Temporal Causal Relationship between Resource Use and Economic Growth in East China

93China & World Economy / 93 – 108, Vol. 19, No. 2, 2011

©2011 The AuthorsChina & World Economy ©2011 Institute of World Economics and Politics, Chinese Academy of Social Sciences

Temporal Causal Relationship betweenResource Use and Economic Growth in

East China

Yuan Wang, Jie Chen, Genfa Lu*

Abstract

The present paper attempts to combine cointegration theory and the material flow analysisapproach to examine the causal relationship between resource use and real GDP in JiangsuProvince in East China. The study considers the period from 1990 to 2007. We use directmaterial input as the proxy variable for resource use. Our estimation indicates that realGDP and resource use are cointegrated and there is only unidirectional long-run Grangercausality running from resource use to real GDP, but not vice versa. The estimation resultsmean that resources are a limiting factor in terms of economic growth, and, therefore,strategies should be adopted for more vigorous economic development and consistentresource use in East China. Furthermore, the novel idea and methodology involved in thepresent study can be readily extended to cover other regions for the analysis of the relationshipbetween resource use and economic growth.

Key words: cointegration, East China, economic growth, material flow analysis, resource

use

JEL codes: C32, Q43, Q56

I. Introduction

The relationship between resource use and economic growth has been analyzed extensively

* Yuan Wang, Associate Professor, State Key Laboratory of Pollution Control and Resources Reuse,School of the Environment, Nanjing University, Nanjing, China. Email: [email protected]; Jie Chen,PhD candidate, State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment,Nanjing University, Nanjing, China. Email: [email protected]; Genfa Lu, Professor, State KeyLaboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University,Nanjing, China. Email: [email protected]. This work is funded by the National Natural Science Foundationof China (No. 40701063).

mailto:[email protected]



94 Yuan Wang et al. / 93 – 108, Vol. 19, No. 2, 2011


over the past few decades, initially motivated by concerns about the security of resourcesupply and later by unease regarding the sustainability of regional development. However,the empirical evidence on their relationship remains ambiguous and insufficient to date.Using material flow analysis (MFA) on resource consumption and time-series analysis, weare able to explore the causal relationship between the two variables by conducting Grangercausality tests.

In the pioneering work of Kraft and Kraft (1978), causality was found to run from grossnational product to energy consumption in the USA, highlighting the relationship betweenresource use and economic growth. Empirical studies were later extended to include otherindustrial countries, such as the UK, Germany, Italy, Canada, France, Japan and Greece (Yuand Choi, 1985; Erol and Yu, 1987; Hondroyiannis et al., 2002; Soytas and Sari, 2003; Lee,2006; Zachariadis, 2007). Rather than relying on the standard Granger causality test, manyrecent studies have used the cointegration and vector error correction model (VECM)technique with a bivariate (Shiu and Lam, 2004; Lise and Van Montfort, 2007; Yuan et al.,2007) or a multivariate methodology (Masih and Masih, 1997; Ghali and El-Sakka, 2004; Ohand Lee, 2004a; Soytas and Sari, 2007; Warr and Ayres, 2010) applied to analyze the causalrelationship between energy consumption and economic growth.

The aforementioned published studies suggest that the directions of causality betweenenergy consumption and economic growth can be categorized into four types. Table 1presents a detailed summary of these four categories of causality and the respective policyimplications from recent studies.

All the empirical analyses have only considered the causality of energy consumptionand economic growth, and not extended to analyze resource use and economic growth.One of the objectives of our work is to fill the void in this direction by applying the MFAmethod. MFA is an instrument used to track and measure the physical flow of materials.MFA accounts for all the natural resources that flow through an industry in each phase ofproduction, such as extraction, processing, use and disposal (Adriaanse et al., 1997;Matthews, 2000). Wernick and Ausubel (1995) were the first to address the MFA frameworkon a national scale. The German Federal Statistical Office’s (1995) book Integrated Envi-ronmental and Economic Accounting: Material and Energy Flow Accounts examined forthe first time the input and output of materials flows at the national level. Working with theWorld Resources Institute, Adriaanse et al. (1997) estimated the material flows associatedwith industrial development in four industrial economies: the US, Japan, Germany and theNetherlands. In 2001, MFA was applied to calculate the input of the material flow within theEU (EU-15) and its member states (Bringezu and Schütz, 2001a; Bringezu et al., 2004).Subsequently, the European Statistics Bureau (Eurostat, 2001) proposed a comprehensiveMFA analytic framework, the Eurostat methodology, which many countries have used to

95Resource Use and Economic Growth


establish material flow accounts, including Austria (Gerhold and Petrovic, 2000; Matthews,2000), Denmark (Pedersen, 1999), Germany (Bringezu, 2002), Finland (Muukkonen, 2000),Italy (De Marco et al., 2000), Japan (Adriaanse et al., 1997), the Netherlands (Adriaanse et al.,1997; Matthews, 2000), Poland (Schütz and Welfens, 2000), the UK (Bringezu and Schütz,2001b) and the USA (Adriaanse et al., 1997; Matthews, 2000; Rogich and Matos, 2002). InChina, researchers have used MFA to analyze the material input and output within theChinese economic system and have made comparisons between resource use in China andother industrial countries (Chen and Qiao, 2000; Liu et al., 2005; Xu and Zhang, 2007; Xu etal., 2008).

The paper applies MFA and the Granger causality test to answer the following importantquestions: Does long-term equilibrium exist between resource use and economic growth inChina, especially in East China, a region of rapid industrialization and urbanization? Howdo resource use and economic growth influence each other in the short term? Answeringthese questions will help to improve policy relating to resource use and conservation inChina. The remainder of the paper is organized as follows. Section II briefly introduces the

Table 1. Comparison of Causal Relationship between EconomicGrowth and Energy Consumption (EC)

Granger causality type Features Policy implication

No causality Neutrality hypothesis

Neither conservative nor expansive energy consumption

has any effect on economic growth (e.g. Asafu-Adjaye,

2000; Hondroyiannis et al., 2002; Wolde-Rufael, 2005).

GDP? EC

(unidirectional causality from

economic growth to energy

consumption)

Less energy-dependent

economy

Policies for reducing energy consumption may be

implemented with only little adverse or no effect on

economic growth (e.g. Oh and Lee, 2004b; Yoo and Kim,

2006; Lise and Van Montfort, 2007; Ang, 2008).

EC? GDP

(unidirectional causality from

economic growth to energy

consumption)

Energy-dependent economy

Energy is a limiting factor in economic growth.

Restrictions on energy consumption might have

significantly adverse effects on economic growth (reduce

economic output), while an increase in energy consumption

might contribute to economic growth (e.g. Masih and

Masih, 1997; Stern, 2000; Shiu and Lam, 2004;

Wolde-Rufael, 2004; Altinay and Karagol, 2005; Lee,

2005; Yuan et al., 2007).

GDP↔EC

(bidirectional causality

between economic growth

and energy consumption)

Energy use and economic growth

complement each other

Economic growth might stimulate more energy

consumption, whereas more energy consumption might

also induce economic growth. Balanced polices of energy

use should apply for harmonious energy consumption and

economic growth (e.g. Yang, 2000; Ghali and El-Sakka,

2004; Jumbe, 2004; Oh and Lee, 2004a; Yoo, 2005).

→

→



study area: Jiangsu Province of China. Section III discusses the methodology and the data.Section IV gives an overview of resource use in Jiangsu Province of China. Our empiricalanalysis is presented in Section V. Section VI details the policy implications of the empiricalanalysis and concludes the paper.

II. Description of Study Area

Jiangsu Province, one of the six provinces and one municipality of East China, is used as acase study in this analysis of resources use and economic growth. The period consideredis from 1990 to 2007. Located in the center of China’s east coast and adjacent to the YangtzeRiver, Jiangsu has an area of 102 600km2, a population of approximately 76 million, and in2007 contributed 10.3 percent of national GDP.1 Jiangsu is one of the most important industrialbases in China, with a broad range of industries covered, including chemical, electronic,machinery, textile and light industries. However, problems such as resource scarcity andenvironmental pollution, have arisen along with the rapid economic growth in Jiangsu.These particular issues have become key constraints to sustainable development in thisregion. Therefore, study focusing on the relationship between resource use and economicgrowth is valuable in terms of the sustainability of the provincial socioeconomic system.

III. Methodology and Data

1. Material Flow AnalysisAccording to the mass balance principle of MFA, material inputs from the environment areprocessed for net material accumulation in the socioeconomic system, with material outputbeing released back into the environment. To describe and quantify the material flowsbetween the economy and the environment, MFA uses a set of physical indicators ormaterial input indicators. Direct material input (DMI) refers to all the materials that enter theeconomy directly and can be measured in terms of monetary value, including domesticextraction (DE) within the boundary of the case study area and imports from other economies.DE comprises such categories as fossil fuels (e.g. coal, oil and gas), minerals (e.g. metalores, industrial mineral and construction material ores) and biomass (e.g. biomass fromagriculture, from forestry and from fishing). Imports include energy, metal, industrial minerals,finished products and semi-manufactured products. Total material requirement (TMR) refersto the sum of demand for domestic material flows and imported material flows in the economy

1 Source: Calculated using the data from National Bureau of Statistics of China (2008).



of the case study area. TMR includes not only DMI but also so-called hidden flows(Adriaanse et al., 1997) or indirect flows (Eurostat, 2001), which refer to materials withoutmarket value, used in economic activities but remaining undocumented in conventionalnational economic accounts.

The framework of MFA used within the present study is consistent with themethodology adopted by Eurostat (2001). This paper will focus on the key input-orientedindicators, DMI and its main components, DE and imports. DMI accounts may be modifieddepending on the data availability from regional economic accounts and the relevantstatistical accounts.

2. Stationarity, Cointegration and Vector Error Correction ModelEngle and Granger (1987) point out that a linear combination of two or more non-stationaryseries (with the same order of integration) may be stationary. If such a stationary linearcombination exists, the series are regarded to be cointegrated and long-run equilibriumrelationships exist. The cointegrated variables must have an error correction representationin which an error correction term (ECT) must be incorporated into the model. Accordingly,a VECM is formulated to test for Granger causation of the series in at least one direction,allowing for long-run equilibrium as well as short-run dynamics. In this paper, the VECM isspecifically adopted to examine the Granger causality between real GDP and resource use(measured by DMI) in Jiangsu Province.

Because using VECM requires the series to be cointegrated with the same order, it isessential to first test the series for stationarity and cointegration. If a series has non-constant (or constant) mean, variance and autocovariance over time, it is considered to benonstationary (or stationary). When a nonstationary series has to be differenced d times tobecome stationary, it is considered to be integrated of order d; that is, I(d). We use unit roottests, the augmented Dickey–Fuller (ADF) and the Phillips–Perron (PP) methods, to test forthe existence of unit roots and to identify the order of integration for GDP and DMI variables.

When both series are integrated of the same order, we can proceed to check for thepresence of cointegration, by using Johansen maximum likelihood approach (Johansen,1991) for the vector autoregressions (VAR) constructed in levels. When cointegration isdetected, the following step is taken to determine evidence of causality by employing theappropriate types of causality tests. According to Engle and Granger (1987), any cointegratingrelationship found between the series will contribute an additional ECT, which is used toformulate the VECM to reintroduce the information lost in the differencing process, therebyallowing for testing long-term equilibrium as well as short-term dynamics. Assuming thatthere is only one cointegrated relationship, the bivariate VECM can be expressed as follows:



titi

iti

tt xiyiECTy 22221122 )()( εααβα +∆+∆++=∆ −−− ∑∑ (1)

titi

iti

tt xiyiECTx 11211111 )()( εααβα +∆+∆++=∆ −−− ∑∑ , (2)

where yt and xt represent natural logarithms of real GDP and DMI, respectively, ty∆ and

tx∆ are the differences in these variables that indicate their short-run dynamics, t1ε and

t2ε are the serially uncorrelated error terms, and ECTt-1 is the ECT, which shows the long-run equilibrium relationship. The statistical significance of the coefficients associated withECT provides evidence of an error correction mechanism, which drives the variables backto their long-run equilibrium from the past disequilibrium.

In each equation, there are two separate sources of causation. Through the ECT, if0≠β , or through the lagged dynamic terms, we can perform three different causality tests:

a short-run Granger causality test, a long-run Granger causality test and a strong Grangercausality test. Using Equation (1), we can examine the statistical significance of the estimatedcoefficients on lagged values of DMI by using the Wald test. Rejection of the null hypothesisimplies that resource use Granger causes real GDP in the short run. The long-run causalitytest is based on observing the significance of the ECT. If 2β is statistically significant, theimplication is that there is a long-run causality from resource use to real GDP. Finally, wecan perform the strong causality test, by imposing stronger restrictions and testing thejoint significance of both the lagged dynamic terms and the ECT. The strong causality testdoes not distinguish between short-run and long-run causality. It is a more restrictive testindicating the overall causality in the system.

3. Data DescriptionOur empirical study uses the time-series data for real GDP and resource use (measured byDMI) during the 1990–2007 period in Jiangsu Province, China. Nominal GDP and the datarelevant to DMI are obtained from the Jiangsu Bureau of Statistics (1991–2008). In thepresent paper, nominal GDP is deflated by the GDP deflator (using 1990 as the base year) toobtain the real GDP figure in billion yuan, while DMI is calculated using the MFA approachproposed by Eurostat (2001).

IV. Profile of Resource Use in Jiangsu Province

In this section, DE, imports and DMI indicators for Jiangsu Province are compiled based onJiangsu statistical data. Due to the deficiency of relevant data in the conventional economic



accounts for Jiangsu Province, the sample range of material input indicators could only betracked back to the year 1990. The development of DE is illustrated in Figure 1. The transitionof DE in Jiangsu Province since 1990 can be characterized as having three phases. From1990 to 1995 with the rapid growth of the economy, DE increased from 80.29 to 122.07million tons; that is, by 8.74 percent annually. However, the year 2000 was a turning point.From 1995 to 2000, DE increased slightly, from 122.07 to 122.50, and by 2007 had increasedto 201.18 million tons, an annual rate of 7.34 percent annually.

As an important industrial base of China, Jiangsu Province requires the extraction of agreat amount of minerals. From 1990 to 2007, the amount of minerals extracted increasedrapidly, from 18.46 to 128.91 million tons; that is, by 12.11 percent annually. In 1990, mineralsaccounted for just 22.30 percent of DE. However, since 2000, minerals have made up themajority of DE, comprising approximately 41.58–64.08 percent of total DE from 2000 to 2007.Biomass and fossil fuels remained at a rather constant level from 1990 to 2007.

The structure of imports in Jiangsu Province is dominated by energy. Even though theshare of energy in materials imports decreased from 95.2 percent in 1990 to 71.3 percent in2007, the amount increased rapidly from 2001, by more than that of fossil fuels in DE. Withthe shortage of local supply of fossil fuels, rapid economic growth is bound to increasedemand for imported energy (see Figure 2).

Figure 1. Composition of Domestic Extractionin Jiangsu Province from 1990–2007

Source: Calculated using the material flow analysis approach based on Jiangsu statistical data.

0.00

50.00

100.00

150.00

200.00

250.00

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Year

Dom

estic

ext

ract

ion

(mill

ion

ton)

Biomass Fossil fuels Minerals



The development of DMI of Jiangsu Province is shown in Figure 3. The share ofmaterial imported in DMI increased sharply and exceeded DE after 2004, ranging between53.3 percent in 2004 and 57.9 percent in 2007. Resource productivity can be evaluated using

Figure 2. Compositions of Imports in JiangsuProvince from 1990–2007


0.00

50.00

100.00

150.00

200.00

250.00

300.00

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Year

Impo

rts (m

illio

n to

n)

Energy Biomass Minerals Products

Figure 3. Compositions of Direct Material Input (DMI)in Jiangsu Province from 1990–2007


0.00

100.00

200.00

300.00

400.00

500.00

600.00

1990

1991

1992

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1996

1997

1998

1999

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2001

2002

2003

2004

2005

2006

2007

Year

DM

I (m

illio

n to

n)

DE Imports



GDP per DMI. The resource productivity of Jiangsu Province increased from 1990 to 2007(Figure 4), by more than the average level for China in the corresponding period (Xu andZhang, 2007). Compared with Japan and 15 countries of the EU (the EU-15), the naturalresource productivity of Jiangsu is much lower. For example, the resource productivity ofJiangsu in 2000 was only equivalent to approximately 45 percent of that of the EU-15 in 2000(Eurostat, 2002), or 15 percent of that of Japan in 1996 (Matthews, 2000).

If the current trend of resource consumption continues, it will be difficult for JiangsuProvince to sustain desirable levels of social and economic development. Having cognizedthe puzzle, the government firstly stipulated specific goals related to energy efficiency anddeclared that it would seek to reduce the energy intensity by 20 percent during the eleventhfive-year plan period (2006–2010).

V. Results and Discussion

1. Unit Root TestTable 2 reports the results of the ADF and PP tests on the integration properties of the realGDP and DMI for Jiangsu Province. Results of the two tests indicate that the two series are

Figure 4. Resource Productivity of JiangsuProvince from 1990–2007

Source: Calculated using the material flow analysis approach based on Jiangsu statistical data.Note: GDP is calculated using 1990 as the base year.

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1990

1991

1992

1993

1994

1995

1996

1997

1998

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2001

2002

2003

2004

2005

2006

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Year

GD

P/di

rect

mat

eria

l inp

ut (t

hous

and

yuan

/ton)



nonstationary in their levels. However, the series are stationary after taking the firstdifference. This indicates that the integration of real GDP and DMI of Jiangsu Province isof order one; that is, I(1) at the 5-percent level of significance.

2. Cointegration TestGiven that integration of the two series is of the same order, we use the Johansen methodto test whether the two series are cointegrated over the sample period. Because the Johansenapproach is sensitive to the lag length used, we conduct a series of nested likelihood ratiotests on level VAR to determine the optimal lag length prior to performing cointegrationtests. Given the sample size, we consider a maximum lag length of two. The optimal laglength is found to be one. We follow this lag structure for the rest of the estimations. Table 3shows the results of the Johansen test. In Table 3, both the results of trace tests andmaximum eigenvalue tests unanimously lead to the same conclusion that there is onecointegrated relationship between real GDP and DMI, at the 1-percent level of significance.The normalized cointegrating coefficient (1.22) is shown in the last row of Table 3, and thesigns of the variables conform to the general cognition about resource use and economicgrowth; that is, there is a positive relationship between real GDP and DMI for JiangsuProvince.

Table 2. Unit Root Test Results of GDP and DirectMaterial Input Logarithmic Series

ADF test PP test Variables

Level First difference Level First difference

LGDP –2.2659 –4.1110*** –1.7724 –3.2148**

LDMI –1.8767 –3.1992** –1.0485 –3.2597**

Note: The optimal lag lengths on the variables in augmented Dickey–Fuller (ADF) regressions areselected using the Akaike Information Criterion. The optimal lag lengths on the variables in Phillips–Perron (PP) regressions are selected using the Newey–West method. *** and ** denote significanceat the 1 and 5% levels, respectively.

Table 3. Johansen Cointegration Estimation Results betweenLogarithmic Series of GDP and Direct Material Input

Number of cointegration Eigenvalue Trace statistic Max-eigenvalue statistic

None 0.8989 37.0620*** 36.6680***

At most 1 0.0243 0.3940 0.3940

Normalized cointegration equation LGDP = 1.22*LDMI

Note: *** denotes significance at the 1% critical level.



3. Granger Causality TestEvidence of cointegration implies the existence of causality, at least in one direction. However,it does not indicate the direction of the causal relationship. Hence, to shed light on thedirection of causality, we perform the VECM-based causality tests. As mentioned in Section IIIwe have performed three tests for Granger causality: (i) short-run causality, the significanceof the sum of lagged terms of each explanatory variable determined using the Wald test; (ii)long-run causality, the significance of the ECT determined using the t-test; and (iii) strongcausality, the joint significance of the sum of lagged terms of each explanatory variable andthe ECT determined using the Wald test.

The results of these tests are displayed in Table 4 and allow us to draw the followingconclusions:

l In the case of real GDP, no Granger causality is detected between real GDP and DMIin the short run. The coefficient of the ECT is found to be significant at the 1-percent levelin the real GDP equation, which indicates that given any deviation in the ECT, both variablesin the VECM would interact in a dynamic fashion to restore long-run equilibrium. Results ofthe significance of interactive terms of change in DMI, along with the ECT in the GDPequation, are consistent with the presence of Granger causality running from DMI to realGDP, which indicates that whenever there is a shock to the system, DMI will make short-run adjustments to re-establish long-run equilibrium.

l Conversely, there is no indication of causality running from real GDP to DMI eitherin the short run or the long run. This indicates that resource use can be treated as exogenouswithin the system.

VI. Policy Implications and Conclusions

This paper has presented the first empirical analysis of the dynamic relationship between

Table 4. Estimation Result of Vector Error Correction Model forLogarithmic Series of GDP and Direct Material Input

Source of causation (independent variable)

Short run Long run Dependent

variable GDP △DMI ECT ECT&△GDP ECT&△DMI

△GDP － 0.4856 –5.4122*** － 14.6902***

△DMI 0.1853 － 0.3780 0.0714 －

Notes: DMI denotes the direct material input. ECT denotes the error correction term. △ denotes the firstdifference of the series. *** denotes significance at the 1% level.



material consumption and economic growth in East China employing a VECM. Using annualdata from 1990 to 2007, we apply the MFA approach to examine material input, focusing onthe key indicator DMI and its composition and structure. To complement the findings ofcointegration analysis, we perform various causality tests to throw light on the causal linksof DMI and real GDP. The empirical results provide support for a long-run relationshipbetween the variables, indicating that economic growth is positively related to resourceuse in the long run. The causality results support the argument that resources are a limitingfactor in economic growth. Resource use exerts a positive causal influence on the economicgrowth of Jiangsu in the long run.

The finding of the existence of unilateral Granger causality running from resource useto economic growth in the long run implies that Jiangsu Province is a resource dependenteconomy. We can explain this result from the perspective of economic structure and resourceuse structure. East China has been on a track of industrialization and urbanization since theintroduction of reform and opening-up policy in 1978. The significant amount of economicgrowth in Jiangsu has been greatly fuelled by industrial growth. This century,industrialization has sped up. The heavy industry sector, which requires intensive use ofresources, continues to contribute to total industry output, making up 57.0 percent ofoutput in 2000 and 67.2 percent in 2006. Thereby, energy consumption in industry hasincreased sharply, at an average annual rate of around 14.8 percent, and the share ofindustry consumption in total energy consumption had increased to 82.2 percent by 2006.2

The economic structure and resource usage allocation structure (the high ratio of industryoutput in GDP and high rate of resource input in industry) mean that the growth of industryresource consumption leads to the growth of GDP by increasing industry output. However,the resource supply shortage will negatively influence the growth of industry output andthen restrain the growth of aggregate output. The increase in resource consumption isinfluenced by not only the growth of GDP, especially industrial output, but also industrialstructure and energy usage efficiency, so there is no Granger causality running fromaggregate output to resource consumption.

The empirical analysis of resource use and economic growth has important policyimplications for economic growth and resource conservation strategies for regions such asEast China undergoing the process of rapid industrialization and urbanization. The unilateralcausal link from resource use to economic growth indicates that, with the current economicstructure remaining in place, the shortage of local resource supplies will hamper the growthof output in the future. Sufficient resources are a necessity condition for continued strongeconomic growth. Therefore, Jiangsu Province must continue to import resources to boost

2 Source: Calculated by using Jiangsu statistical data.



output growth. Given that economic growth is the outcome of growth in inputs and increasesin the productivity of the inputs as well, Jiangsu Province also needs to enhance resourceusage efficiency in production, for example through recycling material and throughtechnologies that reduce heat waste, and to switch to use of renewable energy, bysubstituting coal power with hydropower and nuclear power, for instance.

Jiangsu Province should step up the industrial restructuring and updating process,endeavoring to advance the manufacturing industry and to develop high-technology andmodern service industries to replace high energy dependent industries (ferrous metal,nonferrous metal, textile, chemical and nonmetal mineral product industries). If JiangsuProvince can jump from heavy industrialization to a more efficiency-oriented and lessresource-depleted development mode, more resources can be saved and the quality ofeconomic development can be improved.

The main contribution of this paper is that for the first time an attempt is made toexamine the dynamic relationships between economic development and resource use inEast China. Although the findings of this analysis may be unique to East China due to itsspecific institutional and structural characteristics, the integration of accounting methodsfor resource use and econometric techniques employed in this study can be readily extendedto analyze other regions also undergoing the process of rapid industrialization andurbanization. The test results support the finding of the existence of a causal relationshipbetween resource use and economic growth in East China. However, it is important to notethat given the relatively small span of this analysis, in terms of policy implications, theresults must be interpreted with due caution.

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(Edited by Jing Qiu)

Temporal Causal Relationship between Resource Use and Economic Growth in East China

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