Multi-objective Optimization of China s Export Commodity Structure Based on Non-competitive Input-Output Analysis *

Transcription

1 Mult-objectve Optmzaton of Chna s Export Commodty Structure Based on Non-compettve Input-Output Analyss * Zhru MU Cuhong YANG * Abstract By applyng a non-compettve nput-output model capturng processng exports of Chna, ths paper establshes a mult-objectve programmng model to optmze the export structure. In the nput-output model, Chna s domestc producton s dvded nto three parts, producton for domestc use, processng exports, non-processng export and producton for other FIEs, whch makes possble to dfferentate the effects of dfferent trade patterns. Fnally the paper conducts emprcal analyss and obtaned the optmzng export structure by export type based on data of Key words Optmzng composton of export commodty, Input-output model of non-compettve mports, rocessng export, Mult-objectve programmng 1 Introducton Trade n goods and servces s essentally the trade n factors of producton. That means, to the exporter, when t exports goods and servces to other countres, t exports resources whle at the same tme leaves the polluton caused by export actvtes at home. Smlarly, the mporter not only consumes foregn resources but also keeps the polluton away from ts own country by mportng goods or servces. Zhru MU Bejng Research Center for Scence of Scence, Bejng , Chna. Emal: muzhru@amss.ac.cn. * Cuhong YANG Academy of Mathematcs and Systems Scence, Chnese Academy of Scences, Bejng , Chna. Emal: chyang@ss.ac.cn. * Correspondng author * Ths research s supported by Natonal Natural Scence Foundaton of Chna under Grant Nos , and ), Outstandng Talents Funds of Organzaton Department, Bejng Commttee of CC. 1

2 For Chna, ts trade growth mode s qute extensve. For example, most export goods are labor-ntensve, energy or resource-ntensve and made by smple processng and assembly. There are stll some goods that are restrcted by Techncal Barrers to Trade because of qualty problems such as pestcde resdues n food. On the other hand, Chna has pad heavy prce for extensve trade growth n resource and envronment,.e., the export goods are of hgh resource consumpton and hgh polluton on average. Furthermore, these goods account for a large proporton n Chna s total export. Accordng to the estmaton by WTO anel of Mnstry of Envronmental rotecton of Chna, n 2005 Chna s foregn trade has generated 1.2 bllon ton of CO2 defct, whch amounts to 23% of ndustral CO2 emsson that year; 5.5 mllon ton of SO 2 defct, 39% of ndustral SO2 emsson; and 61.5 bllon ton of water defct, 12% of the total ndustral and agrcultural water consumpton n the same year [6]. The current composton of Chna s export products aggravates the shortage of Chna s energy supply and worsens the envronment. It not only nfluences ndustral and agrcultural producton, but also s harmful to people s health. Thus t s necessary to analyze the comprehensve effects of export on the economy, employment, resource and envronment so as to objectvely optmze the export structure. Ths s very sgnfcant to reduce resource consumpton and envronmental damage as soon as possble whle ensurng the realzaton of soco-economc development goals. The rest of the paper s organzed as follows. Secton 2 descrbes the methodology CO 2 defct here means that export has brought out more CO 2 emsson than mport. SO 2 (water) defct s of the smlar meanng. 2

3 and the sketch of the model. Secton 3 gves a detaled descrpton of Mult-Objectve Input-Output Model (MOIOM). Secton 4 dscusses the results for restructurng of Chna s exports based on several scenaros. Secton 5 concludes. 2 Methodology The composton of export commodtes refers to the share of every knd of commodty n the total export and the relatve relatonshp between dfferent goods [1]. Mult-objectve model s more n lne wth the real complex world snce t consders the varous factors comprehensvely. The nput-output (IO) model reflects the relatonshp among dfferent sectors n the macroeconomc system, but t doesn t necessarly reach the optmum plan. Combnng the two models, however, we can construct a new model utlzng the advantages of these two models, defned as Mult-Objectve Input-Output Model (MOIOM). The most outstandng character of ths model s that the equlbrum equaton of IO model s changed to be a constrant condton. It has ganed a wde applcaton n many felds n practce. [8] Zhang (2001) constructs a dynamc mult-objectve model for a cty ncludng four objectves such as economc growth, energy nput, polluton emsson, as well as overall balance based on the cty s 1997 IO table, she used the model to forecast ts economc growth. Zhang et al. (2003) [9] establsh a lnear programmng model to reflect the tarff adjustment based on non-compettve IO table. Jang et al. (2002) [3] establsh a mult-objectve model based on nput-output analyss to study the nteracton among the populaton, resource, envronment and economy. Based on prevous studes, ths paper s to dscuss the effects of export on economy, 3

4 employment, energy and water consumpton, as well as polluton emsson n the framework of non-compettve nput-occupancy-output model. We establsh the so-called MOIOM to optmze the composton of export goods, to adjust the conflct among economc and socal ncome, resource shortage and envronmental polluton so as to promote the sustanable development of the growth mode of Chna's foregn trade. 3 MOIOM The nput-occupancy-output model of non-compettve mport type capturng processng trade s put forward n the study of Sno-US trade mbalance (Lau et al., 2006). By ths model, one can accurately compute not only the effects of export on domestc value-added, employment and polluton, but also the amount of resources emboded n varous export commodtes. It provdes a clear theoretcal framework for researchng how to optmze the composton of export goods. The table layout of ths model s gven n Appendx table 1. In ths extended table, n order to capture Chna s specal producton structure, the domestc producton of Chna s dvded nto three parts,.e., producton for domestc use, processng exports, non-processng exports and producton for other FIEs (Foregn Invested Enterprses), whch are denoted by D, and N respectvely. In ths table, the extended drect consumpton coeffcent matrx can be denoted as: A A A A A A A A A A DD D DN D N = ND N NN where j A s the drect consumpton coeffcent matrx between type and type j 4

5 (, j = D,, N ). B = ( I A) 1 Then we derve the extended total requrement coeffcent matrx, where I s the unt matrx of the same order as A. Let e, the column vector of export structure, represent decson varable, whose element s e and t satsfes e = 1. n = 1 In the Eleventh Fve-Year lan, Chna has set forth the targets of reducng energy consumpton per unt of GD by 20%, major pollutant emsson (SO 2 and COD) per unt of GD by 10% and of ncreasng the proporton of the value-added of the tertary ndustry n GD by 3 percentage pont by the year 2010, compared wth the levels n Accordng to ths plan and consderng the effects of export on economy, resources, envronment and employment, the paper sets up prncples of evaluatng the pros and cons of the export structure. We establsh a MOIOM based on the non-compettve IO model capturng processng export to optmze the composton of export goods, wth a group of soco-economc development goals to be the objectve functons and wth the economc operaton envronment to be the constrant condtons. 3.1 Objectve Functon We need to consder four aspects,.e. economc growth, employment, resources and envronment when settng the objectve functons n order to ensure the sustanable development of export trade. Therefore, we set forth the followng fve optmzng prncples: 5

6 () Maxmzaton of economc beneft The prmary purpose of optmzng export structure s to obtan economc ncome as hgh as possble. In the IO table, t means maxmzng the total domestc value-added by optmzng export structure,.e. max B V e, where BV = AB V and AV s the row vector of drect value-added coeffcent. () Energy conservaton prncple Energy s the materal bass for economc and socal development. In the ncreasngly tght stuaton of energy supply n Chna, the export of energy-ntensve goods wll only worsen the domestc energy shortage. So t s necessary to try to avod the loss of domestc energy when upgradng export structure. Namely, we should try to mnmze the energy consumpton by export,.e. mn B E e, where BE = AB E and A E s the row vector of drect energy consumpton coeffcent. () olluton emsson reducton prncple Wth the rapd export growth, a noteworthy problem s that export goods manly come from hgh-polluton ndustres, whose export has accelerated the deteroraton of domestc envronment. To change ths stuaton, t s necessary to make the polluton content by export as low as possble. Combnng the correspondng ndex n the Eleventh Fve-Year lan, we choose SO 2 and COD to represent waste gas and waste water. So we come to B e and mn B COD e, where B = A B, mn SO2 SO2 SO2 B = A B and COD COD A A are respectvely the row vector of drect emsson, SO2 COD coeffcent of SO and COD. 2 (v) Increase of employment prncple 6

7 The fact that Chna has a populaton of more than 1.3 bllon people makes employment very mportant for ts economc development and socal stablty. Whle the export enterprses have always been one of the drvng force n provdng job postons, so we expect them to absorb as many labor forces as possble, so as to ease the employment pressure n Chna. Then we set: max B L e, where BL = AB L, AL s the row vector of drect labor occupaton coeffcent. (v) rncple of balance n ndustral development As we all know, the tertary ndustry could meet the demand not only for lvng servce of consumers to mprove ther lvng standard but also for producton servce of producers to mprove the effcency, so t has a strategc role n natonal economy. In other words, the rato of value-added of the tertary ndustry to GD s expected to A ˆV T reach a certan value, lke w, = w, where AˆV s a dagonal matrx, the A [ ] V element on the dagonal s drect value-added coeffcent of each ndustry; T means the set of sectors n the tertary ndustry; stands for the column vector of total output. The numerator of the above formula s the value-added of the optmzed tertary ndustry and the denomnator s the optmzed GD. 3.2 Constrants Economc and socal development s nseparable from the natural and socal condtons lke populaton, resource and envronment. In order to promote the comprehensve and sustanable development of economy and socety, we select the followng fve constrants: 7

8 () The nput-output balance constrant reflectng the nterdependence of producton, dstrbuton and consumpton among dfferent natonal economc parts: 1 ( ) ( ) ( ) = I A Y + E = B Y + ee % where Y and E are the column vector of domestc fnal use and that of export, respectvely; E % s a scalar whch denotes the total export volume. () Constrant of energy supply: AE E S S where E s the total energy supply of that year, AE s a row vector of drect energy coeffcent,.e., energy consumpton per unt of total output value. () Constrant of water supply: AW W S where S W s the total water supply of that year and A W s the row vector of drect water consumpton coeffcent. (v) Upper and lower lmt constrant of the export share of every sector: l e u where u and l are the column vectors of upper and lower lmt of export share of every sector, respectvely. (v) Constrant of vector structure: where I = (1,1, L,1). (v) Non-negatve constrant: I e = 1 e 0 8

9 Now we establsh the MOIOM of optmzng the composton of export goods. 3.3 Soluton of MOIOM It s well known that goal programmng (G) doesn t emphasze the absolute optmalty when decdng and solvng. It could handle varous mult-objectve programmng problems even wthout unform unts of measurement as well as problems wth conflctng objectves. Besdes, t s easy to solve snce t s an extenson of lnear programmng. All the above advantages make t flexble and practcal n dverse felds. So t s chosen to solve ths MOIOM. Specfcally, we frst need to set expectaton value for each objectve functon n reference to the Eleventh Fve-Year lan. The expected changes ( + for up and - for down) for GD, energy consumpton per unt of GD, emsson of SO 2, emsson of COD, the rato of value-added of the tertary ndustry n GD and the employment are respectvely +1%, -2%, -2%, -2%, +1%, +1%, wth OBJ1, OBJ 2, OBJ3, OBJ 4, OBJ5, OBJ 6 denotng the expected values of them respectvely. Then the orgnal MOIOM s transformed as follows. The objectves are: mn OBJ1 BV ( Y + ee% ) (1) BE ( Y + ee% ) mn OBJ 2 BV ( Y + ee% ) (2) mn BSO ( Y + ee% ) OBJ3 2 (3) mn BCOD ( Y + ee% ) OBJ4 (4) 9

10 Bˆ V ( Y + ee% ) T mn OBJ5 BV ( Y + ee% ) (5) where: BˆV mn OBJ 6 BL ( Y + ee% ) (6) s the dagonal matrx of the row vector of total value-added coeffcent. Expresson (1) denotes the macro-economc objectve,.e., the economc benefts generated by export structure after optmzaton should be as close to OBJ1 as possble; Expresson (2) s the energy consumpton per unt of output value objectve,.e., the energy consumpton per unt of output value, under the condton of export structure optmzaton, should not be hgher than OBJ 2 ; Expresson (3) and (4) are the polluton emsson reducton objectves,.e., the SO2 and COD emsson, under the condton of export structure optmzaton, should not be hgher than OBJ3 and OBJ 4, respectvely; Expresson (5) s the balanced ndustral development objectve, meanng the rato of value-added of the tertary ndustry n GD s as close to OBJ5 as possble, where the nomnator and denomnator of the frst term stand for value-added of the tertary ndustry and GD, under the condton of export structure optmzaton, respectvely; Expresson (6) s the employment objectve generated by export,.e., the employment generated by export, under the condton of export structure optmzaton, should not be lower than OBJ 6. The constrants consst of two parts, the nequalty constrants on the resource and the equalty constrants on the objectves. They are: 10

11 ( % V ) BE ( Y + ee% ) BV ( Y + ee% ) SO ( %) 2 COD ( %) ( + %) T V ( + %) L ( %) ( %) ( %) B Y + ee + d d = OBJ d d = OBJ B Y + ee + d d = OBJ B Y + ee + d d = OBJ4 Bˆ V Y ee B Y ee B Y + ee E W E B Y + ee W d d = OBJ B Y + ee + d d = OBJ S S l e u Ie = 1 + ed,, d 0 where: d, d + are the postve and negatve devaton varables for goal constrant respectvely and I s the summaton vector. It should be noted here that the nput-output equaton ( %) B Y + ee = s hard constrant consderng the export and the total output to be endogenous. So be replaced by the left sde. can 3.4 Data Sources The emprcal analyss of ths paper s based on the 2002 non-compettve extended nput-output table capturng processng trade of Chna whch conssts of 42 producton sectors. The SO 2 and COD emssons of every sector refer to Chna Envronment Yearbook The energy supply s taken from Chna Energy Yearbook , and the energy consumpton of every sector refers to the extended non-compettve energy IO table constructed by Wang et al. (2009) [5]. The 11

12 water supply s taken from Chna Statstcal Bulletn of Water 2002, and the water consumpton of every sector refers to ang et al. (2009) [7]. Then we dvde the energy consumpton, water consumpton and polluton emsson nto three parts by D, and N n reference to Detzenbacher et al. (2009) [2]. Other data are from Chna Statstcal Yearbook for varous years. Notably, the energy dataset s composed of 26 sectors. To keep consstency n data, we fnally aggregate 42 producton sectors to 26 ones. The comparson between the two classfcatons s gven n Appendx table 2. 4 Results In the computaton of G, t s frstly requred to determne the prorty level for the unwanted devaton of every goal, wth the mnmzaton of a devaton n a hgher prorty level beng nfntely more mportant than any devatons n lower prorty levels. Here we set four scenaros smulatng dfferent prorty levels of the objectve functons to get dfferent attanment functons (See Table 1), by whch the soluton of G s transformed to solve a sngle-objectve lnear programmng problem. Table 1. The attanment functons under dfferent orders of prorty levels Scenaro Objectve Functon (1) (2) (3) (4) (5) (6) Attanment Functon I f = d + d + + d + + d + + d + d + d mn ( ) ( ) II f = d + + d + d + + d + + d + d + d mn ( ) ( ) III f = d + + d + + d + + d + d + d + d mn ( ) ( ) IV f = d + d + + d + d + + d + + d + d mn ( ) ( ) 12

13 In Table 1, s the prorty level, wth + 1? meanng the prorty level beng nfntely more mportant than the prorty level ( + 1). Snce B and B SO 2 COD share the same unt,.e., ton per 10 thousands, and d + 3 d + 4 can be summed up drectly whch mples that t s equally mportant to reduce the emsson of SO 2 and COD. 4.1 General results To solve the above four G models separately, we can reach the optmzed export structure under dfferent scenaros (See Table 2). Table 2. The optmzed composton of export goods under dfferent scenaros (%) Sector Actual Scenaro Composton I II III IV

14 From Table 2, the sectors whose export shares need to be reduced s characterzed frstly by hgher polluton sectors such as aper and products, prntng and record medum reproducton (sector 10), then by hgher energy or resource consumpton lke Nonmetal mneral products (sector 13), and by lower value-added due to a large number of processng export, just as Transport equpment (sector 17). For smplcty, we defne such sectors as Type I. Obvously, the export shares of etroleum processng, cokng and nuclear fuel processng (sector 11), Nonmetal mneral products (sector 13), Metals smeltng and pressng (sector 14) all need to be reduced to 0% under the former three scenaros. Although the unavodable reasons lke data or model error make t somehow dealstc, the results show a clear trend that t s urgent to reduce the export proportons of these sectors snce they make tremendous negatve mpact on domestc energy and envronment. In a word, the characterstcs of Type I s not consstent wth the optmzed prncples put forward earler. So t s mperatve to lower ther export share. Sectors whose export proportons need to be ncreased nclude Metal ore mnng (sector 4), Non-ferrous mneral mnng (sector 5), Wearng apparel, leather, furs, down and related products (sector 8), Other manufacturng products (sector 21), Constructon (sector 25) and Servces (sector 26). The sector of Servces (sector 26) needs to ncrease mostly. Based on 2002 IO table, 14

15 ts actual export share s 21.23%, and after optmzaton t reaches about 34.50%. A well-known reason s that Servces s not only of low energy consumpton and polluton but also of hgh value-added and employment, thus ts export makes postve comprehensve effects. So t s necessary to expand the export of Servces. Besdes, consderng the partcularty of servce trade n the IO table, t s also necessary to develop the export of sectors closely related to Servces and wth lower energy consumpton and polluton, lke Wearng apparel, leather, furs, down and related products and Other manufacturng products. We call these sectors as Type II. Type III refers to sectors wth nconsstent changng trends under dfferent scenaros, ncludng Manufacture of food products and tobacco processng (sector 6), Textle goods (sector 7) and Sawmlls and furnture (sector 9). These three sectors have a common feature, that s, ther export shares all need to be reduced under the frst three scenaros and to be ncreased under the fourth one. Ths s because that the man characterstcs of these sectors are labor-ntensve. Takng Textle goods for example, by 2008, t has absorbed 20 mllon labors and s the most labor-absorbng sector n manufacturng sectors. Type III s of great mportance to employment but n the mean tme t s wth hgh energy consumpton and polluton. Importantly, these sectors are of long ndustry chan, whch can lead to huge harm to energy and envronment. Overall, the export share of Type I should be reduced and that of Type II be rased The export of Servces n IO table s dfferent from the commonly sad servce trade. The former conssts of the value-added of transportaton sectors and commercal ndustres that goods trade arouses n the transformaton from off shore prce to producer prce. Source: 15

16 under the current techncal condtons. From the dynamc pont of vew, t s mportant to update the ndustry structure by mprovng the energy effcency and reducng the polluton emsson of sectors that are wth postve economc and employment effects. As for those sectors wth lower energy consumpton and polluton, t s mportant to strengthen self-nnovaton to ncrease the technology content. 4.2 Results of dfferentatng trade patterns When dfferentatng processng and non-processng export, the optmzed export compostons of every sector n the two trade patterns are also dfferent (see Table 3). Table 3. The optmzed results under dfferent trade patterns (%) Type Sector rocessng Export Non-processng Export Scenaro Optmzed Actual Scenaro Optmzed Actual I II III IV I II III IV A B C

17 D ~ ~ Sum Note: In Table 3, the mnus - means a declne of the export share after optmzng, the plus + an ncrease, keepng to be zero, and ~ uncertan trend. From Table 3, accordng to the changng trend the sectors can be generalzed to four categores: Type A, Type B, Type C and Type D. Export proporton of Type A should be reduced n both trade patterns after optmzaton, ncludng aper and products, prntng and record medum reproducton (sector 10), etroleum processng, cokng and nuclear fuel processng (sector 11), Chemcals (sector 12), Nonmetal mneral products (sector 13), Metals smeltng and pressng (sector 14), Metal products (sector 15), Common and specal equpment (sector 16), Transport equpment (sector 17), Electrc equpment and machnery (sector 18), Telecommuncaton equpment, computer and other electronc equpment (sector 19), Instruments, meters, cultural and offce machnery (sector 20), Electrcty and heatng power producton and supply (sector 22), Gas producton and supply (sector 23). Moreover, the processng export shares of sector 11, 13, 22 and the non-processng export shares of sector 14, 23 are all reduced to 0% after optmzaton. The major characterstc of Type B s that the export proportons of these sectors show opposte changng trends n two patterns. Sectors whose processng export share needs to be reduced whle that of the non-processng export should be ncreased nclude Manufacture of food products and tobacco processng (sector 6), Textle goods (sector 7), Wearng apparel, leather, furs, down and related products (sector 8), 17

18 Sawmlls and furnture (sector 9) and Other manufacturng products (sector 21). Dfferent from the above, sectors ncludng Coal mnng, washng and processng (sector 2), Crude petroleum and natural gas products (sector 3) and Metal ore mnng (sector 4) need to ncrease ther processng export share whle reduce the non-processng export share. Type C refers to sectors whose export share should be rased n at least one trade pattern, ncludng Constructon (sector 25) and Servces (sector 26). Sectors n Type D show an uncertan changng trend at least n one pattern, lke Agrculture (sector 1) and Non-ferrous mneral mnng (sector 5). For Type A, t s obvous that these sectors are ether resource-ntensve or of hgh polluton or of low domestc value-added. By optmzaton, ther export proporton are reduced, some even to 0%, whch s n accordance wth the optmzaton prncples. At the same tme, some sectors wth hgher value-added, such as Common and specal equpment, Transport equpment and Electrc equpment and machnery, should mprove the techncal level and energy effcency. As to Type B, the reason that dfferent optmzaton results are obtaned under dfferent trade patterns s that the producton structure of these sectors n two patterns are not the same. Takng Crude petroleum and natural gas products for example, the total value-added of non-processng export and producton for other FIEs s 1.5 tmes as much as that of processng trade, but ts total energy consumpton per unt of GD, the total SO 2 emsson per unt of GD and the total COD emsson per unt of GD are 3.1 tmes, 10.2 tmes and 2.4 tmes as much as that of the processng export, 18

19 respectvely. From ths we can see that the postve comprehensve effects of processng export of ths sector s better than that of the non-processng export, so that ts processng export share should be rased. For Servces, ts energy consumpton and polluton emsson level n both patterns are lower than that of the average, whle ts employment and value-added coeffcents are equal to or hgher than that of the average. Therefore t s comprehensvely benefcal to ncrease ts export share n both patterns. In total, the share of processng export should be cut down by 3 percentage pont, whle that of the non-processng export should be accordngly ncreased by 3 percentage pont after optmzaton, snce t has better effects than processng export n general. 5 Concluson Ths paper has establshed a mult-objectve nput-output model for optmzng the composton of export goods n Chna. Based on the results, several polcy mplcatons could be proposed. Frst, mplement the export tarff rebate polcy approprately and tmely to control the export of hgh energy consumpton and heavy polluton. Second, mprove the prcng mechansm of export products. The prce of export products should reflect the cost of varous elements consumed, n partcular the cost of resources and envronment. Thrd, promote the upgradng of processng trade. A problem of the processng export s that at present t s lmted to smple processng and assembly whch s of 19

20 qute low domestc value-added. But based on our analyss, the processng export s better than the non-processng export n energy effcency and polluton emsson. So enterprses commttng the processng trade should be supported to nnovate to enhance ther contrbuton to GD. Fourth, develop servce trade. On one hand, snce the sector of Servces s a sector wth multple benefts, ts drect export should be encouraged. On the other hand, the trade of sectors that are n close relaton to Servces (transport and commerce servces especally) should also expand snce they have ndrect contrbutons. To sum up, t s favorable and necessary to optmze the composton of export goods by settng approprate prncples. Ths s a useful attempt to promote the sustanable development of trade. References [1] W. Cu, L. Y. Wang, and H. W. Wang, redcton of the composton of export commodty n Chna, System Engneerng, 2003, 21(4):56-60 (n Chnese). [2] E. Detzenbacher, J. S. e, and C. H. Yang, The envronmental pans and economc gans of outsourcng to Chna, 17th Internatonal Input-Output Conference, Sao aulo, [3] T. Jang, J. H. Yuan, L. He, and Y. u, Model system of populaton-resources-envronmental-economc, Systems Engneerng Theory and ractce, 2002(12):67-72 (n Chnese). [4] L. Lau,. K. Chen, L. K. Cheng, K. C. Fung, J. S. e, Y. W. Sung, Z.. Tang, Y. Y. ong, C. H. Yang, K. F. Zhu, The estmaton of domestc value-added and 20

21 employment generated by U.S.-Chna trade, Workng paper, Insttute of Economcs, Chnese Unversty of Hong Kong, [5] H. J. Wang, and. K. Chen, The extended energy-nput-occupancy-output model of the non-compettve mports type, Journal of Systems and Mathematcal Scences, 2009, 29 (11): (n Chnese). [6] Y.. Wu,. Y. Shen, and T. Hu, The new trend of green trade polcy under the worldwde fnancal crss: take the restrctons of hgh-polluton, hgh energy-consumng and resource-dependent products for example, Envronmental Economy, 2008(11):32-34 (n Chnese). [7] J. ang, and. L. Lu, Impact on natonal economy of water producton and supply sectors durng , Journal of Economcs of Water Resources, 2009, 27 (1):14-18 (n Chnese). [8] H.. Zhang, A long-term macroeconomc predctng model, method and applcaton, Dalan Unversty of Technology, 2001(n Chnese). [9] W. Zhang, and W. Q. an, An nput-output optmzaton model to reflect the effects of tarff adjustment, Quanttatve & Techncal Economcs Research, 2003(7):93-95 (n Chnese). Appendx Table 1. Chna s non-compettve nput-output table capturng processng trade Intermedate Use Fnal Use Input Output roducton for Domestc Use (D) rocessng Export () Non-processn g Export and producton for other FIEs (N) Domestc Fnal Use Export Gross Output or Imports 1 n 1 n 1 n 21

22 roducton for Domestc Use (D) 1 n DD D DN j j j Y D 0 D Input Domestcally Intermedate Inputs rocessng Export () Non-processng Export and producton for other FIEs (N) 1 n 1 n ND N NN j j j E N N N Y E Intermedate Inputs from Imports 1 n MD M MN j j j Y M 0 M Value-added D V j V j N V j Gross Input D j j N j Occ upan cy Employees Resources D L j D R j L j R j N L j N R j Notes: The superscrpt DD stands for domestc products used by domestc use, D domestc products used by processng exports, DN domestc products used by non-processng exports and producton for other FIEs, and so forth. DD j, D j and denote the ntermedate nput from the domestc products of sector to D, DN j and N of sector ND N j respectvely;, and ndcate the delveres of the non-processng j j NN j export and producton for other FIEs of sector to D, and N of sector j respectvely; D Y Y, and Y N denote the fnal domestc use of D, and N of sector respectvely; E and N E are the volume of processng export and non-processng export of sector respectvely; D N, and are the output of D, and N of sector respectvely; D V j V j N j, and V are the value-added of D, and N of sector j respectvely; D L j N, and are the labour occupancy of D, and N of sector j respectvely;, L j L j D R j R j and N R j are the resource occupancy of D, and N of sector j respectvely. Source: Lau, L., Chen,., Cheng, L. et al. (2006) The Estmaton of Domestc Value-Added and Employment Generated by U.S.-Chna Trade, Workng aper, Insttute of Economcs, Chnese Unversty of Hong Kong, 2. 22

23 Table 2. Comparson table of classfcaton between 26-sector and 42-sector of 2002 IO table n Chna 26-sector 42-sector Code Sector Code Sector 01 Agrculture 01 Agrculture 02 Coal mnng, washng and processng 02 Coal mnng, washng and processng 03 Crude petroleum and natural gas products 03 Crude petroleum and natural gas products 04 Metal ore mnng 04 Metal ore mnng 05 Non-ferrous mneral mnng 05 Non-ferrous mneral mnng 06 Manufacture of food products and tobacco processng 06 Manufacture of food products and tobacco processng 07 Textle goods 07 Textle goods 08 Wearng apparel, leather, furs, down and related Wearng apparel, leather, furs, down and related 08 products products 09 Sawmlls and furnture 09 Sawmlls and furnture 10 aper and products, prntng and record medum aper and products, prntng and record medum 10 reproducton reproducton 11 etroleum processng, cokng and nuclear fuel etroleum processng, cokng and nuclear fuel 11 processng processng 12 Chemcals 12 Chemcals 13 Nonmetal mneral products 13 Nonmetal mneral products 14 Metals smeltng and pressng 14 Metals smeltng and pressng 15 Metal products 15 Metal products 16 Common and specal equpment 16 Common and specal equpment 17 Transport equpment 17 Transport equpment 18 Electrc equpment and machnery 18 Electrc equpment and machnery 19 Telecommuncaton equpment, computer and other Telecommuncaton equpment, computer and other 19 electronc equpment electronc equpment 20 Instruments, meters, cultural and offce machnery 20 Instruments, meters, cultural and offce machnery 21 Other manufacturng products 21 Other manufacturng products 22 Scrap and waste 22 Electrcty and heatng power producton and supply 23 Electrcty and heatng power producton and supply 23 Gas producton and supply 24 Gas producton and supply 24 Water producton and supply 25 Water producton and supply 25 Constructon 26 Constructon 26 Servces 27 Transport and warehousng 28 ost 29 Informaton communcaton, computer servce and software 30 Wholesale and retal trade 31 Accommodaton, eatng and drnkng places 23

24 32 Fnance and nsurance 33 Real estate 34 Rentng and commercal servce 35 Toursm 36 Scentfc research 37 General techncal servces 38 Other socal servces 39 Educaton 40 Health servce, socal guarantee and socal welfare 41 Culture, sports and amusements 42 ublc management and socal admnstraton 24