Energy use embodied in international trade of 39 countries: Spatial transfer patterns and driving factors

Journal Pre-proof Energy use embodied in international trade of 39 countries: Spatial transfer patterns and driving factors Lei Jiang, Shixiong He, Xi Tian, Bo Zhang, Haifeng Zhou PII:

S0360-5442(20)30095-5

DOI:

https://doi.org/10.1016/j.energy.2020.116988

Reference:

EGY 116988

To appear in:

Energy

Received Date: 4 March 2019 Revised Date:

12 January 2020

Accepted Date: 16 January 2020

Please cite this article as: Jiang L, He S, Tian X, Zhang B, Zhou H, Energy use embodied in international trade of 39 countries: Spatial transfer patterns and driving factors, Energy (2020), doi: https:// doi.org/10.1016/j.energy.2020.116988. 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. © 2020 Published by Elsevier Ltd.

Energy use embodied in international trade of 39 countries: spatial transfer patterns and driving factors Lei Jiang a, Shixiong He a, Xi Tianb, Bo Zhang c,*, Haifeng Zhoud a

School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China

b

School of Economics & Management, Nanchang University, Nanchang 330031, China

c

Faculty of Spatial Sciences, University of Groningen, Groningen 9747AD, The Netherlands

d

School of Environment, Beijing Normal University, Beijing 100875, China *

Corresponding author at: Faculty of Spatial Sciences, University of Groningen, Groningen

9747AD, The Netherlands; E-mail: [email protected].

Abstract: The energy embodied in international trade is transferred globally through trade links. The understanding of the energy flows embodied in international trade and what drives the variations in embodied energy use is of great significance for achieving the global goal of saving energy and reducing energy-related emissions. Thus, this research, in its first stage, calculated the energy use embodied in international trade of 39 countries from 1995 to 2011 by building a multiregional input-output model and described the spatial transfer patterns of energy flows using geo-visualization techniques. In the second stage, this paper applied the Logarithmic Mean Divisia Index (LMDI) approach to identify the driving factors of embodied energy use. The findings are as follows. (1) The aggregated embodied energy use of these 39 countries significantly increased during the sample period. (2) Regarding the flows of embodied energy use, these 39 countries can be classified into 3 groups, namely, energy-rich countries with net outflows (Group 1), developed countries (Group 2) and developing countries with net inflows (Group 3). (3) From the decomposition results of the LMDI method, both energy intensity and economic output are the main driving factors that affect embodied energy outflow and inflow changes in international trade. The improvement of energy intensity is the main contributor to reducing the increase in energy use embodied in international trade. Moreover, increases in embodied energy use are attributed to the 1

growth of imports and exports, economic output, and population. On the other hand, upgrading industrial systems and optimizing industrial structure can contribute to reducing embodied energy use growth. Accordingly, policy recommendations are given. International trade plays a crucial role in assigning different shares of responsibility for energy-related emissions reduction. To formulate effective and efficient environmental policies, embodied energy use should be considered. Moreover, solutions to alleviate environmental pollution are improved energy use and extensive use of clean energy. Keywords: energy use embodied in trade; input-output analysis; LMDI; driving factors

1. Introduction Energy plays a fundamental role in sustaining economic and social development. Notably, the ever-growing global economy is heavily dependent on fossil fuel energy, such as oil and coal. Consequently, the recent decades have witnessed a rapid rise of global energy consumption mainly driven by the largest developing countries, including China and India. China’s energy consumption has increased at a rapid pace during the past decades. In 2009, China became the world’s largest energy consumer, surpassing the United States [1]. Currently, China accounts for more than 20% of global energy demand. Additionally, India has become one of the largest energy consumers in the world, in terms of energy use volume and growth rate. In 2018, China and India combined accounted for approximately half of the net increase in energy demand [2]. Furthermore, according to the “BP Energy Outlook 2018”, it is projected that the gross world product will double in 2040, which will also lead to the rapid growth of energy use and energy-related emissions. Specifically, the demand for energy will dramatically increase by more than one third over the next 25 years. Correspondingly, carbon dioxide emissions will increase by approximately 10% over the next two decades [3], which will have a significant effect on the global environment [4,5]. In other words, the overuse of fossil fuel energy has become a major contributor to global environmental degradation, notably to the worsening environmental quality of developing countries, such as China and India [6,7]. Facing the serious problem of global environmental deterioration caused by fossil fuel combustion, the world has already achieved an agreement to implement energy 2

conservation and emissions reduction policies to realize the global goal of improving environmental quality [8]. However, there remains disputes among countries regarding the assignment of differentiated commitments to energy-related emissions reduction. This is because consumers and energy users in the production processes and providers of consumable items have generally been geographically separated through trade links during the rapid economic globalization [9,10]. Specifically, tradable products produced by industrial sectors in one country may be consumed as inputs to produce products and final consumption in other countries through international trade [11]. Accordingly, energy consumption embodied in tradable products is also transferred in geographical space. For example, China, the world’s factory, has provided a large number of industrial products to other countries, such as the U.S., or regions, such as the European Union, through international trade. Correspondingly, part of the obligation to reduce energy-related emissions that should belong to consumers is transferred to the producer, China. Thus, energy use for an export-driven country, such as China, may be overestimated from the perspective of the consumption principle. On the other hand, for those developed economies consuming goods imported from developing countries, such as OECD countries, energy use is likely to be greatly underestimated. Ultimately, to formulate effective and efficient energy-related emissions reduction schemes to mitigate global environmental degradation, fair allocation of energy-related emissions should be prioritized. Thus, the importance of accounting for energy use embodied in international trade grounded in the consumption principle cannot be overstated.

2. Literature review Leontief [12] was the first to develop an input-output analysis method to account economic growth by incorporating environmental data, such as energy use. Subsequently, a growing number of studies have emerged in the literature on accounting embodied CO2 emissions [13-16] and embodied energy in trade. By reviewing the existing literature, empirical studies on embodied energy use accounting may be classified into two strands. One is to focus on a single country or region to account energy use embodied in trade [17-24]. For example, Xie [17] calculated China’s energy use from 1992 to 3

2010 based on input-output tables. Li et al. [18] considered interregional energy trade and the embodied energy in China. Li et al. [19] calculated the embodied energy consumption of Macao’s gaming industry, finding that the total consumption increased by approximately 1.5 times from 2005 to 2010 while the energy intensity decreased by more than a quarter. A similar study was conducted by Shao et al. [20]. The second strand is to focus on worldwide embodied energy use for a single year [25-27]. For example, Chen and Chen [25] studied the embodied energy use of multiple sectors in multiple countries in 2007, finding that it amounted to 1.74×108 TJ. China was the largest energy outflow country with 3.26×107 TJ, while the U.S. was the largest energy inflow country, importing 2.50×107 TJ. Similarly, a recent study by Wu and Chen [26] calculated and discussed worldwide energy consumption in 2013 based on both the production and consumption principles. Another study by Wu et al. [27] analyzed the structure of embodied energy flow networks at the global, regional and national levels. From the aforementioned empirical studies, many researchers have observed energy flows embodied in international trade among countries through trade links. However, these studies may have weaknesses, which must be resolved. Specifically, they either analyzed the embodied energy use of a single country or enlarged the sample size to the entire globe but only for one year. The former studies cannot reveal spatial transfer patterns of embodied energy through international trade links, while the latter studies ignore temporal variations in worldwide embodied energy use year by year. For instance, the 2008 financial crisis led to a great decline in global trade volume by 9% relative to the year 2009, and, thus, the world experienced a decline in the energy embodied in trade [28]. Consequently, these studies could not determine which countries were most affected by the financial crisis and which countries influenced energy use embodied in trade. In summary, very few studies have focused on embodied energy use of multiple sectors in multiple countries over multiple years. We attempt to fill this gap in the research. The existing literature reveals that researchers widely apply two typical decomposition methods, namely, structural decomposition analysis (SDA) and index decomposition analysis (IDA) to analyze driving factors of emissions or energy use [29-31]. The former is based on input and output (IO) tables and is capable of distinguishing between a range of technological effects and final demand effects [32]. For example, Liang et al. [29] applied the SDA method to analyze the socioeconomic factors of U.S. greenhouse gas emissions during the period 1995-2009. A 4

similar study was performed by Huang et al. [30]. IDA uses aggregated sector information and can capture more detailed time and country information. Technically, IDA has two main approaches, namely, the Arithmetic Mean Divisia Index (AMDI) method and the Logarithmic Mean Divisia Index (LMDI) method [32]. The LMDI method is preferred because it has four main advantages, viz., path independence, absence of residue, ability to handle zero values, and consistency in aggregation [33]. Consequently, it has gained popularity in the literature during the past decade. Thus, the second aim of this paper is to employ the widely used LMDI method to decompose the driving factors of embodied energy use of 34 sectors in 39 countries after energy use accounting. However, by searching the existing literature, we find that most researchers have merely focused on the driving factors of energy use within countries or industrial sectors [34-37]. For example, Dai and Gao [34] applied the LMDI method to analyze the driving factors of the energy consumption of China’s logistics industry from 1980 to 2010 and found that transportation modes and intensity drove up the industry’s consumption while energy intensity improvements significantly contributed to curbing the increase in its energy use. However, using the LMDI to decompose embodied energy use to investigate the driving factors has received little attention so far. In this research, we try to fill this gap. Our findings will help better understand what driving factors cause embodied energy use around the globe. Furthermore, it is important to efficiently achieve the global goal of saving energy and reducing energy-related emissions. In its first stage, this study constructs a multiregional input-output (MRIO) model to calculate the embodied energy use of 39 countries (shown in Fig. 1) from 1995 to 2011 and then analyzes spatial transfer patterns of embodied energy flows around the globe. In the second stage, it applies the LMDI decomposition method to identify the driving factors of embodied energy use in the 39 countries. Thus, the contribution of the research may be twofold. On the one hand, we enlarge the sample size to 39 countries for a longer time period, 1995 to 2011. Most important, we clearly describe the spatiotemporal variations in the embodied energy use of these 39 countries by using statistical methods and geo-visualization techniques to better understand spatial transfer patterns of global embodied energy flows. On the other hand, we apply the LMDI method to decompose embodied energy use and thus obtain five important driving factors, which will provide insight into the main driving factors affecting embodied energy use in international trade and help policy-makers implement effective and efficient measures to accomplish the global goal of saving 5

energy and reducing emissions earlier than anticipated.

Fig. 1. Study area of the research

3. Methods and data sources 3.1 Accounting of energy embodied in trade A growing number of researchers have used the input-output model to analyze the energy use embodied in international trade and the division of responsibility for emissions mitigation, since the input-output analysis tracks both direct and indirect supply-demand interdependencies among sectors within the entire economy [38]. According to the previous literature, the widely used

input-output model can be divided into three types: a single-region input-output model (SRIO), a bilateral-region input-output model (BRIO) and a multiregional input-output model (MRIO) [39]. Compared with SRIO and BRIO, MRIO takes into account the different levels of technology in each region, which can significantly improve the accuracy of the calculation of embodied energy [15,16]. As a result, this paper uses the MRIO to explore the embodied energy of 34 sectors in 39 countries. Table 1 Simplified global multi-regional input-output table Intermediate

Final

Total

demand

demand

output

Region 1

Region N 6

Sector

…

1 Sector

Sector

…

Sector

M

Z111M

1

Sector M

…

11 Z1N

…

Z11NM

f111

…

f1N1

Y11

M

M

M

M

M

…

f1MN

Y1M

1

Z1111

M

M

O

M

M

M

O

Z11MM

…

Z1MN1

…

M

Z11M1

…

Z1MNM

f11M

M

M

M

M

M

M

M

M

M

M

M

M

Z1NM1

…

Z1NN

…

Z1M NN

f N11

…

1

Z 11 N1

…

1 f NN

YN1

M

M

O

M

M

M

O

M

M

M

M

M

ZNM11

…

ZNM1M

…

Z1111

…

MM ZNN

f NM1

M f NN

YNM

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

Sector

M

Sector Region N

Intermediate input

Region 1

…

…

Sector M

Total intermediate consumption Value added

ij According to the multi-region input-output table (Table 1), Z rs means the intermediate input i of sector j in region s from sector i in region r. f rs represents the final consumption of region s i from sector i in region r. Yr indicates the total output of sector i in region r. The intermediate ij ij i input coefficients are obtained as a rs = Z rs / Yr , so the intermediate demand coefficient matrix

can be expressed as follows.

 At1,1   M At =  Atn,1   M  AN ,1  t rs

where At

L O

At1,n M

L

Atn,n

M L AtN , n

At1, N   M  L Atn, N  (1)  O M  L AtN , N  L

represents the intermediate demand coefficient of region s from region r in time t. It

indicates the intermediate input of region s from region r’s output per unit. Similarly, we can obtain the final demand matrix:

 ft1,1   M Ft =  ft n,1   M  f N ,1  t

L O

ft1,n M

L ft n,n M L f t N ,n

ft1, N   M  L ft n,N  (2)  O M  L ft N ,N  L

rs where, f t means the final demand of region s from region r in time t. Based on the equilibrium

7

relationship between the columns and the rows of the MRIO table, we can obtain the equation given as follows. N

N

s =1

s =1

N

MM

N

Yri = ∑ Z rs + ∑ f rs = ∑ ∑ arsij Yri + ∑ f rs (3) s =1 i , j =1

s =1

Furthermore, according to equation (3),

ErT,t = Yri * uri (4) i where Er represents the energy use of region r induced by the economical produce activities. ur

denotes the energy consumption of one unit product in region r. In addition, for all regions, −1 −1 Yt = AY t t + Ft , Yt = (I − At ) Ft and then Mt ≡ (I − At ) . M t is the inverse matrix of Leontief.

I means the unit matrix. At indicates the intermediate demand matrix. Hence, the matrix of direct and indirect energy use of output per unit in each region can be expressed as below.

 ( wt1 ) ' M t1,1  M  n Vt =  ( wt ) ' M tn,1  M  ( w N ) ' M N ,1 t  t

( wt1 ) ' M t1,n

L O

M n ' t

( w ) M tn ,n M

L

L ( wtN ) ' M tN ,n

( wt1 ) ' M t1, N   M  L ( wtn ) ' M tn, N  (5)  O M  L ( wtN ) ' M tN , N  L

rs

where Mt represents the inverse intermediate matrix of region r to region s in time t. The direct energy use coefficient of sector i in region r is wi,t = ei,t Yi,t . In other words, it is the ratio of r

r

r

rs

energy use to total output of sector i in region r. Based on equation (5), vi,t indicates the direct and indirect energy use of region r for one unit of final demand in region s for product i produced in region r. Therefore, the embodied energy use from region r to region s can be expressed as below: N N  EEPr − s ,t =  ∑ (vtkr )'  f t rs + (vtrs )' (∑ ft sk ) k =1  k =1 

s ≠ r , s, k ∈ N (6)

where EEPr−s,t denotes the embodied energy that exported from region r to region s in year t. The first item indicates the energy consumption embodied in final demand exported by region r to region s. N denotes the number of regions in MRIO table. The energy use in region r is provided by all regions (region r is included) in the MRIO table. The inter-regional trade must generate 8

N

embodied energy use. Thus,

∑v k =1

region r. f

rs

kr t

denotes the energy use of one unit final product produced by

is the final demand exported from region r to region s. The second item indicates rs

the energy use embodied in intermediate demand exported by region r to region s. vt

denotes

the embodied energy use imported by region s from region r to produce one unit final product. N

∑f k =1

sk t

is all regions’ final demand produced by region s.

Furthermore, the embodied energy outflow (EEE) and embodied energy inflow (EEI) can be written as below: N N N N  N   EEEr ,t = ∑ EEPr −s,t = ∑(vtkr )'  (∑ ft rs ) + ∑(vtrs )' (∑ ft rk ) (7) s≠r s≠r  k =1  k =1  s ≠r  N N N  N  N EEIr,t = ∑ EEPs−r,t = ∑∑(vtks )'  ft sr + ∑(vtsr )'  (∑ ft rk ) (8) s ≠r s ≠ r  k =1   s≠r  k =1

Then, the embodied energy net outflow is written as follows:

ErBEET = EEEr ,t − EEIr ,t (9) ,t There are two approaches, namely, production-based and consumption-based principles to account the energy use. And different accounting approaches have deep influences on allocating the obligation for energy conservation. Thus, we define the energy use based on production principle accounting for region r as below: Erprod = ErT,t (10) ,t On the other hand, the energy use based on consumption principle accounting for region r can be expressed as below: T BEET Ercons (11) ,t = Er ,t − Er ,t

3.2 LMDI decomposition method This research adopts the LMDI method to identify the driving factors of changes in embodied energy use [40,41]. The LMDI decomposition is based on the Kaya identity [42], which was first proposed by Kaya to describe the relationships among social, economic, energy, emissions and other macro-overall factors in a simple mathematical formula and to examine the driving factors 9

of greenhouse gas emissions at the national level. Its initial expression can be written as follows: GHG =

GHG TOE GDP ⋅ ⋅ ⋅ POP (12) TOE GDP POP

where GHG, TOE, GDP, and POP represent the volume of greenhouse gas emissions, total energy consumption, GDP and total population, respectively. The three ratios at the right of the equation and population constitute four driving factors of greenhouse gas emissions, which represent carbon intensity, energy intensity, GDP per capita and population scale, respectively. We follow and modify equation (12) to analyze the driving factors of embodied energy use. Specifically, we consider five influencing factors, namely, input and output scale, energy intensity, industrial structure, economic output, and population. Then, the Kaya identity can be constructed as follows.

EEEi Ei GDPi GDP P (13) Ei GDPi GDP P

EEE = ∑ i

EEI = ∑ i

EEIi Ei GDPi GDP P (14) Ei GDPi GDP P

where E denotes the total energy consumption. i means sector i, P represents the population scale. Then, export scale effect, import scale effect, energy intensity effect, industrial structure effect, economic output effect, population scale effect can be expressed respectively, as follows. Export scale effect on embodied energy outflows:

∆EEE EEE = i

Ei

EEEit − EEEi0 ln EEEit − ln EEEi0

  EEEit ln  t   Ei

  EEEi0   − ln     (15) 0   Ei  

  EEI it ln  t   Ei

  EEI i0   − ln   0   (16)   Ei  

where t denotes year t, the same as below. Import scale effect on embodied energy outflows:

∆EEI EEI = i

Ei

EEI it − EEI i0 ln EEI it − ln EEI i0

Energy intensity effect:

∆EEE

Ei GDPi

∆EEI

Ei GDPi

 Ei0  EEEit − EEEi0   Eit  ln − ln     0  ln EEEit − ln EEEi0   GDPit   GDPi 

(17)

 Ei0  EEIit − EEIi0   Eit  = ln   − ln  0  ln EEIit − ln EEIi0   GDPit   GDPi 

(18)

=

10

Industrial structure effect:

∆EEEGDPi GDP

∆EEI GDPi GDP

 GDPi 0  EEEit − EEEi0   GDPit  = ln   − ln  0  ln EEEit − ln EEEi0   GDPt   GDP 

(19)

 GDPi 0  EEIit − EEIi0   GDPit  = ln   − ln  0  ln EEIit − ln EEIi0   GDPt   GDP 

(20)

 GDP0  EEEit − EEEi0   GDPt  ln − ln     0  ln EEEit − ln EEEi0   Pt   P 

(21)

 GDP0  EEIit − EEIi0   GDPt  = ln   − ln  0  ln EEIit − ln EEIi0   Pt   P 

(22)

Economic output effect:

∆EEEGDP = P

∆EEI GDP P

Population scale effect:

∆EEEP =

EEEit − EEEi0  ln ( P t ) − ln ( P 0 )  (23) t 0   ln EEEi − ln EEEi

∆EEI P =

EEI it − EEI i0  ln ( P t ) − ln ( P 0 )  (24)  ln EEI it − ln EEI i0 

In addition, we define:

 EEEit − EEEi0 , EEEit ≠ EEEi0  t 0  ln EEEi − ln EEEi  t 0 (25) L( EEEi , EEEi ) =  EEEit , EEEit = EEEi0   0, EEEit = EEEi0 = 0  EEI it − EEI i0 , EEI it ≠ EEI i0  t 0  ln EEI i − ln EEI i  t 0 (26) L ( EEI i , EEI i ) =  EEI it , EEI it = EEI i0   0, EEI it = EEI i0 = 0 According to Ang’s [39] summary of the LMDI, the LMDI method has two forms, namely, addition and multiplication. Addition applies to quantity objects, while multiplication is more suitable for intensity objects. In this research we adopt the addition decomposition, since the embodied energy is a quantity object. Thus, the total effect can be written as below. 11

∆EEE = EEEt − EEE0 =∑(∆EEEEEE + ∆EEE

Ei GDPi

i

i

Ei

∆EEI = EEI t − EEI 0 =∑(∆EEI EEI + ∆EEI i

i

Ei

Ei GDPi

+ ∆EEEGDPi + ∆EEEGDP + ∆EEEP ) (27) P

GDP

+ ∆EEI GDPi + ∆EEI GDP + ∆EEI P ) (28) GDP

P

3.3 Data sources The data on energy use at the industry and country levels used in the multiregional input-output model are obtained from the World Input-Output Database (WIOD) [43]. In 2013, the database published data on the input-output and energy use of 34 industrial sectors in 39 countries from 1995 to 2011. It should be noted that in 2016 the database also released a set of input-output tables spanning from 2000-2014. Unfortunately, data on energy use were not included. Due to the unavailability of energy use data, the sample period of this research is restricted to the interval from 1995 to 2011. Furthermore, population and other economic data are available from the World Bank database [44].

4. Empirical results 4.1 Accounting of global energy embodied in trade Based on equations (1)-(6), the aggregated energy use of 39 countries can be obtained, as shown in Fig. 2. Moreover, we find that the energy use clearly presented an increasing trend from 1995-2011. Specifically, 6.6×108 TJ of energy was consumed in 2011—an increase of 65% from 4.0×108 TJ in 1995. This ever-increasing energy use has led to a large amount of energy-related emissions, posing a great challenge for global emissions reduction policies.

12

Fig. 2. Total energy use of 39 countries (1995-2011) Besides, we also calculate the global energy use embodied in international trade from 1995 to 2011. As shown in Fig. 3, similarly, embodied energy consumption gradually increased year by year during the sample period. Specifically, in 2011 it amounted to 7.6×107 TJ, more than twice as much as 3.5×107 TJ in 1995, at an annual average rate of 7%. These findings generate two observations. One is that energy embodied in international trade is also increasing with the rise of global trade. The other is that the geographic separation between producers of exported products along with the energy embodied in the production processes and consumers of tradable items through international trade is expanding year by year. It should be noted that the annual growth rate of embodied energy from 1995 to 2011 presented a similar trend to the share of embodied energy consumption of the global energy consumption. Specifically, it first rose, then remained at a constant rate for 8 years, and then rose again, indicating that the impact of embodied energy consumption on global emissions reduction increased as international trade had grown continuously in recent years.

13

Note: % indicates the share of embodied energy use in the global energy use.

Fig. 3. Energy use embodied in international trade (1995-2011) On the other hand, as displayed in Fig. 3, overall the aggregated energy use embodied in international trade rose from 1995 to 2011, although with two clear interruptions in 2001 and 2009. In 2001, the economic recession occurred, which slowed down international trade [45]. This slowdown reversed the increasing trend of embodied energy use in trade, which declined by more than 10% relative to 2000. Moreover, in 2008 the global economy suffered a serious financial crisis, which resulted in slowed-down international trade in 2009. Specifically, international trade decreased by approximately 9% compared with 2008 [28]. In summary, embodied energy use in trade is heavily dependent on both economic performance and international trade. 4.2 Variations in energy use embodied in trade by country In this subsection, we discuss embodied energy use by country since embodied energy outflows and inflows varied from country to country during the sample period. For simplicity and clarity, we select every 4 years from 1995 to 2011, namely, 1995, 2000, 2005, and 2011.

14

Fig. 4(a). 1995

Fig. 4(b). 2000 15

Fig. 4(c). 2005

Note: Positive values in blue color indicate net outflows while negative values in green color imply net inflows.

Fig. 4(d). 2011 Fig. 4. Top 5 countries of embodied energy net outflows and net inflows 16

As shown in Fig. 4, after sorting these 39 countries by embodied energy use, the top 5 countries in embodied energy net outflows and net inflows are presented. Positive values indicate net outflows, while negative values indicate net inflows. In terms of embodied energy net outflows, the largest country is Russia, whose net outflows amounted to approximately 1×107 TJ, increasing by 105% relative to 4.83×106 TJ in 1995. Canada and the Netherlands, characterized by abundant natural resources, also rank in the top 5 countries in terms of embodied energy net outflows. Regarding net inflows, the U.S., Japan and several EU countries, such as Germany and France, have large volumes. It is worth noting that the net flows of Japan increased from 1995 to 2011; as of 2011, it became the country with the largest net inflows. In 2011, China became the second-largest country in terms of net inflows behind only Japan, with inflows amounting to approximately 6.1×106 TJ. We note in particular that China changed to a net embodied energy inflows country in 1999 from a net embodied energy outflows country, followed by Australia in 2001 and Indonesia in 2006. Furthermore, for clarity, we geo-visualize net inflows and outflows by country on maps. They are plotted in Fig. 5. We use different colors to distinguish countries with net outflows from those with net inflows. Specifically, green indicates countries with net outflows, while blue signifies countries with net inflows. We find that the 39 countries shown in Fig. 5 can be classified into 3 groups. Group 1 are net outflows of embodied energy use countries characterized by abundant natural resources, such as Russia, Canada and the Netherlands. Group 2 are economically developed OECD countries, including the U.S., Japan and several EU countries, featuring large amounts of net inflows of embodied energy use. Finally, Group 3 are such large developing countries as China, Indonesia, India and Brazil, featuring large populations and rapid economic growth.

17

Fig. 5(a). 1995

Fig. 5(b). 2000

18

Fig. 5(c). 2005

Fig. 5(d). 2011 Note: Positive values in blue color indicate net outflows while negative values in green color imply net inflows.

Fig. 5. Net flows of embodied energy of 39 countries We observe that developed and developing countries witnessed rapid increases in embodied energy net inflows, although for different reasons. Developed countries have great demand for imported products from developing countries that have to import energy-embodied raw materials and primary products from natural-resource-rich countries to sustain their production. On the other hand, as the global economy rapidly grows, developing countries encounter ever-expanding gaps 19

between local production and consumption and thus import energy-embodied products to fill the gap. In summary, both developed and developing counties have seen the rise of income levels and embodied energy use. However, they must take responsibility for saving energy and reducing energy-related emissions [15]. 4.3 Spatial transfer patterns of embodied energy use The focus of this subsection is on spatial transfer patterns of embodied energy use in international trade. These patterns are geo-visualized in Fig. 6. For clarity, we combine 27 EU countries into a single unit, namely, the European Union (EU). As a result, the sample size is reduced to 13 countries or regions from 39.

Fig. 6(a). 1995

20

Fig. 6(b). 2000

Fig. 6(c). 2005

21

Fig. 6(d). 2011 Note: Positive values in blue color indicate net outflows while negative values in green color imply net inflows.

Fig. 6(d). Transfers of global energy embodied in trade between regions As displayed in Fig. 6, the two largest countries in terms of net outflows of embodied energy in international trade are Russia and Canada. Abundant in various natural resources, including fossil energy, they export an immense amount of energy resources and energy-embodied raw industrial products to different energy-poor countries. Thus, within Europe, embodied energy basically outflowed from Russia to the EU countries. Specifically, embodied energy from Russia to the EU countries amounted to 3.3×106 TJ in 1995, accounting for 75% of the total energy embodied in Russia’s trade. In contrast, within North America, Canada is the largest energy-exporting country, to the U.S. For instance, it reached 9.7×105 TJ in 1995, accounting for 67% of the total embodied energy of Canada. Moreover, globally the largest embodied energy transfer channels are from China to the EU (4.0×105 TJ), the U.S. (4.0×105 TJ), and Japan (3.3×105 TJ), and from Indonesia to Japan (6.7×105 TJ). Next, we find that in 2011 the two largest countries in terms of net outflows of embodied energy use were still Russia and Canada. Compared with 1995, the spatial transfer patterns of their embodied energy use outflows varied very little. However, the amount of embodied energy use substantially increased from 1995 to 2011. Specifically, net embodied energy outflows from Russia to the EU increased by 54% relative to 1995, while net outflows from Canada to the U.S. amounted to 1.3×106 TJ, increasing by 33% relative to 1995. 22

Additionally, it is worth noting that the main channel of embodied energy flows worldwide is basically from developing to developed countries, for example, from China to the EU (6.8×105 TJ), from China to the U.S. (4.2×105 TJ), and from Indonesia to Japan (6.7×105 TJ). On the other hand, net outflows of embodied energy of India to developed countries also increased year by year, as its economy grew rapidly in recent years. For example, net outflows of embodied energy to the EU increased six-fold, amounting to 6.6×105 TJ, relative to 9.8×104 TJ in 1995. Overall, spatial transfer patterns of global energy use embodied in trade present several clear channels, namely, from natural-resource-rich Russia and Canada to such advanced economies as the U.S., the EU and Japan. In other words, the former two countries exported a large number of energy resources to developed countries to satisfy their demand. The consumption principle approach is obviously preferred for Russia and Canada, since it may help curtail emission reduction requirements assigned in international negotiations. Another significant channel of embodied energy flows is from developing to developed countries. In contrast to Russia and Canada, developing countries basically exported energy-intensive industrial products to developed countries. An immense amount of energy consumed in the production processes not only supported exported-industrial goods produced but also worsened the environmental quality of developing countries, notably China and India, the two most highly polluted populous developing countries. Hence, developed countries should provide advanced technologies to developing countries to help them improve production technologies and reduce various energy-related emissions, since international cooperation may contribute greatly to improving global environmental quality and mitigating climate change. Additionally, for China and India, an increase is urgently needed in the share of highly efficient and clean energy of the total energy use, which can not only reduce embodied energy use and energy-related emissions but also improve local environmental quality and even stimulate the achievement of the global goal of emissions reduction ahead of schedule. 4.4 Decomposition results of LMDI method Based on equations (13)-(28), we decomposed and obtained the scale effect, energy intensity effect, industrial structure effect, economic output effect, population effect, and total effect on embodied energy inflows and outflows by country. These effects are summarized in Fig. 7. Some 23

certain effect has a positive value, indicating it will increase embodied energy inflows or outflows. Negative values signify that the effect hinders embodied energy inflows or outflows.

Fig. 7(a). Outflows

Fig. 7(b). Inflows 24

Fig. 7. Decomposition results of embodied energy outflows and inflows As shown in Fig. 7, for most of these 39 countries, increases in export scale drive up embodied energy outflows. In contrast, for China, the export scale has a negative effect. One possible explanation is that in 1995, embodied energy use accounted for 23% of the total energy use. In 2011, the share sharply declined to 14%. In recent years, China has experienced rapid economic growth, leading to a dramatic increase in energy consumption and reduced environmental quality. Although embodied energy in trade rapidly increased from 1995-2011 (specifically, the aggregated embodied energy in trade in 2011 was double that in 1995), the share of embodied energy use of the total energy use declined year by year. Hence, the effect of the export scale is negative. Similarly, an increase in the share of embodied energy inflows in trade of the total energy use will promote embodied energy inflows. We find that, as displayed in Fig. 7, most countries have positive import scale effects since these shares increased from 1995 to 2011. Energy intensity is a widely used indicator to measure energy efficiency. It is defined as the amount of energy used to produce a unit of economic output, indicating the overall efficiency in the energy-economy nexus. Energy efficiency improvements are usually driven by technological progress. A decline in energy intensity will reduce embodied energy flows, ceteris paribus. We find that the energy intensity of each country presented decreasing trends during the sample period. The energy intensity effect of each country is negative, indicating that decreases in energy intensity can drive down embodied energy inflows and outflows. China, the world factory, has a strong negative energy intensity effect, affecting the quality of products in international trade. This is because energy efficiency improvements imply technological progress, thus improving the quality of exported products. On the other hand, many countries have already increased standards for exported and imported products, which contributes to enhancing the quality of products in international trade. We proceed to analyze how decreases in the energy intensity of primary, secondary and tertiary industries affect embodied energy outflows and inflows. As shown in Fig. 8, for most countries, the contribution of the energy intensity of secondary industry to embodied energy outflows are the largest, followed by those of tertiary and primary industries. In other words, embodied energy inflows and outflows of most countries are determined by changes in their secondary industry. Hence, to save energy and reduce emissions, industrial upgrades and transition 25

from secondary to tertiary industry characterized by high value added and low emissions are highly encouraged to realize the goal of energy use efficiency gains.

Fig. 8(a). Outflows

Fig. 8(b). Inflows 26

Fig. 8(b). Contribution rates of energy intensity of three industries Change in industrial structure is also one of the most important determinants of embodied energy use. From the LMDI decomposition results, industrial structure effect varies by country. For example, in the case of embodied energy outflows, China has a positive industrial structure effect, while the U.S. has a negative one; these effects determine the countries’ embodied energy inflows and outflows. Furthermore, an increase in the share of secondary industry will drive up embodied energy flows. For example, in 1995, secondary industry in China and the U.S. accounted for 62% and 34% of the total economies, respectively. However, in 2011, the share of secondary industry in China rose to 67%, while the share in the U.S. declined to 26%, directly affecting embodied energy flows. Today, China has already become the largest embodied energy consumer, surpassing the U.S. Hence, rapid industrial upgrade, optimization of the industrial structure, and interindustry coordination are urgently needed to reduce the effect of the industrial structure on the embodied energy flows and mitigate energy-related emissions. Moreover, as shown in Fig. 8, we find that the economic output effects of these 39 countries are all positive, indicating that this effect is attributed to embodied energy inflows and outflows. This is because per capita GDP is a major contributor to embodied energy flows, notably for developing countries, such as China and India. One reasonable explanation is that as income levels and living standards rise, a large number of energy-intensive products and services are consumed, such as vehicles, air-conditioners, and central heating systems. Consequently, various energy-related emissions have posed a huge threat to the environment. This is why China and India have suffered serious environmental degradation. On the other hand, developed countries, such as the U.S. and EU countries, are mainly dependent on imported products from developing countries, such as China. This indicates that developed countries with highest GDP have the highest levels of energy use and energy-related CO2 emissions because of large economic scales [46]. Hence, a portion of energy in the form of energy embodied in products through international trade has passed to developing countries. In summary, the energy use of developing countries, such as China, has gradually increased year by year. Hence, technological transfers from developed to developing countries are urgently needed to effectively and efficiently achieve the global goal of energy-related emissions reduction. Finally, the LMDI decomposition results reveal that the population effect is also attributed to 27

embodied energy flows, specifically for populous countries, such as China, India, and the U.S. However, the positive population effect on embodied energy use has a lower magnitude than other positive effects, such as the economic output effect. On the other hand, the populations of most European countries except for Russia and Germany presented increasing trends from 1995-2011. An increase in population will directly lead to large demand for energy use and thus have strong population effects on embodied energy use. As a result, these population increases created a great challenge for global emissions mitigation. Hence, as population and income levels increase, environmental education, environmental awareness enhancement, green consumption and a low-carbon lifestyle are urgently needed since changing consumption patterns is critical for global environmental improvements. Next, we analyze the contributions of the scale effect, energy intensity effect, industrial structure effect, economic output effect and population effect on embodied energy outflows and inflows. The results are plotted in Fig. 9.

Fig. 9(a). Outflows

28

Fig. 9(b). Inflows Fig. 9(b). Contributions of driving factors of embodied energy outflows and inflows As presented in Fig. 9, we find that the contribution of each of these 5 driving factors to embodied energy outflows and inflows is very similar in these 39 countries. Specifically, the energy intensity effect is a major contributor to reducing embodied energy outflows and inflows, while the economic output effect drives up embodied energy use. Additionally, the contribution of industrial structure and population effects is smaller than those of other factors, indicating that there is great potential in upgrading the industrial structure. Hence, to reduce ever-growing energy-related emissions and mitigate environmental pressures, it is vital to promote industrial transition and optimize the industrial structure.

5. Conclusions and policy recommendations In the first stage of this research, we constructed a multiregional input-output model to calculate the embodied energy use of 39 countries during 1995-2011 and then discussed spatial transfer patterns of embodied energy inflows and outflows around the globe. In the second stage, the LMDI decomposition method was applied to examine five effects, namely, the scale effect, 29

energy intensity effect, industrial structure effect, economic output effect, and population effect. The main conclusions and relevant policy recommendations follow. Regarding the aggregated energy use embodied in international trade, we find that as the process of economic globalization sped up and international trade continued to expand, global embodied energy use increased from 1995-2011. This finding suggests that international trade definitely plays a crucial role in assigning countries differentiated commitments to reducing emissions, which cannot be ignored. Hence, to fairly allocate energy-related carbon emissions schemes and effectively and efficiently achieve emissions reduction targets, we must consider the embodied energy use of each country and implement country-targeted emissions reduction policies. Spatial transfers of energy use embodied in international trade exhibit various patterns. For example, Russia, the Netherlands and Canada, abundant in energy resources, are the main embodied energy exporters in the international market. Additionally, countries with net energy inflows can be classified into two groups. One group is economically developed countries, such as the U.S., Japan, and several European OECD countries. The second group is certain typical developing countries, such as China, Indonesia, India, and Brazil. In summary, there are essentially three groups. Energy-rich countries are mainly characterized by embodied energy net outflows. In other words, for those countries, the calculated energy use based on the consumption principle is lower than that based on the production principle. Hence, these energy-rich countries tend to vote for consumption principle schemes. On the other hand, for some developing countries characterized by embodied energy net inflows, such as China and India, their calculated energy use based on the consumption principle is much higher than that based on the production principle, and they tend to favor production principle-based schemes. This is because some of the emissions reduction tasks that should have been assigned to developed countries, such as the U.S. and Japan, have been transferred to developing countries, such as China, through international trade. Thus, in international negotiations related to embodied energy and energy-related emissions mitigation allocation, the two groups should maintain the same stances. The energy use embodied in international trade flows around the globe in two main channels. One channel is from energy-rich Russia and Canada to the U.S., Japan, EU countries, etc. The other is from such developing countries as China, India and Indonesia to developed countries. 30

Unlike Russia and Canada, which are characterized by exports of energy resources, developing countries mainly export industrial products to developed countries in economic globalization, although at the expense of environmental quality, due to the large amount of energy consumed during production processes. Hence, the best solutions to the mitigation of environmental pollution in developing countries are improvements in energy use efficiency and enhancement of the share of clean energy of the total energy use, both of which heavily depend on transfers of advanced technologies, capital, and high-tech equipment from developed to developing countries. Hence, international joint efforts contribute to reducing environmental pollution in developing countries and achieving the global goal of saving energy and reducing emissions. From the decomposition results of the LMDI method, we find that during the sample period, an increase in per capita GDP was a main contributor to embodied energy use. As the global economy continues to grow, increases in income levels of notably developing countries, such as China and India, will lead to huge demand for energy resources and energy-embodied products and pose a great challenge for global emissions mitigation. People in these developing countries also have a right to avoid energy poverty and live better lives. To achieve the global goal of emissions mitigation without undermining the economic growth and environmental quality of developing countries, the best solution in the long run is transfers of advanced environmental protection technologies and cleaner production technologies from developed to developing countries. We also find that energy efficiency improvements can substantially reduce worldwide embodied energy inflows and outflows. However, low energy efficiency of developing countries, such as China and India, is attributed to the high share of the traditional fossil energy, namely, coal. Optimization of energy use structure, for example, developing renewable and clean energy, such as wind, hydro, and solar power and even increasing the share of nuclear power of the total energy, is the best way to enhance the energy efficiency of developing countries and contribute to achieving the goal of energy-related emission mitigation targets ahead of schedule. Finally, industrial upgrades and industrial structure optimization can also reduce embodied energy use, since secondary industry is the largest energy consumer. The low contribution of the industrial structure effect implies that there is great potential to optimize industrial structure to further reduce industrial energy consumption. Hence, preferential policies to upgrade industrial 31

systems and optimize industrial structure in developing countries are urgently needed.

Acknowledgements The authors are grateful for the financial supports provided by the Natural Science Foundation of Guangdong Province (2018B030312004), the Ministry of Education of Humanities and Social Science project of China (17YJC790061), the Zhejiang Provincial Natural Science Foundation of China (LY19G030013), and the National Natural Science Foundation of China (41761021).

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Highlights
We accounted energy use embodied in trade of 39 countries from 1995 to 2011.
Spatial transfer patterns of embodied energy use of 39 countries are geo-visualized.
LMDI method was utilized to identify the driving factors of embodied energy use.
Energy intensity and economic output are main factors affecting embodied energy use.

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All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version.

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This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.

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The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript

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The following authors have affiliations with organizations with direct or indirect financial interest in the subject matter discussed in the manuscript:

Author’s name Lei Jiang Shixiong He Xi Tian Bo Zhang Haifeng Zhou

Affiliation Zhejiang University of Finance and Economics Zhejiang University of Finance and Economics Nanchang University University of Groningen Beijing Normal University