republic|ND<br>|ND|ND<br>ND|AD<br>ND|ND|ND|ND|RD<br>ND|ND<br>ND|RD<br>ND|RD<br>RD<br>AD|ND<br>AD<br>AD<br>ND<br>ND<br>|ND|<br>RD<br>ND<br>RD<br>RD|ND|
|Denmark|ND<br>|AD|ND<br>RD|RD<br>RD|RD|RD|RD|AD<br>RD|ND<br>ND|ND<br>RD|RD<br>ND<br>ND|ND<br>AD<br>AD<br>AD<br>AD<br>|RD|<br>ND<br>RD<br>RD<br>RD|RD|
|Estonia|ND<br>|ND|ND<br>ND|RD<br>ND|RD|ND|ND|AD<br>ND|RD<br>AD|RD<br>RD|RD<br>RD<br>ND|ND<br>ND<br>AD<br>RD<br>AD<br>|RD|<br>ND<br>RD<br>RD<br>RD|RD|
|Finland|ND<br>|ND|ND<br>ND|AD<br>AD|RD|RD|RD|AD<br>RD|RD<br>RD|RD<br>RD|RD<br>RD<br>AD|ND<br>ND<br>AD<br>ND<br>ND<br>|ND|<br>AD<br>ND<br>AD<br>ND|AD|
|France|ND<br>|ND|ND<br>RD|RD<br>AD|RD|ND|RD|RD<br>RD|AD<br>AD|RD<br>AD|RD<br>AD<br>AD|ND<br>RD<br>ND<br>AD<br>AD<br>|RD|<br>RD<br>RD<br>RD<br>AD|RD|
|Germany|RD<br>|RD|ND<br>AD|RD<br>RD|RD|RD|ND|AD<br>AD|ND<br>ND|AD<br>AD|AD<br>AD<br>RD|ND<br>RD<br>RD<br>AD<br>ND<br>|RD|<br>ND<br>RD<br>RD<br>AD|AD|
|Greece|ND<br>|ND|ND<br>RD|RD<br>ND|RD|ND|RD|AD<br>RD|RD<br>RD|RD<br>ND|RD<br>RD<br>ND|ND<br>ND<br>ND<br>ND<br>ND<br>|RD|<br>ND<br>ND<br>AD<br>RD|RD|
|Hong Kong||||||||||||||||
|<br>(China)|ND<br>|ND|ND<br>ND|AD<br>AD|RD|ND|ND|AD<br>AD|AD<br>AD|AD<br>ND|AD<br>AD<br>AD|ND<br>AD<br>AD<br>AD<br>RD<br>|RD|<br>ND<br>ND<br>ND<br>AD|RD|
|Hungary|ND<br>|ND|ND<br>AD|ND<br>AD|ND|ND|ND|AD<br>ND|ND<br>ND|RD<br>ND|ND<br>ND<br>AD|ND<br>AD<br>AD<br>ND<br>AD<br>|ND|<br>ND<br>RD<br>ND<br>ND|ND|
|Iceland|ND<br>|ND|AD<br>ND|ND<br>ND|AD|RD|RD|RD<br>RD|ND<br>RD|RD<br>ND|ND<br>RD<br>AD|ND<br>ND<br>AD<br>AD<br>RD<br>|RD|<br>RD<br>ND<br>ND<br>RD|RD|
|Ireland|ND<br>|ND|RD<br>RD|RD<br>ND|RD|ND|ND|RD<br>RD|RD<br>RD|RD<br>ND|ND<br>RD<br>ND|ND<br>AD<br>AD<br>AD<br>RD<br>|RD|<br>AD<br>ND<br>AD<br>RD|RD|
|Israel|RD<br>|ND|ND<br>ND|ND<br>ND|ND|AD|ND|RD<br>AD|ND<br>ND|AD<br>AD|RD<br>RD<br>ND|AD<br>RD<br>AD<br>ND<br>AD<br>|RD|<br>ND<br>RD<br>RD<br>RD|RD|
|Italy|ND<br>|ND|ND<br>AD|RD<br>ND|RD|ND|ND|RD<br>RD|ND<br>ND|ND<br>AD|RD<br>RD<br>ND|ND<br>AD<br>AD<br>ND<br>ND<br>|ND|<br>AD<br>AD<br>AD<br>ND|RD|
|Japan|ND<br>|ND|ND<br>ND|ND<br>RD|RD|ND|ND|AD<br>ND|AD<br>AD|AD<br>AD|AD<br>AD<br>ND|ND<br>RD<br>ND<br>AD<br>AD<br>|AD|<br>AD<br>AD<br>AD<br>AD|AD|

Note: AD: Absolute decoupling (color-coded: dark green); RD: Relative decoupling (color-coded: light green); ND: No decoupling (color-coded: red). The columns are one-yearinterval time periods from 1990 to 2018.

1
|country|90-<br>91|91-<br>92|92-<br>93|93-<br>94|94-<br>95|95-<br>96|<br>96-<br>97|97-<br>98<br>98-<br>99|99-<br>00<br>00-<br>01<br>01-<br>02<br>02-<br>03|03-<br>04|0<br>0|4-<br>5|05-<br>06|06-<br>07<br>07-<br>08|08-<br>09|09-<br>10|10-<br>11<br>11-<br>12|12-<br>13<br>13-<br>14<br>14-<br>15<br>15-<br>16|16-<br>17<br>17-<br>18|1990-2018|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_High-income coun_**|**_tries_**||||||||||||||||||||
|Korea|ND|ND|<br>ND|RD|ND|RD|<br>RD|ND<br>RD|RD<br>RD<br>RD<br>RD|RD|A|D|AD|RD<br>AD|ND|RD|<br>AD<br>ND|ND<br>RD<br>ND<br>ND|<br>RD<br>AD|RD|
|Kuwait|ND|ND|<br>RD|RD|ND|ND|<br>ND|RD<br>ND|RD<br>ND<br>ND<br>RD|RD|R|D|ND|RD<br>ND|ND|ND|<br>AD<br>RD|ND<br>ND<br>ND<br>RD|<br>ND<br>ND|ND|
|Latvia|ND|ND|<br>ND|AD|ND|AD|<br>RD|AD<br>AD|ND<br>ND<br>RD<br>RD|RD|R|D|RD|ND<br>ND|ND|ND|<br>AD<br>AD|RD<br>ND<br>ND<br>RD|<br>ND<br>RD|RD|
|Lithuania<br>Luxembourg|ND<br>ND|ND<br>ND|<br>ND<br> <br>RD|AD<br>RD|ND<br>AD|ND<br>ND|<br>ND<br> <br>ND|RD<br>ND<br>RD<br>ND|AD<br>ND<br>RD<br>RD<br>ND<br>ND<br>ND<br>ND|ND<br>ND|R<br>N|D<br>D|RD<br>AD|ND<br>AD<br>AD<br>ND|ND<br>ND|ND<br>ND|<br>AD<br>RD<br> <br>ND<br>ND|AD<br>ND<br>ND<br>ND<br>AD<br>AD<br>AD<br>AD|<br>ND<br>ND<br> <br>ND<br>ND|RD<br>RD|
|Malta|ND|RD|<br>ND|ND|ND|AD|<br>ND|AD<br>AD|AD<br>AD<br>AD<br>ND|ND|N|D|ND|RD<br>ND|ND|ND|<br>AD<br>AD|RD<br>RD<br>RD<br>AD|<br>RD<br>ND|RD|
|Mauritius|RD|ND|<br>ND|RD|AD|AD|<br>AD|RD<br>ND|RD<br>RD<br>ND<br>RD|RD|N|D|RD|AD<br>RD|ND|ND|<br>RD<br>RD|RD<br>RD<br>RD<br>ND|<br>RD<br>RD|RD|
|Netherlands|RD|ND|<br>ND|AD|RD|ND|<br>RD|RD<br>RD|RD<br>RD<br>ND<br>ND|RD|R|D|RD|AD<br>RD|ND|ND|<br>RD<br>ND|ND<br>AD<br>RD<br>RD|<br>RD<br>RD|RD|
|New Zealand|ND|ND|<br>RD|ND|ND|RD|<br>RD|ND<br>RD|ND<br>RD<br>RD<br>RD|RD|R|D|RD|RD<br>ND|AD|AD|<br>RD<br>AD|RD<br>RD<br>RD<br>RD|<br>ND<br>RD|RD|
|Norway|RD|RD|<br>ND|AD|ND|RD|<br>RD|ND<br>RD|AD<br>RD<br>RD<br>ND|RD|R|D|ND|ND<br>AD|ND|ND|<br>AD<br>AD|ND<br>RD<br>RD<br>AD|<br>AD<br>ND|RD|
|Oman|ND|RD|<br>ND|RD|RD|ND|<br>ND|ND<br>ND|RD<br>ND<br>ND<br>ND|ND|N|D|ND|ND<br>ND|RD|ND|<br>ND<br>ND|ND<br>ND<br>ND<br>AD|<br>AD<br>AD|ND|
|Panama|RD|RD|<br>ND|ND|ND|ND|<br>ND|ND<br>ND|RD<br>AD<br>ND<br>ND|RD|N|D|RD|AD<br>ND|ND|RD|<br>RD<br>RD|AD<br>ND<br>ND<br>ND|<br>RD<br>AD|RD|
|Poland|ND|RD|<br>AD|RD|RD|ND|<br>RD|ND<br>ND|AD<br>AD<br>AD<br>ND|ND|N|D|ND|ND<br>ND|RD|ND|<br>RD<br>AD|AD<br>RD<br>ND<br>ND|<br>ND<br>RD|ND|
|Portugal|ND|ND|<br>ND|ND|RD|ND|<br>RD|ND<br>ND|ND<br>RD<br>ND<br>ND|ND|A|D|AD|RD<br>RD|ND|AD|<br>ND<br>ND|ND<br>RD<br>RD<br>ND|<br>RD<br>RD|ND|
|Qatar|ND|RD|<br>ND|ND|ND|ND|<br>RD|RD<br>AD|RD<br>ND<br>ND<br>ND|RD|N|D|RD|RD<br>ND|RD|RD|<br>RD<br>AD|ND<br>ND<br>ND<br>ND|<br>ND<br>AD|RD|
|Romania|ND|ND|<br>AD|ND|AD|ND|<br>ND|ND<br>ND|RD<br>ND<br>RD<br>ND|RD|A|D|RD|RD<br>ND|ND|ND|<br>RD<br>ND|AD<br>RD<br>RD<br>ND|<br>RD<br>RD|RD|
|Saudi Arabia|RD|ND|<br>ND|ND|AD|ND|<br>ND|RD<br>ND|RD<br>ND<br>ND<br>RD|RD|R|D|ND|ND<br>ND|ND|RD|<br>ND<br>ND|RD<br>ND<br>ND<br>AD|<br>ND<br>AD|ND|
|Singapore|ND|AD|<br>AD|RD|ND|AD|<br>AD|ND<br>RD|RD<br>ND<br>AD<br>RD|RD|R|D|RD|RD<br>ND|ND|RD|<br>ND<br>AD|AD<br>RD<br>ND<br>AD|<br>AD<br>AD|RD|
|Slovak Republic|ND|ND|<br>AD|ND|ND|AD|<br>ND|RD<br>ND|AD<br>ND<br>ND<br>AD|RD|N|D|AD|RD<br>ND|ND|ND|<br>AD<br>AD|RD<br>AD<br>AD<br>ND|<br>ND<br>AD|RD|
|Slovenia|ND|ND|<br>ND|ND|ND|ND|<br>RD|AD<br>AD|AD<br>ND<br>RD<br>RD|ND|N|D|RD|ND<br>ND|ND|ND|<br>ND<br>ND|ND<br>RD<br>AD<br>ND|<br>RD<br>AD|ND|
|Spain|ND|ND|<br>ND|ND|RD|ND|<br>AD|ND<br>ND|RD<br>ND<br>RD<br>ND|ND|R|D|RD|RD<br>AD|ND|AD|<br>ND<br>ND|ND<br>RD<br>RD<br>ND|<br>ND<br>RD|RD|
|Sweden|ND|ND|<br>ND|RD|RD|AD|<br>RD|RD<br>RD|RD<br>AD<br>RD<br>RD|RD|A|D|RD|RD<br>ND|ND|RD|<br>AD<br>ND|AD<br>AD<br>RD<br>AD|<br>AD<br>AD|AD|
|Switzerland<br>Trinidad and|ND|ND|<br>ND|ND|AD|RD|<br>ND|RD<br>AD|ND<br>AD<br>RD<br>ND|RD|R|D|RD|RD<br>ND|ND|AD|<br>AD<br>RD|AD<br>AD<br>AD<br>AD|<br>AD<br>RD|RD|
|<br>Tobago<br>|ND|AD|<br>ND|ND|ND|RD|<br>ND|RD<br>RD|RD<br>RD<br>RD<br>AD|ND|R|D|ND|ND<br>ND|ND|ND|<br>ND<br>AD|AD<br>ND<br>RD<br>ND|<br>ND<br>ND|RD|
|United Arab<br>Emirates|ND|RD|<br>ND|AD|RD|RD|<br>AD|AD<br>ND|RD<br>ND<br>ND<br>ND|RD|N|D|RD|RD<br>ND|ND|ND|<br>RD<br>RD|ND<br>RD<br>AD<br>ND|<br>ND<br>AD|ND|
|United Kingdom|ND|ND|<br>RD|RD|AD|ND|<br>RD|AD<br>RD|AD<br>AD<br>RD<br>RD|RD|R|D|RD|RD<br>ND|ND|AD|<br>AD<br>AD|AD<br>RD<br>ND<br>ND|<br>RD<br>AD|RD|
|United States|ND|RD|<br>RD|RD|RD|RD|<br>RD|RD<br>RD|RD<br>RD<br>RD<br>RD|RD|R|D|AD|RD<br>ND|ND|ND|<br>AD<br>AD|ND<br>AD<br>ND<br>RD|<br>RD<br>RD|RD|
|Uruguay|ND|RD|<br>ND|ND|ND|ND|<br>ND|ND<br>ND|ND<br>ND<br>ND<br>AD|ND|R|D|ND|ND<br>RD|ND|RD|<br>RD<br>RD|ND<br>AD<br>ND<br>ND|<br>ND<br>AD|ND|

2
|Country|90-<br>91|91-<br>92<br>92-<br>93<br>93-<br>94|94-<br>95|95-<br>96<br><br>|96-<br>97|97-<br>98<br>98-<br>99<br>99-<br>00<br>00-<br>01<br>01-<br>02<br>02-<br>03<br>03-<br>04|04-<br>05<br>05-<br>06|06-<br>07<br>07-<br>08<br>08-<br>09|09-<br>10<br>10-<br>11<br>11-<br>12<br>12-<br>13|13-<br>14|14-<br>15<br>15-<br>16<br>16-<br>17<br>17-<br>18|1990-2018|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Upper-middle-income countri_**|**_es_**||||||||||||
|Albania|ND|ND<br>ND<br>ND|AD|AD|ND|ND<br>ND<br>RD<br>RD<br>ND<br>ND<br>ND|RD<br>AD|ND<br>ND<br>RD|AD<br>ND<br>AD<br>ND|RD|AD<br>AD<br>RD<br>AD|ND|
|Argentina|RD|RD<br>RD<br>AD|ND|ND|AD|ND<br>ND<br>ND<br>ND<br>ND<br>RD<br>RD|RD<br>RD|RD<br>ND<br>ND|RD<br>RD<br>ND<br>ND|ND|ND<br>ND<br>RD<br>ND|RD|
|Armenia|ND|ND<br>ND<br>AD|AD|AD|ND|RD<br>ND<br>ND<br>AD<br>AD<br>RD<br>RD|RD<br>AD|ND<br>ND<br>ND|ND<br>RD<br>RD<br>AD|ND|AD<br>AD<br>RD<br>ND|AD|
|Azerbaijan|ND|ND<br>ND<br>ND|ND|AD|ND|AD<br>AD<br>ND<br>ND<br>ND<br>RD<br>ND|RD<br>RD|AD<br>ND<br>AD|ND<br>ND<br>ND<br>ND|ND|AD<br>ND<br>ND<br>ND|RD|
|Belarus<br>Bolivarian Republic of|ND|ND<br>ND<br>ND|ND|ND|RD|RD<br>AD<br>AD<br>AD<br>ND<br>RD<br>RD|ND<br>ND|RD<br>ND<br>AD|RD<br>ND<br>ND<br>ND|AD|ND<br>ND<br>ND<br>ND|RD|
|Venezuela|RD|AD<br>ND<br>ND|RD|ND|AD|ND<br>ND<br>RD<br>ND<br>ND<br>ND<br>RD|ND<br>RD|RD<br>RD<br>ND|ND<br>ND<br>ND<br>AD|ND|ND<br>ND<br>ND<br>ND|ND|
|Bosnia and Herzegovina|ND|ND<br>ND<br>ND|RD|RD|RD|ND<br>AD<br>ND<br>RD<br>AD<br>RD<br>ND|AD<br>ND|RD<br>ND<br>ND|AD<br>RD<br>ND<br>AD|ND|ND<br>ND<br>ND<br>AD|RD|
|Botswana|ND|ND<br>AD<br>AD|ND|RD|RD|ND<br>RD<br>ND<br>ND<br>AD<br>ND<br>ND|RD<br>RD|RD<br>ND<br>ND|RD<br>RD<br>RD<br>RD|RD|ND<br>ND<br>RD<br>RD|ND|
|Brazil|ND|ND<br>RD<br>RD|ND|ND|ND|ND<br>AD<br>RD<br>ND<br>RD<br>AD<br>ND|RD<br>RD|RD<br>RD<br>ND|ND<br>ND<br>ND<br>ND|ND|ND<br>ND<br>RD<br>AD|ND|
|Bulgaria|ND|ND<br>ND<br>AD|ND|AD|ND|ND<br>ND<br>AD<br>RD<br>RD<br>ND<br>ND|ND<br>RD|AD<br>RD<br>ND|AD<br>ND<br>ND<br>AD|ND|ND<br>RD<br>RD<br>RD|RD|
|Colombia|ND|ND<br>RD<br>AD|ND|ND|ND|AD<br>ND<br>AD<br>ND<br>AD<br>RD<br>ND|AD<br>RD|RD<br>RD<br>AD|RD<br>ND<br>ND<br>AD|RD|ND<br>ND<br>AD<br>RD|RD|
|Costa Rica|ND|ND<br>RD<br>ND|AD|RD|RD|ND<br>RD<br>AD<br>ND<br>ND<br>RD<br>ND|AD<br>RD|RD<br>RD<br>ND|RD<br>RD<br>RD<br>RD|RD|ND<br>ND<br>RD<br>RD|ND|
|Cuba|ND|ND<br>ND<br>AD|ND|ND|ND|AD<br>AD<br>AD<br>RD<br>AD<br>AD<br>RD|AD<br>AD|RD<br>AD<br>AD|AD<br>AD<br>AD<br>ND|ND|ND<br>AD<br>ND<br>ND|AD|
|Dominican Republic|AD|RD<br>ND<br>ND|ND|ND|ND|ND<br>ND<br>RD<br>RD<br>ND<br>ND<br>ND|RD<br>RD|RD<br>AD<br>ND|RD<br>ND<br>ND<br>AD|AD|ND<br>ND<br>AD<br>ND|RD|
|Ecuador|RD|RD<br>RD<br>ND|AD|ND|ND|AD<br>ND<br>AD<br>ND<br>RD<br>ND<br>RD|RD<br>RD|AD<br>AD<br>ND|ND<br>ND<br>RD<br>ND|ND|ND<br>ND<br>ND<br>ND|ND|
|Equatorial Guinea|ND|ND<br>AD<br>AD|ND|ND|RD|AD<br>ND<br>ND<br>AD<br>ND<br>ND<br>RD|RD<br>ND|RD<br>RD<br>RD|ND<br>RD<br>RD<br>ND|ND|ND<br>ND<br>ND<br>ND|RD|
|Gabon|ND|ND<br>ND<br>AD|ND|RD|ND|ND<br>ND<br>ND<br>ND<br>ND<br>AD<br>ND|AD<br>ND|ND<br>ND<br>ND|ND<br>ND<br>RD<br>ND|AD|AD<br>ND<br>AD<br>ND|AD|
|Georgia|ND|ND<br>ND<br>ND|AD|ND|AD|AD<br>AD<br>AD<br>ND<br>RD<br>RD<br>AD|ND<br>RD|ND<br>AD<br>ND|RD<br>RD<br>RD<br>ND|ND|ND<br>ND<br>AD<br>AD|ND|
|Guatemala|ND|RD<br>ND<br>ND|ND|AD|ND|ND<br>ND<br>ND<br>ND<br>ND<br>AD<br>AD|ND<br>RD|RD<br>AD<br>ND|AD<br>AD<br>RD<br>ND|ND|ND<br>ND<br>ND<br>ND|ND|
|Indonesia|ND|RD<br>ND<br>ND|ND|ND|ND|ND<br>AD<br>ND<br>ND<br>RD<br>RD<br>RD|AD<br>AD|RD<br>ND<br>ND|ND<br>ND<br>ND<br>RD|RD|AD<br>ND<br>ND<br>ND|ND|
|Iraq|ND|RD<br>RD<br>RD|AD|AD|AD|RD<br>RD<br>ND<br>ND<br>ND<br>ND<br>RD|ND<br>AD|AD<br>AD<br>RD|ND<br>ND<br>RD<br>AD|AD|AD<br>RD<br>ND<br>ND|RD|
|Islamic Republic of Iran|RD|ND<br>ND<br>ND|AD|ND|ND|ND<br>ND<br>ND<br>ND<br>ND<br>RD<br>ND|ND<br>ND|AD<br>ND<br>ND|AD<br>RD<br>ND<br>ND|ND|ND<br>AD<br>RD<br>ND|ND|

3
|country|90-<br>91<br>91-<br>92|92-<br>93|<br>93-<br>94<br>94-<br>95|9<br>9|5-<br>6|96<br>97|-<br> <br>97-<br>98<br><br>|98-<br>99|99-<br>00|00-<br>01<br>01-<br>02|02-<br>03<br>03-<br>04<br>04-<br>05|05-<br>06<br>06-<br>07|0<br>0|7-<br>8|08-<br>09<br>09-<br>10|10-<br>11<br>11-<br>12<br>12-<br>13<br>13-<br>14|14-<br>15<br>15-<br>16<br>16-<br>17<br>17-<br>18|1990-2018|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Upper-middle-income co_**|**_untries_**||||||||||||||||||
|Jamaica|AD<br>ND|RD|<br>RD<br>ND|N|D|N|D<br>ND<br>|ND|RD|<br>AD<br>ND|AD<br>ND<br>ND|AD<br>AD|N|D|ND<br>ND|AD<br>ND<br>ND<br>ND|AD<br>ND<br>ND<br>ND|ND|
|Jordan|AD<br>RD|RD|<br>RD<br>ND|R|D|N|D<br>RD<br>|RD|ND|<br>RD<br>RD|RD<br>ND<br>ND|AD<br>RD|A|D|ND<br>RD|RD<br>ND<br>ND<br>AD|ND<br>ND<br>ND<br>AD|RD|
|Kazakhstan|ND<br>ND|ND|<br>ND<br>ND|A|D|A|D<br>ND<br>|AD|ND|<br>AD<br>ND|AD<br>RD<br>RD|RD<br>ND|N|D|AD<br>RD|RD<br>ND<br>AD<br>ND|ND<br>ND<br>AD<br>ND|RD|
|Kosovo|||||||||ND|<br>RD<br>ND|RD<br>ND<br>RD|ND<br>RD|N|D|ND<br>AD|RD<br>RD<br>AD<br>ND|ND<br>ND<br>ND<br>ND|ND|
|Lebanon|RD<br>ND|ND|<br>AD<br>ND|R|D|A|D<br>ND<br>|ND|AD|<br>AD<br>RD|ND<br>RD<br>RD|AD<br>AD|N|D|ND<br>AD|RD<br>ND<br>AD<br>ND|ND<br>ND<br>ND<br>AD|RD|
|Libya|RD<br>ND|ND|<br>ND<br>ND|N|D|N|D<br>ND<br>|ND|RD|<br>ND<br>ND|RD<br>RD<br>RD|RD<br>AD|N|D|ND<br>ND|ND<br>RD<br>ND<br>ND|ND<br>ND<br>AD<br>AD|ND|
|Malaysia|RD<br>RD|RD|<br>ND<br>RD|N|D|N|D<br>ND<br>|ND|RD|<br>ND<br>RD|ND<br>ND<br>AD|AD<br>ND|N|D|ND<br>RD|RD<br>AD<br>ND<br>ND|AD<br>RD<br>AD<br>RD|RD|
|Mexico|ND<br>RD|RD|<br>ND<br>ND|R|D|R|D<br>RD<br>|ND|RD|<br>ND<br>ND|ND<br>ND<br>ND|ND<br>ND|N|D|ND<br>RD|RD<br>RD<br>AD<br>RD|AD<br>ND<br>AD<br>ND|RD|
|Montenegro|||||||||||ND|AD<br>ND|R|D|ND<br>ND|AD<br>ND<br>AD<br>AD|ND<br>ND<br>ND<br>ND|ND|
|Namibia|ND<br>ND|ND|<br>ND<br>ND|N|D|R|D<br>ND<br>|RD|AD|<br>ND<br>AD|ND<br>RD<br>ND|AD<br>RD|N|D|ND<br>RD|RD<br>AD<br>ND<br>RD|ND<br>RD<br>ND<br>ND|ND|
|Paraguay|AD<br>ND|ND|<br>ND<br>ND|R|D|N|D<br>ND<br>|ND|ND|<br>ND<br>ND|ND<br>AD<br>AD|ND<br>ND|R|D|ND<br>ND|ND<br>ND<br>RD<br>ND|ND<br>ND<br>ND<br>ND|ND|
|People's Republic of|||||||||||||||||||
|China|RD<br>RD|RD|<br>AD<br>RD|N|D|A|D<br>AD<br>|ND|ND|<br>RD<br>RD|ND<br>ND<br>RD|RD<br>RD|R|D|RD<br>RD|RD<br>ND<br>ND<br>RD|ND<br>RD<br>RD<br>RD|RD|
|Peru<br>|AD<br>ND|RD|<br>ND<br>ND|N|D|R|D<br>ND<br>|ND|AD|<br>AD<br>AD|ND<br>ND<br>AD|RD<br>RD|N|D|ND<br>ND|RD<br>RD<br>ND<br>AD|ND<br>ND<br>RD<br>RD|RD|
|Republic of North|||||||||||||||||||
|Macedonia|ND<br>ND|ND|<br>ND<br>ND|N|D|A|D<br>AD<br>|ND|AD|<br>ND<br>ND|RD<br>RD<br>RD|RD<br>ND|R|D|ND<br>ND|ND<br>ND<br>ND<br>ND|ND<br>ND<br>ND<br>AD|ND|
|Russian Federation|ND<br>ND|ND|<br>ND<br>ND|N|D|A|D<br>ND<br>|RD|AD|<br>RD<br>RD|RD<br>RD<br>AD|RD<br>RD|N|D|ND<br>ND|RD<br>AD<br>RD<br>ND|ND<br>AD<br>ND<br>ND|AD|
|Serbia|ND<br>ND|ND|<br>AD<br>RD|N|D|N|D<br>AD<br>|ND|AD|<br>ND<br>ND|ND<br>ND<br>RD|ND<br>AD|N|D|ND<br>RD|AD<br>ND<br>ND<br>ND|AD<br>RD<br>ND<br>RD|RD|
|South Africa|ND<br>ND|RD|<br>ND<br>ND|A|D|N|D<br>AD<br>|RD|AD|<br>RD<br>RD|ND<br>ND<br>RD|RD<br>ND|A|D|ND<br>ND|ND<br>RD<br>ND<br>AD|ND<br>AD<br>ND<br>RD|RD|
|Suriname|||||||||ND|<br>RD<br>ND|ND<br>RD<br>ND|ND<br>RD|N|D|RD<br>ND|AD<br>ND<br>AD<br>ND|ND<br>ND<br>AD<br>RD|ND|
|Thailand|RD<br>RD|ND|<br>ND<br>ND|N|D|N|D<br>ND<br>|RD|AD|<br>RD<br>RD|RD<br>ND<br>AD|AD<br>ND|A|D|ND<br>RD|ND<br>RD<br>AD<br>AD|ND<br>ND<br>ND<br>RD|RD|
|Turkey|AD<br>RD|ND|<br>ND<br>ND|R|D|A|D<br>AD<br>|ND|RD|<br>ND<br>RD|RD<br>RD<br>RD|ND<br>ND|A|D|ND<br>AD|RD<br>ND<br>ND<br>ND|ND<br>ND<br>RD<br>RD|RD|
|Turkmenistan|ND<br>ND|ND|<br>ND<br>ND|N|D|N|D<br>ND<br>|ND|ND|<br>RD<br>ND|ND<br>RD<br>RD|AD<br>RD|R|D|AD<br>ND|ND<br>RD<br>RD<br>RD|ND<br>ND<br>ND<br>ND|RD|

4
|country|90-<br>91<br>91-<br>92|92-<br>93<br>93-<br>94|94-<br>95<br><br>|95-<br>96|96-<br>97|97-<br>98|98-<br>99<br>99-<br>00|00-<br>01<br>01-<br>02<br>02-<br>03|03<br>04|-<br> <br>04-<br>05|05-<br>06|06-<br>07<br>07-<br>08|08<br>09|-<br> <br>09-<br>10<br>10-<br>11|11-<br>12|12-<br>13<br>13-<br>14<br>14-<br>15<br>15-<br>16<br>16-<br>17<br>17-<br>18|1990-2018|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Lower-middle-income co_**|**_untries_**|||||||||||||||||
|Algeria|ND<br>AD|ND<br>ND|AD<br>|AD|RD|RD|ND<br>RD|RD<br>ND<br>ND|R|D<br>ND|ND|ND<br>ND|ND|<br>ND<br>ND|<br>ND|ND<br>ND<br>ND<br>AD<br>AD<br>ND|ND|
|Angola|ND<br>ND|ND<br>ND|AD<br>|AD|RD|AD|ND<br>AD|ND<br>ND<br>ND|N|D<br>AD|ND|ND<br>ND|ND|<br>ND<br>ND|<br>AD|ND<br>RD<br>RD<br>ND<br>ND<br>ND|ND|
|Bangladesh|ND<br>ND|RD<br>RD|ND<br>|ND|ND|RD|AD<br>AD|ND<br>RD<br>RD|N|D<br>ND|RD|RD<br>ND|ND|<br>ND<br>ND|<br>RD|AD<br>ND<br>RD<br>RD<br>ND<br>ND|ND|
|Benin|AD<br>AD|ND<br>ND|ND<br>|ND|ND|ND|ND<br>ND|ND<br>ND<br>ND|R|D<br>ND|ND|ND<br>RD|ND|<br>ND<br>ND|<br>RD|ND<br>RD<br>ND<br>ND<br>RD<br>ND|ND|
|Cambodia|||ND<br>|RD|RD|ND|AD<br>AD|ND<br>AD<br>RD|R|D<br>RD|RD|ND<br>RD|ND|<br>ND<br>RD|<br>RD|RD<br>ND<br>ND<br>ND<br>RD<br>ND|ND|
|Cameroon|ND<br>ND|ND<br>ND|AD<br>|RD|ND|RD|AD<br>ND|RD<br>ND<br>RD|R|D<br>AD|AD|ND<br>RD|ND|<br>ND<br>RD|<br>RD|RD<br>ND<br>AD<br>RD<br>AD<br>AD|RD|
|Cote d'ivoire|AD<br>ND|ND<br>AD|ND<br>|RD|ND|AD|ND<br>ND|AD<br>ND<br>ND|N|D<br>ND|ND|RD<br>ND|RD|<br>AD<br>ND|<br>ND|RD<br>RD<br>ND<br>ND<br>AD<br>ND|ND|
|Egypt|ND<br>RD|ND<br>ND|ND<br>|ND|ND|ND|ND<br>RD|AD<br>ND<br>ND|R|D<br>AD|ND|ND<br>RD|ND|<br>ND<br>ND|<br>ND|RD<br>ND<br>RD<br>ND<br>AD<br>AD|ND|
|El Salvador|ND<br>ND|ND<br>ND|ND<br>|AD|RD|ND|ND<br>AD|RD<br>AD<br>ND|N|D<br>ND|AD|ND<br>AD|ND|<br>AD<br>RD|<br>AD|RD<br>RD<br>ND<br>ND<br>AD<br>ND|ND|
|Ghana|AD<br>ND|RD<br>ND|ND<br>|ND|ND|ND|ND<br>AD|AD<br>ND<br>AD|N|D<br>AD|RD|ND<br>RD|ND|<br>RD<br>RD|<br>ND|RD<br>RD<br>ND<br>AD<br>AD<br>ND|ND|
|Honduras|ND<br>ND|ND<br>ND|RD<br>|AD|ND|ND|ND<br>AD|ND<br>RD<br>RD|R|D<br>AD|AD|ND<br>AD|ND|<br>RD<br>RD|<br>RD|AD<br>ND<br>ND<br>RD<br>AD<br>ND|ND|
|India|ND<br>RD|RD<br>RD|ND<br>|RD|RD|RD|RD<br>RD|AD<br>RD<br>RD|N|D<br>RD|RD|ND<br>ND|ND|<br>RD<br>ND|<br>ND|RD<br>RD<br>ND<br>RD<br>ND<br>RD|RD|
|Kenya|ND<br>ND|AD<br>AD|ND<br>|ND|AD|RD|AD<br>ND|AD<br>ND<br>AD|N|D<br>RD|ND|RD<br>ND|ND|<br>ND<br>AD|<br>ND|ND<br>ND<br>ND<br>ND<br>AD<br>ND|ND|
|Kyrgyzstan|ND<br>ND|ND<br>ND|ND<br>|RD|RD|AD|AD<br>AD|RD<br>ND<br>ND|N|D<br>ND|ND|ND<br>ND|ND|<br>ND<br>ND|<br>ND|RD<br>AD<br>AD<br>ND<br>AD<br>AD|AD|
|Lao People's Democratic|Republic||||||ND|ND<br>RD<br>RD|R|D<br>RD|RD|ND<br>ND|ND|<br>RD<br>AD|<br>ND|ND<br>ND<br>RD<br>ND<br>RD<br>RD|ND|
|Mongolia|ND<br>ND|ND<br>AD|ND<br>|ND|AD|AD|ND<br>ND|AD<br>ND<br>AD|N|D<br>AD|ND|ND<br>RD|ND|<br>AD<br>ND|<br>ND|AD<br>RD<br>RD<br>AD<br>ND<br>RD|RD|
|Morocco|ND<br>ND|ND<br>RD|ND<br>|RD|ND|RD|ND<br>ND|RD<br>ND<br>RD|R|D<br>ND|RD|ND<br>ND|ND|<br>ND<br>ND|<br>RD|RD<br>RD<br>ND<br>ND<br>RD<br>ND|ND|
|Myanmar|ND<br>AD|ND<br>ND|ND<br>|ND|AD|ND|RD<br>ND|AD<br>ND<br>RD|R|D<br>RD|AD|RD<br>AD|RD|<br>RD<br>RD|<br>ND|ND<br>ND<br>ND<br>RD<br>ND<br>RD|RD|
|Nepal|ND<br>ND|ND<br>ND|ND<br>|RD|ND|ND|RD<br>ND|AD<br>ND<br>AD|N|D<br>AD|ND|RD<br>ND|ND|<br>ND<br>RD|<br>ND|ND<br>ND<br>AD<br>ND<br>ND<br>ND|ND|
|Nicaragua|ND<br>ND|ND<br>ND|ND<br>|RD|ND|ND|RD<br>RD|RD<br>ND<br>AD|A|D<br>AD|RD|RD<br>AD|ND|<br>RD<br>RD|<br>RD|AD<br>ND<br>ND<br>RD<br>ND<br>ND|ND|
|Nigeria|ND<br>ND|ND<br>ND|ND<br>|ND|ND|AD|ND<br>ND|ND<br>RD<br>RD|R|D<br>AD|AD|AD<br>ND|AD|<br>ND<br>ND|<br>ND|ND<br>ND<br>AD<br>ND<br>RD<br>ND|ND|
|Pakistan|RD<br>ND|ND<br>ND|RD<br>|ND|ND|ND|ND<br>AD|AD<br>RD<br>ND|N|D<br>AD|RD|ND<br>AD|RD|<br>ND<br>ND|<br>RD|RD<br>ND<br>ND<br>ND<br>ND<br>RD|ND|
|Philippines<br>|ND<br>ND|ND<br>ND|ND<br>|ND|ND|ND|AD<br>AD|ND<br>RD<br>AD|R|D<br>AD|AD|AD<br>RD|ND|<br>RD<br>AD|<br>RD|RD<br>RD<br>ND<br>ND<br>RD<br>RD|RD|
|Plurinational State of<br>Bolivia|RD<br>AD|ND<br>RD|ND<br>|RD|AD|RD|RD<br>AD|AD<br>RD<br>ND|N|D<br>RD|ND|ND<br>ND|ND|<br>ND<br>ND|<br>ND|ND<br>ND<br>RD<br>RD<br>ND<br>ND|ND|
|Republic of Moldova|ND<br>ND|ND<br>ND|ND<br>|ND|AD|ND|ND<br>AD|RD<br>ND<br>ND|N|D<br>AD|RD|ND<br>ND|ND|<br>ND<br>ND|<br>ND|RD<br>RD<br>ND<br>ND<br>RD<br>RD|AD|
|Republic of the Congo|ND<br>AD|ND<br>ND|ND<br>|AD|ND|ND|ND<br>ND|ND<br>AD<br>ND|N|D<br>RD|ND|ND<br>ND|ND|<br>ND<br>ND|<br>AD|ND<br>RD<br>AD<br>ND<br>ND<br>ND|ND|
|Senegal|ND<br>ND|AD<br>ND|RD<br>|ND|ND|ND|ND<br>ND|AD<br>ND<br>ND|N|D<br>AD|ND|ND<br>ND|AD|<br>ND<br>ND|<br>AD|ND<br>RD<br>ND<br>ND<br>AD<br>RD|ND|
|Sri Lanka|RD<br>ND|ND<br>ND|ND<br>|ND|AD|RD|ND<br>AD|ND<br>RD<br>ND|R|D<br>ND|AD|ND<br>AD|AD|<br>ND<br>RD|<br>AD|ND<br>ND<br>ND<br>ND<br>ND<br>AD|RD|

5
|country|90-<br>91<br>91-<br>92|92-<br>93<br>93-<br>94|94<br>95|-<br> <br>95-<br>96|96-<br>97|97-<br>98|98<br>99|-<br> <br>99-<br>00<br>00-<br>01|01-<br>02<br>02-<br>03<br>03-<br>04<br>04-<br>05<br>05-<br>06|06-<br>07<br>07-<br>08|08-<br>09|09-<br>10<br>10-<br>11<br>11-<br>12|12-<br>13<br>13-<br>14|14-<br>15<br>15<br>16|-<br> <br>16-<br>17<br>17-<br>18|1990-2018|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**_Lower-middle-income countries_**|||||||||||||||||
|Tunisia|RD<br>RD|<br>ND<br>ND|N|D<br>RD|ND|RD|N|D<br>RD<br>RD|ND<br>RD<br>RD<br>ND<br>AD|RD<br>AD|ND|<br>ND<br>ND<br>AD|AD<br>ND|ND<br>ND|<br>ND<br>RD|ND|
|Ukraine|ND<br>ND|<br>ND<br>ND|N|D<br>ND|ND|ND|N|D<br>AD<br>RD|RD<br>AD<br>RD<br>AD<br>AD|ND<br>AD|ND|<br>RD<br>AD<br>AD|ND<br>ND|ND<br>ND|<br>ND<br>AD|ND|
|United Republic of Tanzania|AD<br>ND|<br>ND<br>ND|N|D<br>ND|AD|ND|AD|<br>ND<br>ND|ND<br>RD<br>RD<br>ND<br>RD|AD<br>ND|AD|<br>ND<br>ND<br>ND|ND<br>AD|RD<br>AD|<br>RD<br>RD|ND|
|Uzbekistan|ND<br>ND|<br>ND<br>ND|N|D<br>ND|RD|ND|RD|<br>ND<br>RD|AD<br>AD<br>AD<br>RD<br>RD|AD<br>RD|AD|<br>RD<br>AD<br>AD|AD<br>AD|AD<br>AD|<br>ND<br>ND|RD|
|Viet Nam|AD<br>ND|<br>ND<br>ND|A|D<br>ND|ND|RD|N|D<br>ND<br>ND|ND<br>ND<br>ND<br>RD<br>AD|ND<br>ND|ND|<br>ND<br>AD<br>AD|AD<br>RD|ND<br>ND|<br>RD<br>AD|ND|
|Zambia|ND<br>ND|<br>AD<br>ND|A|D<br>AD|ND|ND|AD|<br>ND<br>RD|ND<br>RD<br>RD<br>RD<br>RD|AD<br>ND|ND|<br>RD<br>ND<br>ND|RD<br>ND|ND<br>AD|<br>AD<br>AD|RD|
|Zimbabwe|AD<br>ND|<br>AD<br>AD|N|D<br>AD|RD|AD|N|D<br>ND<br>AD|ND<br>ND<br>ND<br>ND<br>ND|<br>ND<br>ND|ND|<br>RD<br>ND<br>RD|ND<br>AD|AD<br>AD|<br>RD<br>ND|ND|
|**_Low-income countries_**|||||||||||||||||
|Democratic People's Republic of|||||||||||||||||
|Korea|ND<br>ND|<br>ND<br>ND|N|D<br>ND|ND|ND|N|D<br>ND<br>ND|ND<br>ND<br>ND<br>ND<br>AD|ND<br>ND|ND|<br>ND<br>ND<br>AD|ND<br>AD|ND<br>ND|<br>AD<br>AD|ND|
|Democratic Republic of the Congo|ND<br>ND|<br>ND<br>ND|A|D<br>ND|ND|ND|N|D<br>ND<br>ND|ND<br>ND<br>ND<br>ND<br>ND|<br>ND<br>ND|AD|<br>ND<br>ND<br>AD|ND<br>ND|AD<br>AD|<br>ND<br>AD|ND|
|Eritrea|ND|<br>ND<br>ND|N|D<br>ND|AD|AD|N|D<br>ND<br>ND|ND<br>ND<br>AD<br>AD<br>ND|<br>AD<br>ND|RD|<br>ND<br>ND<br>ND|RD<br>RD|RD<br>ND|<br>ND<br>ND|ND|
|Ethiopia|ND<br>ND|<br>ND<br>ND|N|D<br>RD|ND|ND|RD|<br>ND<br>ND|ND<br>ND<br>RD<br>AD<br>RD|RD<br>RD|RD|<br>RD<br>RD<br>ND|ND<br>ND|ND<br>ND|<br>ND<br>ND|ND|
|Haiti|AD<br>ND|<br>ND<br>ND|N|D<br>ND|RD|ND|N|D<br>ND<br>ND|ND<br>ND<br>ND<br>RD<br>RD|ND<br>AD|RD|<br>ND<br>RD<br>AD|AD<br>ND|ND<br>RD|<br>ND<br>RD|ND|
|Mozambique|AD<br>ND|<br>ND<br>AD|N|D<br>RD|RD|RD|RD|<br>RD<br>AD|RD<br>ND<br>RD<br>AD<br>RD|ND<br>AD|ND|<br>ND<br>ND<br>AD|ND<br>ND|ND<br>RD|<br>ND<br>ND|RD|
|Niger||||||||ND<br>RD|RD<br>ND<br>ND<br>RD<br>RD|ND<br>RD|ND|<br>ND<br>AD<br>ND|ND<br>RD|RD<br>AD|<br>RD<br>ND|ND|
|South Sudan||||||||||||ND|AD<br>ND|ND<br>ND|<br>ND<br>ND|ND|
|Sudan|AD<br>AD|<br>AD<br>ND|A|D<br>AD|ND|AD|AD|<br>ND<br>ND|ND<br>AD<br>ND<br>ND<br>ND|<br>RD<br>ND|AD|<br>ND<br>ND<br>AD|ND<br>ND|ND<br>ND|<br>AD<br>ND|RD|
|Syrian Arab Republic|RD<br>AD|<br>RD<br>RD|A|D<br>ND|ND|RD|N|D<br>ND<br>AD|ND<br>ND<br>ND<br>ND<br>ND|<br>ND<br>RD|AD|<br>AD<br>ND<br>AD|AD<br>ND|ND<br>ND|<br>ND<br>ND|AD|
|Tajikistan|ND<br>ND|<br>ND<br>ND|N|D<br>ND|ND|AD|AD|<br>AD<br>ND|ND<br>AD<br>ND<br>AD<br>ND|<br>ND<br>AD|AD|<br>ND<br>RD<br>ND|RD<br>ND|AD<br>AD|<br>AD<br>RD|ND|
|Togo|ND<br>ND|<br>ND<br>ND|N|D<br>ND|AD|ND|N|D<br>ND<br>ND|ND<br>ND<br>AD<br>AD<br>AD|ND<br>ND|ND|<br>AD<br>AD<br>AD|AD<br>AD|ND<br>AD|<br>ND<br>ND|RD|
|Yemen|ND<br>RD|<br>AD<br>RD|A|D<br>AD|RD|ND|N|D<br>AD<br>ND|AD<br>ND<br>RD<br>ND<br>AD|AD<br>ND|ND|<br>AD<br>ND<br>AD|ND<br>ND|ND<br>ND|<br>ND<br>ND|AD|

Note: AD: Absolute decoupling (color-coded: dark green); RD: Relative decoupling (color-coded: light green); ND: No decoupling (color-coded: red). The columns are one-yearinterval time periods from 1990 to 2018.

6
**Table 2 Correlates of transport-related carbon emissions per capita**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|Random Effects|Fixed Effects|GMM|
|GDP per capita|5.55e-08**|5.53e-08**|5.53e-08**|4.79e-08*|
||(1.72e-08)|(1.94e-08)|(1.94e-08)|(1.99e-08)|
|GDP pc sq|-2.01e-15*|-1.38e-15|-1.38e-15|-1.38e-15*|
||(1.00e-15)|(7.39e-16)|(7.39e-16)|(6.86e-16)|
|Agriculture|-0.0597***|-0.0238***|-0.0238***|-0.0143***|
||(0.00228)|(0.00165)|(0.00165)|(0.00131)|
|Manufacturing|0.00338|0.00232|0.00232|-0.00321*|
||(0.00212)|(0.00160)|(0.00160)|(0.00137)|
|Service|0.00942***|-0.00140|-0.00140|-0.00151|
||(0.00157)|(0.00111)|(0.00111)|(0.000775)|
|Urbanization|0.0317***|0.0765***|0.0765***|0.0211***|
||(0.00377)|(0.00457)|(0.00457)|(0.00385)|
|Urbanization sq|-0.0000710*|-0.000574***|-0.000574***|-0.000183***|
||(0.0000290)|(0.0000373)|(0.0000373)|(0.0000324)|
|Diesel price|1.087***|-0.102*|-0.102*|-0.0632*|
||(0.0856)|(0.0404)|(0.0404)|(0.0254)|
|Gasoline price|-1.218***|-0.0528|-0.0528|-0.00659|
||(0.0843)|(0.0403)|(0.0403)|(0.0257)|
|BRT|0.0383|-0.0720***|-0.0720***|-0.00156|
||(0.0384)|(0.0190)|(0.0190)|(0.0175)|
|L.co2pc||||0.723***<br>(0.0170)|
|Obs.|2816|2816|2816|2689|
|Year fixed effects|Yes|Yes|Yes|Yes|
|Standard errors in p|arentheses||||
|="* p<0.05|** p<0.01|*** p<0.001"|||

1
**Table 3 Correlates of transport-related carbon emissions per capita (with transport policy)**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|Random Effects|Fixed Effects|GMM|
|Log GDP per capita|-8.51e-08***|9.31e-08***|13.0e-08***|2.89e-08***|
||(1.55e-08)|(1.41e-08)|(1.50e-08)|(7.55e-09)|
|Log GDP pc sq|3.88e-15***|-2.39e-15***|-3.13e-15***|-8.21e-16***|
||(8.10e-16)|(4.60e-16)|(4.68e-16)|(2.32e-16)|
|Agriculture|-0.103***|-0.0436***|-0.0351***|-0.0128***|
||(0.00566)|(0.00467)|(0.00474)|(0.00234)|
|Manufacturing|-0.00213|-0.00262|-0.00240|-0.000847|
||(0.00302)|(0.00152)|(0.00148)|(0.000719)|
|Service|0.0149***|-0.000240|-0.000670|-0.00358***|
||(0.00281)|(0.00175)|(0.00170)|(0.000829)|
|Urbanization|0.0321***|0.0777***|0.0574***|0.00889|
||(0.00739)|(0.00874)|(0.00951)|(0.00471)|
|Urbanization sq|-0.000151**|-0.000487***|-0.000403***|-0.0000593|
||(0.0000525)|(0.0000601)|(0.0000637)|(0.0000316)|
|Log diesel price|0.290**|-0.00163|-0.0309|-0.0268|
||(0.101)|(0.0465)|(0.0452)|(0.0220)|
|Log gasoline price|-0.677***|-0.167***|-0.141***|-0.0349|
||(0.0955)|(0.0391)|(0.0378)|(0.0184)|
|Transportation policy|0.117***|-0.0812***|-0.0898***|-0.0105**|
||(0.0214)|(0.00769)|(0.00746)|(0.00393)|
|BRT|-0.128***|0.00421|0.0126|-0.00119|
||(0.0352)|(0.0148)|(0.0142)|(0.00692)|
|L.ln_co2pc||||0.820***<br>(0.0157)|
|Observations|1010|1010|1010|936|
|Year fixed effects|Yes|Yes|Yes|Yes|
|Note: Standard errors i|n parentheses||||
|* p<0.05|** p<0.01|*** p<0.001|||

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