Applied Energy 114 (2014) 301–309

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Applied Energy

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Inflationary effect of coal price change on the Chinese economy

0306-2619/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.apenergy.2013.09.068

⇑ Tel.: +86 10 82500321; fax: +86 10 62511091.E-mail addresses: chenzhanming@ruc.edu.cn, chenzhanming@pku.edu.cn

Zhan-Ming Chen ⇑Department of Energy Economics, School of Economics, Renmin University of China, Beijing 100872, China

h i g h l i g h t s

� The pass-through effect of coal price change on Chinese economy is examined.� The actual tariffs regulation policy is compared with two hypothetical policies.� GDP deflator, CPI, PPI, and export price level changes are calculated.� 5–25% of general price level changes are attributed to actual coal price shocks.� Investors and foreigners afford about three quarters of the inflation expense.

a r t i c l e i n f o

Article history:Received 14 May 2013Received in revised form 21 September 2013Accepted 30 September 2013Available online 20 October 2013

Keywords:Chinese economyEnergy priceInflationInput–output model

a b s t r a c t

This study investigates the pass-through effect induced by coal price fluctuations on the Chinese econ-omy 2007–2011 based on a non-competitive input–output model. Three scenarios with different domes-tic tariff regulation alternatives, i.e., Actual Regulation (AR), No Regulation (NR), and Strong Regulation(SR), are simulated to reflect the effectiveness of different policies. At the sectoral scale, the Coking sectorhas the largest price variation under all scenarios while agriculture sectors and services sectors are theleast sensitive. Nation-level impacts are examined by the weighted price changes of commodities usedfor different purposes. With the government regulation in reality, about 5% of the GDP deflator and CPIchanges as well as 25% of the PPI change over the research period are attributed to coal price increase.Comparison shows the AR scenario brings more stable fluctuations but higher inflation than the NR sce-nario. The SR scenario confirms that authorities can remarkably relieve short-run inflation by controllingdomestic electricity and heat tariffs. The induced inflationary expense sums up to between 0.03% and0.97% of China’s GDP, around three quarters of which are burdened by investors and foreigners. The quan-titative effect investigated in this study can serve as empirical evidence for policy makers regarding infla-tion control in China.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

The high dependency on energy resources brings also highuncertainties to our modern economy, especially when unex-pected shocks are imposed on the energy commodities sustainingits operation. One of the widely concerned uncertainties broughtforward is the pass-through inflationary effect along with itsassociated problems. When oil is the major energy commoditiesin most countries, a series of studies try to explain the conse-quences of external oil price shocks. Berument and Tasci [1]investigated the inflationary effect of crude oil prices in Turkeyby constructing an input–output model. Their results suggestthe oil prices increase might, in some cases, lead to hyperinflationif wages, profits, interest, and rent earnings are flexible. Cunadoand de Gracia [2,3] analyzed the impact of oil price change using

the cases of fifteen European and six Asian countries. Their mainresults suggest that oil prices have a significant effect on bothprice level and economic activity. Doroodian and Boyd [4] ran acomputable general equilibrium model to examine whether oilprice shocks are inflationary in the US economy under two sepa-rate cases and three technological scenarios. Their results showwhile the external shock has fairly severe effect on energy com-modities, the aggregate price level changes can be significantlydissipated over time. Despite different models are employedand mixed evidences are observed from a variety of studies, itis widely accepted that energy price shock will pass through, atleast partially and temporary, into inflation [5].

As one of the fastest developing economies, China is alsobecoming one of the largest energy consumers in the world [6,7].However, comparing with other major energy consumers such asthe United States, China depends heavily on coal [8–11], whichcontributes to approximately 75% of its energy production and70% consumption during the past two decades [12]. This concen-

302 Z.-M. Chen / Applied Energy 114 (2014) 301–309

trated energy structure of China brings even higher uncertaintiesfrom the shocks of energy supply and demand [13,14]. For exam-ple, if the Chinese government is to levy carbon tax on energyproduct, the relative cost of coal as a carbon-intensive energy willincrease comparing to other energy, which might bring significantimpact on China’s domestic energy market.

In the meanwhile, the inflationary effect induced by energyprice fluctuation on the Chinese economy is more complex thanthose on many other countries because of the special energy pricedetermination mechanism in China. For example, in China about80% of the electricity is generated by coal-fired power station,when coal price is determined by market factors, the electricitytariff is strictly regulated by a government authority namedNational Development and Reform Commission. Aside from elec-tricity, price regulation is also applicable to many other energycommodities such as petroleum, natural gas, and heat. As a result,the pass-through effect of energy price change is distorted signifi-cantly by the price regulation policy in China.

Despite several studies attempt to investigate the impact ofenergy price increase in China (see e.g., [15,16]), the inflationaryeffect originated from the price change of its predominant energycommodity, i.e., coal product, has so far not been examined basedon empirical data. Accordingly, this study attempts to contribute toexisting literature by evaluating the quantitative impact of coalprice change for the Chinese economy during 2007–2011 usingempirical data. Besides, when previously studies usually focus onthe price change of domestic energy resources, this study treatsdomestic and imported coal supply separately and thus examinesboth domestically originated and imported inflationary effects.Three scenarios regarding different price regulation alternativesare analyzed in order to provide concrete policy implications forthe government authorities to adopt appropriate policies to keepinflation under control.

The rest of this paper is organized as follows: Section 2 de-scribes about the methodological aspects and the employed datafor this research, Section 3 examines the inflationary effects ofactual coal price fluctuations for the Chinese economy under threescenarios, and the final section provides a brief discussion andconcludes this study.

2. Materials and methods

2.1. Methodology

To track how the coal price change will pass through theproduction chain and lead to general prices changes of othercommodities, this study applies a non-competitive input–output

Table 1Schematic non-competitive input–output table for an open national economy.

Intermediate use

Sector 1 Sector 2 . . . Sect

Local industrial inputs Sector 1Sector 2. . .

Sector n

Imported industrial inputs Sector n + 1Sector n+2. . .

Sector n + m

Non-industrial inputs Wages, taxes,depreciation, surplus, etc.

model to simulate the network structure of the Chinese economy.There are two major merits of the employed model. The first oneis that it provides detailed information of all economic sectorsand thus is applicable to not only nation-level but also indus-try-level analyses [17,18]. A second merit is that the non-compet-itive model configuration makes it possible to distinguishdomestic and imported prices changes [19], which is of impor-tance in the background that energy market is increasing its de-gree of globalization rapidly [20] and local and foreign shockshave very different influences on the economy. But it should bekept in mind that the international feedback effect, which isimportant for global supply chain, is not captured in such a sin-gle-region model (see details in [19,21]).

Portrayed in Table 1 is a schematic non-competitive input–out-put table for an open national economy which consists of n domes-tic industrial sectors (denoted as Sectors 1 to n) and imports goodsfrom m foreign sectors (denoted as Sectors n + 1 to n + m).

On the input side, a domestic industrial sector has to purchaseproducts, pay for wages, taxes, and depreciation, and make reason-able profits to maintain its production. On the output side, the sec-tor sells products to earn sufficient income to sustain its operation.In an equilibrium state, the monetary inflow and outflow of everysector is balance and can be described as

PiQ i ¼Xnþm

j¼1

PjQ j;i þ Vi ði ¼ 1;2 . . . nÞ; ð1Þ

in which Pi and Pj stand for the prices of outputs from Sectors i and j,Qi stands for the volume (in physical unit, e.g., ton, cubic meter, orman-hour) of total output from Sector i, Qj,i stands for the volume(in physical unit) of output from Sector j used by Sector i, and Vi

stands for the value-added (non-industrial input shown in Table 1)of Sector i, which includes wage, depreciation of fixed capital, netproduction tax, and profit.

To test the impact of external price shock on a single sectoras well as on the whole economy, three assumptions areadopted in lights of previous input–output investigations[1,15]: (a) assume that enterprises are incapable to improvetechnology to reduce material input; (b) assume the value-added of each sector keeps constant; and (c) assume the priceelasticities of all products are zero, which means price changewill neither increase nor decrease supply and demand. Underthose assumptions, the sectoral monetary balance after externalprice shock becomes:

P0iQ i ¼Xnþm

j¼1

P0jQ j;i þ Vi ði ¼ 1;2 . . . nÞ; ð2Þ

Final demand

or n Domestic use Foreign use

Ruralconsumption

Urbanconsumption

Governmentalconsumption

Otherdomestic uses

Export

Z.-M. Chen / Applied Energy 114 (2014) 301–309 303

where P0i and P0j are the prices of outputs from Sectors i and j afterthe shock.

Subtract Eq. (1) from Eq. (2) gets

ðP0i � PiÞQi ¼Xnþm

j¼1

ðP0j � PiÞQ j;i ði ¼ 1;2 . . . nÞ: ð3Þ

In Eq. (3) the parts (P0i � PiÞ and (P0j � PjÞ indicate the pricechanges of outputs from Sector i and j. Then divide both sides ofEq. (3) by PiQi and get

ðP0i � PiÞ=Pi ¼Xnþm

j¼1

ðP0i � PiÞQj;i=ðPiQiÞ ði ¼ 1;2 . . . nÞ: ð4Þ

Introduce the notations of relative price change (@i ¼ P0i � PiÞ=Pi

and direct consumption coefficient aj,i = PjQj,i/(PiQi) [22], Eq. (4) canbe simplified into

@i ¼Xnþm

j¼1

aj;i@ j ði ¼ 1;2 . . . nÞ: ð5Þ

When the prices of all imported products (from Sectors n + 1 ton + m) and some domestic products (suppose they are from localSectors k to n where 1 < k 6 n) are exogenously determined (whichare the origins of external shocks examined in this study), Eq. (5)can be assembled into a matrix form as

D1;k�1 ¼ A1;k�1tD1;k�1 þ Ak;nþm

tDk;nþm ð6Þ

in which Di;j ¼@ i

..

.

@j

264

375 and Ai;j ¼

ai;1 � � � ai; k� 1... ..

. ...

aj;1 � � � aj;k�1

264

375 for any inte-

grates i and j satisfying 1 6 i < j 6 n + m. Then Eq. (6) can be solvedto get

D1;k�1 ¼ ðIk�1 � At1;k�1Þ

�1 þ Ak;nþmtDk;nþm ð7Þ

where Ik�1 is an identity matrix with k�1 rows and columns. Notethat @i indicates the relative price change of output from Sector iand thus D1,k�1 provides the relative price changes of outputs fromall endogenous sectors (Sectors 1 to k�1). Thereafter the generalprice level change as well as the inflationary burdens, i.e., the addi-tional expense induced by inflation, by different groups can be dis-tributed according to the relative price changes of their purchasedcommodities. For example, if ci and ei are the outputs (in monetaryunit) from Sector i that are used as consumer goods and exportaccording to the input–output table, then the vectors of

P1 Q1

..

.

Pn Qn

264

375,

C1

..

.

Cn

264

375,

P1Pn

i¼1 Q1;i

..

.

PnPn

i¼1 Qn;i

264

375, and

e1

..

.

en

264

375 represent the

commodity baskets for the national economy (i.e., domestic out-put), for consumers (i.e., consumer goods), for producers (i.e., inter-mediate input), and for foreigners (i.e., export), respectively.Therefore, the changes of Gross Domestic Product (GDP) deflator,Consumer Price Index (CPI), Producer Price Index (PPI), and exportprice level can be calculated as

GDP deflator change ¼ Dt1;n

P1 Q 1

..

.

Pn Q n

2664

3775=Xn

i¼1

ðPiQ iÞ ð8Þ

CPL change ¼ Dt1;n

C1

..

.

Cn

2664

3775=Xnþm

i¼1

Ci ð9Þ

PPI change ¼ Dt1;n

P1

Xn

i¼1

Q1;i

..

.

Pn

Xn

i¼1

Qn; i

266666664

377777775=Xnþm

j¼1

ðPj

Xn

i¼1

Qj;iÞ ð10Þ

Export price level change ¼ Dt1;n

e1

..

.

en

2664

3775=Xn

i¼1

ei ð11Þ

It should be noticed that, via using the input–output data toapproximate the official commodity baskets data, the above indica-tors are approximations of the economy-wide indicators reportedby the government. So whether the obtained results can illustratethe real world price change well depends on that how close the in-put–output data reflect the real economy structure during thestudy period. More conceptual and technological details, includingthe theoretical origin as well as the calculation procedures, can bereferred to the literature (e.g., [23–32]) and thus are not presentedhere. When analyzing the inflationary effect brought forward bythe market-determined coal price fluctuation, three scenarios,i.e., Actual Regulation (AR), No Regulation (NR), and Strong Regula-tion (SR), are applied in this study. Since changing the prices ofmany commodities related to people’s livelihood in China have toget the permissions from National Development and Reform Com-mission, those three scenarios illustrate that in different degreesthe government authority uses its administrative power to regu-late tariffs of domestic electricity and heat which are the majordownstream products of coal. In the AR scenario the domesticand foreign coal prices as well as the domestic electricity and heattariffs change as they do in the reality. At the same time, the pricesof all foreign non-coal products are assumed to be constant. Thenthe introduced changes of other domestic products’ prices can beinvestigated by applying the non-competitive input–output model.Thereafter the other impacts including the distribution of inflationburdens can also be analyzed. In the NR scenario all factors are setas in the AR scenario except the domestic electricity and heat tar-iffs are now endogenous factors determined by the prices of otherproducts instead of by the actual value. This scenario reflects thehypothetical inflationary effect of coal price increase if the govern-ment downstream price regulation is ruled out. The SR scenario isanother revision of the AR scenario, in which all specifications ofthe latter are adopted except that the electricity and heat tariffskeep constant during the research period. In this scenario thecapacity of the government to use administrative power to stabi-lize price level is examined. In summary, the AR scenario meansusing the actual electricity and heat tariffs changes data, the NRscenario means the tariffs are endogenously determined, whilethe SR scenarios means keeping the tariffs at constant levels.

2.2. Data descriptions

In the current study, the 135-sector input–output table for the2007 Chinese economy [33] is applied to calculate the direct con-sumption coefficients as well as the monetary values of differentflows. According to [34], investigations based on previous Chineseinput–output tables tend to overestimate the energy/emission costof processing exports and underestimate that of normal exports.However, the 2007 Chinese input–output table applies a new rulethat ignoring the re-export fraction of processing exports, thus it isunnecessary to separate the processing and normal exports now. Itshould be noted that in the original table the input–output flows ofdomestic and imported commodities are combined while allimported commodities are classified according to the domestic

Table 2Price variables specifications for the three scenarios.

AR NR SR

Domestic coal Ex Ex ExDomestic electricity and heat Ex En CDomestic other commodities En En EnImported coal Ex Ex ExImported other commodities C C C

Note: Ex: Exogenously determined by actual value. En: Endogenously determinedby modeling results. C: Exogenously defined as constant.

304 Z.-M. Chen / Applied Energy 114 (2014) 301–309

industrial sectors. In order to separate the domestic and importedflows, the total imported flow is assumed to be distributed into dif-ferent intermediate and non-export final demand flows based on auniform proportion for each sector (see [35–37]). In this study thesector order of the original input–output table is rearranged, i.e.,now sectors 134 and 135 are ‘‘Production and Supply of ElectricPower and Heat Power’’ and ‘‘Mining and Washing of Coal’’, respec-tively (see Appendix). The notation k in Eq. (5) equals 134, 134, and135 for AR, SR, and NR scenarios, respectively, which mean coalprices are exogenously in all scenarios while electricity and heattariffs are exogenously only in the AR and SR scenarios (seeTable 2).

The present study investigates the impacts induced by the pricechanges of both domestic and foreign coal products. To track theforeign coal price change, the monetary value and net weight dataof the coal product (Harmonized System Commodity Code of 2701)imported by China during 2006–2011 are extracted from the UNComTrade database (http://comtrade.un.org/) while correspondingexchange rates are calculated according to the OANDA database(http://www.oanda.com/lang/cns/currency/historical-rates/) (seeTable 3). It is obvious that China is increasing its coal import duringthe research period in terms of trade value as well as trade weightexcept a slight decline in trade weight in 2008, reflecting a generaltrend that China is relying more and more on foreign coal supply.

Because the coal type structure (such as the compositions ofpeat and lignite) of the imported coal product is unavailable, it isassumed to be constant during the concerned period. Thereforethe relative price changes are calculated based on the changes ofaverage unit price, i.e., annual trade value divided by annual tradeweight. Meanwhile, the domestic coal price changes as well as theelectricity and heat tariffs changes during 2007–2011 arecalculated according to the National Bureau of Statistics of China

Table 3Coal product import by China during 2006–2011.

Year Trade value ($) Trade weight (kg) Exchange rate (¥/$)

2006 1,617,785,286 38,105,176,053 7.96462007 2,421,770,302 51,015,853,725 7.59722008 3,509,799,302 40,340,095,045 6.94042009 10,573,665,912 125,834,393,755 6.82122010 16,921,987,577 164,568,497,280 6.76052011 20,883,862,450 182,054,129,216 6.4544

Table 4Relative price changes of coal and electricity and heat.

Item 2007(%)

2008(%)

2009(%)

2010(%)

2011(%)

2007–2011(%)

Domestic coal 3.80 28.73 1.88 9.95 10.26 65.04Imported coal 6.65 67.44 �5.08 21.28 6.51 118.96Total coal 3.86 29.47 1.74 10.17 10.19 66.07Electricity and

heat2.22 1.85 2.35 1.97 1.56 10.33

database (http://www.stats.gov.cn/). The recorded price changedata are listed in Table 4, according to which the imported coalproduct has higher overall price increase as well as larger pricefluctuation than the domestic one. At the same time the electricityand heat tariffs have lower (except 2009) and steadier increasescomparing with the coal prices.

3. Results

3.1. Impact on sectoral output price

Presented in Appendix are the aggregate price changes of all135 domestic sectors’ outputs over the period 2007–2011, accord-ing to which the AR scenario (median price change of 1.27% andaverage of 2.35%) has similar overall sectoral inflationary effectas the NR scenario (median price change of 1.24% and average of2.32%) and both impose significantly larger impacts than the SRscenario (median price change of 0.69% and average of 1.64%).The Coking sector (S37) is affected remarkably by the coal pricechange, with more than one fifth output price increase over theperiod under all three scenarios. According to the result, the gov-ernment regulation in reality does not lower the electricity tariffin the long run: when the actual price increase of the domesticElectricity and Heat Production and Supply sector (S134) is10.33%, the withdrawal of government regulation even reducesthis value slightly to 9.76%. The other industries being affectedheavily include chemical manufacture (S38 and S39), metal pro-duction (S56–S58), building materials production (S49–S51), andgas production and distribution (S91). Meanwhile, the priceschanges of agricultural sectors (S1–S5) and services sectors (S94–S133) are generally lower than the medians as well as the averages,indicating the fact that the primary and tertiary industries rely lessheavily on coal.

3.2. Impact on national price level

The GDP deflator is an economic metric that accounts for theprice level of all final goods and services produced domesticallyduring a concerned period. Presented in Table 5 are the changesof GDP deflators for each year and the whole period of 2007–2011 under the three scenarios as well as in the reality.

In general the SR scenario brings noticeable lower price changecomparing with the other two scenarios, which confirms thepossibility for the government to control price level throughadministrative power, at least in the short run. However, one prob-lem of the SR scenario is that, since the regulated tariffs of electric-ity and heat are much lower than the equilibrium prices (tariffs inthe NR scenario), the Electricity and Heat Production and Supplysector (S134) is not profitable and investors would quit. As such,to maintain sufficient electricity and heat provision governmenthas to provide subsidy to this industry, which by itself might intro-duce inefficiency (e.g., suffering deadweight loss).

Meanwhile, the AR scenario has slightly higher inflationaryeffect than the NR scenario, which indicates that free market

Table 5GDP deflator changes during 2007–2011.

Scenario 2007(%)

2008(%)

2009(%)

2010(%)

2011(%)

2007–2011(%)

AR 0.21 0.64 0.18 0.31 0.28 1.63NR 0.11 0.85 0.05 0.29 0.29 1.60SR 0.07 0.53 0.03 0.18 0.18 0.99Realitya 7.23 7.31 0.23 6.88 7.46 32.46

a Data from [12].

Table 6CPI change during 2007–2011.

Scenario Group 2007(%)

2008(%)

2009(%)

2010(%)

2011(%)

2007–2011 (%)

AR Ruralresidents

0.17 0.43 0.15 0.22 0.20 1.18

Urbanresidents

0.18 0.39 0.17 0.22 0.19 1.15

Allresidents

0.17 0.40 0.16 0.22 0.19 1.15

NR Ruralresidents

0.08 0.61 0.04 0.21 0.21 1.15

Urbanresidents

0.08 0.59 0.03 0.20 0.20 1.12

Allresidents

0.08 0.60 0.04 0.21 0.21 1.13

SR Ruralresidents

0.04 0.33 0.02 0.11 0.11 0.62

Urbanresidents

0.04 0.27 0.02 0.09 0.09 0.51

Allresidents

0.04 0.29 0.02 0.10 0.10 0.54

Realitya Ruralresidents

5.37 6.50 �0.31 3.60 5.80 22.62

Urbanresidents

4.48 5.58 �0.85 3.20 5.30 18.85

Allresidents

4.77 5.86 �0.69 3.30 5.40 19.92

a Data from [12].

Table 7PPI change during 2007–2011.

Scenario 2007(%)

2008(%)

2009(%)

2010(%)

2011(%)

2007–2011(%)

AR 0.40 1.32 0.33 0.61 0.56 3.26NR 0.22 1.68 0.10 0.58 0.58 3.20SR 0.15 1.11 0.07 0.38 0.38 2.10Realitya 3.10 6.90 �5.40 5.54 6.03 13.17

a Data from [12].

Table 8Price level change of export commodities during 2007–2011.

Scenario 2007(%)

2008(%)

2009(%)

2010(%)

2011(%)

2007–2011(%)

AR 0.22 0.71 0.18 0.33 0.31 1.76NR 0.12 0.91 0.05 0.32 0.32 1.73SR 0.08 0.60 0.04 0.21 0.21 1.12

Z.-M. Chen / Applied Energy 114 (2014) 301–309 305

mechanism can slightly relieve the price shock comparing with thecurrent system. Nevertheless, the AR scenario has more stableprice level than the NR scenario, e.g., the former brings fluctuationsbetween 0.18% and 0.64% while the latter between 0.05% and0.84%, which is considered to be brought forward by the gradualprice adjustment with buffering capacity for sharp coal pricechange. Besides, the current study finds that coal price increasescontribute to about 5% of the actual GDP deflator change duringthe research period.

Besides the general impact on final products and services, theinflationary effect of coal price change on domestic consumptionand production as well as on international trade are also analyzedin this study. Presented in Table 6 are the changes of CPI for ruralresidents, urban residents, and all residents under different scenar-ios as well as in the reality.

It is obvious in all the three scenarios rural CPI has higherincrease than urban CPI, revealing the different energy consump-tion structures between rural residents and urban residents inChina (see [38]). Regarding the effects of different scenarios, simi-lar observations are caught as in the GDP deflator analysis: (1) theSR scenario induces the lowest price level increase, (2) the ARscenario induces slightly higher price level increase than the NRscenario, and (3) the AR scenario can effectively stabilize CPI fluc-tuation comparing with the NR scenario. Meanwhile, according toour results, the coal price increases between 2007 and 2011contribute to more than 5% of the actual consumer price level vari-ations under the actual electricity and heat tariffs regulation policy.

The relative changes of PPI for China during 2007–2011 are pre-sented in Table 7. It is obvious that PPI has much higher increasethan the CPI under all regulation scenarios, indicating that coalproduct contributes more to the production processes than to theconsumption processes in China. Under the AR scenario the PPIincreases between 0.33% and 1.32% annually with an aggregateincrease of 3.26% over the five years, which accounts for nearlyone quarter of the recorded change. If government regulation iseliminated, the aggregate increase declines to 3.20% but the annualprice level change becomes less stable (vary between 0.10% and

1.68%). On the other hand, the aggregate increase can be reducedsignificantly to 2.10% when strict price control is executed. Besides,the results also show that PPI is more sensitive to coal price, whilenear one quarter of the change of this index during 2007–2011 canbe attributed to coal price variation. This finding suggests the coalprice stabilization is more important to the general business pro-cess than only to consumption side.

As coal is the primary energy input for China, coal price increasewill definitely affect the competitiveness of Chinese commoditiesin the world market because of cost reason. This point is extremelyimportant since the Chinese economy relies heavily on interna-tional trade, e.g., export contributes to about one quarter of theChinese GDP in 2011 [12]. According to this study, the coal pricechanges during 2007–2011 introduce between 0.18% and 0.71%of annual price increases for the Chinese export commodities un-der the AR scenario. Meanwhile, the overall price increases are1.76% and 1.73% under the AR and NR scenarios, respectively. Ifthe government executes strict price regulation on electricity andheat, i.e., to keep their tariffs constant, over one third of the pricelevel increase of export commodities can be mitigated (seeTable 8).

3.3. Incidence of inflation

Besides the quantitative price level change, policy makers alsopay close attention to the affordability and equality issues regard-ing the coal price shock. Presented in Fig. 1 is the total extraexpense paid for final goods and services under all three scenariosdue to inflation. When the annual GDP of China within the researchperiod are between 26.60 and 38.13 trillion RMB in 2007 price, theinflationary burden sums up to between 0.03% (SR scenario in2009) and 0.97% (NR scenario in 2008) of corresponding economicscale. Since the price level change of the SR scenario is the smallest,this scenario also induces the least inflation burden for each year.The overall inflationary burdens induced by the AR scenario andthe NR scenario are approximately the same over the period, butthe former scenario relieves the high burden in 2008 and reinforcesthe low ones in 2007 and 2009. As a result, the AR scenario leads toa more stable temporal trend with the actual inflationary expensevaries between 0.18% (in 2008) and 0.71% (in 2007) of nationalGDP.

Fig. 2 portrays the groups who absorb the inflationary effectsand the fractions they respectively afford. The scenarios of ARand NR have similar shares between groups, in which investorspay over 41% of the inflationary expense and foreigners pay nearly

67

207

58

99 90

36

272

16

94 94

22

169

10

58 58

0

50

100

150

200

250

300

2007 2008 2009 2010 2011

Bill

ion

RM

B

AR

NR

SR

Fig. 1. Inflation burdens for final goods and services between 2007 and 2011.

5.48% 5.46% 4.76%

15.83% 15.71% 11.59%

5.57% 5.54%4.59%

41.04% 41.16%45.35%

32.08% 32.13% 33.71%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

AR NR SR

Foreigner

Investor

Government

Urban resident

Rural resident

Fig. 2. Shares of inflation burdens between different groups over 2007–2011.

306 Z.-M. Chen / Applied Energy 114 (2014) 301–309

one third. Besides, domestic residents pay slightly over 20% and theremaining fraction is afforded by the government. Comparing withthese two scenarios, the investors and foreigners pay larger por-tions of the inflationary expense in the SR scenario. At the sametime, urban residents appear to reduce significant fraction of theirburden while rural residents and the government also relieveburdens. Nonetheless, taking into account the total inflationaryeffects of different regulation alternatives, the SR scenario intro-duces the least net burdens to every group in terms of the extraexpense they have to pay.

4. Discussions and conclusions

As an economic indicator connecting to people’s daily livesclosely, the general price level is concerned by the public andconsidered as an important regulation aim by the governmentalauthorities as well. Understanding the quantitative connectionbetween external shock, such as unexpected energy price change,and general price level is of significance to decision maker regard-ing inflation control. Besides, the substantial effects of differentpolicies options should also be identified in order to adopt appro-priate regulation measures.

Owing to its particularity in resources endowments, China hasan energy structure depending highly on coal. Moreover, thispredominant energy commodity has a rapid price increase duringthe past few years, which attracts broad concerns regarding itspass-through effect for the Chinese economy. As such, this studyexamines the inflationary effect of coal price change during2007–2011 in China based on a non-competitive input–outputmodel. Three basic scenarios are tested to reflect the effects underdifferent price regulations policies.

According to the results, the Coking sector has the largest pricechange while primary and tertiary sectors are less impacted by thecoal price fluctuations. Meanwhile, the strong regulation policy cansignificantly relieve the impact of coal price change on generalprice level, which confirms the possibility to stabilize economicfluctuation through administrative regulation on key product.However, the revenue reduction originated from the strong regula-tion is burdened by the producers and/or the government, wherenegative side effects are likely to be introduced. Comparing tothe strong regulation scenario, both the actual alternative withpartial regulation and the market alternative with no regulationensure adequate accountant profits for the enterprises and is morefiscally neutral in the long run. Moreover, these two scenariosbring similar average price level change but the former inducesmuch smaller temporal variation. It suggests that the actual partialprice control brings forward the effect of smoothing price fluctua-tion instead of eliminating it. Nonetheless, the inherent problem ofgovernment regulation is how to determine the target priceadequately.

The present research also analyzes the incidences of the infla-tionary effect under different regulation scenarios. Results suggestthat strong regulation on electricity and heat tariffs can signifi-cantly reduce the burdens originated from coal price increase.Meanwhile, under all the three scenarios, investors bear the largestportion of the inflation burden, followed by foreigners, domesticurban residents, domestic rural residents, and the government. Itis not trivial to mention that the short-term burdens of the govern-ment and investors will ultimately be transmitted to final consum-ers, either domestic or foreign ones, at the long run by means oftaxing and changing price, whose effects are not investigated inthe present research.

In the current study, the domestic and foreign products areseparately treated. Despite comparing to the domestic coal pricechange, the foreign coal price change has less impact on thegeneral price level owing to its much smaller consumption in the2007 Chinese economic structure, i.e., less than 2% of coal con-sumption is imported in terms of expenditure. However, as noticedby the data, the foreign coal price is changing with a much largeroscillation than the domestic one. When the foreign coal priceincreases by nearly 70% in 2008, the inflationary effect is stillconsiderable. Therefore, enhancing China’s bargaining power ininternational coal market to get stable import price is an importantmeasure to stabilize domestic price level, especially under thebackground that China keeps expanding its coal import in therecent years, e.g., coal import increases by over 2.5 times from2007 to 2011 in terms of trade weight. As such, taking the effectof foreign coal price change into account provides a more compre-hensive and extendable framework for future research. Besides, thetreatment of separating domestic and imported energy commodi-ties is more applicable to the cases of other countries which relyheavily on energy import, such as Japan and South Korea.

Limitations of this study should also be noticed. First of all, thecalculated price level indicators such as the CPIs are derived fromthe input–output data instead of from the officially definedcommodity baskets, which means the analyzed indicators wouldbe different from the economy-wide indicators reported by thegovernment. However, as the input–output data are the reflectionof economic activities, the calculated indicators can simulate theactual price level approximately. Second, this investigation reliesheavily on the assumption that when price changes different cate-gories of input cannot substitute each other. This assumption ismore applicable for short-term analysis but introduces unavoid-able errors when studying the economy in the long run as theeconomic structure actually changes. Unfortunately there is nogenerally accepted division between short and long terms ineconomics study, especially when focusing on the nation-scale

Table A.1Sectoral price changes over 2007–2011.

Sector code Sector content AR (%) NR (%) SR (%)

S1 Farming 1.04 1.02 0.66S2 Forestry 0.54 0.53 0.35S3 Animal husbandry 0.48 0.47 0.27S4 Fishery 0.47 0.46 0.23S5 Services in support of agriculture 0.62 0.60 0.27S6 Extraction of petroleum and natural gas 1.46 1.41 0.54S7 Mining of ferrous metal ores 2.70 2.60 0.70S8 Mining of non-ferrous metal ores 2.09 2.02 0.75S9 Mining and processing of nonmetal ores and other ores 1.77 1.72 0.74S10 Grinding of grains 0.99 0.97 0.51S11 Processing of forage 0.86 0.84 0.46S12 Refining of vegetable oil 0.93 0.91 0.55S13 Manufacture of sugar 1.12 1.09 0.62S14 Slaughtering and processing of meat 0.52 0.51 0.27S15 Processing of aquatic product 0.54 0.52 0.25S16 Processing of other foods 0.96 0.94 0.49S17 Manufacture of convenience food 1.29 1.27 0.81S18 manufacture of liquid milk and Dairy products 1.21 1.19 0.79S19 Manufacture of flavoring and ferment products 1.46 1.43 0.93S20 Manufacture of other foods 1.18 1.15 0.70S21 Manufacture of alcohol and wine 1.21 1.19 0.80S22 Processing of Soft Drinks and Purified Tea 1.28 1.26 0.77S23 Manufacture of tobacco 0.46 0.45 0.27S24 Spinning and weaving, printing and dyeing of cotton and chemical fiber 1.70 1.66 0.87S25 Spinning and weaving, dyeing and finishing of wool 1.04 1.02 0.63S26 Spinning and weaving of hemp and tiffany 1.37 1.34 0.75S27 Manufacture of textile products 1.38 1.35 0.72S28 Manufacture of knitted fabric and its products 1.49 1.46 0.87S29 Manufacture of textile wearing apparel, footwear and caps 1.27 1.24 0.73S30 Manufacture of leather, fur, feather(down) and its products 0.85 0.83 0.47S31 Processing of timbers, manufacture of wood, bamboo, rattan, palm and straw products 1.77 1.74 1.09S32 Manufacture of furniture 1.52 1.49 0.90S33 manufacture of paper and paper Products 1.92 1.88 1.14S34 Printing, reproduction of recording media 1.32 1.29 0.74S35 Manufacture of articles for culture, education and sports activities 1.49 1.46 0.85S36 Processing of petroleum and nuclear fuel 1.11 1.08 0.48S37 Coking 21.66 21.59 20.72S38 Manufacture of basic chemical raw materials 4.91 4.80 2.90S39 Manufacture of fertilizers 6.23 6.15 4.81S40 Manufacture of pesticides 2.19 2.14 1.20S41 Manufacture of paints, printing inks, pigments and similar products 2.43 2.38 1.49S42 Manufacture of synthetic materials 2.15 2.10 1.21S43 Manufacture of special chemical products 3.45 3.39 2.40S44 Manufacture of chemical products for daily use 1.39 1.36 0.85S45 Manufacture of medicines 1.17 1.14 0.54S46 Manufacture of chemical fiber 2.02 1.97 1.09S47 Manufacture of rubber 1.94 1.90 1.18S48 Manufacture of plastic 1.74 1.69 0.87S49 Manufacture of cement, lime and plaster 6.24 6.15 4.67S50 Manufacture of products of cement and plaster 4.50 4.43 3.33S51 Manufacture of brick, stone and other building materials 5.99 5.92 4.88S52 Manufacture of glass and its products 4.60 4.55 3.61S53 Manufacture of pottery and porcelain 3.43 3.39 2.56S54 Manufacture of fire-resistant materials 4.10 4.06 3.23S55 Manufacture of graphite and other nonmetallic mineral products 4.45 4.39 3.40S56 Iron-smelting 7.34 7.28 6.38S57 Steelmaking 5.41 5.36 4.45S58 Rolling of steel 4.46 4.40 3.42S59 Smelting of ferroalloy 3.60 3.51 2.00S60 Smelting of non-ferrous metals and manufacture of alloys 2.91 2.84 1.56S61 Rolling of non-ferrous metals 2.52 2.46 1.30S62 Manufacture of metal products 2.89 2.84 1.77S63 Manufacture of boiler and prime mover 1.90 1.87 1.26S64 Manufacture of metalworking machinery 2.03 2.00 1.36S65 Manufacture of lifters 2.10 2.06 1.41S66 Manufacture of pump, valve and similar machinery 2.33 2.30 1.59S67 Manufacture of other general purpose machinery 2.33 2.29 1.51S68 Manufacture of special purpose machinery for mining, metallurgy and construction 2.22 2.18 1.37S69 Manufacture of special purpose machinery for chemical industry, processing of timber and nonmetals 2.25 2.21 1.43S70 Manufacture of special purpose machinery for agriculture, forestry, animal husbandry and fishery 1.89 1.86 1.28S71 Manufacture of other special purpose machinery 1.96 1.92 1.18S72 Manufacture of railroad transport equipment 2.06 2.03 1.31S73 Manufacture of automobiles 1.59 1.56 0.98S74 Manufacture of boats and ships and floating devices 1.56 1.53 0.97

(continued on next page)

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Table A.1 (continued)

Sector code Sector content AR (%) NR (%) SR (%)

S75 Manufacture of other transport equipment 1.47 1.45 0.93S76 Manufacture of generators 1.73 1.69 1.04S77 Manufacture of equipments for power transmission and distribution and control 1.64 1.60 0.96S78 Manufacture of wire, cable, optical cable and electrical appliances 1.93 1.88 1.04S79 Manufacture of household electric and non-electric appliances 1.46 1.43 0.84S80 Manufacture of other electrical machinery and equipment 1.74 1.70 0.94S81 Manufacture of communication equipment 0.84 0.82 0.45S82 Manufacture of radar and broadcasting equipment 0.79 0.78 0.42S83 Manufacture of computer 0.63 0.62 0.31S84 Manufacture of electronic component 1.26 1.22 0.62S85 Manufacture of household audiovisual apparatus 0.76 0.74 0.39S86 Manufacture of other electronic equipment 0.82 0.80 0.39S87 manufacture of measuring instruments 1.13 1.11 0.68S88 Manufacture of machinery for cultural activity & office work 1.18 1.16 0.67S89 Manufacture of artwork, other manufacture 1.71 1.68 1.04S90 Scrap and waste 0.26 0.26 0.16S91 Production and distribution of gas 4.79 4.76 4.20S92 Production and distribution of water 2.51 2.40 0.30S93 Construction 2.50 2.46 1.73S94 Transport via railway 1.19 1.16 0.61S95 Transport via road 0.75 0.74 0.43S96 Urban public traffic 0.76 0.74 0.35S97 Water transport 0.60 0.59 0.31S98 Air transport 0.81 0.79 0.42S99 Transport via pipeline 1.56 1.50 0.34S100 Loading, unloading, portage and other transport services 0.74 0.72 0.41S101 Storage 1.03 1.02 0.68S102 Post 0.69 0.67 0.34S103 Telecom & other information transmission services 0.58 0.56 0.16S104 Computer services 0.51 0.50 0.26S105 Software industry 0.50 0.48 0.23S106 Wholesale and retail trades 0.49 0.47 0.19S107 Hotels 1.51 1.45 0.33S108 Catering services 0.62 0.60 0.31S109 Banking, security, other financial activities 0.21 0.20 0.07S110 Insurance 0.73 0.71 0.26S111 Real estate 0.23 0.22 0.11S112 Leasing 0.72 0.71 0.38S113 Business services 0.96 0.94 0.52S114 Tourism 0.62 0.60 0.26S115 Research and experimental development 0.96 0.93 0.49S116 Professional technical services 0.52 0.51 0.28S117 Services of science and technology exchanges and promotion 0.63 0.62 0.36S118 Geological prospecting 0.93 0.91 0.46S119 Management of water conservancy 0.61 0.59 0.27S120 Environment management 1.17 1.15 0.75S121 Management of public facilities 1.05 1.03 0.54S122 Services to households 0.84 0.82 0.45S123 Other services 1.08 1.06 0.69S124 Education 0.86 0.83 0.37S125 Health 1.17 1.14 0.65S126 Social security 0.59 0.57 0.23S127 Social welfare 0.40 0.39 0.16S128 Journalism and publishing activities 0.83 0.82 0.46S129 Broadcasting, movies, televisions and audiovisual activities 0.98 0.96 0.54S130 Cultural and art activities 0.76 0.75 0.40S131 Sports activities 1.01 0.98 0.38S132 Entertainment 0.60 0.58 0.28S133 Public management and social organization 0.74 0.73 0.38S134 Production and supply of electric power and heat power 10.33 9.76 0.00S135 Mining and washing of coal 65.04 65.04 65.04

median (exclude S134 and S135) 1.26 1.22 0.69average (exclude S134 and S135) 1.87 1.84 1.23Median 1.27 1.24 0.69Average 2.35 2.32 1.64

308 Z.-M. Chen / Applied Energy 114 (2014) 301–309

case. Nevertheless, this research presents a scenario analysis toshow the short-term market response on coal price change, whichprovides knowledge for the government authorities regarding tem-porary regulation to cope with sudden external price shock. Third,it should be noticed that using the 2007 input–output table toevaluate the actual scenario for 2007 is not an appropriate treat-ment since the table itself is in fact a consequence of the actual

price shock. However, due to result consistency to compare withthe other two scenarios, the 2007 actual price impacts are still2reported. Last but not least, uncertainties might be introducedfrom data input and assumptions. For instance, when evaluatingthe impact of government regulation for the period 2007–2011,the conclusion that about one quarter (3.26% out of 13.17%) ofthe PPI change is attributed to coal price change stands on the

Z.-M. Chen / Applied Energy 114 (2014) 301–309 309

assumption that the economy-wide PPI change derived from theinput–output data by considering all actual price changes hap-pened (including coal price change) is exactly equal to 13.17%.However, as the actual price changes take place along with theevolution of the economy (from 2007 to 2011) while the appliedinput–output data only capture the economic structure of 2007,this assumption might bring forward significant errors if the2007 data are not good approximation of the whole the researchperiod. This point is especially important for China as a fast devel-oping country experiencing rapid economic structure change.Another example is the average coal price calculation, which is aweighted indicator considering coal prices of different types suchas peat and lignite. Thus the relative coal price change is a resultof both coal type structure change and actual coal price change,but the former factor is ignored in this study. It is obvious thatthe aforementioned manipulations lead to uncertainties on the re-sults to some degree. Nevertheless, the manipulations are the besttreatments when further data are not available.

To sum up, the overall inflationary effect brought forward bycoal price fluctuation during 2007–2011 to the Chinese economyis evaluated, i.e., contributing to between 5% and 25% of the generalprice level changes and introducing between 0.18% and 0.71% ofadditional social expenditures. Three different scenarios are ana-lyzed according to the assumption that whether government con-trols the electricity and heat tariffs or not: ‘‘actual regulation’’means the actual electricity and heat tariffs changes happened;‘‘no regulation’’ means the two factors are endogenously deter-mined; and ‘‘strong regulation’’ means keeping both factors at con-stant levels. It is interesting to combine current results with theelectricity market reform discussion in China. One of the majortopics of the discussion is whether the electricity tariff should bechanged in accordant to coal price fluctuation. According to thisstudy, the market determined electricity tariff will enable broadershort-term oscillation but does not introduce significant impact inthe long run. Results in this study are also critical for policy makerwho concerns about inflation control in China, despite furtherinvestigations are still needed to determine the best responsemechanism for the government to use its power.

Acknowledgements

I thank the three anonymous reviewers for the comments andsuggestions on the previous versions of this paper. This study hasbeen supported by the Ministry of Education Foundation of China(No. 12YJCZH021).

Appendix A.

Table A.1

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