199

The Dematerialization of Natural Resources andResource Efficiency During 1990 to 2010
Jue YANG1* and Shunsuke MANAGI2*Economic Research Center, Fujitsu Research Institute,16-1, Kaigan 1-chome, Minato-Ku, Tokyo 105-0022, Japan2
Departments of Urban and Environmental Engineering, School of Engineering, Kyushu University,
744 Motooka, Nishi-ku, Fukuoka, 819-0395, Japan*email: yang.jue@jp.fujitsu.com
1

Abstract
Various indicators have been developed for measuring the physical metabolism of economies andassessing the pressure on natural systems of material throughput. Among them, resource efficiency, which
can represent the progress toward dematerialization, has become a popular indicator since the early 1990s.
Improved resource efficiency, however, may not save resources, but result in even greater consumption
through the so-called ‘Jevons paradox.’ This paper first compares countries’ achievement of relative
decoupling and absolute decoupling from four types of natural resources over the 1990 − 2010 period. Wethen apply a panel analysis to identify the interaction between the improvement of resource efficiency andeconomic growth. Based on the results, we further estimate the impacts of efficiency improvement, taking
rebound effects into account in the short run. The results show that almost one in three countries achieved
absolute good decoupling during the last two decades. Among the four types of natural resources we
examined in this study, fossil fuel, ores and biomass exhibited a strong relationship between efficiency andeconomic growth, but industrial minerals did not. Higher resource efficiency can contribute to economic
growth and dematerialization, while the development of technology may lead to a rebound effect in the shortrun. Finally, we analyze implications of our findings for post-2015 SDG designation as setting various targets
based on the priority between renewable and nonrenewable resources and levels of resource efficiency acrossdifferent scales of countries.
Key words: decoupling, dematerialization, rebound effects, resource efficiency

1. Introduction
The UN Sustainable Development Summit adopted a
final document named ‘Transforming Our World: the2030 Agenda for Sustainable Development’ on Sep.
25 − 27, 2015 in New York. The draft contains 17 goals
and 169 proposed targets designed for sustainable
development (UN, 2015a). Among them, improving
resource use efficiency was included as target 12.2, ‘By
2030, achieve the sustainable management and efficientuse of natural resources.’ Without a numerical goal,
however, it is unclear how the improvement of resource
use efficiency can be assessed at the global scale andnational level. Adding numerical goals into theinternational agenda could effectively help governments
think about what level of effort they should make, howmuch it may cost and whether they can afford it. Thus,
the next issue should be setting indicators and aframework for estimating the achievement of resource
Global Environmental Research
19/2015: 199−206
printed in Japanuse efficiency. Various types of indicators have beendeveloped for assessing resource use. In a technicalreport published by BIO Intelligence Service (BIO, 2012)
in May 2015, two indicators were proposed for
estimating efficient use of materials: domestic material
consumption (DMC) or DMC/ capita and material
footprint (MF) or MF/capita. The feasibility of adopting
these two indicators is ranked as BBB, which indicates
they cannot be easily applied by many nations (UN,2015b). As Wiedmann et al. (2015) noted in theirreview, DMC or DMC/ capita, which is based on amaterial flow accounting approach has been used as aleading indicator by the European Commission andUnited Nations Environment Program. MF, which
represents the global allocation from raw material
extraction to the final demand, contains information on
the industrial structure of the given economy as well as
its trade relationship with other countries. Grasping MF,
however, requires detailed information about all the©2015 AIRIES

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J.YANG and S.MANAGI

sectors in each country, which is still difficult to gauge
for most developing countries. To further compare theachievement of resource use efficiency using MF, a setof international regulations would be required. Other
studies have focused on resource use efficiency orintensity. The intensity concept has been applied todifferent types of resources and environmental impacts,
e.g., energy intensity or CO2 intensity (Canadell et al.,2007).
For developing countries, investigating the drivers ofhigher resource use efficiency and the costs ofdematerialization at the country level is a crucial issue.
UNEP (2011) provides a new concept for assessing
resource use, so-called ‘resource decoupling.’ It is not
used for stopping the growth of resource use, but for
further defining relevant relationships between economic
growth and resource productivity improvement.
Another issue for setting numerical goals is the socalled ‘rebound effect,’ which use to describe increases
in resource use after improvements in efficiency throughtechnology or regulations. This occurs because improved
efficiency can result in lower costs of resource use andeconomic growth, which increases the demand forresources.
This paper aims at grasping resource efficiency andestimating the impacts of efficiency improvements on
economic growth, taking rebound effects into accountfor about 130 countries during the last two decades. We
focus on four types of resources: industrial minerals,
fossil fuel, ores and biomass. As Pothen and Schymura(2015) summarized, there are three perspectives forinvestigating the drivers of material use and efficiency
improvement in the literature: country level-based index
decomposition analysis, structural decomposition
analysis, and econometric methodology, e.g., crosssectional analysis and panel analysis. In this paper, weuse cross-sectional analysis and panel analysis forinvestigating the relationship between resource
efficiency and economic growth.
In Section 2, we present a brief overview ofachievements in decoupling resource use from economic
growth across different income groups over the 1990 −2010 period. We then explore the interaction between
resource efficiency and economic growth in Section 3.
We further examine the impacts of improving resource
productivity, taking rebound effects into account in the
short run. Our findings are then discussed in Section 4
and conclusions presented in Section 5.

bads and economic goods.’ UNEP (2011) redefined it as
the ‘rate of primary resource use per unit of economic
activity.’ In ‘Decoupling Natural Resource Use andEnvironmental Impacts from Economic Growth,’ UNEP
further defined relative decoupling and absolute
decoupling. Relative decoupling of resources means thatthe growth rate of use of the environmentally relevant
resources in question is lower than the growth rate of a
relevant economic indicator such as GDP (Mudgal et al.,2010), while absolute decoupling means resource use
decreased but the economic growth rate did not. Thus,
absolute reduction in resource use is more difficult toachieve. It can occur only when the growth rate ofresource productivity exceeds the growth rate of theeconomy.
In this study, we first grasp the achievement of
decoupling resource use from economic growth bycalculating relative and absolute decoupling. We focus
on industrial minerals, fossil fuel, ores and biomass,
because these four types of resources are used moregenerally, with less country specificity.
The data used in this study are from two sources: theWorld Bank database (World Bank, 2013) and Sustainable
Europe Research Institute (SERI) material flows (SERI,
2014). Both of these datasets have been widely used insocial science studies.

2.1 Total Use of Natural Resources
To investigate decoupling, we chose the total Gross
Domestic Product (GDP) based on purchasing power
parity in constant 2011 international dollars of about 130
countries to represent economic growth at the global
scale. We plotted the total resource use growth and GDP
growth during the period between 1990 and 2010 (Fig.
1). The data of year 1990 are set at 100 as a baseline.
The figure shows that the growth of resource use,
especially fossil fuel and mineral use, was higher thaneconomic growth in the early 1990s. After 1997,
however, ore became the only resource to continue ahigher growth rate of usage with GDP. From 2001, theGDP growth rate was much faster compared to mineraluse, fossil fuel use and biomass use. Generally speaking,during the last two decades, both resource use and theeconomic base have been increasing, and a relative

2. The Achievement of Relative Decoupling
and Absolute Decoupling from Natural
Resources
As stated in ‘Decoupling Natural Resource Use andEnvironmental Impacts from Economic Growth’(UNEP,
2011), the OECD was the first international body toadopt the concept of resource decoupling (2001), anddefines it as ‘breaking the link between environmental

Fig.1 Resource use and GDP.

The Dematerialization of Natural Resources and Resource Efficiency During 1990 to 2010

Fig.2 Per capita resource use and GDP per capita.

decoupling has been achieved in the recent 10 years.
Ore use, however, has risen faster than the GDP since2006. In the dataset, ores, which represent the extractionof major metals and minerals in countries, and include
iron, antimony, copper, lead, nickel, zinc, uranium andgold, are highly correlated with urbanization andindustrialization. Developing countries probably
contribute to the rapid growth of ores.
Because of the rapid population growth during thepast two decades, comparing per capita resource
consumption and economic growth is also crucial.
Figure 2 shows per capita resource use and GDP
during 1990 to 2010 at the global scale. The curves ofmineral, fossil fuel and ores use are very similar to thecurves in Fig.1. The per capita growth rates of thesethree resources are lower than GDP per capita, especiallyafter year 2001, which indicates relatively good resource
decoupling from economic growth. It is notable that percapita biomass use has shown a downward trend since1992. A kind of absolute decoupling seems to have beenachieved for biomass use. The biomass use here includes
biomass food (e.g., sugar crops), biomass animals (e.g.,marine fish catch), biomass feed (e.g., crop residue
straw), biomass forestry (e.g., wood), and other types(e.g., natural rubber). Per capita minerals use has alsochanged little since 2004. Here, we only use industrial
minerals data from SERI, because data on construction
minerals consist of estimated values based on countries’
income level. Again, however, ores have shown rapid
growth during the past five years. A possible reason isthat with industrializations and urbanization, the rate ofmodern energy accessibility is increasing and there may
have been a shift from biomass use for energy to fossil
fuel use and from biomass wood use for housing to oreuse.
To confirm our thoughts, we further investigated thedifferences across the developing and developed world.
Levels of countries’ development is created based on the
UN 2012 classification. Figure 3 shows the relationshipbetween resource use and economic growth fordeveloping countries. Compared to Fig.2, all four typesof resource use have been increasing more gently.
Mineral use was much higher than GDP growth until

201

Fig.3 Resource use and GDP growth of developing countries.

Fig.4 Resource use and GDP growth of developed countries.

2003, while biomass use has continued increasing, but at
a much lower speed compared to the growth of GDP,
since 1991.
If we look at the resource use of developed countries,
however, we see a totally different tendency (Fig. 4).
In the case of developed countries, the growth rate ofresource use has become lower than economic growth intotal since 1999. Especially for mineral and biomass use,absolute decoupling was achieved in the period 2004 −2008. The growth rates of ore and fossil fuel use in the
2000s slowed compared to that of the 1990s, and relative
decoupling seems to have been achieved. Environmental
regulations designed for improving resource use andtechnology may have contributed to the decoupling.

2.2 Comparison of Countries
To get a better grasp of patterns of resource use andachievement of decoupling, we examined the relationshipbetween economic growth and resource use acrossdifferent income groups. The income groups of countries
are from the classification of the World Bank’s Gross
National Income (GNI) per capita.
Figures 5 to 8 represent the relevant relationshipduring the 1990s and 2000s. We use the resource use
growth rate as the horizontal axis, and the GDP growthrate as the vertical axis in the figures. The growth rates
here are the growth rates of the total values during the202

J.YANG and S.MANAGI

Fig.5 Decoupling of mineral use from economic growth (N=109).

1990s and 2000s, which measure the changes for the
past 20 years. The nodes representing countries in the
figures are colored according to the countries’ income
levels. For instance, low-income countries are colored
blue; lower-middle-income countries, green; uppermiddle-income countries, orange; and high- income
countries, red. We can distinguish four types of resource
use situations in each figure: nodes in the first quadrant
can be defined as representing weak decoupling (bothresource use and GDP growth rate are positive); nodes
in the second quadrant indicate absolute good
decoupling (while the economy grows, resource use
decreases); nodes in the third quadrant indicate relative
bad/ good decoupling (while the economy grows,
resource use decreases); and nodes in the fourth quadrant
indicate absolute bad decoupling (when the economy
and resource use are both decreasing). We have further
added a 45 degree line in the first quadrant. The space
between this 45 degree line and the vertical axis is therelative good decoupling zone, because economic
growth outpaces the growth of resource use. The space
between the 45 degree line and the horizontal axis could
be defined as the weak decoupling zone. Our goal for
most of the countries would be for them to enter the
relative good decoupling zone or the second quadrant.
Figure 5 shows the relationship between mineral use and
GDP growth rate across countries. Among the 130
countries, 109 countries were extracting minerals, so thetotal observations here were 109. Countries withdifferent income levels were mixed together and most ofthem were located in the first quadrant, which indicatesweak decoupling from mineral use for the past 20 years.
There are some countries, however, in the second
quadrant, which means they have already achieved
absolute good decoupling. They are Hungary, Poland,
Uruguay, Armenia, Yemen and Uganda. Most of thesecountries are not big mineral suppliers. There is only
one country located in the fourth quadrant, Jamaica.
Jamaica has been a big minerals exporting country sincethe 1970s, and is now suffering a serious economic crisis

Fig.6 Decoupling of ore use from economic growth (N=95).

Fig.7 Decoupling of fossil fuel use from economic growth (N=92).

and environmental pressure (Harris & Omoregie, 2008).
Thus, its economic growth rate is negative while its
resource use growth rate is positive. For Jamaica, thegoal for sustainable resource use should be to shift fromthe fourth quadrant to the first quadrant.
Figure 6 shows the state of decoupling from ore use.
Due to limited data, only 95 observations were available.
Compared to the previous figure, here we can see aclearer cluster of income groups. Almost all the highincome countries are located in the relative good
decoupling zone and the second quadrant, while lots of
low-income and lower-middle-income countries arelocated in the weak decoupling zone. Upper-middleincome countries are variously located in the figure.
There is a kind of pattern here in that ore use is highly
similar in the case of rich countries. Combining what we
found in Fig.4, that developing countries have been
contributing the largest amount of the ore use recently,
the results here are understandable. Again, Jamaica is
the only country located in the fourth quadrant. Jamaica
is also one of the biggest producers of bauxite andalumina, but exporting ores has no longer been able toThe Dematerialization of Natural Resources and Resource Efficiency During 1990 to 2010

203

using a panel analysis. We first create a panel dataset for
exploring the factors impacting the efficiency of
resource use. Then we examine the effects of resource
use efficiency on countries’ income growth.

Fig.8 Decoupling of biomass use from economic growth (N=129).

support its economy since the 1980s.
Figure 7 is a scatterplot of fossil fuel use and GDP
growth. One third of the countries are located in thesecond quadrant. We find a different tendency of lowincome countries compared to the previous figure. Most
low-income countries are located near the vertical axis.
A possible reason is most low-income countries do notpossess fossil fuels and are more dependent on exports.
No countries are located in the fourth quadrant here.
With higher extraction of fossil fuels, the GDP growthrate also increases. Countries like Cote d'Ivoire, Senegal
and Israel, however, face unstable political situations
that result in low economic growth, despite increasing
fossil fuel prices and exports.
Figure 8 shows the relationship between biomass use
and economic growth. There are fewer countries locatedin the second quadrant, and they range along thehorizontal axis from − 0.5 to 2.5.
The figure demonstrates that more countries haveachieved relative good decoupling in the case of biomass
use. With population growth, it may be difficult to reachan absolute decoupling from biomass use. Except for
Croatia, Estonia, Israel and Kuwait, most of the highincome countries have achieved relative good
decoupling. Developing countries like Cambodia andLiberia, have achieved high growth rates and low
biomass use growth rates. Less than one third of thecountries have achieved absolute good decoupling. The
remaining two thirds of the countries have the potential
to improve their relationship between resource use andeconomic growth. We find, however, that countries withunstable political situations or large debts seem toexperience inefficient economies.

3. Mechanism of Interaction between Improved
Resource Efficiency and Economic Growth
In this section, we consider the empiricaldeterminants of relative and absolute good decoupling

3.1 Cross Country Analysis of Resource Efficiency
There are abundant studies that have investigated theinter-effects between resource use and economic growth,
especially on energy, emissions and economic growth.
As Soytas and Sari (2009) summarized, these studies
can be divided into two areas. One uses theEnvironmental Kuznets Curve (EKC) hypothesis, whichassumes an inverted U-shaped relationship as well as
quadratic and cubic relationships between per capita
income and environmental pollutants (Stern, 2004; Fujii& Managi, 2013). The other area focuses on identifying
the Granger causality link between energy consumption
and economic growth or employing cointegration
models (Ozturk, 2010). Recently, a combined approach
using both methods for investigating the dynamic
relationships between resource use and economic growthhas been applied (Zhang & Cheng, 2009). We have
simply followed their findings and tried to capture thetendencies for the last 20 years. Our model for
estimation has the following form:
1990

lnefficiencyi,t = α0 + α1 lnGDPi+ εi,t+ Xβ + ci(1)where the indices t and i represent time and country,
respectively. There are two periods in our dataset: the1990s and 2000s. The dependent variable resource
efficiency is the ratio of the sum GDP to the total
domestic resource use for the last two decades, divided
by population. Higher values represent better efficiency.
We use the GDP of year 1990 for controlling the initial
income level of each country. X denotes the matrix ofvariables used for investigating their impacts on resource
efficiency. The average values of each decade are used
for the variables in X. CAP is the gross domestic fixed
capital investment, which is proportional to the GDP. It
includes land improvements, plant, machinery andequipment purchases, and the construction of roads andrailways, which may affect resource use in a given
country. CPI is the consumer price index, which
represents changes in the cost of goods and services. It
is also worth considering that changes in costs may
impact changes in the demand side of resource use. The
GDP deflator is the annual change in the GDP implicit
deflator, which shows the rate of price changes in agiven country. We assume it would also be an importantfactor affecting resource use. The number of patents is
employed for capturing improvements in technology ina given country. Renewable energy as a share of energy
use is used to examine its impacts on fossil fuels andbiomass use. ‘ε’ is the error vector. Fixed effect andrandom effect are examined using Hausman tests.
Table 1 presents the results of the panel analysis. Labor
is the only variable that is relevantly consistent in the fourmodels. Countries with large populations, such as China

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J.YANG and S.MANAGI

Table 1 Resource efficiency in the panel analysis.
Model I
GDP1990(logged)mineralsfossil fuels

Model III

Model IV

− 0.0630.095

1.167**− 0.1380.502− 0.116+
0.059
0.982+
0.559− 0.790+
0.452
0.083
0.296
− 0.3850.404− 0.005
0.01
0.028
0.018
− 1.009+
0.57
11.0568.616
144
0.522**
Fixed effect
0.01470.388− 0.094*
0.042
0.593+
0.346− 1.212**0.335− 0.2940.18− 0.206
0.137
0.013+
0.007
0.007
0.012
− 1.213*
0.481− 6.134
6.296
149
0.147***
Random effect0.56990.137− 0.024+0.013− 0.1030.15− 0.333**0.117
0.175*
0.079
0.006
0.069
0.002
0.003
− 0.004
0.004
0.14
0.154
8.945**
2.342
188
0.579**
Fixed effect0.03710.178− 0.0170.02
CPI(logged)
0.091
0.169
Labor(logged)− 0.757**
0.145
Patent(logged)
0.006
0.082
GDP deflator− 0.033
0.068
Trade (%)
0.007**
0.002
Renewable (%)
0.020**
0.005
Period0.013
0.223
constant
15.029**
2.82
N
178
R2
0.615***
Model type
Random effectHausman test
0.3244CAP

Table 2 The impacts of efficiency on economic growth.

Model IIores

biomass

Note : The first column is the coefficient; the second column isthe robust standard error.
+=p<.1; *= p<.05; **= p<.01; ***= p<.001.

and India, have lower resource efficiency. The income
level in 1990 shows a positive effect on the efficiency ofore use only, indicating that the level of development
may not so important in improving resource efficiency.
As we showed in the previous section, the growth ofore use shows a different tendency compared to theother three types. It is a more income-oriented resource.
The result of trade shows that a country more dependent
on exports seems to have a lower efficiency of mineraluse. The renewable energy share also has a positiveeffect on the efficiency of mineral use. There are a large
number of developing countries with less access tomodern energy that have high renewable energy shares.
Thus, it is not simple income level but the structure of
the economy and the level of human development that
affect efficiency. The number of patents only impacts
biomass use.

3.2 Panel Analysis of the Relationship between
Resource Efficiency and Economic Growth
Here we apply a dynamic panel analysis of therelationship between resource efficiency and economic
growth. We focus on whether resource efficiency inprevious times has had an impact on the GDP per capita
in subsequent times.
Our model for estimation has the following form:
lnGDPpci,t = α0 + α1 lnefficiencyi,t-1 + Xβ + ci
+ εi,t(2)where X includes variables that have been examinedin the existing studies. CPI, patents, GDP deflator andrenewable energy share are used in the analysis, alongwith life expectancy, legal origin (LaPorta et al., 2008),
regions (Barro & McCleary, 2003) and a geographic
variable, tropical area (Center for International

Model I
Efficiency minerals
Efficiency fossil

minerals

0.008
0.005

Efficiency ores

Model II

fossil fuels

0.035**
0.005

Efficiency biomass
Life expectancy
Islam
Legal origin
Tropical area
CPI (logged)
Patent (logged)
GDP deflator
Trade (% of GDP)
Renewable energy (%)
Constant
N
R2
BIC

0.00020.0004− 0.017*0.007− 0.001
0.004
0.001
0.01
− 0.024**
0.008
0.009
0.006
− 0.002
0.002
0.00030.0002− 0.006**
0.001
9.279**− 0.0661501
0.697**− 2377.87

1.13E+05
3.83E+05− 0.009
0.008
0.002
0.004
− 0.0090.011− 0.0120.008
0.022**
0.006
0.005*
0.002
0.001**
0.0003− 0.005**
0.001
9.162**
0.075
1222
0.725**− 2009.71

Model IIIores

0.012**
0.004
0.00030.0003− 0.013+0.008
0.0003− 0.004
0.005
0.011− 0.024**0.008
0.021**
0.006− 0.003+
0.002
0.00030.0003− 0.007**
0.001
9.204**
0.075
1237
0.700**− 1986.47

Model IV
biomass0.154**
0.01
1.36E+05
0.0003− 0.016*0.007− 0.002
0.003
0.003
0.01
− 0.019**0.007
0.012*
0.005− 0.006**
0.002− 0.0002
0.0002− 0.004**
0.001
9.421**
0.057
1538
0.733**− 2654.75

Note : The first column is the coefficient; the second column is therobust standard error. *= p<.05; **= p<.01; ***= p<.001.

Development, 2001). Table 2 shows the results of thepanel analysis. Year dummies are omitted in the table.
We can see that the previous efficiencies of resource use
have a positive effects on economic growth except forthe case of mineral use. The CPI index, renewableenergy share and Islam have negative impacts on GDP
per capita which have been confirmed by previous
studies. There are various methods of dealing with theeffects of technologies. Here we simply use the numberof patents and find positive effects on three of the four
resource types.

3.3 Rebound Effects
In 1865, William Stanley Jevons first put forward thepossibility that energy-efficiency improvements would
increase rather than reduce energy consumption. Later,abundant studies have debated the existence and thepossible amount of rebound effects. Recently,
researchers have concentrated especially on the macroeconomic side of the issue, using neoclassical growthmodels, econometric evidence and general equilibrium
models (Dimitropoulos, 2007). In this study, we
conducted a panel analysis focusing on the effects ofimproved resource efficiency on resource use in thesubsequent years.
Our model for estimation has the following form:lnResourceusei,t +1 = α0 + α1 lneffgrowthi,t+ Xβ + ci + εi,t(3)where lnResourceuse is the total use of resources andlneffgrowth is the growth rate of efficiency at time t − 1.
Table 3 shows the results of the rebound effects test

205

The Dematerialization of Natural Resources and Resource Efficiency During 1990 to 2010
Table 3 Rebound effects of efficiency improvement.
Model I

Model II

Mineral use(t+1) fossil fuels use(t+1)

Efficiency growth ofminerals
Efficiency growth offossil fuels
Efficiency growth ofores
Efficiency growth ofbiomass
CPI(logged)
Patent(logged)
GDP deflator
Trade (% of GDP)
Renewable energy (%)
ConstantN
R2
Model type

− 0.083**
0.011

0.119**
0.045− 0.0240.032− 0.002
0.01
0.004**
0.001
0.001
0.005
6.684**
0.351
1424
0.115**
Fixed effect− 0.014**0.002

0.084*
0.041
0.136**
0.032− 0.0090.011− 0.001
0.001− 0.036**0.005
9.363**
0.364
1156
0.155**
Fixed effectModel III

Ores use(t+1)− 0.014+0.008
0.224**
0.065
0.175**
0.049− 0.0090.013− 0.004+
0.002− 0.019**
0.007
6.841**
0.621
1172
0.062**
Random effect

Table 4 Countries with the lowest resource efficiency.
Model IV

Biomass use(t+1)− 0.257**
0.031− 0.025*0.01− 0.008
0.007
0.004+
0.002− 0.001**
0.0003− 0.002+
0.001
10.701**0.078
1460
0.119**
Fixed effectNote : The first column is the coefficient; the second column isthe robust standard error.
* = p<.05; ** = p<.01; *** = p<.001. ( + = p<.1)

in the short run. A higher growth rate of efficiency at themacro level has a negative impact on total domestic
resource use. The number of patents, however, hassignificant positive impacts on resource use. This may
indicate that improvement of resource efficiency at the
micro level has a rebound effect on resource use. The
rebound effects are around 13.6% on fossil fuel use and17.5% on ore use, which are under 30% , as mostresearchers in the energy field have found. Other
variables such as CPI and trade have different impacts
on resource use. For instance, CPI positively affects
mineral, fossil fuel and ore use, but is negatively related
to biomass use. It is thought that higher prices directly
affect demand from households.

4. Discussion
We investigated the relationships among resource use
growth, economic growth and the efficiency of resource
use in the previous sections. Only one third of thecountries examined have achieved absolute good
decoupling, and two thirds of the countries could
achieve a better decoupling by improving the efficiencyof their resource use. Our panel models provide evidence
that improved efficiency could contribute to economic
growth with no rebound effect on future resource use at
the macro level.
It is unclear, however, how detailed a sustainable
development goal should be designed and whether it canbe adopted in international negotiations. Establishing a
resource-efficient economy is a big environmental andmacroeconomic challenge for most developing
countries, especially for emerging economies. At thepractical level, basically there are three means ofimproving resource efficiency: (1) installing treatment
technology in the resource use sector or searching for
substitutes for rare resources; (2) promoting reuse,recycling and reduction of resource use; and (3)

1
2
3
4
5
6
7

MineralFossil fuels

Ores

Biomass

India

China

South Africa

Uganda

China

India

Chile

VietnamVietnam

RussiaZambiaKenya

MoroccoIndonesia

United States

Niger

UnitedStatesRussiaUnitedStatesNigeria

Papua New Guinea

Indonesia

Australia

Mozambique

TurkeySouth
AfricaPolandDemocratic Republicof the Congo
UkraineUnited States

Iran

RussiaMali

Vietnam

Kazakhstan

Russia8 Mexico
9 Poland
10 Brazil

Nepal

adjusting the industrial structure. All these efforts
require considerable inputs (including human and social
capital) in the long run. At present, economic activity is
still very materials-intensive in emerging economies.
Their extensive economic growth depends on the low
cost of resource use. Targets can hardly be adopted ifthey may cost them too much. It is critical to clarify
whether a higher resource efficiency could cover thecosts of improving the efficiency of resource use.
Table 4 shows countries with the smallest averagevalues of per capita efficiency of resource use during thepast two decades. The United States and Russia appear
in all four resource categories. Major countries like
Vietnam (3), India (2), China (2), Indonesia (2) and
Poland (2), also appear more than once in the table. For
these countries, the total costs could be much higher
than for other countries. Negotiation with these countries
would probably be very difficult. Among these countries,
however, Indonesia, Chile, Turkey, Mexico and Vietnam
have weak decoupling. As we found in Section 2, thelevels of resource efficiency and decoupling vary acrossincome groups and countries at the same income levels.
We suggest designing SDGs without setting definite
values for resource efficiency, but goals that canencourage countries to achieve a better decoupling. For
instance, improving resource efficiency from a relative
good decoupling to absolute decoupling. Since income
level does not play a major role in efficiency
improvement, other factors such as institutions and traderelationships may be more important for mostdeveloping countries. We also find a shift fromrenewable resource use (e.g., biomass) to nonrenewable
resource use (e.g., ores). Thus, we should set orders ofpriority for different types of resources and levels oftargets for various statuses of countries.

5. Conclusions
Sustainable development goals for dematerializationof absolute material use of limited resources by
increasing resource efficiency are urgently required.
This paper investigates the relationship between resource
use and economic growth by grasping decoupling levels

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J.YANG and S.MANAGI

and using an interaction analysis of minerals, fossil
fuels, ores and biomass. We further examine the reboundeffects on resource use and analyze implications of thefindings for SDG designing. This paper provides
evidence towards designing resource-related sustainable
development goals. Our results also indicate that not
only do the income level and economic plight impact theefficiency of resource use, but also the economic
structure, the political situation and the degree of humandevelopment inside the country have effects. Our
analysis, however, is simple and has limitations. Future
research will be needed to estimate the rebound effects
in the long run with micro and macro data combined. As
stated in the UNEP Report (UNEP, 2011), that said,“UNEP’s International Resource Panel will be
addressing the challenges of applying the concept of
decoupling more comprehensively in separate reports,
including applications to water, land and soil, and otherkey natural resources,” the impacts of efficiency
improvement of other resources use should also be
investigated in the future.

Acknowledgements
We acknowledge the Project on Sustainability
Transformation beyond 2015 from the Ministry of theEnvironment, Japan, for providing funding for thisresearch.

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Jue YANG
Jue YANG is a senior associate at the Economic
Research Center at Fujitsu Research Institute(FRI), Japan. She received her M.Sc. in policy
and planning science in 2008 and her Ph.D. inenvironmental science from the University ofTsukuba, Japan, in 2011.
In 2008, she joined the National Institute forEnvironmental Studies, Japan as an assistant fellow and in 2011 became a
research associate. From 2012, she became a postdoctoral fellow at theGraduate School of Environmental Studies, Tohoku University. She
joined Fujitsu Research Institute in April, 2015. Her current researchinterests include sustainability and inequality, the formation ofenvironmental regulations, and the formation of environmental regimes.

Shunsuke MANAGI
Shunsuke MANAGI is a professor of technology
and policy at Kyushu University, Japan. He
received his Ph.D. in Environmental and Natural
Resource Economics from the University of
Rhode Island in 2004. He is an editor ofEnvironmental Economic and Policy Studies
and on the editorial boards of six journals,
including associate editor of Resource and Energy Economics, a leadauthor for the Intergovernmental Panel on Climate Change, and author ofTechnology, Natural Resources and Economic Growth: Improving theEnvironment for a Greener Future, published by Edward Elgar
Publishing Ltd. He has authored 12 books and more than 100 academic
journal papers. He has been published in peer reviewed journals such as
the Journal of Environmental Economics and Management; Journal of
Economic Dynamics and Control; and Land Economics.
(Received 31 May 2015, Accepted 20 October 2015)