Sys­tem­atic Op­ti­miza­tion of China’s Man­u­fac­tur­ing In­dus­trial Struc­ture


China Economist - - Contents - 1 2 Shi Dan ( ) and Zhang Cheng ( )史丹 张成 1In­sti­tute of In­dus­trial Eco­nom­ics (IIE), Chi­nese Academy of So­cial Sci­ences (CASS), Beijing, China 2School of Eco­nom­ics, Nan­jing Univer­sity of Fi­nance and Eco­nom­ics (NUFE), and In­sti­tute of In­dus­trial Eco­nom­ics


Us­ing China’s two-digit man­u­fac­tur­ing sec­tors as sam­ples, this paper first an­a­lyzes China’s out­put struc­ture op­ti­miza­tion ob­jec­tives and en­ergy con­ser­va­tion and emis­sions abate­ment po­ten­tials in 2015, then ex­am­ines var­i­ous fac­tor in­puts’ match­ing, and es­ti­mates their ca­pac­ity uti­liza­tion sta­tus, fo­cus­ing on cap­i­tal stock fac­tor. Re­sults of our study sug­gest that: (1) China’s man­u­fac­tur­ing out­put struc­ture has great po­ten­tials of op­ti­miza­tion to re­duce en­ergy in­ten­sity and car­bon in­ten­sity by 18.08% and 17.42% re­spec­tively over the orig­i­nal val­ues; (2) to re­duce fac­tor mis­match, var­i­ous sup­port­ing in­put fac­tors need to be in­tro­duced after man­u­fac­tur­ing out­put struc­ture op­ti­miza­tion. The level of cap­i­tal stock, in par­tic­u­lar, re­quires a sub­stan­tial change; (3) China’s man­u­fac­tur­ing ca­pac­ity uti­liza­tion (56.14%) in 2015 was far below its av­er­age level (73.27%) in the mid and late stage of the 11th Five-Year Plan pe­riod (2008-2010). The low ca­pac­ity uti­liza­tion was at­trib­ut­able to eco­nomic slow­down and in­vest­ment in­er­tia. After in­put fac­tor match­ing, ca­pac­ity uti­liza­tion may rise to the lat­ter level.


out­put struc­ture, fac­tor struc­ture, over­ca­pac­ity, en­ergy con­ser­va­tion and emis­sions abate­ment JEL Clas­si­fi­ca­tion Codes: O21; Q01; Q56 DOI: 1 0.19602/j .chi­nae­conomist.2018.11.01

1. In­tro­duc­tion

Over the past four decades, China’s man­u­fac­tur­ing in­dus­try has con­trib­uted a sig­nif­i­cant share to its rapid eco­nomic growth, job cre­ation and the “China mir­a­cle” through rapid in­dus­trial struc­ture evo­lu­tion. To­day, China boasts the largest man­u­fac­tur­ing in­dus­try in the world, with sig­nif­i­cant im­prove­ments in its na­tional power and in­ter­na­tional com­pet­i­tive­ness. In its cur­rent stage, China has to si­mul­ta­ne­ously deal with the slow­down in eco­nomic growth, make dif­fi­cult struc­tural ad­just­ments, and ab­sorb the ef­fects of pre­vi­ous stim­u­lus poli­cies. Its man­u­fac­tur­ing in­dus­try is faced with the dilemma be­tween growth sta­bil­ity and struc­tural ad­just­ment, as well as com­pet­i­tive pres­sures from both de­vel­oped coun­tries and emerg­ing economies. While low-cost ad­van­tages di­min­ished, new com­pet­i­tive edges are yet to de­velop.

These chal­lenges cast shadow on the fu­ture out­look of China’s man­u­fac­tur­ing in­dus­try. There has been a great deal of in­ter­est among re­searchers re­gard­ing how to make China’s man­u­fac­tur­ing in­dus­trial struc­ture more ad­vanced and rea­son­able in or­der to pro­mote the qual­ity and ef­fi­ciency of eco­nomic de­vel­op­ment.

The ques­tion to be dis­cussed in this paper is how to ad­just China’s man­u­fac­tur­ing in­dus­trial struc­ture1. Ex­ist­ing stud­ies at­tempt to an­swer rel­e­vant ques­tions in this field. Based on a sci­en­tific eval­u­a­tion of the his­tor­i­cal role of China’s out­put struc­ture evo­lu­tion (Liu and Zhang, 2008; Zhang, 2010), stud­ies sim­u­late the di­rec­tions of in­dus­trial struc­ture op­ti­miza­tion and its coun­ter­fac­tual ef­fects (Wang and Xiang, 2014; Zhu et al., 2014; Zhang and Zhao, 2015). In op­ti­miz­ing out­put struc­ture, ex­ist­ing stud­ies in­tro­duce such fac­tors as en­ergy con­ser­va­tion and emis­sions abate­ment, em­ploy­ment se­cu­rity and in­dus­trial co­or­di­na­tion. How­ever, these stud­ies are con­fined to an iso­lated econ­omy’s per­spec­tive with­out us­ing rel­e­vant in­for­ma­tion of an open econ­omy. Al­though ex­ist­ing stud­ies dis­cuss out­put struc­ture op­ti­miza­tion and pro­duc­tion fac­tor al­lo­ca­tion (Ngai and Pis­sarides., 2007; Yuan and Jie, 2011; Ben­hima, 2013; Dong, 2015), the two is­sues are not prop­erly in­te­grated. In op­ti­miz­ing out­put struc­ture, al­most all stud­ies only pro­vide the de­sir­able out­put lev­els of var­i­ous sec­tors with­out re­veal­ing the ex­tent to which cap­i­tal, la­bor and other in­puts should be ad­justed ac­cord­ingly. Fac­tor struc­ture match­ing anal­y­sis, which is ab­sent in these stud­ies, can be in­tro­duced in the in­dus­trial struc­ture op­ti­miza­tion.

To fa­cil­i­tate the­o­ret­i­cal re­search and pro­vide pol­icy rec­om­men­da­tions, this paper con­ducts a sys­tem­atic in­dus­trial struc­ture op­ti­miza­tion us­ing China’s two-digit man­u­fac­tur­ing sec­tors as sam­ples. In ad­di­tion to an­a­lyz­ing out­put struc­ture op­ti­miza­tion ob­jec­tives and en­ergy con­ser­va­tion and emis­sions abate­ment po­ten­tials, this paper also in­ves­ti­gates ques­tions of in­put fac­tor match­ing and cap­i­tal stock ca­pac­ity uti­liza­tion. This paper has the fol­low­ing con­tri­bu­tions: (1) In op­ti­miz­ing man­u­fac­tur­ing out­put struc­ture, this paper takes into ac­count other fac­tors in a more com­pre­hen­sive and sci­en­tific man­ner, in­clud­ing de­mand and sup­ply in­for­ma­tion. In par­tic­u­lar, this paper con­sid­ers de­mand-side im­port/ex­port po­ten­tials and sup­ply-side tech­nol­ogy con­tri­bu­tion, which are sel­dom men­tioned in ex­ist­ing stud­ies; (2) un­like ex­ist­ing stud­ies which sep­a­rately ex­am­ine the struc­tural op­ti­miza­tions of out­put and fac­tor, this paper in­te­grates the anal­y­sis of struc­tural op­ti­miza­tion with fac­tor in­put match­ing; (3) in in­ves­ti­gat­ing fac­tor struc­ture match­ing, this paper fol­lows an ap­proach of suc­ces­sion and crit­i­cal­ity. In match­ing fac­tor struc­ture by ex­tract­ing his­tor­i­cal in­for­ma­tion, we of­fer a deeper anal­y­sis of cap­i­tal fac­tor al­lo­ca­tion to ad­dress the po­ten­tial prob­lem of cap­i­tal fac­tor over­ca­pac­ity.

2. Model and Re­search Method­ol­ogy

With re­spect to re­search method­ol­ogy, this paper adopts the fol­low­ing steps: Step 1: Non-lin­ear Pro­gram­ming is em­ployed to op­ti­mize China’s man­u­fac­tur­ing out­put struc­ture of 2015 from an en­ergy con­ser­va­tion and emis­sions abate­ment per­spec­tive, tak­ing into ac­count fac­tors such as em­ploy­ment se­cu­rity, in­dus­trial equi­lib­rium, im­port/ex­port po­ten­tials and tech­nol­ogy con­tri­bu­tion. Step 2: After ob­tain­ing a non-lin­ear re­la­tion­ship be­tween fac­tor in­put and eco­nomic out­put, trans-log pro­duc­tion func­tion model is em­ployed to match an ap­pro­pri­ate fac­tor pat­tern for op­ti­mized out­put struc­ture. Step 3: Data En­velope­ment Method (DEA) is em­ployed to es­ti­mate ca­pac­ity uti­liza­tions be­fore and after op­ti­miza­tion, fo­cus­ing on cap­i­tal stock fac­tor.

2.1 Cre­ation of Non-lin­ear Pro­gram­ming Model

Based on above the­o­ret­i­cal dis­cus­sions, this paper as­sumes that to­tal en­ergy con­sump­tion and CO2 emis­sions can­not ex­ceed ceil­ings un­der the con­di­tions of em­ploy­ment se­cu­rity, in­put-out­put equi­lib­rium, fi­nal do­mes­tic con­sump­tion po­ten­tials, im­port/ex­port po­ten­tials, as well as tech­nol­ogy con­tri­bu­tion. In or­der to min­i­mize over­all na­tional re­source and en­vi­ron­men­tal in­ten­sity (weighted en­ergy and car­bon in­ten­si­ties), the fol­low­ing Non- lin­ear Pro­gram­ming is spec­i­fied to seek man­u­fac­tur­ing in­dus­trial struc­ture op­ti­miza­tion2: i(j), t and b in equa­tions (1) through (11) re­spec­tively de­note sec­tor3 ( i= 1,2… m;j= 1,2… m+n), year and en­ergy type ( b= 1,2… k), and t0 denotes a year be­fore year t. * denotes the re­sult after op­ti­miza­tion. TP is re­source and en­vi­ron­men­tal in­ten­sity. EP, CP and LP are en­ergy in­ten­sity, car­bon in­ten­sity and la­bor in­ten­sity re­spec­tively. and are the weight ra­tio co­ef­fi­cients of EP and CP re­spec­tively. Y, E, C, L, XF, IM, EX and RT are out­put, en­ergy, CO2, la­bor, other con­sump­tion4, im­port value, ex­port value and tech­nol­ogy con­tri­bu­tion. and are changes in im­port value and ex­port value. is di­rect con­sump­tion co­ef­fi­cient, and is change in di­rect con­sump­tion co­ef­fi­cient. is change in other con­sump­tion. is change in na­tional to­tal la­bor.

Equa­tion (1) is ob­jec­tive func­tion, i.e. seek­ing the min­i­miza­tion of over­all na­tional re­source and en­vi­ron­men­tal in­ten­sity. Equa­tions (2) through (11) are con­straints. Among them, equa­tions (2) through (4) cre­ate re­la­tion­ships be­tween out­put and en­ergy con­sump­tion, and CO2 emis­sions and la­bor quan­ti­ties of var­i­ous sec­tors. Equa­tion (5) re­strains sec­tor out­puts from an in­ter-sec­tor equi­lib­rium and im­port/ex­port

per­spec­tive5. Equa­tion (6) en­sures from a tech­nol­ogy con­tri­bu­tion per­spec­tive that after op­ti­miza­tion, the to­tal con­tri­bu­tion of var­i­ous sec­tors’ tech­nol­ogy lev­els to out­put is at least no less than that of their orig­i­nal level be­fore op­ti­miza­tion. Equa­tions (7) through (9) pro­vide con­straints on to­tal en­ergy con­sump­tion, CO2 emis­sions and em­ploy­ment se­cu­rity. Equa­tions (10) and (11) re­spec­tively pro­vide the meth­ods for cal­cu­lat­ing na­tional en­ergy in­ten­sity and car­bon in­ten­sity.

2.2 Cre­ation of Trans-Log Pro­duc­tion Func­tion Model

Un­der rel­e­vant as­sump­tions and con­straints, this paper is able to ob­tain the out­put size of China’s two- digit man­u­fac­tur­ing sec­tors after op­ti­miza­tion. But a new ques­tion is how var­i­ous sec­tors should make use of in­put fac­tors to ef­fi­ciently pro­vide de­sir­able out­put and re­duce fac­tor mis­match. For this pur­pose, his­tor­i­cal data can be em­ployed to es­ti­mate the non-lin­ear re­la­tion­ship be­tween in­put fac­tors and out­put, and cal­cu­late a rea­son­able fac­tor al­lo­ca­tion pat­tern ac­cord­ing to the needs of out­put.

In es­ti­mat­ing the non- lin­ear re­la­tion­ship be­tween fac­tor in­put and out­put, this paper em­ploys sto­chas­tic fron­tier anal­y­sis (SFA) method since this method is able to not only de­com­pose tech­ni­cal ef­fi­ciency val­ues from pro­duc­tiv­ity but con­trol for the dis­tur­bance aris­ing from sto­chas­tic er­ror term, so as to more ac­cu­rately de­pict the re­la­tion­ship of sub­sti­tu­tion or sup­ple­ment be­tween fac­tor in­puts, as well as the non-lin­ear re­la­tion­ship be­tween fac­tor in­puts and out­puts. Based on Bat­tese and Coelli’s (1995) SFA model and ref­er­enc­ing ex­ist­ing lit­er­a­ture, this paper adopts a func­tion form in­clud­ing cap­i­tal ( K), la­bor ( L), in­ter­me­di­ate prod­uct in­put ( M) and tech­nol­ogy level ( T). In or­der to ex­am­ine fac­tor in­put’s mar­ginal out­put and elas­tic­ity in more de­tail, this paper spec­i­fies pro­duc­tion func­tion in the trans-log form, whose spe­cific form is as fol­lows:

Where, β is pa­ram­e­ter to be es­ti­mated; U is out­put in­ef­fi­ciency, which con­forms to iid and denotes out­put loss caused by dif­fer­ences in the in­ter­nal man­age­ment lev­els of de­ci­sion-mak­ing units. V is sto­chas­tic de­vi­a­tion term, which sat­is­fies iid , and denotes luck’s sto­chas­tic im­pact on out­put.

Once the size of de­sir­able out­put of each man­u­fac­tur­ing sec­tor is ob­tained, the la­bor quan­tity and in­ter­me­di­ate in­put quan­tity which sec­tors need to ab­sorb can be es­ti­mated. Then, equa­tion (12) can be used to es­ti­mate the ap­pro­pri­ate size of cap­i­tal stock.

2.3 Ap­pli­ca­tion of Data En­velope­ment Method

This paper em­ploys the in­put-ori­ented non- dis­cre­tionary vari­able model ( NDSC) cre­ated by Cooper et al. ( 2004) with con­stant re­turn to scale. This model is able to ex­tract in­for­ma­tion of dis­cre­tionary vari­able (cap­i­tal stock) and non-dis­cre­tionary vari­ables (la­bor and in­ter­me­di­ate in­put), and fo­cus on an­a­lyz­ing in­put ef­fi­ciency of dis­cre­tionary vari­able un­der the con­di­tion of spec­i­fy­ing the non-dis­cre­tionary vari­ables as con­stants. Due to limit of length, this model will not be de­scribed in de­tail.

After cal­cu­lat­ing cap­i­tal re­dun­dancy ( ), cap­i­tal uti­liza­tion ( ) can be ob­tained us­ing the fol­low­ing equa­tion (13):

3. Model Cre­ation and Data Ex­pla­na­tion

This paper em­ploys 2003-2015 panel data of man­u­fac­tur­ing two-digit sec­tors of 30 pro­vin­cial-level re­gions (ex­clud­ing Ti­bet, Hong Kong, Ma­cao and Tai­wan), and data is ar­ranged and cal­cu­lated ac­cord­ing to prov­ince-spe­cific sta­tis­ti­cal year­books, DRCnet data­base and the China Sta­tis­ti­cal Ap­pli­ca­tion Sup­port Sys­tem. In or­der to ex­clude the im­pact of price fac­tor, all price-re­lated data in this paper is ad­justed to the 2000 price level ac­cord­ing to rel­e­vant price in­dex or growth in­dex. Given the dif­fer­ences in the 2002 and 2011 edi­tions of na­tional eco­nomic sec­tor clas­si­fi­ca­tion, this paper con­ducts nec­es­sary data splits and merges to form 29 man­u­fac­tur­ing sec­tors6.

In em­pir­i­cal anal­y­sis, the fol­low­ing vari­ables are cre­ated: (1) out­put ( Y): Ac­tual ag­gre­gate in­dus­trial value after ad­just­ing for the ex-fac­tory prices of in­dus­trial goods; (2) la­bor in­put ( L): De­noted by yearend to­tal em­ploy­ment; (3) cap­i­tal in­put ( K): Cal­cu­lated us­ing per­pet­ual in­ven­tory method with equa­tion

. In cal­cu­la­tion, the method pro­vided by Dong et al. (2015) is em­ployed. Where, It is new in­vest­ment value, de­noted by the dif­fer­ence be­tween the orig­i­nal prices of fixed as­sets of two ad­ja­cent years; Pt is the price in­dex of in­vest­ment goods, de­noted by fixed as­set in­vest­ment price in­dex; de­pre­ci­a­tion rate is mea­sured by the mean value7 of es­ti­mated de­pre­ci­a­tion rates of var­i­ous years; base-pe­riod cap­i­tal stock is ex­pressed by the dif­fer­ence be­tween orig­i­nal price of fixed as­sets and cu­mu­la­tive de­pre­ci­a­tion in 2000. (4) In­ter­me­di­ate prod­uct in­put ( M): After sub­tract­ing in­dus­trial val­ueadded and payable tax in­crease from gross in­dus­trial out­put value, the re­sult is di­vided by raw ma­te­rial pur­chase price in­dex. In­dus­trial value-added data of 2001-2007 for two-digit in­dus­trial sec­tors is from China In­dus­trial Sta­tis­ti­cal Year­book, and the data of 2008-2015 is ex­pressed by the prod­uct be­tween cur­rent-year gross in­dus­trial out­put value and av­er­age in­dus­trial value-added ra­tios of 2003-2007. (5) Tech­nol­ogy progress ( T): If tech­nol­ogy progress needs to be in­tro­duced into trans-log pro­duc­tion func­tion model, it should be de­picted us­ing time spans 1-13. (6) En­ergy con­sump­tion ( E): End-user en­ergy con­sump­tion adopted in our cal­cu­la­tion in­cludes raw coal, cleaned coal, other washed coal, bri­quette, coke, coke oven gas, other coal gas, crude oil, petroleum, coal oil, diesel, fuel oil, liq­ue­fied petroleum gas, re­fin­ery dry gas, nat­u­ral gas, other petroleum prod­ucts, other coke prod­ucts, heat power and elec­tric power, and is con­verted into stan­dard coal equiv­a­lent us­ing stan­dard coal con­ver­sion co­ef­fi­cient pro­vided by the Na­tional Bureau of Sta­tis­tics (NBS). (7) CO2 emis­sions ( C): CO2 emis­sion fac­tor of con­ven­tional fos­sil en­ergy is sub­ject to data pro­vided by IPCC (2006). The CO2 emis­sion fac­tor of elec­tric power as sec­ondary en­ergy is from the na­tional bench­mark data pro­vided by the Na­tional Cen­ter for Cli­mate Change Strat­egy and In­ter­na­tional Co­op­er­a­tion (NCSC). It is as­sumed that all heat power is gen­er­ated from raw coal com­bus­tion and con­verted ac­cord­ing to raw coal’s emis­sion fac­tor. (8) En­ergy in­ten­sity ( EP), car­bon in­ten­sity ( CP) and la­bor in­ten­sity ( LP): De­noted by the ra­tio of en­ergy con­sump­tion, CO2 emis­sions and la­bor quan­tity to in­dus­trial gross out­put. (9) Im­port ( IM), ex­port ( EX), di­rect con­sump­tion co­ef­fi­cient ( ) and other con­sump­tion ( XF): Based on China’s in­put-out­put ta­bles of 2012, data of rel­e­vant sec­tors is cal­cu­lated in com­bi­na­tion. Where, other con­sump­tion is mea­sured

by the sum be­tween the to­tal in­di­rect con­sump­tion of man­u­fac­tur­ing prod­ucts by all sec­tors other than a coun­try’s man­u­fac­tur­ing sec­tors and the fi­nal con­sump­tion by house­holds, govern­ment and cap­i­tal. (10) En­ergy in­ten­sity’s weight­ing co­ef­fi­cient ( ): The weight­ing co­ef­fi­cients of en­ergy in­ten­sity and car­bon in­ten­sity are both 0.5. (11) Con­tri­bu­tion of tech­nol­ogy level ( TP): Ra­tio be­tween out­put growth in­duced by tech­nol­ogy level and to­tal out­put7. (12) Change in na­tional to­tal work­force ( ): The mean value of ab­so­lute val­ues of change in na­tional to­tal work­force in re­cent three years is the up­per and lower re­stricted line. (13) Change in im­port value ( ), change in ex­port value ( ), change in di­rect con­sump­tion co­ef­fi­cient ( ) and change in other con­sump­tion ( ). Ac­cord­ing to ex­ist­ing data, dif­fer­ence method is em­ployed to es­ti­mate the change in 2015 rel­a­tive to 2012.

4. Em­pir­i­cal Re­sults and Anal­y­sis

4.1 Man­u­fac­tur­ing In­dus­trial Struc­ture Op­ti­miza­tion

Based on the Non-lin­ear Pro­gram­ming pro­vided in the above sec­tion, this paper es­ti­mates the size of de­sir­able out­put for var­i­ous man­u­fac­tur­ing sec­tors in 2015 and their en­ergy con­sump­tions and CO2 emis­sions from an en­ergy con­ser­va­tion and emis­sions abate­ment per­spec­tive, i.e. min­i­miza­tion of re­source and en­vi­ron­men­tal in­ten­sity.

(1) Po­ten­tial ef­fect after man­u­fac­tur­ing out­put struc­ture op­ti­miza­tion

In 2015, China’s man­u­fac­tur­ing gross out­put value was 89,856.41 bil­lion yuan, and op­ti­mized gross out­put value may in­crease to 93,883.30 bil­lion yuan, up 4.48%. Mean­while, the en­ergy con­ser­va­tion and emis­sions abate­ment ef­fects are fa­vor­able: To­tal en­ergy con­sump­tion may re­duce from 2,110.17 mil­lion tce to 1,806.19 mil­lion tce (down 14.41%), and to­tal CO2 emis­sions may re­duce from 6,096.11 mil­lion tons to 5,260.01 mil­lion tons (down 13.72%). In this man­ner, re­source and en­vi­ron­men­tal in­ten­sity re­duced from 4,566 tons/100 mil­lion yuan to 3,763 tons/100 mil­lion yuan (down 17.59%). Specif­i­cally, en­ergy in­ten­sity re­duces from 2,348 tce/100 mil­lion yuan to 1,924 tce/100 mil­lion yuan (down 18.08%), and car­bon in­ten­sity re­duces from 6,784 tons/100 mil­lion yuan to 5,603 tons/100 mil­lion yuan (down 17.42%).

(2) Man­u­fac­tur­ing sec­tor out­put struc­ture op­ti­miza­tion and anal­y­sis

This paper drafts the fol­low­ing Fig­ure 2 to more clearly re­veal the di­rec­tion and de­gree of out­put size ad­just­ment after a com­par­i­son be­tween op­ti­mized val­ues for man­u­fac­tur­ing sec­tors in 2015 and their orig­i­nal val­ues in 2015 and 2010. Mean­while, this paper in­tro­duces the fol­low­ing six cat­e­gories to give a clear pic­ture of sec­tors’ ad­just­ments and pat­terns: (1) strong ab­so­lute pro­duc­tion in­crease, (2) weak ab­so­lute pro­duc­tion in­crease, (3) rel­a­tive pro­duc­tion in­crease, (4) ab­so­lute pro­duc­tion re­duc­tion, (5) strong rel­a­tive pro­duc­tion re­duc­tion and (6) weak rel­a­tive pro­duc­tion re­duc­tion. Re­fer to Ta­ble 1 for the cri­te­ria of clas­si­fi­ca­tion for each cat­e­gory. Strong ab­so­lute pro­duc­tion in­crease means that the sec­tor’s out­put size is not only greater than the orig­i­nal value of 2015 but also greater than the av­er­age growth rate of the op­ti­mized value of 2015 rel­a­tive to 2010 (66.57%, re­ferred to as “bench­mark growth rate”). One may only need to ob­serve the “●” and “▲” la­bels of var­i­ous sec­tors in Fig­ure 2. If they are all above their crit­i­cal lines of 0% and 66.57%, the sec­tor is of ab­so­lute pro­duc­tion in­crease. After an ob­ser­va­tion, we know that the fol­low­ing nine sec­tors meet this cri­te­rion, in­clud­ing man­u­fac­ture of medicines, man­u­fac­ture of spe­cial pur­pose ma­chin­ery, man­u­fac­ture of elec­tri­cal ma­chin­ery and equip­ment, man­u­fac­ture of com­mu­ni­ca­tion equip­ment, com­put­ers and other elec­tronic equip­ment and re­cy­cling and dis­posal of waste.

Strong rel­a­tive pro­duc­tion re­duc­tion means that de­spite the in­crease of a sec­tor’s out­put size over 2010, it is smaller than bench­mark growth rate and the orig­i­nal value of 2015, i.e. “●” and “▲” sym­bols should be smaller than 0% and in the range of [0%,66.57%]. Ob­vi­ously, 17 sec­tors are of this cat­e­gory, in­clud­ing pro­cess­ing of food from agri­cul­tural prod­ucts, man­u­fac­ture of foods, man­u­fac­ture of paper and paper prod­ucts, man­u­fac­ture of rub­ber and fer­rous metal smelt­ing and press­ing.

Weak ab­so­lute pro­duc­tion in­crease means that a sec­tor’s out­put size is greater than the orig­i­nal value of 2015, and has some growth com­pared with 2010, but is smaller than bench­mark growth rate, i.e. the sec­tor’s “●” and “▲” sym­bols should be higher than 0% and in the range of [0%,66.57%]. Sec­tors of this cat­e­gory in­clude trans­port equip­ment man­u­fac­tur­ing.

Weak rel­a­tive pro­duc­tion re­duc­tion means that a sec­tor’s out­put size is smaller than orig­i­nal value of 2015, but is greater than bench­mark growth rate. If a sec­tor’s “●” sym­bol is smaller than 0% crit­i­cal line, but “▲” sym­bol is higher than 66.57% crit­i­cal line in Fig­ure 2, this sec­tor is of weak rel­a­tive pro­duc­tion re­duc­tion. Only man­u­fac­ture of bev­er­age and man­u­fac­ture of cul­tural, ed­u­ca­tional, fine arts, sports and en­ter­tain­ment goods are of this cat­e­gory. This in­di­cates that even com­pared with the orig­i­nal val­ues of 2015, the out­put size of these sec­tors should be ap­pro­pri­ately re­duced, but com­pared with the man­u­fac­tur­ing in­dus­try’s over­all bench­mark growth rate, they are still higher than av­er­age level. No sec­tor can be clas­si­fied into the other cat­e­gories.

Ob­vi­ously, the nine man­u­fac­tur­ing sec­tors of strong ab­so­lute pro­duc­tion in­crease in­clude not only high-tech ad­vanced man­u­fac­tur­ing and high-end equip­ment man­u­fac­tur­ing, but the promis­ing in­ter­net in­dus­try, as well as “ve­nous in­dus­try” which tends to be over­looked. With­out doubt, ad­vanced man­u­fac­tur­ing, high-end equip­ment man­u­fac­tur­ing and in­ter­net sec­tors, in­clud­ing “in­ter­net+” sec­tors, are key to the suc­cess of China’s “In­dus­try 4.0” and “Made in China 2025” roadmap. De­spite its lim­ited share of out­put, re­cy­cling and dis­posal of waste as a “ve­nous in­dus­try” en­joys su­pe­rior growth

mo­men­tum after op­ti­miza­tion among sec­tors of strong ab­so­lute pro­duc­tion in­crease: Its orig­i­nal out­put value needs to be in­creased from 327.99 bil­lion yuan to 591.76 bil­lion yuan, up 80.42%.

Sec­tors of strong rel­a­tive pro­duc­tion re­duc­tion should be prop­erly un­der­stood. Gross out­put re­duc­tions of these sec­tors with high re­source and en­vi­ron­men­tal in­ten­si­ties rep­re­sent an over­all op­ti­miza­tion based on an in­put- out­put frame­work un­der the con­di­tion of sat­is­fy­ing con­sump­tion, in­vest­ment and all sec­tors’ de­mand for in­ter­me­di­ate in­puts, im­port/ex­port re­stric­tions and tech­nol­ogy con­tri­bu­tions. In or­der to min­i­mize over­all re­source and en­vi­ron­men­tal in­ten­sity and avoid over­ca­pac­ity, these sec­tors should give way to sec­tors of strong ab­so­lute pro­duc­tion in­crease to some ex­tent. Of course, this does not mean that each firm should sim­ply cut pro­duc­tion. Rather, more re­source-con­sum­ing and pol­lut­ing firms in var­i­ous sec­tors should be closed or change pro­duc­tion through a sur­vival-of-th­e­fittest process to meet the gross out­put size tar­gets of var­i­ous sec­tors. Firms with rel­a­tive com­par­a­tive ad­van­tages should ex­pand to achieve economies of scale and economies of scope. As for sec­tors of weak rel­a­tive pro­duc­tion re­duc­tion, their ba­sic con­di­tions are sim­i­lar to those of sec­tors of strong rel­a­tive pro­duc­tion de­crease. The only dif­fer­ence is that while their out­put value needs to re­duce to some ex­tent, their op­ti­mized growth rates are still higher than bench­mark growth rate.

4.2 Fac­tor Struc­ture Match­ing for Op­ti­mal Man­u­fac­tur­ing Out­put Struc­ture

(1) Es­ti­ma­tion re­sult and anal­y­sis of trans-log pro­duc­tion func­tion model

When sto­chas­tic fron­tier pro­duc­tion func­tion model is em­ployed to es­ti­mate the eco­nomic out­put ef­fects of fac­tor in­puts, we need to first as­sess the ap­pro­pri­ate­ness and spe­cific form of sto­chas­tic fron­tier pro­duc­tion func­tion. Us­ing like­li­hood ra­tio test and sig­nif­i­cance test, we find that the cross­mul­ti­ply­ing term be­tween cap­i­tal stock and in­ter­me­di­ate prod­uct in­put, the cross-mul­ti­ply­ing term be­tween tech­nol­ogy level and cap­i­tal stock, as well as the cross-mul­ti­ply­ing term be­tween tech­nol­ogy level and in­ter­me­di­ate prod­uct in­put, should be ex­cluded, and fi­nal re­sults are shown in Ta­ble 2 below. It can be seen that the co­ef­fi­cients of all in­de­pen­dent vari­ables of the model are sig­nif­i­cant at least at

10% sig­nif­i­cance level, with γ value as high as 0.9693 and sig­nif­i­cant at 1% level. This in­di­cates that tech­nol­ogy in­ef­fi­ciency gen­er­ally ex­ists, and that the er­ror of fron­tier pro­duc­tion func­tion is pri­mar­ily caused by tech­nol­ogy in­ef­fi­ciency, which fur­ther demon­strates that the use of sto­chas­tic fron­tier pro­duc­tion func­tion is nec­es­sary and valid.

Rel­e­vant re­sults of Ta­ble 2 pro­vide pos­si­bil­i­ties for the anal­y­sis of fac­tor in­puts of 2015. Be­fore spe­cific anal­y­sis, this paper spec­i­fies cap­i­tal stock as a dis­cre­tionary vari­able given the uni­ver­sal ex­is­tence of cap­i­tal fac­tor over­ca­pac­ity in man­u­fac­tur­ing sec­tors. This is in­tended to re­duce cap­i­tal fac­tor over­ca­pac­ity through fac­tor struc­ture match­ing. In or­der to fol­low the above an­a­lyt­i­cal ap­proach, this paper spec­i­fies the la­bor in­ten­sity co­ef­fi­cient of spe­cific years as in­de­pen­dent from the level of eco­nomic out­put, i.e. la­bor in­ten­sity co­ef­fi­cient ob­tained from the pre­vi­ous sec­tion can be used to cal­cu­late the em­ploy­ment that each sec­tor is able to sus­tain based on op­ti­mized out­put level. Lastly, con­sid­er­ing the fun­da­men­tal sup­port­ive role of in­ter­me­di­ate prod­uct in­puts in man­u­fac­tur­ing process, the in­ter­me­di­ate prod­uct in­put in­ten­sity co­ef­fi­cients of spe­cific years can be spec­i­fied as in­de­pen­dent from the level of eco­nomic out­put, so as to ob­tain the level of in­ter­me­di­ate prod­uct in­put based on the op­ti­mized out­put level.

(2) Fac­tor struc­ture match­ing re­sult of man­u­fac­tur­ing sec­tors and anal­y­sis

Based on the de­sir­able out­put val­ues of man­u­fac­tur­ing sec­tors, we cal­cu­late the rea­son­able lev­els of cap­i­tal stock, la­bor and in­ter­me­di­ate prod­uct in­put. Man­u­fac­tur­ing in­dus­try as a whole re­quires a cap­i­tal stock worth 14,708.56 bil­lion yuan and in­ter­me­di­ate prod­uct in­puts worth 48,506.76 bil­lion yuan, down

26.02% and up 0.74% com­pared with orig­i­nal val­ues re­spec­tively, and may pro­vide an em­ploy­ment ex­tremely close to the orig­i­nal value of 2015. Com­par­a­tively speak­ing, man­u­fac­ture of com­mu­ni­ca­tion equip­ment, com­put­ers and other elec­tronic equip­ment, man­u­fac­ture of trans­port equip­ment, and man­u­fac­ture of elec­tri­cal ma­chin­ery and equip­ment pro­vide the most jobs, and use the most in­ter­me­di­ate prod­uct in­puts. This sug­gests that these three sec­tors play an ex­tremely im­por­tant role in pro­mot­ing em­ploy­ment and sup­port­ing the de­vel­op­ment of other sec­tors.

In or­der to com­pare the in­creases and de­creases of cap­i­tal stock of var­i­ous man­u­fac­tur­ing sec­tors, this paper car­ries out an anal­y­sis by clas­si­fy­ing sec­tors into those with ab­so­lute strong in­vest­ment in­crease, weak ab­so­lute in­vest­ment in­crease, rel­a­tive in­vest­ment in­crease, ab­so­lute in­vest­ment re­duc­tion, strong rel­a­tive in­vest­ment re­duc­tion and weak rel­a­tive in­vest­ment re­duc­tion, with clas­si­fi­ca­tion cri­te­ria shown in Ta­ble 3.

Based on Ta­ble 3 and Fig­ure 3, we find that six sec­tors in­clud­ing man­u­fac­ture of medicines, man­u­fac­ture of trans­port equip­ment, man­u­fac­ture of com­mu­ni­ca­tion equip­ment, and com­put­ers and other elec­tronic equip­ment are sec­tors of ab­so­lute strong in­vest­ment in­crease; 11 sec­tors in­clud­ing man­u­fac­ture of rub­ber, man­u­fac­ture of raw chem­i­cal ma­te­ri­als and chem­i­cal prod­ucts, and man­u­fac­ture of tex­tile are sec­tors of ab­so­lute in­vest­ment re­duc­tion; four sec­tors in­clud­ing man­u­fac­ture of leather, fur, feather and re­lated prod­ucts, man­u­fac­ture of tex­tile wear­ing ap­parel, footwear and caps, and man­u­fac­ture of non-metal­lic min­eral prod­ucts are sec­tors of strong rel­a­tive in­vest­ment re­duc­tion, while eight sec­tors in­clud­ing pro­cess­ing of food from agri­cul­tural prod­ucts, man­u­fac­ture of bev­er­age, and re­cy­cling and dis­posal of waste are sec­tors of weak rel­a­tive in­vest­ment re­duc­tion.

In com­par­i­son be­tween the size of cap­i­tal stock and the size of out­put, we may no­tice a con­sis­tent trend in their changes, with the ex­cep­tion of some sec­tors. We no­tice that iron and steel, elec­trolytic alu­minum, ce­ment, coal chem­i­cals, fan equip­ment, poly­crys­talline sil­i­con and paper-mak­ing sec­tors are con­sid­ered as sec­tors with se­ri­ous over­ca­pac­ity. In in­dus­try clas­si­fi­ca­tion, they cor­re­spond to six sec­tors, in­clud­ing smelt­ing and press­ing of non-fer­rous met­als, smelt­ing and press­ing of fer­rous met­als, man­u­fac­ture of raw chem­i­cal ma­te­ri­als and chem­i­cal prod­ucts, man­u­fac­ture of paper and paper prod­ucts, man­u­fac­ture of non-metal­lic min­eral prod­ucts and man­u­fac­ture of spe­cial pur­pose ma­chin­ery (Dong et al., 2015). In the fore­go­ing out­put struc­ture op­ti­miza­tion, the first four sec­tors all re­quire a slow­down in out­put growth and a more sub­stan­tial re­duc­tion in the size of cap­i­tal stock; but such a re­duc­tion is merely an ad­just­ment of the size of cap­i­tal stock un­der the con­di­tion of ac­cept­ing his­tor­i­cal over­ca­pac­ity and ob­so­lete ca­pac­ity. The fol­low­ing sec­tion will ex­am­ine to what ex­tent such an ad­just­ment is able to re­solve the prob­lem of over­ca­pac­ity.

4.3 Es­ti­ma­tion and Re­duc­tion of Man­u­fac­tur­ing Cap­i­tal Fac­tor Over­ca­pac­ity

The Chi­nese govern­ment has at­tached great im­por­tance to ad­dress­ing ob­so­lete ca­pac­ity in man­u­fac­tur­ing in­dus­try, and achieved ini­tial re­sults. Ac­cord­ing to the NBS sur­vey of 60,000 large and medium-size en­ter­prises since 2014, al­most all en­ter­prises have ca­pac­ity uti­liza­tion rates below 80%. As China’s econ­omy en­ters into the new nor­mal, if slow­ing growth is not matched by a cap­i­tal stock ad­just­ment, the prob­lem of over­ca­pac­ity will per­sist and de­te­ri­o­rate. Hence, it is of great sig­nif­i­cance to as­sess the cap­i­tal stock of China’s man­u­fac­tur­ing in­dus­try.

This paper will em­ploy in­put- ori­ented non- dis­cre­tionary vari­able model with con­stant re­turn to scale cre­ated by Cooper et al. (2004) to es­ti­mate the cap­i­tal fac­tor ca­pac­ity uti­liza­tion of var­i­ous man­u­fac­tur­ing sec­tors, in­clud­ing orig­i­nal val­ues and op­ti­mized val­ues of 2015. In or­der to cre­ate an ef­fi­ciency fron­tier for each sec­tor, rel­e­vant data of var­i­ous economies is re­quired. Con­sid­er­ing data avail­abil­ity, this paper con­ducts an anal­y­sis of sec­tor-spe­cific data of 30 pro­vin­cial re­gions and na­tional

over­all data with 31 DMU in­put-out­put data en­tries as sam­ples.

(1) Es­ti­ma­tion of cap­i­tal fac­tor over­ca­pac­ity be­fore and after man­u­fac­tur­ing fac­tor match­ing

Fig­ure 4 re­ports the orig­i­nal and op­ti­mized val­ues of ca­pac­ity uti­liza­tion of man­u­fac­tur­ing sec­tors in 2015. The re­sult shows that man­u­fac­tur­ing in­dus­try’s orig­i­nal over­all uti­liza­tion is about 56.14%. Rel­a­tively, light in­dus­tries and high-tech in­dus­tries boast higher ca­pac­ity uti­liza­tion rates. For in­stance, medicine man­u­fac­tur­ing, man­u­fac­ture of tex­tile wear­ing ap­parel and footwear and caps rank rel­a­tively high. How­ever, the ca­pac­ity uti­liza­tions of heavy in­dus­tries are rel­a­tively low. For in­stance, re­cy­cling and dis­posal of waste, smelt­ing and press­ing of non-fer­rous met­als and pro­cess­ing of petroleum, cok­ing, pro­cess­ing of nu­clear fuel rank as the bot­tom three man­u­fac­tur­ing sec­tors with the low­est ca­pac­ity uti­liza­tion rates. Over­all rank­ings of ca­pac­ity uti­liza­tion rates of var­i­ous sec­tors cal­cu­lated in this paper are gen­er­ally con­sis­tent with Han et al. (2011) and Dong et al. (2015).

Through op­ti­miza­tion of man­u­fac­tur­ing in­dus­try’s out­put struc­ture and fac­tor struc­ture in 2015, we are able to greatly in­crease man­u­fac­tur­ing in­dus­try’s uti­liza­tion rate to an over­all mean value of 72.04%. This value is still smaller than the de­sir­able ca­pac­ity uti­liza­tion level of­ten ref­er­enced by de­vel­oped coun­tries like the U. S. ( Zhong and Pan, 2014) by seven to ten per­cent­age points. From a sec­tor­spe­cific per­spec­tive, ex­cept for medicine man­u­fac­tur­ing whose ca­pac­ity uti­liza­tion re­mains al­most un­changed after op­ti­miza­tion, ca­pac­ity uti­liza­tion rates have more or less in­creased for all other sec­tors. In par­tic­u­lar, ca­pac­ity uti­liza­tion of tex­tile in­dus­try may in­crease to 85.84%, which is the high­est. This paper no­tices that the ca­pac­ity uti­liza­tion of re­cy­cling and dis­posal of waste is still as low as 55.12% after op­ti­miza­tion, which is among the low­est. The rea­son is that in re­spond­ing to the pol­icy to de­velop “ve­nous in­dus­try,” var­i­ous lo­cal­i­ties fell into low-level repet­i­tive con­struc­tion and vi­cious com­pe­ti­tion. De­vel­op­ing “ve­nous in­dus­try” is an in­evitable choice for China, but is­sues re­lated to the cross-re­gional trans­porta­tion of waste re­sources should be ad­dressed. “Ve­nous in­dus­try” should be de­vel­oped ac­cord­ing to var­i­ous fac­tors such as pop­u­la­tion den­sity, busi­ness den­sity and cost of trans­porta­tion.

(2) Re­duc­tion of man­u­fac­tur­ing cap­i­tal fac­tor over­ca­pac­ity

Man­u­fac­tur­ing in­dus­try’s ca­pac­ity uti­liza­tion may in­crease after match­ing the fac­tor struc­ture of op­ti­mized de­sir­able out­put merely by ex­tract­ing his­tor­i­cal in­for­ma­tion. How­ever, over­ca­pac­ity still ex­ists. Here, cap­i­tal stock of var­i­ous man­u­fac­tur­ing sec­tors is ad­justed based on the non-lin­ear re­la­tion­ship be­tween cap­i­tal stock and eco­nomic out­put in his­tor­i­cal sam­ple data. Ad­justed ca­pac­ity uti­liza­tions re­sult should be equiv­a­lent to his­tor­i­cal mean val­ues. Ca­pac­ity uti­liza­tion rates of 2008-2010 in Fig­ure 4 are 73.27%, which is rather close to the value after fac­tor struc­ture match­ing for 2015 (72.04%). In this sense, ad­just­ment based on his­tor­i­cal sam­ples tech­ni­cally only ap­proached the his­tor­i­cal mean value. In an at­tempt to shore up slow­ing econ­omy, lo­cal gov­ern­ments re­sorted to an in­vest­ment spree. But this is only the first level for man­u­fac­tur­ing in­dus­try to in­crease ca­pac­ity uti­liza­tion and re­duce over­ca­pac­ity. After cap­i­tal stock is ad­justed to his­tor­i­cal mean value ac­cord­ing to the level of de­sir­able out­put, there is of­ten a gap with the ca­pac­ity uti­liza­tion with do­mes­tic high-ef­fi­ciency econ­omy as ef­fi­ciency fron­tier. Re­duc­ing this gap be­comes the se­cond level where China’s man­u­fac­tur­ing in­dus­try may in­crease ca­pac­ity uti­liza­tion and re­duce over­ca­pac­ity.

On the ba­sis of re­solv­ing over­ca­pac­ity at the first level, we should fo­cus on and re­solve the fol­low­ing prob­lem: The ca­pac­ity uti­liza­tion rates of re­cy­cling and dis­posal of waste, smelt­ing and press­ing of non-fer­rous met­als, pro­cess­ing of petroleum, cok­ing, and pro­cess­ing of nu­clear fuel are sig­nif­i­cantly smaller than de­sir­able lev­els, and fur­ther ad­just cap­i­tal stock value based on their gaps to achieve the tran­si­tion of ca­pac­ity uti­liza­tion from level 1 to level 2. This tran­si­tion is a qual­i­ta­tive change, and will be much more dif­fi­cult than achiev­ing level 1. As noted by Coel­lie et al. (2002) and Dong et al. (2015), since var­i­ous economies may have equal fixed in­puts but dif­fer­ent pro­duc­tiv­i­ties (i.e. dif­fer­ence in tech­ni­cal ef­fi­ciency), ca­pac­ity uti­liza­tion may be fur­ther de­com­posed into equip­ment uti­liza­tion and tech­ni­cal ef­fi­ciency (Coelli et al., 2002; Dong et al., 2015). Then, the most di­rect so­lu­tion to cap­i­tal fac­tor over­ca­pac­ity is to de­ter­mine a rea­son­able size of firms based on out­put re­quire­ment

to avoid dis­ec­onomies of scale aris­ing from ex­ces­sively small or large scale, fo­cus­ing on equip­ment uti­liza­tion im­prove­ment. In ad­di­tion to im­prov­ing firms’ tech­ni­cal and man­age­rial lev­els, as well as re­vealed tech­ni­cal ef­fi­ciency, it is also im­por­tant to re­duce im­plicit tech­ni­cal in­ef­fi­cien­cies by phas­ing out ob­so­lete ca­pac­i­ties and “bub­ble fixed as­sets” with ex­ag­ger­ated cost and value.

5. Con­clud­ing Re­marks and Pol­icy Im­pli­ca­tions

Rea­son­able in­dus­trial struc­ture is the key to in­dus­trial be­hav­iors and per­for­mance. In this sense, whether China is able to op­ti­mize its in­dus­trial struc­ture ac­cord­ing to its na­tional con­di­tions is vi­tal to the suc­cess of “Made in China 2025.” Us­ing two-digit man­u­fac­tur­ing sec­tors, this paper sys­tem­at­i­cally op­ti­mizes China’s man­u­fac­tur­ing in­dus­trial struc­ture. Our find­ings sug­gest that (1) man­u­fac­tur­ing out­put struc­ture op­ti­miza­tion may re­duce en­ergy in­ten­sity and car­bon in­ten­sity by 18.08% and 17.42% re­spec­tively; (2) to re­duce fac­tor mis­match, in­put fac­tors need to be matched after man­u­fac­tur­ing out­put struc­ture im­prove­ment. The level of cap­i­tal stock, in par­tic­u­lar, re­quires a 26.02% re­duc­tion; (3) es­ti­ma­tion re­sult of cap­i­tal fac­tor ca­pac­ity uti­liza­tion fur­ther re­veals that China’s man­u­fac­tur­ing ca­pac­ity uti­liza­tion in 2015 was far below the av­er­age level in the mid-and late stage of the 11th Five-Year Plan pe­riod (2008-2010). After in­put fac­tor match­ing, ca­pac­ity uti­liza­tion may rise to the lat­ter level.

Based on this paper’s find­ings, we may ar­rive at the fol­low­ing pol­icy im­pli­ca­tions: First, “Made in China 2025” strat­egy should not be in­tended for all sec­tors in­dis­crim­i­nately. In­stead, there should be pri­or­i­ties for the de­vel­op­ment of spe­cific sec­tors and re­treat of some oth­ers. We sug­gest giv­ing pri­or­ity to de­vel­op­ing nine sec­tors, in­clud­ing man­u­fac­ture of medicines and man­u­fac­ture of spe­cial pur­pose ma­chin­ery, to speed up eco­nomic growth; prop­erly con­trol­ling the growth rates of man­u­fac­ture of bev­er­age, man­u­fac­ture of cul­tural, ed­u­ca­tional, fine arts, sports and en­ter­tain­ment goods, as well as other man­u­fac­tur­ing (these sec­tors should out­pace man­u­fac­tur­ing in­dus­try’s bench­mark growth rate); growth rates of other man­u­fac­tur­ing such as pro­cess­ing of food from agri­cul­tural prod­ucts and man­u­fac­ture of foods should stay below man­u­fac­tur­ing in­dus­try’s bench­mark growth rate and avoid ex­ces­sive growth.

Se­cond, the het­ero­gene­ity of pro­duc­tion fac­tors re­quires the govern­ment and mar­ket to play dif­fer­ent roles in syn­ergy. The govern­ment should elim­i­nate in­sti­tu­tional la­bor mar­ket seg­re­ga­tion, re­duce in­sti­tu­tional priv­i­leges re­lated to house­hold reg­is­tra­tion ( hukou) and quota, and thus lower the cost of la­bor mi­gra­tion and pro­mote the free flow of la­bor. How­ever, po­ten­tial un­em­ploy­ment aris­ing from ex­ces­sive in­crease of profit-seek­ing cap­i­tal com­po­nent should be avoided. Ac­cord­ing to the needs of out­put struc­ture op­ti­miza­tion, the cen­tral govern­ment should iden­tify a rea­son­able level of cap­i­tal stock for man­u­fac­tur­ing sec­tors, avoid dis­pro­por­tion­ate in­vest­ment in over­all sec­tor plan­ning, and reg­u­late and re­strain lo­cal govern­ment in­vest­ment to curb ex­ces­sive in­vest­ments. The govern­ment should with­draw from its role as an in­vest­ment en­tity, and re­frain from in­ter­ven­ing in cap­i­tal fac­tor al­lo­ca­tion. In­stead, it should reg­u­late and guide the mar­ket, and en­sure mar­ket-based in­vest­ment ac­tiv­i­ties.

Fig­ure 2: Pro­duc­tion In­crease and Re­duc­tion of Man­u­fac­tur­ing Sec­tors Note: solid line is crit­i­cal line of cap­i­tal stock in­crease or de­crease with 2015 as bench­mark for com­par­i­son, and crit­i­cal point is 0%. Dot­ted line is crit­i­cal line of out­put in­crease or de­crease with 2010 as bench­mark for com­par­i­son, and crit­i­cal val­ues are 0% and 66.57%.Source: Es­ti­mated by the task group.

Fig­ure 3: Cap­i­tal Stock In­crease and De­crease of Man­u­fac­tur­ing Sec­tors Note: Solid line is crit­i­cal line of cap­i­tal stock in­crease or de­crease with 2015 as bench­mark for com­par­i­son, and crit­i­cal point is 0%. Dot­ted line is crit­i­cal line of out­put in­crease or de­crease with 2010 as bench­mark for com­par­i­son, and crit­i­cal val­ues are 0% and 18.03%.Source: Es­ti­mated by the task group.

Fig­ure 4: Com­par­i­son of Ca­pac­ity Uti­liza­tions of Var­i­ous Man­u­fac­tur­ing Sec­tors Source: Es­ti­mated by the task group.

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