Energy saving, GHG abatement and industrial growth in OECD countries: A green productivity approach

Journal Pre-proof Energy saving, GHG abatement and industrial growth in OECD countries: A green productivity approach Yun Wang, Xiaoling Liu, Xiaohua Sun, Baocai Wang PII:

S0360-5442(19)32528-9

DOI:

https://doi.org/10.1016/j.energy.2019.116833

Reference:

EGY 116833

To appear in:

Energy

Received Date: 27 February 2019 Revised Date:

24 June 2019

Accepted Date: 23 December 2019

Please cite this article as: Wang Y, Liu X, Sun X, Wang B, Energy saving, GHG abatement and industrial growth in OECD countries: A green productivity approach, Energy (2020), doi: https:// doi.org/10.1016/j.energy.2019.116833. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Yun Wang: Conceptualization, Methodology, Formal Analysis, WritingOriginal draft Xiaoling Liu: Data curation, Writing- Review&Editing, Visualization Xiaohua Sun: Investigation, Supervision, Project Administration Baocai Wang: Software, Validation.

Energy saving, GHG abatement and industrial growth in OECD countries: A green productivity approach Yun Wang1, Xiaoling Liu1, Xiaohua Sun*,1, Baocai Wang2 (1School of Economics and Management, Dalian University of Technology, China; 2 School of Software, Dalian University of Technology, China) *Corresponding author at: School of Economics and Management, Dalian University of Technology, No.2 Linggong Road, Dalian, Liaoning Province 116024, China. Tel:+86 411 84707221. E-mail address: [email protected]

Biography Yun Wang is a lecturer and Ph.D. in School of Economics and Management at Dalian University of Technology. Her research interests include industrial economics, evolutionary economics and innovation economics, etc. Email:[email protected] Xiaoling Liu is a Ph.D. candidate in School of Economics and Management at Dalian University of Technology. Her research interests include development economics and evolutionary economics. *Xiaohua Sun is a professor and Ph.D. advisor in School of Economics and Management at Dalian University of Technology. His research interests include industrial economics, evolutionary economics and innovation economics, etc. Email:[email protected] Baocai Wang is a Ph.D. candidate in School of Software at Dalian University of Technology. His research interests include artificial intelligence algorithm and modeling analysis. 1

Abstract Environmentally-friendly sustainable economic development has been a worldwide concern, whereas the existing literature has not studied the specific directions and potential evaluation of green growth. This study compares the development trends of green growth in OECD countries and estimates the potentials of energy saving, GHG abatement and industrial growth based on the green productivity measurement. Using green productivity approach provides a new idea of organizing three aspects of green growth in one research framework. The results show that OECD countries have different performances and potentials for green growth. Three pathways of green growth: technological breakthrough-orientation, energy saving and emission reduction-orientation, balanced growth-orientation are proposed for different types of OECD countries. The findings provide implications for OECD countries to promote green growth in the future. Key words: energy saving; greenhouse gas abatement; industrial growth; OECD; green productivity

2

1 Introduction The increasing threat of resource exhaustion and global climate change has been a worldwide concern. Both production and life of humankind require intensive use of fuels, most of which are non-renewable fossil fuels. With the development of economy and industrialization process, the increasing consumption of energy resources naturally leads to increased emissions of carbon dioxide (CO2), among other greenhouse gases (GHG), which are the primary cause of the climate warming and sea level rise. The irreversible reduction in the country’s energy resources or degradation of its environment will have far-reaching implications for the country’s ecosystem and quality of life. In the past two decades, several international organizations and national programs are working on environment protection for sustainable development. The 1997 Kyoto protocol implements the objective of the United Nations Framework Convention on Climate Change (UNFCCC) to reduce GHG emissions. After the Kyoto protocol expired in 2012, Paris Agreement, which will take effect in the year 2020, sets the long-term goal to keep the increase of global temperature to well below 2 °C above pre-industrial levels. Based on sustainable development, United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) first promote the concept of “Green growth” in 2005, introducing a new sustainable development model for fast-developing countries. Further, Organization for Economic Cooperation Development (OECD)’s “Green Growth Strategy” in 2011, and the United Nations’ 3

“Towards a Green Economy” in 2011 have made a positive influence on many countries and regions. According to OECD’s definition, green growth means fostering economic growth and development, while ensuring that natural assets continue to provide the resources and environmental services on which our well-being relies. Different from sustainable development, green growth stresses the quality of growth and seeks to encourage economic growth and development in a way that balances ecological environmental protection and long-term economic growth. In light of current environmental crises and resource depletion around the world, green growth is imperative for all the industrialized countries. It is recognized that the significant amount of economic growth in OECD has been fueled by industrial growth. Thus, industrial growth, energy saving and pollution abatement have become the three equally-important goals of the countries. However, as industrial sector relies heavily on energy resource input, it is believed that the reduction of energy consumption or pollution abatement affects the economic output of industries (Lee and Chang, 2008; Begum et al., 2015; Esso and Keho, 2016). It seems like to be a contradiction between the three goals for a country. The previous literature has discussed the relationship between economic development, energy consumption and CO2 emissions (Mirza and Kanwal, 2017; Nordin and Kun, 2018; Bekhet et al., 2017), but it lacks theoretical research on integrating three goals into one framework. Although the extensions of Solow Model have verified that both energy-augmenting technical progress (Stern and Kander, 2012) and technological 4

progress in abatement (Brock and Taylor, 2010) are efficient ways to keep the economic output growing, it is still not clear how to identify the growth potential and development direction of the three goals at the current technical level. OECD countries have been among the highest growth economies in the world over the last three decades, and rapid economic growth is associated with the problem of excessive energy consumption and GHG emissions. As one of the most important economic organizations in the world, OECD countries have made considerable efforts to foster and support environmentally friendly “green growth” strategies, and the development of emerging economies. Especially, OECD has taken the lead in arguing for a reduction in greenhouse gas emissions worldwide through international negotiations. As the pioneer of advocating green growth, OECD countries can be used as a globally representative sample for research. Besides, the energy consumption structure of industrial sector differs across OECD countries. In addition to fossil fuel energy (such as oil, coal and natural gas), renewable energy consumption has been increased. According to the data of World Bank, the average ratio of fossil fuel energy consumption in OECD members is 80.7% in 2010, in which the ratio is 94.4 in Australia, 84.2% in the United States, 79.6% in Germany, 49.8% in France and 11.5% in Iceland, etc. And energy efficiency of the national energy system is different as the gaps in technological level and development stage. So the potentials of energy-saving differ across OECD countries and remain to be estimated, to provide evidence for adjusting the energy consumption structure and energy efficiency. Therefore, it makes sense to study the status and potentials of energy saving, GHG 5

abatement and industrial growth of OECD countries, which is the aim of this study. Based on the methodology of green productivity measurement, three goals of industrial growth, energy saving and GHG abatement are integrated into one framework. Slack based measures-Directional Distance Function (SBM-DDF) is further extended to estimate the green productivity and green growth potentials of 27 OECD countries from 2004 to 2010. Our research not only fills the research gap in studying the potentials of green growth, but also provides the quantitative research framework to study the performance in green growth. In contrast to the previous research, the contribution of this study is threefold. First, the main topic is to investigate the potentials of energy saving, GHG abatement and industrial growth in OECD countries through quantitative analysis, rather than a qualitative study of the relationships between them. Second, green productivity approach is applied to integrate the three goals of energy saving, GHG abatement and industrial growth into one framework. The advantage of this framework is that it is possible to expand desirable outputs while contracting the undesirable outputs, which is in accordance with the national development goals. Third, in the context of methodology, this study contributes to the literature by extending the SBM-DDF model. The merit of this approach is that it allows the direction vectors to be endogenously determined and avoid artificial direction settings. The remainder of the paper proceeds as follows. Section 2 outlines the literature review. Section 3 states the methodology. Section 4 describes the data sources and statistics analysis. Section 5 presents and discusses the empirical results. Finally, 6

section 6 summarizes the main conclusions and policy implications.

2 Literature review There are three research groups in the literature related to the relationships between energy consumption, CO2 (or GHG) emissions and economic growth. The first group which concentrates on the causal relationship between energy consumption, environmental degradation and economic growth have intensively analyzed empirically over the past two decades. As different methodologies and samples have been employed, no unanimous conclusion has been reached. The presence of bi-directional causalities between energy consumption, economic growth and CO2 emissions are found in Pakistan (Mirza and Kanwal, 2017). Similar results are found in Gulf Cooperation Council countries by Bekhet et al. (2017). The conclusion indicates the long-run and causal relationships among carbon emission, gross domestic product and energy use. A large number of studies assess the empirical evidence by employing VAR Granger causality test and cointegration tests, such as Nordin and Kun (2018) reveal evidence of both a long-run and short-run relationship between energy consumption, GDP and CO2 in 13 oil importing countries and 11 oil exporting countries. However, Al-mulali et al. (2013) examine the long-run relationship between urbanization, energy consumption and carbon dioxide emission, but find no correlation between urbanization, energy consumption, and carbon dioxide emissions in most of the low-income countries. Pao and Tsai (2011) in the study of Brazil present that the long-run equilibrium emissions have an inelastic relationship 7

with both energy consumption and economic growth. The second category of the literature focuses on the causal links between energy consumption and economic growth. This nexus suggests that economic growth and energy consumption may be jointly determined, because energy consumption is a necessary element for national economic growth. A positive relationship between economic growth and energy consumption in both the short and long run is proved (Paul and Bhattacharya, 2004; Mahadevan and Asafu-Adjaye, 2007; Tang and Tan, 2014). There are also conflicting conclusions for different countries. Lee and Chang (2008) find that energy consumption and economic growth lack short-run causality, and there is long-run unidirectional causality running from energy consumption to economic growth in Asian countries. The third strand of the literature investigates the relationship between environmental pollutants and economic growth. This relationship has been examined that economic growth leads to a gradual degradation of the environment (Shafik and Bandyopadhyay, 1992; Omri et al., 2015). And most extant studies test the validity of the Environmental Kuznets Curve (EKC) hypothesis. The EKC hypothesis postulates that the relationship between economic development and the environment resembles an inverted U-curve. An increasing number of studies have verified the inverted U-shaped EKC hypothesis graphically and analytically (Jaunky, 2011; Heidari et al., 2015). However, some research does not support the ECK hypothesis. One reason is that individual countries have not yet reached a certain level of economic development (Jebli and Youssef, 2015). It is also found to be not held for greenhouse 8

gas emissions, because greenhouse gases are particular pollutants that created globally (Ansuateqi and Escapa, 2002). Since productivity growth is the engine driving the economic development and the competitiveness of a country, the research on productivity has always been of great interests to researchers and policymakers (Salter et al., 1969; Coelli et al., 2005; Fernald, 2015). However, traditional measures of total factor productivity do not account for the by-products, such as GHG emissions. Considering the impact of economic development on the environment, recent studies have estimated green productivity with the presence of undesirable outputs. In the beginning, the studies treat pollution as one of the inputs in the production function, and some research reformulates the pollution as a desirable output (Seiford and Zhu, 2002). Further, Chung et al. (1997) provide the basis to represent the joint production of desirable and undesirable outputs by extending the Shephard’s output distance function to the directional output distance function. A voluminous literature using the Directional Distance Function (DDF) to estimate the green productivity at macro-economic as well as at micro-economic level has emerged since 21st century (Hur et al., 2004; Cao, 2007; Lin et al., 2013; Chen and Golley, 2014). The limited research on OECD countries mainly investigates the economic development (Carree et al., 2002), fiscal policies (Kneller et al., 1999) and labor market (Ohanian et al., 2008), etc. With regard to OECD countries’ energy consumption, GHG emissions and industrial development, few studies focus on the comparison of green growth performance and further decompose it into specific 9

aspects. To research on green growth potentials, OECD countries are worth studying as a leader all around the world. Thus, this study fills the gap in exploring the potentials of green growth using data of OECD countries.

3 Methodology 3.1 DDF The Directional distance function (DDF) approach, introduced by Chung et al. (1997), can handle the green productivity by increasing desirable outputs and simultaneously decreasing undesirable outputs (Färe et al., 1989), which is consistent with the three goals for green growth of a country. Assume the industrial sector of each OECD country is involved in a sustainable production process, in which desirable outputs and undesirable outputs are jointly produced. The input vectors are capital ( k ), labor ( l ) and energy ( e ).The desirable output is industrial added-value ( y ) and undesirable outputs are GHG emissions ( b ). Then, the production technology T is defined as T = {(k , l , e , y , b ) : (k , l , e )can produce

( y , b )}

(1)

The production technologies ( T ) are assumed to follow all the standard axioms of production theory (Färe and Grosskopf, 2005), including the assumptions of the bounded set, bounded convexity, strong disposability of production inputs and desirable outputs, weak disposability and null-jointness of desirable outputs and undesirable outputs. The directional distance function represents the maximum contraction of input and expansion of output in the direction defined by the vector g , which maintains the 10

input and output combination within (on the boundary of) the production possibilities set (Chambers et al., 1996). The directional vector is g = ( g k , g l , g e , g y , g b ) , representing the direction in which the combination of inputs-outputs is projected onto the technology frontier. →

D ( k , l , e, y , b; g ) = max {β ∈ R : ( k − β k , l − β l , e − β e, y + β y , b − β b ) ∈ T }

(2)

However, traditional DDF given in Eq. (2) has some deficiencies. It assumes that the direction vector g is determined by ( k , l , e, y, b ) . The ad hoc choices of researchers may not be the optimal directions for each Decision Making Unit (DMU). In this study, the green growth directions for each country should be heterogeneous. Some countries may focus more on industrial growth to pursue development quantity, while the other countries may put more emphasis on energy saving or GHG abatement to solve environmental problems. Besides, Eq. (2) assumes that all distances under various dimensions are equal to β . Part of the input or output redundancies can not be minimized, and there is a possible presence of non-zero slack. The above problems may lead to bias results of green productivity estimation. 3.2 SBM-DDF In this study, a SBM-DDF approach is developed based on the works of Färe et al. (2013). Three aspects are worth emphasizing in this approach. First, the direction vector will be endogenously determined rather than subjectively chose, so each country under evaluation has a different direction to the production boundary. Second, distances under various dimensions are transformed to be relative distances by the ratio of the slack to the actual value. Third, exogenous weights α1 , α2 , α 3 , α4 , α5 are assigned to various distances to calculate the weighted summation, and 11

α1 + α2 + α3 + α4 + α5 = 1 (α i ∈ [0,1], i = 1, 2,..., 5) . Different weights combination indicates different constraints and objectives. → β gy  β gk β gl β ge β gb  D ( k , l , e, y , b; g ) = max  α 1 ⋅ + α2 ⋅ + α3 ⋅ + α4 ⋅ + α5 ⋅  k l e y b  

N

N

N

N

n=1

n =1

n =1

n =1

(3)

s.t. ∑ λn kn ≤ k − β gk , ∑ λnln ≤ l − β gl , ∑ λn en ≤ e − β ge , ∑ λn yn ≥ y + β g y N

N

n =1

n =1

∑ λnbn = b − β gb , ∑ λn = 1 , λn ≥ 0 , n = 1,L , N and β g k β gl β g e β g y β gb k

,

l

,

e

,

y

,

∈ [ 0,1]

b

Where λn is the intensity variable for the nth observation.

N

∑λ n =1

n

= 1 and λn ≥ 0

( n = 1, L , N) reflect the assumption of variable returns to scale.

However,

(g

k

Eq.

(3)

is

non-linear

programming

since

both

β and

, g l , g e , g y , g b ) are decision variables. Therefore, solving the programming problem

often leads to a local optimal solution rather than a global optimal solution. Let Sk = β gk , Sl = β gl , Se = β ge , S y = β g y , Sb = β gb ( Si implies slack of each dimension). Therefore, Eq. (3) can be rearranged into Eq.(4). → Sy  S S S S  D ( k , l , e, y , b; g ) = max  α 1 ⋅ k + α 2 ⋅ l + α 3 ⋅ e + α 4 ⋅ + α5 ⋅ b  k l e y b  

N

N

N

N

N

n=1

n=1

n =1

n =1

n=1

(4)

s.t. ∑λn kn ≤ k − Sk , ∑ λnln ≤ l − Sl , ∑ λnen ≤ e − Se , ∑ λn yn ≥ y + S y , ∑ λnbn = b − Sb , N

∑λ n =1

n

= 1 , λn ≥ 0 , n = 1,L , N and

12

Sk Sl Se S y Sb , , , , ∈ [ 0,1] k l e y b

By solving Eq. (4), the slack value in each direction is estimated and the value →

of D ( k , l , e, y , b; g ) is obtained, which is the weighted summation of slacks and also the degree of inefficiency. Then, Eq. (5) presents the green total factor productivity ( GTFP ). →

GTFP = 1 − D ( k , l , e, y , b; g )

(5)

Values of GTFP range from 0 to 1. Value equals to 1 suggests that a country under evaluation is on the technology frontier with the highest green growth efficiency. Otherwise, the country under evaluation is inefficient and has potentials in various directions.

3.3 Potentials estimation model Based on the measurement of green productivity by SBM-DDF, the potentials in each direction can be estimated through the following model: → Sy  S S  D ( k , l , e, y , b; g ) = max  α 1 e + α 2 ⋅ + α3 ⋅ b  e y b  

(6)

N

N

N

N

N

n=1

n=1

n =1

n =1

n=1

s.t. ∑ λn kn ≤ k , ∑ λnln ≤ l , ∑ λnen ≤ e − Se , ∑ λn yn ≥ y + S y , ∑ λnbn = b − Sb , N

∑λ n =1

n

= 1 , λn ≥ 0 , n = 1,L , N and 0 ≤ Se ≤ e,0 ≤ S y ≤ y,0 ≤ Sb ≤ b

Different from Eq.(5), Eq.(6) does not take the slacks of labor and capital stock into account, implying that the DMUs should try to increase economic output and decrease energy consumptions as well as GHG emissions under the conditions of a fixed labor force as well as fixed capital stock. It is essential for OECD countries to increase (or at least maintain) employment. Similarly, no country wants to reduce its capital stock in the industrial sector. Therefore, every country has no motivation to 13

reduce labor and capital input, which is very different when the DMUs are enterprises (Pang et al., 2015). By solving Eq. (6), the slack values of energy saving Se , GHG abatement Sb and added-value growth S y are estimated. As ∑ g = 1 , let Ω = Se + S y + Sb , Ω = β ( ge + g y + gb ) = β

Then,

the

optimal

β ∗ = Se + S y + Sb ; g e∗ =

values

β

of

(7)

g

and

can

be

estimated:

S Se S , g ∗y = y , g b∗ = b . Therefore, energy potential saving Ω Ω Ω

index (EPSI), GHG potential abatement index (GPAI), added-value potential growth index (APGI) are computed in Eq. (8). The values of potential indexes range from 0 to 1.

EPSI =

Se

e

, GPAI =

Sb

b

, APGI =

Sy

(8)

y

As illustrated in Fig.1, three goals of green growth can be integrated in one three-dimensional coordinate axis. If one country is in point A (not on the frontier), →

→

AB ( g e , g y , g b ) is the direction vector to production frontier. Then, AB can be →

decomposed: AB = g e + g y + gb . In the direction of energy saving ( g e ), energy consumption could be reduced by Se at most. In the direction of added-value growth ( g y ), industrial added-value could be increased by S y maximally. While in the direction of GHG abatement ( gb ), GHG emission could be reduced by Sb at most.

14

Added -value

Energy consumption Frontier

gy

B y

Sy

e

gb

Se

A ( e, y, b )

ge

Sb b

GHG emission

Fig. 1. Illustration of potential index estimation

4 Data 4.1Data sources and variables For the balanced panel data, collected annual data for 27 OECD countries over the period 2004-2010 from OECD.Stat is used as the sample, excluding Chile, Iceland, Ireland, Israel, Latvia, New Zealand, Switzerland and Turkey due to data availability. The variables used in this study include the following: (1) Capital input. The gross capital formation in each country is used as the proxy of capital stock. OECD.Stat offers the data of gross capital formation by activity. The gross capital formation in VB (Mining and quarrying), VC (Manufacturing), VD (Electricity, gas, steam and air conditioning supply), and VE (water supply, sewerage, waste management and remediation activities) are summed and transformed into constant prices of US dollars (Millions) in 2010. (2) Labor input. Considering data availability, the total number of employment is 15

used as a proxy. OECD.Stat offers the number of total employment by activity. The data from different industrial activities is summed as the labor input in the industrial sector of each country. (3) Energy input. The product of total national energy consumption and energy consumption in the industry (%) is calculated as the energy input. The energy is converted into thousand tons of oil equivalent. (4) Desirable output. The industrial added-value is used as the proxy for desirable output. OECD.Stat offers the data of gross value added by activity. After the summation, the data are all transformed into constant prices of US dollars (Millions) in 2010. (5) Undesirable output. Due to data availability, greenhouse gas emissions are used as the proxy for undesirable output. OECD.Stat offers the data of GHG emissions (thousand tons of CO2 equivalent) in industrial processes and product use. The summary statistics of the variables used in this study is shown in Table 1.

Table 1. Summary statistics of the variables Variable

Unit

Mean

Min

Max

Std.Dev

Capital input

US dollars, million

73954.2

284.8

589863

121642.9

Labor input

Persons, thousands

2876.6

37.1

18675

3998.1

Energy

thousand tons of oil equivalent

29312.8

524.8

289675

53473.9

Added-value

US dollars, million

285511.3

3235.2

2500000

492590.2

GHG emission

thousand tons of CO2 equivalent

38494.8

475.9

380484

66044.8

16

4.2 Trends of energy consumption, GHG emissions and industrial growth in OECD countries Fig. 2 shows the changing trends of energy consumption, GHG emissions and industrial added-value in the industrial sector of 27 OECD countries. It is evident that industrial added-value has a sharp decrease in 2008 and 2009, and then begins to rise in 2010. While energy consumption and GHG emissions are relatively stable and have a slight decrease with the decline of industrial added-value from 2008. In the aspect of energy consumption, total energy consumption in the industrial sector of OECD countries is about 800 million tons of oil equivalent from 2004 to 2010. The ratio of energy consumption in industrial sector has a downward trend from 23.4% in 2004 to around 21% in 2010. Specifically, energy consumption in industry sector of the United States accounts for 33.4% of total industrial energy consumption in 27 OECD countries, Japan 12%, and Germany 6.54% in 2010. The industry sector of three countries has consumed over half of energy consumption in the industry sector of OECD countries. In terms of energy consumption per unit of output, the value of most OECD countries is at about 100 tons/million US dollar and shows a downward trend, except for Luxembourg, Mexico, Norway and Portugal.

17

Fig. 2. The trends of energy consumption, GHG emission and added-value in industrial sectors of OECD countries

In the aspect of GHG emission, total GHG emissions in the industrial sector of OECD countries are over 10 billion tons of CO2 equivalent from 2004 to 2010. In accordance with energy consumption, the top three sources of GHG emission in industrial production and use of OECD countries are the United States (32.68%), Japan (7.42%) and Germany (5.79%). In terms of GHG emission per unit of output, the average value is 172 tons CO2 equivalent/million US dollar. Nevertheless, the GHG emissions per unit of output in Japan and Germany are almost the lowest, 62 and 79 tons CO2 equivalent/million US dollar, respectively. In the aspect of industrial added-value, the gross value added in the industry sector of OECD countries is between 0.7 and 0.8 billion US dollars from 2004 to 2010. As the financial crisis and economic downturn, most OECD countries experience a 18

negative growth rate of industrial added value in 2008 and 2009. In addition to the three largest economies of United States (29.7%), Japan (16.6%) and Germany (9.7%), France, Canada, Mexico and Australia all contribute more than 3% of industrial added-value in OECD countries.

5 Results and Discussion The empirical study estimates the green total-factor productivity and potentials of energy saving, GHG abatement and industrial growth using data of OECD countries. It is conducted from three aspects: the first step is to compare the green total-factor productivity across years and countries; the second is to estimate and analyze the potentials of energy saving, GHG abatement and industrial growth of each OECD country; the third part discusses the pathways to promote green growth for different types of OECD countries.

5.1 The analysis of green productivity When inputs, desirable output and undesirable output are regarded as equally

(

)

important, the weighted combination is set as 1 , 1 , 1 , 1 , 1 to get green 9 9 9 3 3 total-factor productivity. If labor and capital input are not taken into account, energy input, desirable output and undesirable output are regarded as equally important. In

(

this case, the weighted vector is set as 0, 0, 1 , 1 , 1 3 3 3

)

and energy-environment

performance productivity is estimated. The two typical weighted combinations are most frequently used in exsiting research of green productivity measurement. And the higher values of productivities reflect the better performance of the country in green 19

growth under evaluation. (1) Analysis from the time dimension 0.79 0.78 0.77 0.76 0.75 0.74 0.73 0.72

green total-factor productivity

0.71

energy-environment performance productivity

0.7 0.69 2004

2005

2006

2007

2008

2009

2010

Fig. 3. Trends of green productivity in time dimension

From Fig. 3, the average value of green total-factor productivity and energy-environment performance productivity show a similar rising tendency. Both the indexes are respectively low at about 0.73 before 2006, and increase gradually until 2008 and then has an obvious decline between 2008 and 2010. Besides, the gap between the two lines expands from 0.01 further to about 0.02. Since the 21st century, industrialized countries have further developed with the trend of economic globalization. After the financial crisis in 2008, many developed countries even had a de-industrialization tendency, and the arising of information services have critically influenced the industrial structure. In this context, green growth in the industrial sector has an opportunity of substantial development. 20

(2) Cross-country comparison According to the estimation results, some countries are always on the frontier, whereas some countries have relatively low productivities. To explore the differences between countries, the sample of 27 countries is split equally into three groups, according to the ranking of the average value of GTFP from 2004 to 2010. The groups are: high GTFP (ranking 1-9), medium GTFP (ranking 10-18) and low GTFP (ranking 19-27).

Table 2. Green productivity differences across countries High GTFP

Medium GTFP

Low GTFP

Country

AGTFP

Country

AGTFP

Country

AGTFP

Denmark

1

Canada

0.8510

Korea

0.6489

Estonia

1

Australia

0.8289

Mexico

0.6055

Luxembourg

1

Italy

0.8193

Austria

0.6032

Norway

1

France

0.8048

Hungary

0.5231

Slovenia

1

Netherlands

0.7749

Portugal

0.5047

Germany

0.9968

Sweden

0.7712

Belgium

0.4901

United States

0.9913

Greece

0.7289

Poland

0.4575

United Kingdom

0.9879

Spain

0.6858

Czech Republic

0.4261

Japan

0.9768

Finland

0.6581

Slovak Republic

0.2994

Mean

0.9948

Mean

0.7692

Mean

0.5065

Note: AGTFP represents the average value of GTFP from 2004 to 2010.

21

In the group with the highest rankings, Denmark, Estonia, Luxembourg, Norway, Slovenia, Germany, United States, United Kingdom and Japan are included. The mean value of these countries is over 0.99, indicating that all these countries are just on or adjacent to the frontier and have the highest efficiencies. The group with the medium GTFP includes Canada, Australia, Italy, France, Netherland, Sweden, Greece, Spain and Finland. The mean value of this group is 0.7692. However, the mean value of the low GTFP group is the lowest at 0.5065. The group includes Korea, Mexico, Austria, Hungary, Portugal, Belgium, Poland, Czech Republic, and Slovak Republic.

5.2 Potential analysis After measuring and analyzing the green productivities of OECD countries, based on Eq. (6), the potentials in directions of energy saving, GHG abatement and industrial growth of each country from 2004 to 2010 are further estimated. Take the results of 2010 for example, the potentials in three directions are listed in Table 3 according to the GTFP groups. In high GTFP group, most of the countries are on the frontier. United States, United Kingdom and Japan, which are adjacent to the frontier, have few potentials (less than 4%) in all of the three directions. In medium GTFP group, there is almost no potential in the direction of industrial growth, whereas many potentials in both directions of energy-saving and GHG abatement. It indicates that the countries with a medium level of green productivities, such as Canada, Australia, and some European countries, mainly have improvement spaces in energy consumption and GHG emissions, rather than industrial growth. 22

Table 3. Potentials in three directions of OECD countries in 2010 Group

High GTFP

Medium GTFP

Low GTFP

Country Denmark Estonia Luxembourg Norway Slovenia Germany United States United Kingdom Japan Canada Australia Italy France Netherlands Sweden Greece Spain Finland Korea Mexico Austria Hungary Portugal Belgium Poland Czech Republic Slovak Republic

0.38%

Potential in industrial growth 0.16%

Potential in GHG abatement 1.72%

-

-

-

3.54% 52.61% 30.89% 17.85% 21.61% 61.7% 44.32% 38.59% 76.53% 32.63% 4.79% 53.46% 24.07% 55.54% 60.01% 61.07%

0.43% 0% 0% 0% 0% 0% 0% 0% 0.67% 36.15% 81.96% 1.61% 22% 29.87% 15.74% 9.04%

3.75% 55.13% 50.51% 34.92% 57.46% 22.2% 83.24% 62.92% 67.24% 48.56% 40.11% 73.7% 76.85% 74.10% 76.93% 71.89%

53.71%

21.26%

77.35%

39.47%

62.16%

83.06%

Potential in energy saving

Notes: “-” means the country under evaluation is at the frontier with the highest green productivity.

23

In low GTFP group, the potentials in the direction of energy-saving and GHG abatement are even higher. In addition, the potentials in the direction of industrial growth are obviously higher than those of the other two groups. It presents that the countries with a low level of green productivities, such as Korea, Mexico, Belgium and eastern European countries, could have been more efficient in energy consumption, GHG emissions and industrial growth.

5.3 Discussion: pathways of green growth Based on the above measurement results, OECD countries have different levels of green productivities and potentials in energy saving, GHG abatement and industrial growth. Besides, 27 OECD countries have been in different development stages of industrialization and urbanization since the 21st century. The experience of developed countries shows that industrialization level affects energy consumption and GHG emissions differently across developmental stages (Li and Lin, 2015). Therefore, the pathways of green growth and future growth prospects for different types of OECD countries are further discussed. First, technological breakthrough-orientation. Nine developed countries locating on or close to the frontier (Denmark, Estonia, Luxembourg, Norway, Slovenia, Germany, United States, United Kingdom and Japan) have the highest green productivity and income per capita. As industrialization process basically finished in these countries, the share of industry in GDP has begun to decrease with the rapid development of the service sector, and even entered into the deindustrialization stage. Take the potential estimation results of 2010 for example, except the Unites States and 24

Japan has few potentials in three directions, the potentials of other seven countries which are locating on the frontier could not be estimated. But it does not mean that these countries have no potentials for green growth. With regard to this group of countries, the key is to keep advanced in technological levels and innovation. As the production frontier has dynamic features, the frontier will expand or contract under the changes of the economic environment and technical condition. Some countries even have begun the stage of reindustrialization and revived manufacturing industries. Under a new round of technological progress and the industrial revolution, relying on technological breakthrough, e.g., disruptive technologies creation or informatization, will promote the outward frontier shift and bring in new growth spaces for these countries.

Green Productivity Growth

Balanced growthorientation Korea Mexico Austria Hungary Portugal Belgium Poland Czech Republic Slovak Republic

Energy saving and emission reductionorientation Canada Australia Italy France Netherlands Sweden Greece Spain Finland

Technological breakthroughorientation

Denmark Estonia Luxembourg Norway Slovenia Germany United States United Kingdom Japan Disruptive technology Informatization Clean energy

Green innovation Industrial growth Environment protection

Industrialization stage

Fig. 4. The pathways for different types of OECD countries

25

Second, energy saving and emission reduction-orientation. Some developed countries (Canada, Australia, Italy, France, Netherlands, Sweden, Greece, Spain and Finland) with a high industrialization level have a medium level of green productivities, and the growth potentials mainly concentrate on the directions of energy saving and GHG abatement. It reflects that the problems of energy overconsumption and environment pollution are to be solved in the industrialization process. To improve the level of green productivities, the pathway for this type of countries is to advocate the use of clean technology in production and green innovation. Clean energy, which includes renewable energy (e.g., solar, geothermal, biomass, ocean, wind, hydro) and nuclear energy sources, can solve both the growing energy demands and the carbon emission problem. Increasing the ratio of clean energy in the industrial sector can significantly reduce the related GHG emissions and their negative impact on the environment. Besides, green innovation implies that innovations in products, processes or business models lead to higher levels of environmental sustainability (Triguero et al., 2013). According to the Porter hypothesis, well-designed and stringent environmental regulation is believed to stimulate green innovations, which in turn increase productivity. Thus, both clean energy and green innovation are efficient ways for countries to increase green productivity and reduce pollution emissions. Third, balanced growth-orientation. Some emerging countries (Korea, Mexico, Austria, Hungary, Portugal, Belgium, Poland, Czech Republic and Slovak Republic) have relatively low green productivities and distinct potentials in all the directions of 26

energy saving, GHG abatement and industrial growth. The economy of these countries is growing fast and the market system has been gradually improved. The countries have been in the later stages of industrialization, and the share of industry in GDP is higher than the other OECD countries. The industrial sector is an important part of national economic growth, and rapid growth requires large consumption of energy with a high level of emissions. Concerning this type of countries, to catch up with the developed countries, the heavy tasks of industrialization is still increasing industrial added-value of manufactured products, through improving industrial production capacity and technology level. Meanwhile, the balance between industrial growth and environmental protection should be considered, to avoid rapid growth but with heavy pollution. Thus, a balanced growth mode between industrial growth and environmental protection is more suited to emerging economies.

6 Conclusion and implications Irreversible resource exhaustion and global climate warming led to an increasingly strict requirement for energy saving and GHG abatement. The concept of “Green growth” is promoted to seek for the quality of growth, which balances ecological environmental protection and long-term economic growth. Therefore, industrial growth, energy saving and GHG abatement are three equally-important goals for a country. This study integrates the three goals into one framework and extends the methodology of SBM-DDF to measure green productivity and the green growth potentials. Using panel data of OECD countries from 2004 to 2010, the estimation 27

results of green productivities and the potentials in three directions of each country are compared and analyzed. Further, the pathways of green growth for different types of OECD countries are discussed. This study finds that OECD countries differ in the levels of green productivities and green growth potentials in energy saving, GHG abatement and industrial growth. The countries with high green productivity are almost on or adjacent to the frontier and have few potentials in green growth. The only way for these developed countries to achieve green growth is the technological breakthrough to push frontier outward shift. The countries with medium-level green productivity and high industrialization level have distinct growth potentials in both directions of energy saving and GHG abatement. The main pathway for these countries is to use clean energy and develop green innovation in the industrial sector. The other emerging OECD countries have been in the late stage of industrialization and have relatively low green productivity. As there are significant potentials in all the three directions, the pathway for these countries is to balance the industrial growth and environment protection. Therefore, three pathways of technological breakthrough-orientation, energy saving and emission reduction-orientation and balanced growth-orientation are provided for different types of OECD countries. And the countries could adopt the multi-pronged strategy of green growth. The implication for OECD countries is that achieving green growth requires potential actions for sustainable development. Using clean energy and increasing renewable energy consumption is one of the effective ways, which can not only 28

increase energy efficiency but also reduce the GHG emissions to combat global warming. From the perspective of undesirable outputs in the industrial sector, environmental regulation on industrial pollutions should be well-designed for industrial enterprises. Moreover, R&D activities of disruptive technologies are continued to encourage in developed countries for a new round of industrial revolution. The implication for developing countries is drawing lessons from the experience of developed countries. The continued rapid economic growth of developing countries requires more attention to energy saving and GHG abatement, to avoid serious environmental problems during the development process. Last, both the developed countries and developing countries should be committed to promoting global cooperation in green growth.

Acknowledgements This study was supported by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (Project No.18YJC790171).

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34

data used country

Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan

id

year

labor input energy input capital input industrial added-value GHG emissions 1298.48 22405.6 84243.8 216015 32795.9 682.77 6835.37 16282 72599.9 14863.5 649 9996.98 19479.5 72737.6 27663.9 2403.73 57622.4 81286 276291.622 58187.2

1 2 3 4

2004 2004 2004 2004

5

2004

1444.96

8253.97

14635.4

41728.5

15745.3

6 7 8 9 10 11 12 13 14 15

2004 2004 2004 2004 2004 2004 2004 2004 2004 2004

384 156.9 439.8 3521 7950 527.587 1021.19 4868.8 11760 4313.6

2984.9 645.472 12257.7 32098.3 53396 4042.01 2934 37570 104331 40033.6

12309.3 1074.34 13101.3 89994.7 152670 4615.08 7428.05 107339 383201 89964.8

57747.2 3364.8 49061.8 334464 734900 36536.8 27513.3 379732 1.30E+06 229196

3332.68 741.411 6406 53145.8 78555.2 14673.3 8703.21 45064.2 85579.4 57619

16

2004

37.963

845.934

1198.51

5101.48

754.043

17

2004

5975.65

29710.4

72592.7

260588

51281.8

18

2004

928

13052.6

21492.1

123321

17142.7

19 20 21

2004 2004 2004

302 3205.3 977.502

6158.13 14745.2 5225.37

23464.7 18812.6 9916.87

149040 74151.3 36285.4

10955.1 25456.7 8112.36

22

2004

556.026

3317.96

7165.77

15436.3

10701.3

23 24 25

2004 2004 2004

258.25 3080.2 735

1452.19 28076.8 11961.9

339.803 45822.4 25149.7

9141.13 225955 91886.1

1337.27 40363.6 7940.03

26

2004

3352.42

31534.8

49955

344996

40502.4

27

2004

18595

287626

483088

2.20E+06

353038

1 2 3 4

2005 2005 2005 2005

1325.11 677.4 643.3 2402.22

22294.4 7376.47 9275.38 53907.2

105524 217515 16462.8 74225.6 19625.9 73494.8 84314.2 297526.479

32060.6 15612.5 26395 54397.4

5

2005

1479.53

8132.57

14781.5

45698.6

14591.8

6 7 8 9 10 11 12 13 14

2005 2005 2005 2005 2005 2005 2005 2005 2005

378 157.1 441.2 3454 7818 540.427 991.63 4832.9 11531

2960.12 676 11315.9 30010.3 53923.8 4148.97 2868.07 36568.2 105137

11645.9 1317.37 15381.6 91090.8 148119 4180.73 7498.1 105144 383872

57090.7 3620.25 50646.1 336171 743128 37687.6 28307.5 382169 1.30E+06

2809.05 726.419 6497.24 53058.8 75488.1 15425.6 9338.31 45660 86721.2

Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States

15

2005

4273.1

39062.6

97392.1

242562

54321

16

2005

37.991

819.354

1242.57

5087.27

725.487

17

2005

5981.34

30593.3

73614.1

265485

46900

18

2005

911

12814.7

22191.8

123788

17243.4

19 20 21

2005 2005 2005

308 3313.8 937.601

6104.62 13763.1 5211.19

24224.5 20135.1 10117.1

148091 76819.5 35575.4

10617.8 25424.4 8138.95

22

2005

556.58

3433.45

8445.57

15799.2

10257.6

23 24 25

2005 2005 2005

253.51 3091.2 727

1550.55 29033.4 11621.4

333.566 44659.2 27377.5

9499.57 230924 94586.1

1416.76 42397.7 7884.98

26

2005

3217.76

30949.4

50670.3

340833

39715.4

27

2005

18460

277570

514689

2.30E+06

353419

1 2 3 4

2006 2006 2006 2006

1351.9 677.09 639 2351.47

22219.9 7372.41 9913.47 51523.2

91128.9 19886.8 21474.5 88490.7

228598 78151.4 74278.2

302316.3

32412.7 16251.7 25674.4 54811.5

5

2006

1486.22

8134.95

15478.3

52732.8

15663.7

6 7 8 9 10 11 12 13 14 15

2006 2006 2006 2006 2006 2006 2006 2006 2006 2006

378 150.8 445.3 3395 7734 542.57 992.324 4883 11812 4210

2997.71 649.04 12425.1 29043.4 54721.1 4223.93 2881.39 35318.2 110213 40670.7

12773.1 1408.42 15039.5 93861.8 156160 4886.37 7834.67 111258 386545 100892

58317.3 3955.46 55467.1 341680 784391 37403 30012 397074 1.40E+06 260048

2868.58 764.251 6535.87 52168 75739.8 12739.5 8895.29 41568.9 89543 52888

16

2006

37.949

883.658

1286.18

4525.81

779.763

17

2006

6051.93

32836.9

76609.4

273682

57254.2

18

2006

905

12302.8

24073

125218

16882

19 20 21

2006 2006 2006

327 3466.2 924.034

5950.48 13747.6 5329.2

25309.6 22844 10254.4

142471 82665.6 36147.7

9745.16 27762.1 7934.81

22

2006

566.112

3380.01

10535.9

18756.6

11137

23 24 25

2006 2006 2006

249.77 3063.3 723

1603.25 24037.1 11669.9

328.645 50185.5 29015.3

10157.7 237801 98400.3

1468.46 43928.4 7869.93

26

2006

3172

30327.9

52035.7

341625

38905.2

27

2006

18675

289675

546399

2.40E+06

362312

Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea

1 2 3 4

2007 2007 2007 2007

1423.31 693.53 635.9 2311.67

22779.4 7473.48 9710.27 51578.9

99764.3 21567.7 23691.4 91668

235650 82521.4 78261.4 350350

34393.5 16940.7 23913.8 53497.6

5

2007

1524.06

7889.62

18786.3

55307

16482.3

6 7 8 9 10 11 12 13 14 15

2007 2007 2007 2007 2007 2007 2007 2007 2007 2007

388 145.3 450 3362 7839 540.639 979.866 4905.9 12021 4178

2914.37 731.628 12088.5 28310.7 56041.3 4617.19 2826.67 34228.5 106519 41606.7

13156.4 1248.55 17514.5 101203 167020 4806.49 7600.36 115056 387295 109150

56860.5 4196.7 60669.7 348159 820808 38418 32284.4 406152 1.40E+06 280964

2898.08 957.725 7105.46 52579 76954.3 13173.8 8652.82 41761.8 88651.8 50802

16

2007

37.749

847.213

1392.3

5074.64

775.92

17

2007

5969

32358.4

80057.3

273667

55949.1

18

2007

905

12282.3

24962

129920

16399.1

19 20 21

2007 2007 2007

341 3634.2 903.605

5861.17 14466.1 5440.73

26872 26859.6 10446.1

138891 90975.2 36993.5

9850.01 30322.4 8788.26

22

2007

573.281

3381.5

10819

20996

10978.2

23 24 25

2007 2007 2007

251.9 3017.8 740

1514.67 26088.5 11801.2

331.447 50532.9 32154.4

10910.2 242082 103379

1475.21 44213.7 7811.92

26

2007

3098.39

29763.1

52815.3

342491

41263.8

27

2007

18655

286523

567159

2.50E+06

380484

1 2 3 4

2008 2008 2008 2008

1354.09 698.9 636.6 2216.43

23172.3 7645.49 9538.12 48124

96112.4 20572.6 23470.7 96664.2

235750 82514.4 77291.5 339315

34578.7 17273.8 24282.2 52723.5

5

2008

1535.54

7473.85

18671.7

59795.3

16438.6

6 7 8 9 10 11 12 13 14 15

2008 2008 2008 2008 2008 2008 2008 2008 2008 2008

391 148.5 456.6 3317 8022 557.82 967.497 4855.8 11969 4189

2800.61 713.484 11438.3 27983.1 55635.2 4220.19 2877.19 32447.3 97111.8 42521.5

14425.1 1312.37 16618.4 101517 173811 5830.58 8264.28 114825 392417 108893

55624.6 3939.58 59043.4 335293 810009 34998 30760.8 396518 1.40E+06 291217

2596.98 964.344 7459.57 50398.2 73148.9 12987.4 7489.25 39051.8 84176.8 50046

Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States Australia

16

2008

38.238

826.325

1812.61

4030.86

721.507

17

2008

5853.32

32939.4

83946.1

267279

58167.2

18

2008

909

12146.7

26525.9

129589

11883.5

19 20 21

2008 2008 2008

351 3784.8 879.774

5966.28 13187.6 5229.83

28711.6 29280.1 10692.2

137130 96334.4 36282.2

9708.7 28896.5 8623.19

22

2008

591.665

3403.2

10159.2

21489.2

10850

23 24 25

2008 2008 2008

251.02 2992.4 743

1414.17 24634.7 11317.9

330.289 54110.5 31975.3

11020.9 240205 100005

1332.35 42900.9 7575.34

26

2008

3031.87

28455.2

54757.7

333666

39396.9

27

2008

18385

280411

589863

2.40E+06

357759

1 2 3 4

2009 2009 2009 2009

1366.99 675.85 611.5 2059.3

22061.5 7251.84 8798.61 43983.2

82100.4 17671.2 21604.3 99789.3

245302 72570 71391.4 300743

32318.2 13948.4 18560.6 45815.3

5

2009

1422.36

6950.34

15508

52949.1

13964.1

6 7 8 9 10 11 12 13 14 15

2009 2009 2009 2009 2009 2009 2009 2009 2009 2009

346 126.6 415 3189 7844 538.522 915.5 4633.3 11438 4068.4

2409.93 524.78 9406.48 24497.1 49551.4 3454.82 2257.3 26820.5 88302.1 39952.7

14293.1 1013.03 10817.1 88423.7 152739 5854.04 8565.66 96389.1 381022 103710

49553.9 3235.24 47292.1 314721 683917 34677.2 26463.2 334107 1.20E+06 290738

2113.16 475.912 5738.31 45502.7 65495.7 11185.1 6455.76 33730.7 76848.3 47042

16

2009

37.332

718.093

1259.91

3364.55

651.3

17

2009

5463.65

28704

95278.5

246342

55308.2

18

2009

884

11133.5

24925.3

117961

11668.3

19 20 21

2009 2009 2009

339 3607.2 811

4989.45 12075.8 5029.41

30080.5 27683.8 10445.9

132645 96124.2 33319.7

7385.29 22794.1 6943.92

22

2009

530.921

2941.4

5832.49

17758.5

9292.97

23 24 25

2009 2009 2009

229.28 2640.4 677

1162.76 20228.4 10499.1

301.684 57015.8 27830.8

9452.78 216212 83220.1

999.198 38038.5 5632.8

26

2009

2852.1

23767.6

56145.8

304105

32789.7

27

2009

16576

248076

579061

2.30E+06

310295

1

2010

1394.42

22626.1

102980

247293

35363.4

Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States

2 3 4

2010 2010 2010

667.02 593.9 2072.19

7695.84 9908.29 44573.3

18514.6 20784.3 100727

76886.4 75722.6 319001

15926.1 21423.9 48474.7

5

2010

1378.6

6866.19

16111.4

56114.7

14965.3

6 7 8 9 10 11 12 13 14 15

2010 2010 2010 2010 2010 2010 2010 2010 2010 2010

316 121.8 396.4 3066 7705 494.016 891.285 4470.2 11338 4244.8

2504.8 563.697 10643 26121.3 52515.7 3399.96 2413.57 28124.2 96305 44906.4

12098.8 1063.03 12032.9 95025 153251 5428.89 8276.75 96619.7 380036 125554

51218.5 3734.87 50651.3 320763 790051 29431 28502.8 356026 1.30E+06 328359

2056.51 537.578 6260.15 47008 62534.3 11662 6610.27 34556 80158.5 53956

16

2010

37.071

807.252

1311.24

3500.9

675.521

17

2010

5469.99

31701.8

94856.5

261174

61226.9

18

2010

863

11765.3

24193.4

125196

12145.3

19 20 21

2010 2010 2010

331 3403.8 790.545

5608.67 12815.2 5225.23

30592.5 26896.7 10441.2

128778 103889 34992.3

8203.14 24815.7 7367.93

22

2010

511.758

3269.7

7298.09

21272.7

9609.94

23 24 25

2010 2010 2010

216.48 2559.2 666

1210.94 20532.7 11519.9

284.842 52894.7 28384.2

10067.1 223655 98889

1000.67 40817.1 7498.95

26

2010

2811.08

25173.3

56067.7

313229

35547

27

2010

16543

268216

584090

2.40E+06

353196

Results country

Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan

id

year 1 2 3 4

2004 2004 2004 2004

GTFP 0.96937471 0.5860833 0.42432687 0.63511759

EPSI 47.02% 35.78% 46.51% 61.93%

APGI 0.00% 40.92% 71.76% 20.90%

GPAI 53.36% 48.34% 66.39% 60.56%

5

2004 0.29893306

53.98%

100.00%

63.00%

6 7 8 9 10 11 12 13 14 15

2004 2004 2004 2004 2004 2004 2004 2004 2004 2004

1 1 0.6219901 0.98674564 0.9774025 0.70388171 0.4508089 0.98777614 0.97926497 0.52456479

0.00% 0.00% 73.81% 0.81% 0.99% 21.33% 24.68% 31.23% 2.45% 39.29%

0.00% 0.00% 30.91% 0.15% 0.30% 0.00% 62.48% 0.00% 0.26% 57.86%

0.00% 0.00% 39.53% 0.79% 1.17% 73.35% 58.18% 42.50% 2.07% 57.05%

16

2004

1

0.00%

0.00%

0.00%

17

2004 0.62570892

47.25%

0.00%

64.53%

18

2004

0.6865013

57.12%

7.76%

43.96%

19 20 21

2004 1 2004 0.40185793 2004 0.48390604

0.00% 64.66% 45.98%

0.00% 63.04% 67.82%

0.00% 64.57% 41.14%

22

2004 0.23798982

44.88%

100.00%

80.82%

23 24 25

2004 1 2004 0.67428448 2004 0.75521506

0.00% 55.00% 65.13%

0.00% 0.00% 0.00%

0.00% 58.21% 22.13%

26

2004 0.97250365

1.04%

0.60%

3.23%

27

2004 0.99074464

0.63%

0.18%

1.64%

1 2 3 4

2005 2005 2005 2005

0.97190727 0.58561287 0.44931281 0.67295447

45.85% 43.55% 45.59% 57.98%

0.00% 23.86% 62.56% 14.46%

52.58% 62.85% 67.27% 57.11%

5

2005 0.34053275

49.27%

100.00%

53.57%

2005 2005 2005 2005 2005 2005 2005 2005 2005

0.00% 0.00% 65.59% 25.78% 0.00% 0.00% 21.27% 0.85% 2.47%

0.00% 0.00% 66.09% 0.00% 0.00% 0.00% 55.48% 0.13% 0.26%

0.00% 0.00% 21.07% 56.57% 0.00% 0.00% 63.40% 0.73% 2.26%

6 7 8 9 10 11 12 13 14

1 1 0.60782673 0.73060375 1 1 0.46971896 0.76578516 0.97631752

Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States

15

2005 0.55443642

32.80%

57.67%

51.95%

16

2005

1

0.00%

0.00%

0.00%

17

2005 0.64160216

47.06%

0.00%

60.82%

18

2005 0.68493101

56.33%

7.75%

45.74%

19 20 21

2005 1 2005 0.40968499 2005 0.47484378

0.00% 61.31% 45.11%

0.00% 61.90% 69.52%

0.00% 64.43% 44.15%

22

2005 0.23137177

45.50%

100.00%

82.29%

23 24 25

2005 1 2005 0.6835428 2005 0.75045358

0.00% 55.45% 63.38%

0.00% 0.00% 0.00%

0.00% 56.76% 23.57%

26

2005 0.97075022

0.00%

0.00%

0.00%

27

2005

0.9891449

0.75%

0.20%

2.00%

1 2 3 4

2006 2006 2006 2006

0.70777177 0.57668738 0.45164912 0.98181473

41.08% 36.61% 46.71% 53.06%

0.00% 35.72% 64.46% 19.88%

53.07% 58.34% 66.99% 56.76%

5

2006 0.42449744

47.88%

71.43%

58.58%

6 7 8 9 10 11 12 13 14 15

2006 2006 2006 2006 2006 2006 2006 2006 2006 2006

1 1 0.66212381 0.73027207 1 0.72656218 0.5151064 0.78482922 0.97696416 0.57244721

0.00% 0.00% 71.58% 22.64% 0.00% 18.09% 28.01% 23.38% 2.80% 31.73%

0.00% 0.00% 32.57% 0.00% 0.00% 0.00% 37.27% 0.00% 0.34% 55.97%

0.00% 0.00% 37.09% 57.09% 0.00% 68.03% 64.26% 37.69% 2.87% 50.00%

16

2006

1

0.00%

0.00%

0.00%

17

2006 0.61486876

36.79%

17.31%

63.19%

18

2006 0.69028725

54.00%

7.15%

46.29%

19 20 21

2006 1 2006 0.41801201 2006 0.50231992

0.00% 59.84% 51.00%

0.00% 56.55% 52.76%

0.00% 67.84% 48.48%

22

2006 0.25154946

37.90%

100.00%

81.47%

23 24 25

2006 1 2006 0.68241617 2006 0.76163208

0.00% 43.23% 62.26%

0.00% 0.00% 0.00%

0.00% 61.35% 21.93%

26

2006 0.97181764

1.13%

0.52%

3.30%

27

2006 0.99130072

0.36%

0.17%

1.74%

Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea

1 2 3 4

2007 2007 2007 2007

0.71095972 0.60205798 0.49174006 0.98179692

40.37% 36.82% 45.00% 0.00%

0.00% 29.87% 57.87% 0.13%

53.78% 57.73% 63.01% 0.99%

5

2007 0.41005901

42.49%

80.63%

55.72%

6 7 8 9 10 11 12 13 14 15

2007 2007 2007 2007 2007 2007 2007 2007 2007 2007

1 1 0.68408797 0.73772844 1 0.74800167 0.58608208 0.79702264 0.97374884 0.61110826

0.00% 0.00% 68.15% 20.30% 0.00% 19.17% 32.93% 20.55% 0.00% 30.69%

0.00% 0.00% 36.68% 0.00% 0.00% 0.00% 0.01% 0.00% 0.00% 51.90%

0.00% 0.00% 28.13% 56.40% 0.00% 64.77% 76.97% 36.42% 0.00% 45.20%

16

2007

1

0.00%

0.00%

0.00%

17

2007 0.61946124

35.98%

18.75%

61.61%

18

2007 0.72665946

54.34%

0.00%

43.18%

19 20 21

2007 1 2007 0.43276502 2007 0.51691277

0.00% 59.50% 41.72%

0.00% 52.60% 61.34%

0.00% 67.53% 46.90%

22

2007

0.2828812

32.04%

100.00%

78.59%

23 24 25

2007 1 2007 0.69748135 2007 0.79498277

0.00% 47.70% 61.14%

0.00% 0.00% 0.00%

0.00% 57.24% 12.44%

26

2007

1

0.00%

0.00%

0.00%

27

2007 0.98991684

0.66%

0.18%

2.00%

1 2 3 4

2008 0.71499634 2008 0.6262405 2008 0.51592086 2008 0.98148918

43.54% 45.55% 46.26% 0.43%

0.00% 9.92% 47.29% 0.50%

55.11% 67.25% 66.39% 3.50%

5

2008 0.48640524

41.72%

54.73%

58.97%

2008 2008 2008 2008 2008 2008 2008 2008 2008 2008

0.00% 0.00% 71.27% 27.57% 0.00% 45.59% 36.74% 23.92% 0.00% 38.71%

0.00% 0.00% 15.40% 0.00% 0.00% 1.32% 1.89% 0.00% 0.00% 42.69%

0.00% 0.00% 50.55% 57.55% 0.00% 77.24% 75.57% 35.97% 0.00% 47.80%

6 7 8 9 10 11 12 13 14 15

1 1 0.68141731 0.72202481 1 0.63021629 0.57564589 0.79400323 0.97275144 0.98120249

Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States Australia

16

2008

1

0.00%

0.00%

0.00%

17

2008 0.59499085

39.86%

23.06%

63.87%

18

2008 0.82159556

49.72%

0.00%

18.76%

19 20 21

2008 1 2008 0.47209747 2008 0.52491217

0.00% 53.68% 53.47%

0.00% 44.40% 20.75%

0.00% 66.00% 73.77%

22

2008 0.30932414

28.82%

100.00%

78.33%

23 24 25

2008 1 2008 0.69734583 2008 0.79479036

0.00% 45.19% 48.66%

0.00% 0.00% 0.00%

0.00% 58.33% 29.00%

26

2008

1

0.00%

0.00%

0.00%

27

2008 0.99377933

0.30%

0.12%

1.18%

1 2 3 4

2009 2009 2009 2009

0.97131495 0.61403272 0.52565884 0.72374894

29.90% 57.48% 55.67% 58.09%

0.00% 6.65% 36.68% 0.00%

43.06% 68.20% 70.00% 58.45%

5

2009 0.49605522

55.16%

36.66%

69.49%

6 7 8 9 10 11 12 13 14 15

2009 2009 2009 2009 2009 2009 2009 2009 2009 2009

1 1 0.68357368 0.98390194 1 0.6777587 0.51220968 0.79109779 0.98376128 0.64883534

0.00% 0.00% 75.14% 1.13% 0.00% 29.87% 25.36% 22.77% 3.23% 43.76%

0.00% 0.00% 0.00% 0.14% 0.00% 0.00% 39.29% 0.00% 0.20% 22.65%

0.00% 0.00% 53.76% 0.99% 0.00% 74.14% 64.82% 39.24% 2.09% 53.32%

16

2009

1

0.00%

0.00%

0.00%

17

2009 0.58031422

28.70%

34.34%

63.31%

18

2009 0.81412737

46.67%

0.00%

28.45%

19 20 21

2009 1 2009 0.50591102 2009 0.50679675

0.00% 61.23% 50.96%

0.00% 27.68% 54.32%

0.00% 69.85% 54.28%

22

2009 0.37227607

36.31%

82.07%

76.48%

23 24 25

2009 1 2009 0.67345418 2009 0.76028085

0.00% 43.02% 67.09%

0.00% 0.00% 0.00%

0.00% 65.75% 24.56%

26

2009

1

0.00%

0.00%

0.00%

27

2009 0.99313265

0.33%

0.14%

1.35%

1

2010 0.75603937

30.89%

0.00%

50.51%

Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Italy Japan Korea Luxembour g Mexico Netherland s Norway Poland Portugal Slovak Republic Slovenia Spain Sweden United Kingdom United States

2 3 4

2010 0.63196268 2010 0.57238375 2010 0.97989205

53.46% 60.01% 52.61%

1.61% 15.74% 0.00%

73.70% 76.93% 55.13%

5

2010 0.52623988

53.71%

21.26%

77.35%

6 7 8 9 10 11 12 13 14 15

2010 2010 2010 2010 2010 2010 2010 2010 2010 2010

1 1 0.66582234 0.7423636 1 0.61612896 0.55238423 0.81466167 0.97493507 0.64940937

0.00% 0.00% 76.53% 21.61% 0.00% 44.32% 24.07% 17.85% 3.54% 32.63%

0.00% 0.00% 0.67% 0.00% 0.00% 0.00% 22.00% 0.00% 0.43% 36.15%

0.00% 0.00% 67.24% 57.46% 0.00% 83.24% 76.85% 34.92% 3.75% 48.56%

16

2010

1

0.00%

0.00%

0.00%

17

2010 0.56186591

4.79%

81.96%

40.11%

18

2010

1

0.00%

0.00%

0.00%

19 20 21

2010 1 2010 0.56196354 2010 0.52306726

0.00% 61.07% 55.54%

0.00% 9.04% 29.87%

0.00% 71.89% 74.10%

22

2010 0.41059689

39.47%

62.16%

83.06%

23 24 25

2010 1 2010 0.69228996 2010 0.7813514

0.00% 38.59% 61.70%

0.00% 0.00% 0.00%

0.00% 62.92% 22.20%

26

2010

1

0.00%

0.00%

0.00%

27

2010 0.99137451

0.38%

0.16%

1.72%

· Energy saving, GHG abatement and industrial growth are essential to green growth. · The potentials of green growth are estimated based on an extended SBM-DDF model. · OECD countries have different performances and potentials for green growth. · Three different pathways of green growth are proposed.