China Economic Review 31 (2014) 288–302

Contents lists available at ScienceDirect

China Economic Review

Benchmark wealth capital stock estimations across China's 344prefectures: 1978 to 2012

Jidong WU⁎, Ning LI⁎, Peijun SHI⁎Key Laboratory of Environmental Change and Natural Disasters, MOE, Beijing Normal University, Beijing 100875, ChinaState Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, ChinaAcademy of Disaster Reduction and Emergency Management, MCA & MOE, Beijing Normal University, Beijing 100875, China

a r t i c l e i n f o

⁎ Corresponding authors at: Academy of Disaster RedBeijing 100875, China. Tel.: +86 13488750072.

E-mail addresses: wujidong@bnu.edu.cn (J. WU), nin

http://dx.doi.org/10.1016/j.chieco.2014.10.0081043-951X/© 2014 Elsevier Inc. All rights reserved.

a b s t r a c t

Article history:Received 23 January 2014Received in revised form 26 October 2014Accepted 26 October 2014Available online 6 November 2014

Measures of wealth (‘net’) capital stock (WKS) can be used for measuring economic exposure tonatural disasters and thus are essential for disaster risk management in terms of both quick lossestimation during emergency responses and post-disaster planning for recovery and reconstruc-tion. Today, the improved availability of statistical data and the progress of capital stock estima-tion methods have made it possible to produce datasets of WKS on the prefecture level. Byapplying the perpetual inventory method (PIM) to estimate prefecture-level WKS in China from1978 to 2012, this paper aims to illustrate both the methodology for generating theWKS datasetand the utility of theWKS as a useful indicator of economic exposure to potential hazards. The es-timation results indicate that the accumulatedWKS forMainland Chinahad reached RMB152 tril-lion by 2012, and it hasmaintained an average annual growth rate of 14% since 1990. Spatially, theuneven distribution ofWKS is distinct, with approximately 47% being concentrated in the easterneconomic region, and approximately 60% to 22% of China's prefectures. Methodologically, thedataset can easily be extended tomore recent yearswith available data. Furthermore, a systematicsensitivity analysis indicates that the depreciation rate is the most important parameter for WKSestimates. Notwithstanding certain limitations, the paper concludes that such WKS estimates, inparticular with its finer spatial resolution, offer a useful baseline for quick disaster loss estimation.

© 2014 Elsevier Inc. All rights reserved.

JEL classification:Q54E01O16R12G31

Keywords:Wealth capital stock (WKS)Perpetual inventory method (PIM)PrefecturesChina

1. Introduction

Many researchers have attributed growing disaster economic losses from weather and climate-related disasters to the increasingexposure of people and assets to such hazards (e.g., Field et al., 2012). Consequently, estimation of the asset values exposed becomescrucial in the cycle of disaster risk management. For pre-event risk appraisal and assessment, such estimation serves as a good indi-cator of economic exposure. In the critical period immediately after an event, such estimates can be useful in assessing the extent ofthe damage and determining the severity of the event. Rapidly developed estimates can support relief efforts by determining theroughquantities of reliefmaterials that are needed. In the post-disaster reconstruction phase, such estimates can give a first indicationof how much funds will be necessary to restore the economic system to its pre-disaster state.

In general, it is useful to have approximations of the total physical fixed assets at risk (i.e., exposure) as rapidly as possible once adisaster occurs. Such fixed assets are not merely that of the structures themselves but of the tools, machinery, equipment and infra-structure. The amount ofmoney invested in fixed capital formationwill determine the amount of potential losses once disasters occur.In otherwords, thefixed capital stock value in the affected region can provide a benchmark of themaximumpotential direct economic

uction and Emergency Management, Beijing Normal University, No. 19 Xinjiekouwai Street, Haidian District,

gli@bnu.edu.cn (N. LI), spj@bnu.edu.cn (P. SHI).

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losses due to disasters. Furthermore, the temporal and spatial changes of fixed capital stock can be indicative of the dynamics of vul-nerability to disasters and thus are instrumental for disaster risk analysis and management. In recent decades, applications of assetstock estimates in risk and disaster studies have grown rapidly. Kleist et al. (2006) estimated Germany's regional stock of residentialbuildings as a basis for a comparative risk assessment, and Seifert, Thieken, Merz, Borst, and Werner (2010) estimated countrywideasset values for commercial and industrial properties of Germany for hazard risk assessment. Te Linde, Bubeck, Dekkers, de Moel,and Aerts (2011) also employed economic assets to estimate future flood damage along the Rhine River. In the 2013 Global AssessmentReport on Disaster Risk Reduction (De Bono & Mora, 2014; UNISDR, 2013), capital stock (a ‘stock’ indicator) has been used to replacegross domestic product (GDP, a ‘flow’ indicator) to represent economic exposure to natural disasters because a natural disastercould cause asset damage greater than the annual GDP, such as the 2010 Haiti earthquake (Bilham, 2010).

Depending on analytical purposes, capital measurements can be distinguished into thewealth side (e.g., in the formofwealth cap-ital stock (WKS)) and the production side (e.g., in the form of productive capital stock) (OECD, 2009). The wealth capital stock mea-sures the (market) value of fixed assets still in use, valued at current replacement cost and is therefore a current pricewealthmeasure.Capital's evolution over time is governed by flows of investment and depreciation, i.e., the consumption of fixed capital (SNZ, 2013).The productive capital stockmeasures the cumulative value of theflow of productive capital services from capital assets to productionafter correcting for the efficiency loss that has occurred since it was new. While the wealth and production sides of capital are notindependent of each other, capital's age-price profile and its age-efficiency profile (i.e., an asset's ability to produce goods and servicesover the course of its service life) hang together (OECD, 2009). As the asset's productive efficiency declines, it undergoes a processof physical depreciation. As all capital assets suffer wear-and-tear, an older asset has less opportunity to generate revenue than ayounger asset. In this paper, for the main purpose of disaster economic loss estimation, we choose to focus on wealth capital stock(WKS).

While fixed capital stock occupies an important place in economic policy debates and in the analysis of economic growth for aregion, such as the change of productivity, technical progress, and efficiency, China has no officially published capital stock data.Consequently, estimations have been made by many researchers using the perpetual inventory method (PIM) (see Table 1 for asummary).

Over the years, considerable advances have been achieved in estimating the capital stock. Before 1990, due to the constraints ofstatistical data, the original value of fixed assets (Chen, Jefferson, Rawski, Wang, & Zheng, 1988; Jefferson, Rawski, & Zheng, 1996)or the accumulation of fixed assets (Chow, 1993; He, 1992; Perkins, 1998; Zhang, 1991) was used as an annual investmentmeasurement1 to estimate the capital stock of China. At that time, the System of Material Product Balance was used in China. Themost widely referred to study was conducted by Chow (1993), who estimated a value of RMB 175 billion for the aggregate capitalstock in 1952 (at 1952 prices). This estimated value was employed by many researchers who followed (e.g., Hu & Khan, 1997;Wang & Yao, 2003). Chow (1993) also provided the method to construct the implicit deflator for capital accumulation, which waswidely utilized to construct the implicit deflator for fixed capital formation (IdFCF) in later studies (e.g., He, Chen, & He, 2003;Young, 2003; Zhang, 2008). The total investment in fixed assets (TIFA) was considered a basic representative measurement of theinput of annual fixed assets, which was extensively adopted by many researchers at that time, and the retail price index, priceindex of building materials and the GDP deflator were adopted to deflate the investment of fixed assets between years (e.g., Hall &Jones, 1999; Huang, Ren, & Liu, 2002; Islam, Dai, & Sakamoto, 2006).

With the publishing of the Historic Data of China National Accounting for Gross Domestic Products: 1952–1995 in 1997 (NBS,1997), the provincial gross fixed capital formation (GFCF) as well as its growth rate series under the composition of the GDPby expenditure were available as an annual fixed-asset input for capital stock estimation (e.g., Gong & Xie, 2004; He et al.,2003; Wang & Yao, 2003). This enabled researchers to construct IdFCF series to deflate the GFCF between years (Holz, 2006;Lei, 2009; Shan, 2008; Ye, 2010). It is worth mentioning that such earlier studies set a low depreciation rate for fixed capital as-sets, e.g., 5% for China, referring to the published depreciation rate of state-owned industries from 1952 to 1991 (Wang & Yao,2003). Since the published data were also an estimated value, Zhang (2008) indicated that the lower depreciation rate mayoverestimate the capital stock. Huang et al. (2002) began to consider capital service lives for different capital goods, and assum-ing a geometric diminishing of the relative efficiency of capital goods in theory, then, according to the investment structure, theaverage depreciation rate can be obtained. Based on this idea, Zhang (2008) estimated a 9.6% depreciation rate for the totaleconomy of China from 1952 to 2004, while Fan (2012) estimated an 11.28% depreciation rate from 1952 to 2009 for China.Ye (2010) utilized time-varying depreciation rates from 8.82% to 11.16% for China in the period 1952–2008. Holz (2006) provideda comprehensive review of the key issues related to capital stock estimations for China, such as the meaning of each investment flowindicator and its adaptability. Wang and Szirmai (2012) make a clear distinction between capital services from a productivityperspective and a wealth capital stock perspective.

While considerable efforts have beenmade for capital stock estimation in China as described above, the estimation by far has beenalmost exclusively at national or provincial levels. Such coarse spatial resolution forms a major obstacle limiting the use of such stockestimation in disaster economic loss assessment. For example, nearly 40% of the direct economic losses of the 2008Wenchuan earth-quake occurred in 10 of 183 counties in the Sichuan province (Wu et al., 2013), and over 80% of the direct damage occurred in theworst hit prefectures (NCDR & MOST, 2008). In such case, capital stock estimation at finer spatial resolution would have been ex-tremely useful in the damage analysis and loss assessment.

1 Other than the OECD (2000), Holz (2006) andWang and Szirmai (2012) also provided a detailed explanation of the relevant statistical concepts and data of China.

Table 1Selected published papers on the capital stock estimation of Mainland China employing perpetual inventory method.

References Investmentmeasurement

Fixed capital price deflators Initial capital stock estimationmethod

Depreciation rates Period and spatialscale

Resultsavailable

Chen et al. (1988) OFA Implicit deflator forcomponents of industrialinvestment

n.a. Time-varying,3.6%–4.6%

1952–1985, China Yes

Perkins (1988) AFA Industrial price index Capital–output ratio method 5% 1953–1985, China NoZhang (1991) AFA Capital accumulation index Capital–output ratio method

(assumed that the capital–outputratio was 3 in 1953)

n.a. 1952–1990, China Yes

He (1992) AFA Capital accumulation index Author's estimation relied on thestatistics of “accumulation of fixedassets”

n.a. 1952–1990, China Yes

Chow (1993) AFA n.a. Author's estimation relied on thestatistics of “accumulation offixed assets”

n.a. 1953–1985, China Yes

Jefferson et al.(1996)

OFA Average deflator forconstruction–installation works& equipment–instruments

Estimate a baseline net value offixed assets in 1979

n.a. 1980–1992, China Yes

Hu and Khan(1997)

TIFA Price index of buildingmaterials & OFAI

Reference Chow (1993) 3.6% 1952–1994, China No

Hall and Jones(1999)

TIFA n.a. Growth rate approach 6% 1988, 127 countries No

Wang and Fan(2000)

TIFA Price index of investment infixed assets

Author's own estimation in 1952 5% 1952–1999, China Yes

Chow and Li(2002)

AFA n.a. Reference Chow (1993) 4% for 1978–1992,5.4% for 1993–1998

1978–1998, China No

Huang et al.(2002)

TIFA Retail price index n.a. 17% for equipmentand 8% forconstructions

1978–1995, China Yes

He et al. (2003) GFCF IdFCF & OFAI Capital–output ratio method n.a. 1952–2001, China YesLi and Tang(2003)

GFCF Fixed capital formation priceindices of Shanghai

Reference Chow's (1993)estimation in 1978

n.a. 1978–2000, China No

Song, Liu, andJiang (2003)

TIFA Price index of buildingmaterials

Reference the capital estimationresult in 1958 by Hu and Khan(1997)

3.6% add GDPgrowth rate

1983–1995, provincial No

Wang and Yao(2003)

GFCF IdFCF & OFAI Reference Chow (1993) 5% 1952–1999, China Yes

Young (2003) GFCF IdFCF & OFAI Growth rate approach 6% 1978–1998, China NoZhang and Zhang(2003)

AFA & TIFA Shanghai's price index offixed asset investment

Author's estimation relied on“accumulation of fixed assets”statistics

n.a. 1952–2001, China Yes

Gong and Xie(2004)

GFCF IdFCF & OFAI n.a. 10% 1970–1999, provincial No

Wu (2004) GFCF Author's calculation relied onincome's price indices

Author's estimation in 1952given that the value of capitalstock in 1900 was zero

7% 1952–2000, provincial Yes

Holz (2006) NIFA IdFCF & OFAI n.a. Time-varying,3.7%–5.9%

1953–2003, China Yes

Islam et al. (2006) TIFA The GDP deflator Growth rate approach 3% for 1952–1978,4% for 1979–1992,5% since 1993

1952–2002, China Yes

Wu (2007) GFCF Reference Wu (2004) Reference Wu (2004) Region-varying,2.2%–6.9%

1952–2005, provincial No

Shan (2008) GFCF IdFCF & OFAI Growth rate approach 10.96% 1952–2006, provincial YesZhang (2008) GFCF IdFCF & OFAI Fixed asset investment growth

rates9.6% 1952–2004, Provincial Yes

Lei (2009) GFCF IdFCF & OFAI Reference Zhang and Zhang (2003) 9.732% 1952–2007, China YesYe (2010) GFCF IdFCF & OFAI Author's own estimation Time-varying,

8.82%–11.16%1952–2008, China Yes

Wang andSzirmai (2012)

TIFA & NIFA National specific price indices& aggregate price index

Capital–output ratio method n.a. 1953–2007, China;1978–2007, provincial

Yes

Fan (2012) GFCF GDP deflator Growth rate approach 11.28% 1952–2009, China Yes

Notes: OFA = original value of fixed assets, AFA = accumulation of fixed assets, GFCF = gross fixed capital formation, TIFA = total investment in fixed assets,NIFA = newly increased fixed assets, IdFCF = implicit deflator for fixed capital formation, and OFAI = official fixed asset investment index.

290 J. WU et al. / China Economic Review 31 (2014) 288–302

To address this gap, this paper aims to estimate the economy-wide prefecture-level wealth capital stock of China usingPIM. The hope is to provide a “benchmark” value for the maximum economic exposure analysis as well as disaster lossassessment (e.g., the cost of damaged construction or infrastructures). The estimation is carried out for the 344 prefectures

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(shi, diqu)2 of Mainland China. In addition to making the wealth capital stock available at a finer spatial scale, a uniform database ofassets at risk is also essential for a consistent quantitative comparison of economic losses from different risks. Furthermore,the prefecture-level wealth capital stock estimation is also useful for further downscaling of the WKS estimates to regular grids(cell-level) for better matching of economic data with disaster impacted areas.

This paper is constructed as follows. In Section 2, we present the wealth capital stock estimation methods (i.e., the PIM) and dataissues for measurement inputs in the Chinese context. In particular, we focus on the construction of provincial economic depreciationrates. Section 3 presents the first estimatedWKS series for the total economy across 344 prefectures and applies the constructed dataseries in the context of natural disasters. Section 4 provides a sensitivity analysis of the main parameters employed in the WKSestimation, which focuses on the parameters of economic depreciation rates and initial capital stock values, and also discusses thelimitations and ways to improve this study. Section 5 presents the main conclusions.

2. Methodology and data

2.1. Methodology

Direct surveys and the PIM are the two popular methods to acquire capital stock data. Since there are no official data exist for theChinese capital stock, this study employs the PIM (Goldsmith, 1951), which was widely applied in the estimation of capital stock bythe OECD (2009) and other researchers (Kamps, 2006; SNZ, 2013). The objective is to generate an estimate of the prefectures' WKSseries by accumulating the past purchases and disposals of assets over their estimated service lives across China.

Four primary inputs are required for the PIM— the annual investment flows of fixed assets, the price deflators of fixed assets, theinitial capital stock value, and the depreciation of fixed capital. For the traditional PIM, capital stock at year t, Kt, can be expressed as afunction of the capital stock in the previous year (t − 1), Kt − 1, of gross investment (i.e., GFCF in this paper) in year t, It, and ofconsumption of fixed capital (CFC) in year t, Dt:

2 TheZhang, 2

3 CFCvalued a

4 Thisvariablepresentdata to

5 Wa

Kt ¼ Kt−1 þ It−Dt þ Xt ; ð1Þ

where Xt is other changes in volumes of the group of assets, including the loss value of fixed assets destroyed by war or major naturaldisasters that occur very infrequently, which are not included under CFC. This effectwill not be considered for themoment in this paper.

CFC is usually obtained using a depreciation function such as straight-line or geometric (OECD, 2009). Based on a geometricdiminishing model of relative efficiency (that is, the consumption of fixed capital is at a constant rate — the economic depreciationrate,3 δ, or better known as the replacement rate), then Eq. (1) can be rewritten as

Kt ¼ Kt−1 1−δð Þ þ It : ð2Þ

Historically, available investment data are limited, so assume that the initial capital stock was K0; by repeatedly substitutingEq. (2), Eq. (2) is replaced by the following expression:

Kt ¼ 1−δð ÞtK0 þ∑t−1i¼0 1−δð ÞiIt−i: ð3Þ

Applying Eq. (3), themain inputs required and assumptions for PIM in this paper are the following. First, for initial capital stock, wetake 1978 as the initial year because of the availability of investment data on the prefecture level. Second, for a time series of annualinvestment flows, GFCF data in current prices is selected. Third, the constructed IdFCF indices are employed to deflate GFCF betweenconstant prices and current prices. Finally, the capital depreciation rate under the assumption of the geometric efficiency declinemode is utilized for all of the prefectures. The estimation procedure can be seen in Fig. 1.

2.1.1. Gross fixed capital formation (GFCF)For the gross investment flow time series, this paper relied on GFCF. For the prefecture level, however, there are only TIFA data

available from the China Statistical YearbooksDatabase (CSYD).4 To derive prefecture level GFCF fromTIFA,we introduce the provincialrate of GFCF to TIFA (rt), which transfers prefectures' TIFA (Investt) into GFCF (i.e., new investment flow) (It)5:

It ¼ Investt � rt t ¼ 1978;1979;…;nð Þ; ð4Þ

Constitution dictates three administrative division levels (i.e., province, county and township) in China; in fact, there are five administrative division levels (Qin &014), i.e., province, prefecture, county, township and village-level division from top to down, and there are 344 prefecture divisions in 2010 in Mainland China.is used in preference to “depreciation” to emphasize that fixed capital is spent in the process of generating new output, and because unlike depreciation it is nott historic cost but at current market value (so-called “economic depreciation”).database includes national, provincial and prefecture statistical yearbooks, which not only include investment data but also a large set of othermacroeconomics. Most of these data are available via the Internet at tongji.cnki.net. National Bureau of Statistics (NBS) also provides the latest (mainly from 1990 up to the) national and provincial statistical data in 2013 via the Internet at www.stats.gov.cn/tjsj. For the data employed in this paper, we utilized the latest publishedensure the consistency of the data, as some data were revised according to the statistical system.ng and Fan (2000) employed this method to calculate the capital stock of China from 1980 to 1999.

Fig. 1.Wealth capital stock estimation diagram utilizing the perpetual inventory method (source: authors).

292 J. WU et al. / China Economic Review 31 (2014) 288–302

where rt is the provincial rate of GFCF (calculated by GFCF divided by TIFA). Limited by data availability and for simplification incalculation, the assumption here is a uniform rt for all prefectures within the same province.

2.1.2. Implicit deflator for fixed capital formation (IdFCF)The price index series forfixed asset investment on theprovincial levelwere only available after 1991. Therefore,most studies seek

proxies for this price index. In our analysis, we follow the method of Zhang (2008) and others (Young, 2003; Wang & Yao, 2003; Heet al., 2003; Gong & Xie, 2004) to calculate the IdFCF based on Chinese official statistics from the publication of Historic Data of ChinaNational Accounting for Gross Domestic Products: 1952–1995 (NBS, 1997), which provides the time series of both GFCF and its growthindex at the provincial level. The provincial IdFCF (previous year = 1), Ideflatort, can be calculated by

Ideflatort ¼Invest Ft

Invest Ft−1 � InvestFindextt ¼ 1978;1979;…;nð Þ; ð5Þ

where InvestFt is the GFCF in the year t, and InvestFindext is the index of GFCF (previous year= 1). The same deflator based on a baseyear (i.e., base year = 1978) can also be calculated by employing this method. Then, for simplicity, the provincial deflator of GFCF isapplied to all prefectureswithin the same province becausemost businesseswill face the samemarket priceswithin a province, and atleast the same market price changes.

2.1.3. Initial value of wealth capital stockThere is no official information on the initial capital stock for any prefectures or for any province of China. In the absence of a full

time series of investment or historical GDP data, a simple approximation (Kohli, 1982) can be used to construct the net stock at thebeginning of the benchmark year t0; in particular, when geometric age-price profiles apply, a prefecture's initial WKS, Kprefecture, in1978 can be written approximately as

Kprefecture ¼ Invest Ft0= δþ θð Þ; ð6Þ

where InvestFt0 is the prefecture's investment expenditure in 1978, δ represents the depreciation rate, and θ is the long-run growthrate of investment (i.e., constant-price GFCF). It is clear that this initial capital stock is a rough approximate but the measurementerrors introduced into the stock figures will diminish over time and matter much less for the most recent estimates.

293J. WU et al. / China Economic Review 31 (2014) 288–302

2.1.4. Depreciation rateHerein, depreciation means economic depreciation, i.e., the decline in asset value (or asset price) associated with aging (Fraumeni,

1997). Like many other researchers (Matheson, 1910; Zhang, 2008), this paper also employs geometric efficiency profiles to calculatethe depreciation rate:

6 Theasset ow

7 Theuntil 19amined1949, suthese covincial o

8 In pRegion,groups'

9 Aftetistical Y

dT ¼ 1−δð ÞT ; ð7Þ

where dT represents the relative efficiency for a cohort of assets, δ represents the rate of geometric depreciation (a rate of CFC) as themajority of empirical research on asset depreciation has concentrated on it (Fraumeni, 1997; Huang et al., 2002), and T representsaverage service lives of a group of a cohort (assets).

2.2. Data

2.2.1. Investment dataAs aforementioned, there is no prefecture-level annual statistical data on GFCF in China. At the provincial level, however, statistical

data series of both TIFA6 and GFCF (under the GDP accounting-by-expenditure approach) are available, which enable the calculationof the provincial rate of GFCF. Such conversion rate is then used to derive the prefecture level GFCF from the prefectural level historicaldata on TIFA. All data are taken from the China Statistical Yearbooks Database (CSYD), which collects almost all national, provincial andprefecture-level statistical yearbooks published by the China Statistics Press. This database provides a long time series of statisticalmacroeconomic data for a large panel of regions,7 including GDP, the provincial indices of GDP, the provincial index of GFCF, andthe provincial fixed asset investment price indices (since 1991).

At the prefecture level, the TIFA series for the period of 1978–2012were largely available in the CSYD butwere not complete for allprefectures. For themissing data, we must employ some data filling-in methods to complete the data series, especially concentratingon nine provinces in the period from 1978 to 1989, such as Anhui, Hubei, Guangxi, Sichuan, Chongqing, Yunnan, Tibet, Ningxia andXinjiang. Given the provincial TIFA series that are available, an investment-ratio approach was utilized to distribute provincial TIFAinto the prefecture to fill these gaps. Some county-level statistical data were also used to aggregate and acquire the prefecture-level data values, i.e., for Chongqing before 1997.8

Investment data series are expressed in current prices, so provincial fixed asset investment price indices from CSYD areemployed to deflate the new investment data between years. While there were no fixed asset investment price indices availablebefore 1991, based on the dataset from theHistoric Data of China National Accounting for Gross Domestic Products: 1952–1995, theprovincial IdFCF data, calculated from the provincial index of GFCF and annual GFCF as described above (Eq. (5)), are used tosubstitute the GFCF price indices from 1978 to 1990, and the available provincial fixed asset investment price indices are utilizedsince 1991.9

At the provincial level, the provincial fixed capital price index series are still not complete even after employing constructed IdFCFseries, including those for Tianjin (1978–1988), Hainan (1978–1990), Chongqing (1978–1996) and Tibet (1978–2012). Therefore, weresort to some substitutes for completing these data. For Tianjin, we utilize China's IdFCF to substitute Tianjin's missing IdFCF datafrom 1978 to 1988, given its great similarity to the national IdFCF. For Hainan, we employ Guangdong's fixed-capital price datafrom 1978 to 1990 for substitution because these regions are geographically similar. For Chongqing, we directly employ Sichuan'sfixed-capital price series from 1978 to 1996 for substitution because Chongqing belonged to Sichuan before 1997. In the case ofTibet, there are no such fixed-capital price statistics; we utilize the average value of Xinjiang and Qinghai (both of them close toTibet in western China) as a substitution. Finally, we can acquire the evolution of the deflator for fixed-capital formation in 31provinces since 1978.

The aggregated prefectural-level TIFA data are not always equal to the provincial TIFA statistical data and the differences varyamong provinces: the former is usually lower than the latter especially before 1990, with some provinces (e.g., Guangxi, Guizhou,Jiangsu, and Shaanxi) even 30% lower in some years. The main reason for this discrepancy is due to the fact that the prefecturalTIFA statistics, especially before 1990, did not include the central and provincial project investments (i.e., railway construction)that traverse the country. Such difference implies that the WKS will be underestimated for the whole province if we utilize the pre-fecture level TIFA data unadjusted. To reduce this possible underestimation ofWKS,we redistribute those ‘cross-regional’ investmentsto prefectures by the corresponding GDP share of the prefecture to ensure the validity of theWKS estimation at the province-level. On

TIFA statistics fromChina's NBS gives the value of the fixed assets that are built or purchased by residents plus the related costs of the installation and transfer ofnership during the accounting period (OECD, 2000).regional (or provincial) System of National Accounts (SNA) was first compiled in the 1980s in China and then was adopted as the official accounting system93 (OECD, 2000). Fortunately, on the 60th anniversary of the founding of the People's Republic of China, historical macroeconomic data were collected and ex-, which verified the quality of the historical data and extended the data series. Most provincial statistical bureaus compiled their histories of statistical data sincech as 60 years statisticalmaterials of Zhejiang 1949–2009, which include the prefecture-level statistical data, and the China Statistics Press successively publishedmpilations in 2010. Most of the prefecture-level investment data can be acquired from this source. If the data were not complete, we resort to historical pro-r prefecture statistical yearbooks.articular, Xinjiang is a special statistical area. There are 14Xinjiang Production and Construction Groups distributed throughout theXinjiangUygur Autonomousand theyhave an independent statistics institution. These groups' statistical datawere not summarized in theXinjiang Statistical Yearbook, sowe aggregate theseinvestment data to Xinjiang's relevant prefectures according to their location to better reflect their real investment scale.r comparing the resulting provincial implicit deflators with those mentioned above (i.e., provincial fixed-asset investment price indices) published in the Sta-earbook of China from 1991 to 2004, it is clear that they are almost the same.

294 J. WU et al. / China Economic Review 31 (2014) 288–302

the other hand, for data after 2000, in some province in some years (e.g., Jiangsu, 2002 to 2010), the aggregated prefectural-level TIFAvaluewas greater than the provincial TIFA. The difference however is smaller than 15%. Given that the difference in this case is due tothe employment of different data sources and calculationmethods, as is indicated in the preface of the statistical yearbook, no correc-tion is attempted for those prefecture level TIFA data in this paper.

2.2.2. Depreciation ratesThe depreciation rate becomes a controversial issue as the extent to which rates of economic depreciation can be adequately used

to act as proxies to rates of physical replacement when making perpetual inventory estimates of capital stock (Jorgenson, 1996).Following Huang et al. (2002) and Zhang (2008), we consider the average rate of residual value to the total capital goods' valueto be 4% because the official value is between 3% and 5% for China, which implies that the retired capital goods' relative efficiencyis only 4% of that of the new capital goods. However, the NBS's fixed capital investment statistics contain three components:construction and installation, equipment purchase, and other investments that are not included in the first two groups (OECD,2000). To obtain the depreciation rate of fixed capital formation, both the service lives of the three parts (calculating their respectivedepreciation rates as mentioned in Eq. (7)) and its relevant average weight for GFCF are needed.

For capital service lives, China's Ministry of Finance specified the asset's service life of state-own economy by asset types for dif-ferent sectors in 1994 (Wang & Wu, 2003), i.e., 40 years for urban dwellings, 30 to 50 years for non-residential constructions, 8 to20 years for equipment and 20 years for other fixed assets. As these fiscal service lives reflect a principle of prudence, they tend tounderestimate the true economic service lives and hence, we assume that the average service lives for construction and installation,equipment purchase, and other investments, are 45 years, 20 years and 25 years, respectively. Next, the capital depreciation rates canbe calculated accordingly as 6.9%, 14.9% and 12.1%, respectively.

For the relativeweight of the three parts that constitute theGFCF, Zhang (2008) calculated it at the national level and assumed thatit is the same for the provincial level; he then calculated the depreciation rate at 9.6% for each province. Fortunately, recently pub-lished provincial statistical yearbooks released more historical data about the composition of TIFA, which allows one to calculatethe relative share of each component of the TIFA on the provincial level averaged for the period of 1983–2012. The average weightof the composition of GFCF then can be calculated for each province, and in turn, the provincial depreciation rates can be calculatedunder the assumption of geometric diminishing modes of relative efficiency of capital goods as described above and as presentedin Table 2. For Guangxi, there are only intermittent statistical data for the share of the three components of the TIFA; we employChina's value for substitution because of their similarity in the existing annual data.

Table 2Depreciation rates across 31 provinces in Mainland China.

Province Depreciation rate (%)

Beijing 9.76 (9.12, 10.58)Tianjin 9.56 (8.93, 10.36)Hebei 9.83 (9.18, 10.65)Shanxi 9.53 (8.91, 10.33)Inner Mongolia 9.22 (8.62, 10.00)Liaoning 9.49 (8.86, 10.28)Jilin 9.43 (8.81, 10.23)Heilongjiang 9.28 (8.67, 10.06)Shanghai 10.05 (9.39, 10.89)Jiangsu 9.62 (8.98, 10.42)Zhejiang 9.53 (8.90, 10.33)Anhui 9.75 (9.11, 10.57)Fujian 9.44 (8.82, 10.24)Jiangxi 9.68 (9.04, 10.49)Shandong 9.49 (8.87, 10.29)Henan 9.45 (8.83, 10.24)Hubei 9.58 (8.95, 10.38)Hunan 9.20 (8.60, 9.98)Guangdong 9.35 (8.74, 10.14)Guangxi 9.44 (8.82, 10.24)Hainan 9.35 (8.74, 10.14)Chongqing 9.21 (8.61, 9.99)Sichuan 9.27 (8.66, 10.05)Guizhou 9.35 (8.73, 10.13)Yunnan 9.03 (8.44, 9.79)Xizang 7.95 (7.42, 8.63)Shaanxi 9.87 (9.22, 10.70)Gansu 9.30 (8.69, 10.08)Qinghai 8.74 (8.17, 9.48)Ningxia 9.32 (8.71, 10.11)Xinjiang 9.08 (8.48, 9.84)

Note: Data source: Depreciation rates are calculated by the authors, thedepreciation rate inside of the parentheses is calculated by the averagerelative efficiency rate of 3% and 5%, respectively, as described in the text.

Fig. 2. Estimated wealth capital stocks across Mainland China in 2012 (a) and its proportional distribution in China's four economic zones (b) and cumulativedistribution (c) (source: authors).

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In summary, for the application of the PIM, three important assumptions are made in this paper. First, the year 1978 is chosen forconstructing the initial capital stock. This study utilizes the growth rate approach, i.e., utilizing the level of investment expenditure forthefirst period (i.e., in 1978) and a combination of parameters of the long-term growth rate of investment and the depreciation rate toestimate the initial capital stock for each prefecture. Second, regarding the level of economic depreciation rates, this paper assumesthat prefecture-level depreciation rates are the samewithin a province. Third, for the depreciationmethod, this study employs geometricdepreciation. In this study, the estimatedwealth capital stock does not include cash, bank deposits, and inventories, and both natural cap-ital (e.g., agricultural land, minerals, and forests) and intangible capital (e.g., human capital, and social capital) are also excluded in theWKS, i.e., it is an estimation of the fixed-asset value. The sensitivity of the main parameters and assumptions will be discussed in thefollowing text.

3. Results

3.1. Wealth capital stock estimates

Following the lengthy stage of data survey, collection, and processing, including seeking substitution for themissing data, the pre-fecture wealth capital stock series were estimated across China.10

Fig. 2a displays a map visualizing the estimated wealth capital stock at current prices for the 344 prefectures of Mainland China in2012using thequantile classificationmethod (for details, see Table A1). Unsurprisingly, there are clusters of prefectureswith relativelyhigh capital stocks along China's coastal regions, especially in the Beijing–Tianjin–Hebei region, the Shandong Peninsula, and theYangtze River Delta. As expected, four particularmunicipalities of China, i.e., Shanghai, Tianjin, Beijing, and Chongqing, had the highestwealth capital stock in 2012, with Guangzhou, Suzhou, Chengdu,Wuhan, Nanjing and Shenyang following Chongqing. The next groupof prefectures with similar aggregate capital stock consisted of Qingdao, Dalian, Hangzhou,Wuxi, Changsha and Zhengzhou; theWKSin these prefectures surpassed RMB 1.6 trillion in 2012.

Mainland China's cumulative 2012wealth capital stock (WKS) reached RMB 152.0 trillion (at current prices). Ofwhich, nearly 60%was distributed within 22% of the prefectures, and about 40% of China's WKS was distributed within 10% of the prefectures (Fig. 2c).Overall, China's eastern economic region represented 47% of the total WKS, while China's western and central economic regionsaccounted for 22% and 20% of the WKS, respectively, while the north-eastern economic region accounted for the remaining approx-imately 11% of the capital stock (Fig. 2b).

By the end of the period 1990–2012, the aggregate wealth capital stock of the 344 prefectures grew to nearly 20 times greater,growing from RMB 7.3 trillion in 1990 to RMB 141.8 trillion in 2012 (at 2010 price levels), with an average annual growth rate of

10 More detailed WKS series presented in this paper are available in electronic format to interested researchers on request.

Fig. 3.Disaster-hit area of the 2008Wenchuan earthquake (Ms 8.0) and the 2013 Lushan earthquake (or Ya'an Earthquake,Ms 7.0). The colors show the seismic inten-sity, which indicates the disaster damage magnitude, from the epicenter to the surrounding area (source: authors).

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14%. At the same time, the standard deviation of the aggregateWKS also increased significantly from RMB 29.2 billion in 1990 to RMB522.6 billion in 2012.

3.2. Application in disaster loss estimation

Comparing to provincial level WKS estimation, with finer spatial resolution, data for prefecture level WKS can offer much more toassessing economic losses to disaster. Especially for rapid assessment upon the occurrence of a disaster, the extent of pre-existingwealthcapital stock loss or damage is usually used as the indicator for deciding whether and to what extent external assistance would be re-quired. In the following, we illustrate this applicationwith the cases of 2008Wenchuan earthquake and 2013 Lushan earthquake (Fig. 3).

Logically, we can assume that the direct economic loss of any disaster-affected prefecture can be expressed as

DLoss ¼ W � α; ð8Þ

where DLoss is the direct economic loss of the disaster-affected prefectures,W is the wealth capital stock of the prefecture (i.e., asset ex-posure), andα is the direct economic loss ratio related closely to the severity of the hazard (e.g.,magnitude of the earthquake). The ratioαunder different hazard severity levels can be estimated for a specific region based on its physical structure vulnerability to the hazardevent. Such hazard intensity and damage/loss relationship can be established either empirically based on historical data of the regionor adjusted through relevant references fromexisting literature. Thus, with a knownW (in this case, thewealth capital stock of the affect-ed prefecture), the direct economic loss can be quickly estimated once the damagemagnitude (i.e., seismic intensity for the earthquake)of the hazard is provided. While invariably such estimation will be rough approximation, it is still of great value in the early stage ofdisaster emergency response. Consequently, as further field data comes in, the estimates could be progressively revised and refined.

Table 3Relationship betweenwealth capital stock (WKS) and direct damage inworst hit prefectures of twomajor earthquakes in Sichuan, China (RMB billion at current prices).

WKS in worst hitprefectures

GDP pre-disaster inworst hit prefectures

Direct losses reported inworst hit prefectures

Estimated directlosses

2008 Wenchuan earthquake (Ms 8.0) 1455.3 558.2 676.5 /2013 Lushan earthquake (Ms 7.0) 151.6 39.8 66.5 69.7

Notes: 1. Earthquakes are measured on the Richter scale. 2. Data source: 1) The wealth capital stock value here is calculated by the previous year'sWKS (i.e., estimatedby the authors) adding up the accumulated TIFA (i.e., from local bureau of statistics) in themonth just before the disaster occurred in the current year; 2) the GDP fig-ures were originally from Sichuan Statistical Yearbook the year before the disaster, and then accumulated from each prefecture toworst hit prefectures as shown here;3) the direct losses excluding non-fixed assets in theworst-hit prefectures for theWenchuan earthquake amounted to RMB 676.5 billion (NCDR &MOST, 2008), whilefor the Lushan earthquake, the amount of the direct damage reportedwas RMB 66.5 billion (CEA, 2014); and 4) the estimated direct losses from the Lushan earthquakeare calculated by the authors as described in the text.

297J. WU et al. / China Economic Review 31 (2014) 288–302

Table 3 shows the relationshipbetween seismic intensity anddirect economic losses in the affected areaof the 2008Wenchuanearth-quake. According to the estimated prefecture level WKS in this paper, the worst-hit area (where the seismic intensity is greater thanseven, including Guangyuan, Mianyang, Deyang, Chengdu, Aba, Ya'an, Longnan, and Hanzhong) had wealth capital stock value of ap-proximately RMB 1.5 trillion prior to the earthquake, and we knew that the officially published damage number was RMB 845.1 bil-lion for theWenchuan earthquake, including RMB41.6 billion of non-fixed capital assets (e.g., the loss of natural resource and culturalheritage), and 84.2% of the damage occurred in the worst hit prefectures (NCDR &MOST, 2008), i.e., the direct economic loss in thoseareas amounted to RMB 676.5 billion. Thus, we derived an α value of 0.46 for similar regions with given seismic intensity of greaterthan seven.

After the 2013 Lushan earthquake, we knew that the wealth capital stock in the worst-hit prefecture (i.e., Ya'an) was RMB 151.6billion. According to the similarity of the fixed asset vulnerability to earthquake in the affected areas (i.e., underdeveloped mountainregion in the same province), we applied the α ratio derived from theWenchuan earthquake for the quick assessment of direct eco-nomic loss in the Lushan earthquake, which was RMB 69.7 billion. Although this initial quick assessment turned out to be somewhathigher than the final reported total direct loss (RMB 66.5 billion) after comprehensive evaluation, the initial rapid assessment wassufficient and very useful to indicate the extent of the loss and damage,11 and in turn, offered valuable support for preparing the emer-gency rescue strategies.

Furthermore, it is worth to note that, comparingwith using GDP as an economic exposure indicator, the advantage of usingWKS isthat it offers a ‘benchmark’ for maximum direct economic losses for a given region. For both the Wenchuan earthquake and Lushanearthquake cases, the direct loss and damage totally surpassed the pre-disaster GDPs in both the Wenchuan earthquake and theLushan earthquake in the worst hit prefectures (as Table 3 presents). Thus, the GDP indicator would not provide any useful guidancefor loss assessment especially when the disaster impact is severe.

4. Discussion

4.1. Sensitivity analysis of the main parameters

A number of factors may affect the estimates of the wealth capital stock that we reported. In this section we discuss some of themain ones, supported by a sensitivity analysis. The primary purpose of the sensitivity analysis is to test how the estimation resultsvary in relation to each of the main parameters employed in the estimation methodology.

4.1.1. The effect of alternate depreciation rates on the wealth capital stockAs Eq. (7) demonstrates, both service lives of the fixed assets and the average rate of residual value to the total capital goods' value

(dT in Eq. (7))will affect the depreciation rate estimate. First, the depreciation rate changewill be proportional to the change of servicelives of thefixed assets, i.e., a 10% increase infixed assets' service liveswill result in an approximately 10%decrease of the depreciationrate for these assets.

Second, for dT the threshold valuewas between3% and 5% in theChinese context as described above. Taking those boundary values(i.e. 3% and 5%) for dT, we see a resultant depreciation rate change from 6.55% and 8.50%, respectively (see Table 2, figures inside of theparentheses, also Fig. 4a). The impact of this variation on the finalWKS estimation result (for year 2012) varies across prefectures butall are less than 5%, while most of them around 2% to 3%.

Finally, we adopted a consistent change in value of the depreciation rate of 6%, which possibly resulted from the change ofasset service lives or the average rate of residual value to the total capital goods' value, or both of them, and we analyzed its

11 There existed exaggerated reports of disaster losses by the local government after the 2013 Lushan Earthquake, and some of the media also reported this issue(http://view.news.qq.com/zt2013/yadz5/index.htm), i.e., the three severely affected counties (inside the prefecture of Ya'an) reported almost RMB 170.0 billion of di-rect economic losses, which surpasses thewealth capital stock estimates in this paper, and theWKS estimates are helpful in giving a referencemaximumdisaster loss inthe disaster areas for officials for further decision-making in disaster rescuing and preliminary reconstruction planning.

6.55% 8.50%1

2

3

4

5

Depreciation rate change (%)

WKS

cha

nge

in 2

012

(%)

1

2

3

4

5

1980 1985 1990 1995 2000 2005 20100

20

40

60

80

100

Wea

lth c

apita

l sto

ck c

hang

e (%

)

c

b

a

Fig. 4. Box-plot of wealth capital stock sensitivity to the depreciation rate change. (a) Prefectures' WKS change in 2012 under the depreciation rate change (due to theparameter change of relative efficiency for a cohort of assets at 3% and 5%, respectively). (b) WKS change over time with an absolute change of 6% in the depreciationrate for 344 prefectures from 1978 to 2012. (c) WKS sensitivity to a doubling of the initial wealth capital stock in 344 prefectures — an exponential decline type(source: authors).

298 J. WU et al. / China Economic Review 31 (2014) 288–302

effect on the wealth capital stock estimates with other parameters unaltered. As a result, as Fig. 4b exhibits, the prefectures'yearly wealth capital stock estimate changes were below 6% over time, and most of the prefectures' WKS changes were concen-trated at 2% to 3%.

Overall, the depreciation rate is a very sensitive parameter forWKS estimates. Yet due to the lack of actualmonitoring/survey data ondepreciation, it remains practically impossible at the current time to verify its reliability, except by making cross reference with theexisting research as what we have done in this paper. Furthermore, the assumptions of a constant depreciation rate over time and of asingle one within a province are also major simplifications that undermine the accuracy of the WKS estimates (Gellatly, Tanguay, &Beiling, 2002).

4.1.2. The effect of initial capital stock on wealth capital stockThe effect of initial capital stock in the estimation of theWKS declines exponentially over time. Many analysts have demonstrated

thatmeasurement errors of the initial stock, given sufficiently long time series, would thenmattermuch less for the estimates for the

299J. WU et al. / China Economic Review 31 (2014) 288–302

most recent year (Zhang, Wu, & Zhang, 2004; Shan, 2008; Ye, 2010). In our estimates, this is certainly the case. As Fig. 4c presents, adoubling of the initial WKS (year 1978) in 344 prefectures only resulted in less than 0.6% difference for the WKS estimates in 2012.Given that our main purpose is to use prefecture level WKS estimates as a measure of economic exposure to future risk, i.e., usingthe estimates for the most recent year, the measurement error of the initial capital stock is acceptable.

4.2. Other limitations or uncertainty

There are several other factors that affect the accuracy of the capital estimation beyond the issues described above. First, for the selec-tion of annual investment measurements on a prefecture level, while this paper uses TIFA data to derive the GFCF series, a more appro-priate series for WKS estimation purposes should have been the newly increased fixed assets — if only those were available on theprefecture level. By far, TIFA remains to be the only available statistical indicator for a prefecture's capital estimation. Furthermore, be-cause the price index for fixed capital formation on the prefecture level is evenmore deficient, we have to substitutewith provincial data.

Second, the statistical criteria for TIFA changed over time, except for real estate, rural collective and individual investment, and thecut-off point for collecting statistics data for fixed asset investment was first raised from aminimum of RMB 50,000 to a minimum ofRMB500,000 in 1997, and since 2011, this cut-off pointwas further raised fromRMB500,000 to RMB 5million. However, there are nosuch exact prefecture-level statistical data to analyze the effect of this criterion change to the TIFA value, even on the provincial level.According to the available statistical data from the 2013 China Statistical Yearbook, these statistical criteria changes caused a decreasein TIFA by 0.3% in 1996 and 9.5% in 2010 for the entire Mainland China, whichwill underestimate the finalWKS value to some extent,especially since 2011, while the effect of the former should have been extremely limited in 2012 under 17 years of depreciation.Furthermore, undoubtedly, the extent of underestimation will vary across prefectures.

Third, constrained by data availability, other debatable assumptions are also made, such as assuming that both the ratio of GFCF toinvestment and the asset price change are uniformwithin a province. In fact, asset values vary both in time because of economic devel-opment (e.g., inflation, new investments and innovation) and in space because of regional context or differences in material costs andwages, etc. As prefectural-level data, instead of provincial data, becomes available in the coming years, the quality of the estimate resultscanbe significantly improved. All considered, thewealth capital estimation reportedhere shouldbe regarded as preliminary, and ongoingwork will continue to improve, refine, and extend these estimates. Nevertheless, we maintain that the current rough estimation of theprefecture level WKS database still offers a valuable starting point for further investigation. Not the least, this database provides thebasis for a better assessment of asset exposure to natural disasters in China, as well as ‘benchmarks’ for maximum disaster losses atthe prefecture level.

5. Conclusions

The primary objective of this paper is to estimate China's prefecture-level capital stock from a wealth measure perspective usingthe perpetual inventorymethod (PIM). With a finer spatial resolution, the resultant database, i.e., wealth capital stock (WKS) for 344prefectures inMainland China, can beused for assessment of economic exposure to natural hazards, aswell as rapid appraisal of directeconomic losses in the early phase of disaster emergency response. According to the estimates, the total wealth capital stock in 2012for Mainland China amounted to RMB 152 trillion (at current prices), and the average annual growth rate has been 14% from 1990 to2012. Not surprisingly, the uneven spatial distribution of wealth capital stock is most distinctive, with close to half (47%) of the totalwealth capital stock concentrated in the East economic region and approximately 22% of China's prefectures contains 60% of itswealthcapital stock.

Second, the application of the derived wealth capital stock data to disaster loss estimation demonstrates the potential of utilizingthese data in the context of natural disasters; however, more case studies are certainly needed for further validation. The estimateddataset can be extended simply and consistently to satisfy the latest need.

The sensitivity analysis of themain parameters indicated that the depreciation rate is themost important parameter thatwill affectultimate capital estimates, while the initial capital stock estimates have increasingly less influence on the near-term wealth capitalestimation because of the consumption of fixed assets over time (i.e., depreciation). The application of the prefecture levelwealth cap-ital stock data to disaster loss estimation demonstrates the potential value of utilizing such data in the context of natural disaster riskresponse and management.

It is worth to note that the current estimates are a rough approximation at the best, limited by a range of assumptions and simpli-fications on keyparameters. Its application for disaster loss and damage assessment also requiresmuchmore cases and empirical test-ing. Both areas will be the focus of future study.

Acknowledgment

We gratefully acknowledge the financial support from the National Basic Research Program of China (2012CB955402), NationalNatural Science Foundation of China (41101506), and Programme of Introducing Talents of Discipline to Universities (B08008). Theauthors would like to thank Guoyi Han, the editor, and three anonymous referees for their constructive suggestions and comments.The remaining errors are the responsibility of the authors.

300 J. WU et al. / China Economic Review 31 (2014) 288–302

Appendix A

Fig. A1. Study area: 344 prefectures across Mainland China's 31 provinces (prefectures' names are shown in Table A1).

Table A1344 prefectures' wealth capital stock across 31 provinces of Mainland China in 2012 (in RMB billion of current price).

ID Name 2012 ID Name 2012 ID Name 2012 ID Name 2012

1 Beijing 3849.2 44 Yingkou 370.8 87 Hangzhou 1724.6 130 Ganzhou 307.12 Tianjin 3883.6 45 Fuxin 139.4 88 Ningbo 1390.6 131 Jian 260.53 Shijiazhuang 1373.6 46 Liaoyang 191.6 89 Wenzhou 773.8 132 Yichun 272.74 Tangshan 1103.1 47 Panjin 315.9 90 Jiaxing 856.9 133 Wuzhou 213.35 Qinhuangdao 268.8 48 Tieling 306.7 91 Huzhou 464.1 134 Shangrao 321.26 Handan 830.7 49 Chaoyang 221.5 92 Shaoxing 807.4 135 Jinan 1130.67 Xingtai 484.1 50 Huludao 177.2 93 Jinhua 543.2 136 Qingdao 1778.68 Baoding 729.9 51 Changchun 1480.5 94 Quzhou 294.1 137 Zibo 731.39 Zhangjiakou 372.5 52 Jilin 958.1 95 Zhoushan 253.4 138 Zaozhuang 437.110 Chengde 332.1 53 Siping 264.7 96 Taizhou 628.3 139 Dongying 815.411 Cangzhou 640.6 54 Liaoyuan 231.4 97 Lishui 215.1 140 Yantai 1559.312 Langfang 547.1 55 Tonghua 343.9 98 Hefei 946.9 141 Weifang 1303.613 Hengshui 269.2 56 Baishan 244.6 99 Wuhu 369.8 142 Jining 781.814 Taiyuan 665.2 57 Songyuan 461.8 100 Bengbu 193.1 143 Taian 707.015 Datong 337.2 58 Baicheng 216.4 101 Huainan 165.0 144 Weihai 740.716 Yangquan 178.9 59 Yanbian 330.4 102 Ma'anshan 275.6 145 Rizhao 403.617 Changzhi 358.1 60 Harbin 1432.5 103 Huaibei 137.7 146 Laiwu 184.218 Jincheng 287.0 61 Qiqihar 254.8 104 Tongling 124.2 147 Linyi 823.119 Shuozhou 233.4 62 Jixi 115.0 105 Anqing 264.5 148 Dezhou 638.720 Jinzhong 320.1 63 Hegang 91.5 106 Huangshan 136.5 149 Liaocheng 490.921 Yuncheng 366.1 64 Shuangyashan 156.6 107 Chuzhou 217.8 150 Binzhou 507.222 Xinzhou 236.6 65 Daqing 633.2 108 Fuyang 147.3 151 Heze 366.423 Linfen 340.7 66 Yichun 76.3 109 Suzhou 145.4 152 Zhengzhou 1605.024 Lvliang 304.8 67 Jiamusi 151.2 110 Chaohu 194.9 153 Kaifeng 298.125 Hohhot 611.6 68 Qitaihe 81.5 111 Liuan 178.7 154 Luoyang 984.226 Baotou 1086.2 69 Mudanjiang 255.1 112 Bozhou 112.9 155 Pingdingshan 418.927 Wuhai 143.3 70 Heihe 97.6 113 Chizhou 101.7 156 Anyang 488.928 Chifeng 534.6 71 Suihua 181.4 114 Xuancheng 218.0 157 Hebi 182.6

Table A1 (continued)

ID Name 2012 ID Name 2012 ID Name 2012 ID Name 2012

29 Tongliao 484.2 72 Daxinganling 30.4 115 Fuzhou 1235.2 158 Xinxiang 637.130 Erdos 1081.1 73 Shanghai 4568.2 116 Xiamen 671.9 159 Jiaozuo 522.831 Hulunbeir 391.7 74 Nanjing 1961.1 117 Putian 293.4 160 Puyang 324.932 Bayannur 332.3 75 Wuxi 1681.0 118 Sanming 426.0 161 Xuchang 476.333 Ulanqab 250.2 76 Xuzhou 1073.7 119 Quanzhou 771.8 162 Luohe 232.234 Hinggan 152.1 77 Changzhou 1147.8 120 Zhangzhou 477.7 163 Sanmenxia 380.735 Xilingol 325.7 78 Suzhou 2329.1 121 Nanping 337.0 164 Nanyang 794.736 Alxa 106.3 79 Nantong 1222.5 122 Longyan 323.4 165 Shangqiu 472.437 Shenyang 1889.2 80 Lianyungang 604.6 123 Ningde 225.4 166 Xinyang 565.838 Dalian 1764.8 81 Huaian 610.6 124 Nanchan 737.9 167 Zhoukou 462.539 Anshan 527.2 82 Yancheng 861.4 125 Jingdezhen 144.5 168 Zhumadian 378.040 Fushun 291.5 83 Yangzhou 725.3 126 Pingxiang 212.7 169 Jiyuan 123.341 Benxi 212.7 84 Zhenjiang 621.8 127 Jiujiang 344.1 170 Wuhan 1981.542 Dandong 261.1 85 Taizhou 682.8 128 Xinyu 204.5 171 Huangshi 244.243 Jinzhou 223.1 86 Suqian 450.1 129 Yingtan 94.0 172 Shiyan 214.8173 Yichang 577.7 216 Qingyuan 295.7 259 Ganzi 91.4 302 Shangluo 136.2174 Xiangyang 446.4 217 Dongguan 780.8 260 Liangshan 239.2 303 Lanzhou 377.0175 Ezhou 145.6 218 Zhongshan 483.7 261 Guiyang 578.2 304 Jiayuguan 38.9176 Jingmen 242.3 219 Chaozhou 133.0 262 Liupanshui 197.3 305 Jinchang 56.0177 Xiaogan 302.1 220 Jieyang 324.9 263 Zunyi 315.0 306 Baiyin 95.6178 Jingzhou 324.6 221 Yunfu 203.4 264 Anshun 86.6 307 Tianshui 114.3179 Huanggang 364.7 222 Nanning 946.9 265 Bijie 241.6 308 Wuwei 103.0180 Xianning 221.9 223 Liuzhou 599.0 266 Tongren 135.5 309 Zhangye 67.0181 Suizhou 149.7 224 Guilin 569.9 267 Qianxi 109.4 310 Pingliang 107.3182 Enshi 140.4 225 Wuzhou 272.3 268 Qiandong 158.4 311 Jiuquan 173.4183 Xiantao 89.2 226 Beihai 271.7 269 Qiannan 154.7 312 Qingyang 188.3184 Qianjiang 85.4 227 Fangchenggang 206.5 270 Kunming 1121.7 313 Dingxi 83.6185 Tianmen 79.8 228 Qinzhou 261.0 271 Qujing 386.5 314 Longnan 94.0186 Shennongjia 7.4 229 Guigang 228.6 272 Yuxi 188.2 315 Linxia 46.2187 Changsha 1644.6 230 Yulin 374.5 273 Baoshan 111.6 316 Gannan 39.7188 Zhuzhou 402.2 231 Baise 394.8 274 Shaotong 205.7 317 Xining 294.4189 Xiangtan 328.6 232 Hezhou 212.8 275 Lijiang 112.3 318 Haidong 95.2190 Hengyang 371.6 233 Hechi 215.3 276 Puer 133.7 319 Haibei 30.1191 Shaoyang 300.6 234 Laibin 192.7 277 Lincang 141.5 320 Huangnan 22.6192 Yueyang 434.5 235 Chongzuo 190.1 278 Chuxiong 150.2 321 Hainan 52.5193 Changde 344.5 236 Haikou 235.7 279 Honghe 275.5 322 Guoluo 12.9194 Zhangjiajie 79.3 237 Sanya 151.1 280 Wenshan 139.6 323 Yushu 44.3195 Yiyang 236.9 238 Else of Hainan 410.2 281 Xishuangbanna 69.7 324 Haixi 190.1196 Chenzhou 389.1 239 Chongqing 2981.3 282 Dali 165.8 325 Yinchuan 404.9197 Yongzhou 319.6 240 Chengdu 2094.1 283 Dehong 70.1 326 Shizuishan 165.1198 Huaihua 214.2 241 Zigong 146.3 284 Nujiang 30.8 327 Wuzhong 146.4199 Loudi 215.3 242 Panzhihua 162.6 285 Diqing 65.2 328 Guyuan 72.8200 Xiangxi 105.7 243 Luzhou 200.6 286 Lhasa 126.7 329 Zhongwei 101.4201 Guangzhou 2332.2 244 Deyang 246.8 287 Changdu 43.1 330 Urumchi 464.9202 Shaoguan 284.0 245 Mianyang 325.1 288 Shannan 39.5 331 Karamay 213.5203 Shenzhen 1549.8 246 Guangyuan 170.7 289 Shigatse 48.9 332 Turpan 85.8204 Zhuhai 388.5 247 Suining 196.6 290 Naqu 27.1 333 Hami 119.3205 Shantou 297.6 248 Neijiang 147.0 291 Ali 14.2 334 Changji 252.3206 Foshan 1197.9 249 Leshan 216.2 292 Linzhi 37.5 335 Bortala 48.3207 Jiangmen 436.4 250 Nanchong 263.2 293 Xi'an 1572.7 336 Bayingolin 283.0208 Zhanjiang 309.5 251 Meishan 194.5 294 Tongchuan 67.0 337 Aksu 171.8209 Maoming 207.4 252 Yibin 236.6 295 Baoji 424.2 338 Kizilsu 32.7210 Zhaoqing 396.9 253 Guangan 172.8 296 Xianyang 539.8 339 Kashgar 173.1211 Huizhou 592.7 254 Dazhou 273.9 297 Weinan 355.1 340 Hetian 59.9212 Meizhou 150.7 255 Yaan 142.8 298 Yan'an 355.7 341 Erie 206.9213 Shanwei 206.8 256 Bazhong 111.2 299 Hanzhong 174.6 342 Tacheng 106.4214 Heyuan 167.9 257 Ziyang 163.0 300 Yulin 547.2 343 Altay 69.4215 Yangjiang 208.5 258 Aba 132.5 301 Ankang 156.7 344 Shihezi 88.4

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Notes: 1. The prefecture's ‘ID’ number is corresponding to the number labeled in Fig. A1. 2. Data source: Data compiled by authors.

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