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Ecological Indicators xxx (2014) xxx–xxx

Contents lists available at ScienceDirect

Ecological Indicators

j o ur na l ho me page: www.elsev ier .com/ locate /eco l ind

riving force analysis of water footprint change based on extendedTIRPAT model: Evidence from the Chinese agricultural sector

hunfu Zhaoa, Bin Chena,b,∗, Tasawar Hayatb,c, Ahmed Alsaedib, Bashir Ahmadb

State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, ChinaNAAM Group, Faculty of Science, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Mathematics, Quaid-i-Azam University, 45320 Islamabad, Pakistan

r t i c l e i n f o

rticle history:eceived 13 January 2014eceived in revised form 22 April 2014ccepted 28 April 2014

eywords:ater footprint

TIRPAT modelriving force analysis

a b s t r a c t

The key impact factors of Chinese agricultural sector water footprint change play an important role inaddressing China’s current water resource pressures. However, commonly used water footprint account-ing method can only describe the situation related water resource consumption and is difficult to analyzethe driving forces that shape such a situation. In this study, by taking the Chinese agricultural waterfootprint from 1990 to 2009 as a case, we explore the influencing factors of population, affluence, urban-ization level, and diet structure on agriculture products-related water footprint change based on anextended STIRPAT model. Empirical results reveal that the all examined factors as positive driving forceshave pushed forward Chinese agricultural sector water footprint increases from 549.68 Gm3 in 1990 to

3

hinese agricultural sector 1016.64 Gm in 2009. Furthermore, the absolute values of normalized coefficients indicate that popu-

lation has the largest positive contribution to increase the Chinese agricultural water footprint with anelastic coefficient of 0.9995, followed by affluence, diet structure, and urbanization level with elasticcoefficients of 0.0874, 0.2662, and 0.2174, respectively. Finally, policies aiming at addressing currentChinese agriculture-related water resource crisis have been discussed.

© 2014 Elsevier Ltd. All rights reserved.

. Introduction

Human activities have placed great pressure on the environ-ent’s water systems by freshwater resource appropriation andater resource pollution (Liu and Yang, 2012). China, as a devel-

ping country with the largest population in the world, is undern unprecedented severe water crisis because of rapid industri-lization and urbanization. Among China’s different sectors, thegriculture sector, with the largest water consumption share andater consumption intension, has played a key role in the water

esource crisis due to its relative water consumption and low waterfficiency (Jiang, 2009). With population growth and economicevelopment, such a crisis is expected to further intensify in comingecades.

Conventional water resource accounting methodology has

Please cite this article in press as: Zhao, C., et al., Driving force analysEvidence from the Chinese agricultural sector. Ecol. Indicat. (2014), ht

ocused on utilizing domestic water resources to satisfy the finalater demand (Hoekstra and Mekonnen, 2012). However, trade

lobalization has created a linkage among different countries,

∗ Corresponding author at: No. 19, Xinjiekouwai Street, Beijing 100875, China.el.: +86 10 58807368; fax: +86 10 58807368.

E-mail address: chenb@bnu.edu.cn (B. Chen).

ttp://dx.doi.org/10.1016/j.ecolind.2014.04.048470-160X/© 2014 Elsevier Ltd. All rights reserved.

which makes it possible for consumers in a specific nation toconsume “embodied water” beyond their borders through goodsand services trade. Therefore, to broaden the scope of traditionalwater resource accounting, Hoekstra and Hung (2002) introducedthe concept of the water footprint in an analogy of the ecolog-ical footprint to measure the impact of human consumption onthe freshwater appropriation, which is defined as the total vol-ume of water resource used, directly or indirectly, to producethe goods and services consumed by the inhabitants of a certaingeographical region. However, Pfister et al. (2009) argued thatthe volumetric-oriented water footprint methodology developedby Water Footprint Network ignored the environmental impactrelated to water use. Ridoutt and Pfister (2010) thereby sug-gested incorporating water stress characterization factors into themodified impact-oriented water footprint methodology. Despite aconsistent water footprint concept is still not reached, both of previ-ous two water footprint assessment approaches have been widelyemployed to assess the impact of human activity on freshwaterresource withdrawal at local, national and global scales (Dong et al.,

is of water footprint change based on extended STIRPAT model:tp://dx.doi.org/10.1016/j.ecolind.2014.04.048

2013; Mekonnen and Hoekstra, 2011; Pfister et al., 2011; Ridouttet al., 2012; Shao and Chen, 2013; Zhao et al., 2010). Although thesewater footprint assessments can illustrate the situation of waterresource consumption for a specific geographical location, it cannot

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eveal the driving forces that shape the situation, which makes itifficult to develop most potential policies to control anthropogenicater resource pressures. Therefore, investigations that explore the

elationship between drivers and corresponding water footprinthange should be conducted.

Despite the complexity of factors influencing water footprinthanges, the major direct determinant factors of the water foot-rint for a specific accounting boundary essentially depend on:he volume of consumption, consumption pattern, and the waterootprint of the commodities consumed (Hoekstra and Chapagain,007; Hoekstra et al., 2011). All the other factors can influence theater footprint change mainly by posing a direct or indirect effect

n these three direct factors. The volume of consumption is closelyelated to the income level because consumers with higher incomeenerally consume more products, which ultimately translates into

water footprint increase (Hoekstra and Chapagain, 2007). Addi-ionally, the population growth will enlarge the requirement ofood consumption, which also results in the increased water foot-rint. For the consumption pattern, it is significantly affected by theiet composition because the water footprint for different agricul-ural product varies remarkably and water resource input per meats generally much higher than grains (Molden, 2007). The waterootprint of the commodities consumed is not only highly related tohe growth condition but also significantly influenced by the wateresource use efficiency (Hoekstra and Mekonnen, 2012). Undoubt-dly, previous studies have presented a clear qualitative descriptionegarding the relationship between major influencing factors andater footprint change. However, the precision of the relationship

etween the influencing factors and the water footprint continueso be insufficient, in particular based on econometric techniques.onsidering the fact that quantitative relationship between China’sgriculture related water resource pressure and potential drivingorce can provide important reference for decision maker to for-

ulate policies from consumption side to address China’s currentater crisis, a quantitative exploration of drivers of Chinese agri-

ulture related water footprint change is urgent.Many previous methods such as structural decomposition anal-

sis (SDA), Logarithmic-Mean Divisia Index (LMDI), and stochasticmpact by regression on population, affluence and technologySTIRPAT) have been employed to examine the social and economicriving force of anthropogenic environmental degradation (e.g.,ng, 2005; Rosa and Dietz, 2012; Zhang et al., 2012). Among these,MDI and STIRPAT are the two most widely used techniques forxamining such drivers. The STIRPAT model, which is derived fromhe IPAT model, has been widely employed to analyze the drivingorce of energy consumption, CO2 emission, and ecological foot-rint (Dietz and Rosa, 1994; Shafiei and Salim, 2013). Comparedith the LMDI model, the STIRPAT model can examine more fac-

ors and provide more detailed and reliable information for shapingnvironment friendly strategies (Wang et al., 2013). Therefore, wettempt to employ the STIRPAT to analysis the driving force ofhinese agricultural water footprint changes.

Current studies examining the driving force analysis for anthro-ogenic environmental impacts based on the STIRPAT modelxactly provide useful references for the exploration of the driversf Chinese agricultural water footprint changes, while they still suf-er from the following limitations: (1) the influencing factors focusn the population, technology level, and economic level; the poten-ial factors still need to be further explored; (2) the anthropogenicnvironmental impacts related drivers analysis mainly emphasizeshe carbon emission, while the environment’s water system haseceived few attention; (3) the multicollinearity among indepen-

Please cite this article in press as: Zhao, C., et al., Driving force analysEvidence from the Chinese agricultural sector. Ecol. Indicat. (2014), ht

ent variables should be addressed with an effective approach.herefore, the objective of this study includes following threespects: (1) examining the main factors that affect Chinese agricul-ural water footprint change based on an extended STIRPAT model.

PRESStors xxx (2014) xxx–xxx

(2) The proportion of calories from meat products is introducedas a useful indicator to measure the diet structure transition andthe effect of such an indicator on water footprint change has beenexplored; and (3) to avoid the multicollinearity, we employ theridge regression to fit the extended STIRPAT model.

The remaining sections of this study are organized as fol-lows: Section 2 reviews the previous application of the STIRPATmodel. Section 3 presents the concrete method of water footprintaccounting; the basic the STIRPAT model form and the model afterextension, the methodology to deal with multicollinearity, and thedata sources. Section 4 describes the main results in this study. Sec-tion 5 discusses the results and some potential policies to alleviateChinese agricultural water resource pressure. Finally, limitationsand further research are discussed.

2. Literature review

2.1. Evolution of the IPAT model

The “IPAT” model was initially introduced by Ehrlich andHoldren (1971) to analyze the principle driving forces of anthro-pogenic environmental impacts. The main idea of IPAT is thatenvironmental impacts are the multiplicative function of popula-tion size (P), affluence (A) described as per capita consumption orproduction, and technology (T) described as impact per unit of con-sumption or production. However, the basic form of IPAT can onlyexamine the effect of the aforementioned three drivers on the envi-ronmental impact. To explore the additional factors that affect theenvironmental impact, Waggoner and Ausubel (2002) decomposedT from original IPAT model into C and T and developed the ImPACTmodel. Furthermore, Xu et al. (2005) noted that social develop-ment and society’s capability factor to decrease environmentalimpact should be added into ImPACT and developed an extendedImPACTS. Similarly, Schulze (2002) argues that human behavior isalso a key driving force of environmental impact, and expands IPATto IPBAT, where human behavior is represented by the parame-ter of B. However, Roca (2002) and Diesendorf (2002) noted thatSchulze’s application was problematic due to unclear mathemat-ically clarification, asserting that the previous models can onlydisplay the linear variation between independent variables anddependent variables. Therefore, York et al. (2003) modified theoriginal IPAT model by offering the innovative stochastic modelSTIRPAT to overcome the limitations of the original model andmake it useful for hypothesis testing. Since then, the STIRPAT modelhas been widely applied in the influencing factors analysis of eco-logical footprint (EF), greenhouse gas (GHG) emissions and energyfootprint changes.

2.2. Drivers of anthropogenic environmental impact

A number of studies have explored driving forces that contributeto anthropogenic environmental impacts based on the STIRPATmodel, most of which use EF to denote the anthropogenic envi-ronmental impact of human activities, indicating that the effect ofpopulation and affluence factors are the most significant factors. Forexample, Rosa et al. (2004) concluded that population and affluenceare two principal anthropogenic drivers for environmental impact.Dietz et al. (2007) further examined the effect of population size,affluence, urbanization, economic structure, and age distributionon anthropogenic environmental stressors based on cross-nationalEF data, which yielded similar results compared with those of Rosa

is of water footprint change based on extended STIRPAT model:tp://dx.doi.org/10.1016/j.ecolind.2014.04.048

et al. (2004). In recent years, similar research has also been con-ducted for China. At a national scale, Xu and Cheng (2005) employedthe EF data of China’s provinces in 1999 to explore drivers of anthro-pogenic environmental impacts, revealing population is the major

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riving force. At the local scale, Jia et al. (2009) found populations the most dominant driver to push forward the EF increase ofenan province, China. Taking Sichuan province as a case, Tangt al. (2011) illustrated that population was the principal drivingorce for the EF variation in Sichuan province from 1995 to 2008.

GHG emission is also a commonly used indicator to reflecthe anthropogenic environmental impact, which has also receivedttention with regards to global climate change. Therefore, theITIRPAT model can be applied to parse the driving force of suchn environmental impact. At the global scale, York et al. (2003)mployed 146 nations’ CO2 emission data to analyze the drivingorce of the CO2 emission and found population as the major driver.an et al. (2006) investigated the impacts of population, affluencend technology on the total CO2 emissions of countries at differ-nt income levels over the 1975–2000 period and concluded thatconomic growth had the highest impact. At the national scale,oth Li et al. (2012) and Zhu and Peng (2012) have confirmed thathe effect of affluence is much more significant compared withther influencing factors to drive China’s CO2 emission. However,y examining the effect of population, urbanization level, GDP perapita, industrialization level and energy intensity on China’s CO2mission from 1978 to 2006, Lin et al. (2009) stated that popula-ion has the largest potential effect on CO2 emission for China. Athe local scale, taking the Shanghai and Guangdong provinces as axample, respectively, Wang et al. (2011, 2013) demonstrated thatopulation has the greatest impact for city CO2 emission increaseased on the STIRPAT model.

Additional factors, including urbanization level and techno-ogical advancement, have also been proven to have significantffects on anthropogenic environmental impact. Poumanyvong andaneko (2010) investigated the influence of urbanization on CO2mission in a panel of 99 countries over the period 1975–2005nd suggested urbanization has a positive and significant impactn CO2 emissions for each income group but its impact differsmong various income groups. Wang et al. (2010) also concludedhat urbanization was the most dominant driving forces of ecolog-cal footprint change in West Jilin province, Northeast China from986 to 2006. When taking the regional difference into account,hang and Lin (2012) found that urbanization has a bigger impactn CO2 emissions than other factors in most provinces in China.dditionally, Martínez-Zarzoso and Maruotti (2011) analyzed the

mpact of urbanization on CO2 emission in developing countriesrom 1975 to 2003 and the urbanization’s impact differs consider-bly for different groups of countries. More recently, Wang et al.2012) verified urbanization level as the most fundamental factoreading to increase in Beijing’s CO2 emission by conducting empir-cal research on influencing factors for CO2 emission. In addition,he results from Wang et al. (2013) indicated that technology levellso has a significant effect on anthropogenic CO2 emission.

Previous research based on the STIRPAT model has deepenedur understanding regarding the relationship between anthro-ogenic environmental impacts and the driving force of such an

mpact. Overall, the potential influencing factors mainly includeopulation, affluence, technology and urbanization. However, thenvironmental impact in the current study focused on EF, GHGmission, while little progress has been made to determine the rela-ionship between driving forces and the water footprint. This gapas generated a barrier to formulate the most potential policies toeduce human effects on environment’s water systems. Althoughong et al. (2006) attempted to employ the STIRPAT model to ana-yze the impact of population, GDP per capita, crop transpirationemand per area and land demand per food yield in China, this

Please cite this article in press as: Zhao, C., et al., Driving force analysEvidence from the Chinese agricultural sector. Ecol. Indicat. (2014), ht

odel can be further be improved. For example, multicollinear-ty needs to be addressed in this model. Additionally, the potentialnfluencing factors such as diet structure and urbanization levelave not been explored in their research. In this paper, we extend

PRESStors xxx (2014) xxx–xxx 3

the STIRPAT model to analyze the drivers of the Chinese agriculturalsector’s water footprint by innovatively introducing the propor-tion of calories from meats product as an indicator to measure dietstructure transition effects. In addition, ridge regression has beenemployed to address the problem of multicollinearity.

3. Methodology

3.1. Water footprint accounting

The top-down and bottom-up approaches are two alternativeapproaches to calculate national water footprint consumption.Compared with the bottom up approach, the top-down has twomain limitations: (1) the results based on top-down approach areslightly higher if the stocks of water-intensive products increaseover a year and (2) a delay may occur between the time of water usefor production and the time of trade (Hoekstra et al., 2011). There-fore, we calculate the Chinese agricultural sector’s water footprintbased on a bottom-up approach, which can be expressed as follows:

WFChi,agr =∑

n

C[p] ∗ WFpro,ave[p] (1)

where C[p] is consumption quantity of p by inhabitant within China,WFpro,ave[p] refers to the water footprint for product p. Because partof the consumed p comes from the China itself and others fromimport, WFpro,ave[p] is thereby estimated by employing the averagewater footprint, which can be described as:

WFpro,ave[p] =M[p] ∗ WFpro,Chi[p] + ∑

ne(Ti[ne, p] ∗ WFpro[ne, p])

M[p] +∑

neTi[ne, p]

(2)

where M[p] is the production quantity of product p in China.WFpro,Chi[p] is the water footprint of product p when produced inChina. Ti[ne, p] refers to the import quantity of product p from coun-try ne. WFpro[ne, p] represents the water footprint of product p whenproduced in country ne. As it is difficult to trace the import originfor each product, we substitute the WFpro[ne, p] with global aver-age water footprint of the product p. The assumption here is thatthe water footprint for product p depends on the relative volumeof domestic production and import.

3.2. Extended STIRPAT model

The basic STIRPAT model can be expressed as follows:

I = aPbAcTde (3)

where I denotes the anthropogenic environmental impact, P standsfor the population, A reflects the affluence and T is the technologicallevel. For the coefficients, a is the constant term and b, c, d is theexponential terms of P, A and T; and e is the error term.

It should be pointed out that the meaning of technology (T) haschanged when the IPAT is extended as STIRPAT. Technology (T)in IPAT is deterministic, while technology (T) in STIRPAT model isimplicitly assumed depending on affluence, population and otherdrivers, which should be included in the error term rather thanseparately estimated (Wei, 2011). In addition, we have developeda linear regression by converting all factors to natural logarithmicform to facilitate estimation and hypothesis testing. The modifiedSTIRPAT model thereby can be further expressed as follows:

ln I = a + b(ln P) + c(ln A) + ε (4)

is of water footprint change based on extended STIRPAT model:tp://dx.doi.org/10.1016/j.ecolind.2014.04.048

where ε is the value of e after taking logarithm form. Consideringthat Eq. (4) can only examine a limited number of variables includ-ing population, affluence and technological level, to further explorethe potential influencing factors such as urbanization level and diet

IN PRESSG ModelE

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Table 1Matrix of correlation between variables.

ln I ln P ln D ln A ln U

ln I 1 0.996** 0.980** 0.987** 0.981**

ln P – 1 0.989** 0.985** 0.983**

ln D – – 1 0.961** 0.950**

ln A – – – 1 0.985**

ous multicollinearity among variables. It can be judged that thecoefficient from OLS regression is not reliable. Therefore, to yieldmore reliable parameter estimates, a regression that can eliminatemulticollinearity should be conducted.

Table 2Influencing factors of water footprint by OLS.

Unstandardizedcoefficients

Std. error t-Statistic Sig. VIF

C −6.365 2.59 −2.457 0.027P 5.755 1.537 3.744 0.002 388.889D −0.417 0.242 −1.722 0.106 120.866

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tructure change on water footprint change, we extend the originalTIRPAT model by taking the urbanization level and the diet struc-ure into consideration. The extension of the STIRPAT model can beurther expressed as follows:

n I = a + b(ln P) + c(ln A) + f (ln U) + g(ln D) + ε (5)

here U stands for the urbanization level, which is reflected by theercent of urban population on the total population. D displays the

nfluencing factors of diet structure change, and the proportion ofalories of meat products is proposed to measure such an effect.ased on the concept of ecological elasticity introduced by Yorkt al. (2003), the driving force coefficients of b, c, g, and f, respec-ively refer to the percentage change in I in response to a 1% changen the driving force with other factors held constant.

.3. Multicollinearity test

The multicollinearity refers to a statistical phenomenon thatwo or more predictor variables in a multiple regression are highlyorrelated, which can be tested by valuing the variance inflationactors (VIF) of the variables. If the VIF is larger than 10, it indi-ates that a severe multicollinearity existed. Multicollinearity willesult in large standard error among related independent variables.hese errors are mainly reflected by regression model parameters’arge variance, which will significantly reduce the stability of theegression model.

.4. Ridge regression

The ridge regression can handle multicollinearity by incorpo-ating a small positive k to the diagonal of the quantity (Hoerl andennard, 2000). The ridge estimator is obtained by

∧= (X ′ X + kI)−1g (6)

here g = X ′ Y , k is the ridge parameter or the biasing parameterhat satisfies k ≥ 0 where I is an identity matrix.

Although ridge regression is a biased estimation, a minimal bias can significantly improve the estimation and reduce the overallean squared error. The ridge regression can be converted back to

rdinary least square (OLS) regression as a special case given that = 0 (Dietz and Rosa, 1994). Considering the fact that mean squarerror is reduced at the cost of a variance in parameter estimation,n appropriate ridge regression coefficient k thereby should notnly reduce the VIF under acceptable range but also should be asmall as possible. Within the econometric literatures, several wayso decide the optimal k value have been proposed. The ridge tracelot method is a widely applied method that has been employed in

his case. The∧ˇ coefficients are plotted by changing K with a step

ength of 0.02 within [0,1] and the optimal value for k is chosen at

he point where∧ˇ appears to stabilize.

.5. Data description

The food consumption data used in this study are extractedrom food balance sheets (FBSs) by FAO (http://faostat3.fao.org/aostat-gateway/go/to/download/FB/FB/E), which has made a dis-inction between the quantities fed to livestock, used for seed,sed for food consumption and used for others. The item of foodonsumption excluding the agricultural products fed to livestockas been selected to calculate the water footprint for cereals, cash

Please cite this article in press as: Zhao, C., et al., Driving force analysEvidence from the Chinese agricultural sector. Ecol. Indicat. (2014), ht

rops, fruit, and vegetables in order to avoid double accounting. ForFpro[ne, p] and WFpro,Chi[P] except for cereals and most of cash

rops, we directly obtain the values from Chapagain and Hoekstra2004). Considering the large share of cereals and cash crops in

ln U – – – – 1

** Significant at the 1% level.

the agriculture structure, we estimate the water footprint of cere-als and cash crops in China based on the crop water requirements(m3/ha) and the yield of cereals and cash crops (ton/ha). The dataof crop water requirement comes from Chapagain and Hoekstra(2004), and the yield data is extracted from FAO database. The pop-ulation, domestic gross product (GDP), and urbanization rate dataare collected from China Statistical Yearbook from 1990 to 2009. Toeliminate the effect of inflation, we adjust GDP data at 2000 con-stant price. The diet structure indicator is also collected from FAOdatabase.

4. Results

4.1. Accounting for water footprint in the Chinese agriculturalsector

Overall, the Chinese agriculture related water footprintincreased from 549.68 Gm3 in 1990 to 1016.64 Gm3 in 2009 (Fig. 1),which nearly doubled during the research period. In terms of theindividual types of Chinese agricultural water footprint, the shareof water footprint increased for all types except for cereals. In par-ticular, the proportion of the water footprint from livestock hasincreased from 15.12% in 1990 to 30.15% in 2009, which reflects thatliving standards in China have improved significantly during pasttwo decades. The water footprint structure transformation in theChinese agriculture sector is consistent with the fact that Chinesediet structure has shifted toward a meat-intensive structure.

4.2. Ordinary least square regression of the model

Initially, a correlation analysis of each variable is performedusing SPSS 18.0 software. According to Table 1, ln I has a significantcorrelation with ln P, ln D, ln A, and ln U.

Next, we conducted OLS regression to estimate the coefficientsof the model and summarized the results in Table 2. The coeffi-cient of determination (R2) for the model is 0.994, and the F-test ishighly significantly with an F-static of 643.397 at 0.1% significantlevel. However, the variable of D, U, and A cannot pass the t-test.Furthermore, the VIF value shows that all variables are larger than10 ranging from 49.89 to 388.889, which indicates there is seri-

is of water footprint change based on extended STIRPAT model:tp://dx.doi.org/10.1016/j.ecolind.2014.04.048

U −0.247 0.168 −1.471 0.162 79.094A 0.061 0.051 1.209 0.245 49.89R square 0.994 Adjusted R square 0.993F 643.397 Sig. 0.000

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and th

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Fig. 1. The total Chinese agricultural water footprint

.3. Ridge regression

As shown in Fig. 2, we obtain a ridge trace curve by plotting thealues of the coefficients against the successive values of k with atep length of 0.02. The number 0.2 is selected as the ridge param-ter because the coefficients of independent variables tend to betable at this point. When k = 0.2, the overall fit is very good with aoefficient of determination (R2) of 0.9880. The F-test of the models highly significant with an F-statistic of 309.2846 at the 0.1% signif-cance level. All estimated coefficients passed the significance tests

ith t-statistic at 0.1% significance level. The VIF for all estimatedoefficients is far less than 10, ranging from 0.1183 to 0.5413. There-ore, the STIRPAT model in this study can be expressed as follows:

n I = 0.9995 ln P + 0.2662 ln D + 0.2174 ln U + 0.0874 ln A

+ 1.7891 (7)

. Discussion

As shown in Eq. (6), the signs of all the estimated parameters areositive, which indicates that all the variables in this study promotehe water footprint for the Chinese agricultural sector. Further-

ore, the absolute values of the normalized coefficient in Table 3eflect the contributions of these influencing factors. The contri-ution of influencing factors can be ranked in descending orders follows: population (25.20%), affluence (24.01%), diet structure23.74%), and urbanization (22.59%).

China’s population, as the largest positive driving force, hasxperienced continuous growth from 1.143 billion in 1990 to.335 billion in 2009. The regression model in Eq. (6) indicateshat the elasticity of the water footprint in relation to popula-ion during the period of this study is 0.9995, which indicates that

Please cite this article in press as: Zhao, C., et al., Driving force analysEvidence from the Chinese agricultural sector. Ecol. Indicat. (2014), ht

very 1% increase in population will result in 0.9995% water foot-rint increase. Although China’s One Child Policy has resulted in aontinuous decline for the natural growth rate of China’s popula-ion for more than 30 years, the population increase is still large

e share for each individual type from 1990 to 2009.

during the study period because of the substantial populationbase. Such a large population increase has inevitably resulted inmore agriculture product requirements, which immediately trans-lates into an increased water footprint. Considering China’s alreadylarge population base, the population will continue to increase byapproximately 100 million in next two decades (Peng, 2011). There-fore, the water resource pressure caused by population size willintensify in the next two decades years and China should continueto stabilize the population size.

Affluence, as the second largest positive driving force, also hada prominent influence on the Chinese agricultural water footprintincrease during the study period. The significant contribution ofaffluence factor owes to the fact that China has been witnessed therapid economic growth since the adoption the reform policies inthe late 1970s, which has significantly improved the income percapita during the research period. Such an income improvementwill encourage people to pursue a high quality lifestyle with highquality food comfortable living rather than satisfied with enoughprovision of food supply, which may impose a great pressure onfreshwater resource (Hubacek et al., 2009). However, the relation-ship between income level and water footprint consumption isnot a simple linear correlation because the water consumptionintensity of each type good or service or service differs and theimprovement in production technology constantly varies. Whenthe income per capita reaches a higher level, the water resourceconsumption can benefit from economic growth because peoplemay employ advanced technologies to improve the water use effi-ciency, thus alleviating the water resource pressure to some extent.Such a relationship between environment pressure and affluenceis commonly described by an inverted-U curve (Cavlovic et al.,2000; Dietz et al., 2007). Although China still does not reach theturning point of the environment Kuznets curve, great efforts havebeen made to enhance agricultural water productivity (Yang et al.,2012). This explains why the growth rate of water footprint is lowerthan the affluence level in China during the study period, with the

is of water footprint change based on extended STIRPAT model:tp://dx.doi.org/10.1016/j.ecolind.2014.04.048

elasticity of affluence being only 0.0874.Diet structure also shows a significant positive influence on

the Chinese agricultural water footprint increase during the studyperiod with an elastic coefficient of 0.2662. Such an elastic

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Fig. 2. Ridge trace curve.

Table 3Ridge regression results (k = 0.2).

Items Non-normalized coefficient Normalized coefficient Std. error t-Statistic Sig. t VIF

P 0.9995 0.2520 0.03855 25.92941933 0.0000 0.1183D 0.2662 0.2374 0.02331 11.42124532 0.0000 0.5413U 0.2174 0.2259 0.0179 12.14589735 0.0000 0.4335A 0.0874 0.2401 0.0062 14.12824436 0.0000 0.3616Constant 1.7891 0.1432 12.4933228 0.0000

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2ustolfhiaiutn

R square 0.9880F 309.2846Sig. F 0.0000

oefficient reveals that every 1% increase in the proportion of calo-ies from livestock will lead to 0.2662% water footprint increase.he significant contribution of a meat-biased diet structure tran-ition to the water footprint increment can be explained byivestock’s consumption of feed crops, drinking water and service ints lifetime before it produces some output compared with cereals,

hich ultimately results in the fact that a meat-biased diet struc-ure is more water intensive compared with a cereals-biased diettructure (Hoekstra and Chapagain, 2007). Similar to the results inhis study, Vanham et al. (2013) found that diet structure affects theater footprint of the EU significantly and a shift toward a vegetar-

an diet will result in a substantial water footprint reduction. In lightf the significant contribution of the diet structure factor, shiftingrom a meat intensive diet to a vegetarian diet is a promising way toelieve China’s current agriculture related water resource pressure.n addition, a vegetarian diet transition will also promote humanealth. Therefore, it is a win–win strategy.

The urbanization rate of China’s population has risen from6.41% in 1999 to 46.59% in 2009. Although the contribution ofrbanization is much smaller than population, affluence, and diettructure factor, it still has a huge impact for the Chinese agricul-ural sector’s overall water footprint increase. The elastic coefficientf urbanization is 0.2174, suggesting that every 1% urbanizationevel increase will cause a 0.2174% water footprint increase. In fact,rom the perspective of agricultural production, urbanization mayave an inhibitory effect on the agricultural sector’s water footprint

ncrease because urbanization will transfer the labor force fromgricultural sectors in rural areas to industrial and service sectors

Please cite this article in press as: Zhao, C., et al., Driving force analysEvidence from the Chinese agricultural sector. Ecol. Indicat. (2014), ht

n urban areas. However, from the perspective of agriculture prod-ct consumption, urbanization exhibits a significant positive effecto agricultural sector’s water footprint increase because there is sig-ificant food consumption disparity between urban and rural areas

due to the dualistic structure of China. The high living standards andaccessibility to richer diets in urban areas therefore bring more agri-culture related water pressure compared with rural areas. AlthoughChina has experienced significant urbanization in the last twodecades, the urbanization level will continue to increase becauseChina’s current urbanization level is still low compared with devel-oped countries (Zhu and Peng, 2012). Thus, the agriculture-relatedwater resource pressure caused by urbanization would be inten-sified in the nearest future, which implies that the policymakersshould control the urbanization rate of China at a reasonable level.

6. Conclusion

Based on the extended STIRPAT model, we explored the effectsof population, diet structure, urbanization and affluence on the Chi-nese agricultural water footprint from 1990 to 2010. To addressthe multicollinearity among the independent variables, a ridgeregression method has been employed to give a more reliable esti-mation for the coefficient under acceptable bias. The results showchanges in population size are the largest driving force to leading toincreases in the Chinese agricultural water footprint. Additionally,affluence, diet structure and urbanization have also shown signifi-cant positive effects on promoting Chinese agricultural sector waterfootprint. Still, the results are preliminary because potential influ-encing factors such as population age structure and the degree offoreign trade need further examinations.

Acknowledgements

is of water footprint change based on extended STIRPAT model:tp://dx.doi.org/10.1016/j.ecolind.2014.04.048

This work was supported by the Major Research plan of theNational Natural Science Foundation of China (No. 91325302),Fund for Creative Research Groups of the National Natural

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cience Foundation of China (No. 51121003), National Natural Sci-nce Foundation of China (No. 41271543), and Specialized Researchund for the Doctoral Program of Higher Education of China (No.0130003110027).

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