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techsuch May 9, 2021 0 Comments

Measuring Green Innovation Efficiency for China’s High-Tech ManufacturingIndustry: A Network DEA ApproachEarlier studies on the innovation process in the high-tech manufacturingindustry failed to take environmental pollution into account, making itdifficult to estimate green innovation efficiency in the industry. From aperspective of innovation value chain, this paper decomposes green innovationprocess in the high-tech manufacturing industry into two stages: R&D stage andachievement transformation stage; a network DEA approach consideringundesirable outputs is utilized to estimate the green innovation efficiency inChina’s high-tech manufacturing industry. Compared with the method ofconventional innovation efficiency without considering environmentalpollution, the estimation method for green innovation efficiency can not onlyavoid bias of estimation results of provinces producing low pollutionemissions like Inner Mongolia and Hainan but also reflect the volatility inefficiency of the high-tech manufacturing industry before and after theimplementation of the environmental law.#### 1. IntroductionWith increasing attention to the global competitiveness of the high-techmanufacturing industry since the 1990s, China has been steadily increasing itsinputs in this area. A series of breakthroughs have been made in areas ofmanned space programs, the BeiDou Navigation Satellite System, and high-speedrail equipment. However, China’s high-tech industries are still facingsignificant problems in terms of innovation capabilities and environmentalpollution. Take Guangdong, a leading province in high-tech manufacturing, forexample. According to statistics of the Development and Reform Commission ofGuangdong Province, the province relies on importation for 90% of its keytechnologies and components, and 80% of the high-end CNC machines in theprovince are made in foreign countries. A large number of high-techmanufacturing firms without adequate innovative capabilities have been lockedin low-value-adding, marginally profitable manufacturing processes, which hasnot only inhibited structural upgrade of the manufacturing industry and theimprovement in the value chain but also caused a series of environmentalpollution problems. In the meantime, China’s high-tech manufacturing industryis also confronted with increasingly tightened constraints pertaining toresources and environment. In 2008, the State Environmental ProtectionAdministration was upgraded to the Ministry of Environmental Protection. TheCircular Economy Promotion Law of the People’s Republic of China, which wasofficially implemented in January 2009, imposes more stringent controls overpollutant discharges from manufacturing firms. As such, with a severe scarcityof innovation resources and increasingly stringent environmental regulations,characterizing green innovation efficiency in the high-tech manufacturingindustry is of a great significance for facilitating policy-making andsustainable development of industries in China.Research literature focusing on innovation efficiency in China’s high-techmanufacturing industry can be divided into three categories: the firstcategory of literature deals with assessment of provincial innovationefficiency in the high-tech manufacturing industry with an emphasis onanalyzing interprovincial differences in innovation efficiency [1–3]; anotherstrand of literature has focused on industry-level evaluations throughexamining industry-specific heterogeneity of innovation efficiency in thehigh-tech manufacturing industry [4–7]; finally, a small body of literatureanalyzed the influencing factors on innovation efficiency in the high-techmanufacturing industry based on survey data [8–11]. The most common method forestimating innovation efficiency in China’s high-tech manufacturing industryis the DEA method [12–14], which has evolved from one-stage overall efficiencyevaluation to multistage network efficiency evaluation [13–16].Despite a large multitude of literature on innovation efficiency in China’shigh-tech manufacturing industry, few studies have estimated green innovationefficiency in the high-tech manufacturing industry while consideringenvironmental factors [17, 18]. A small fraction of literature did considerenvironmental pollution when estimating innovation efficiency in the high-techmanufacturing industry, but, in these studies, the overall innovation systemwas treated as a “black box” without further analyzing the internal structureof the innovation system [19–22]. Has there been any volatility in greeninnovation efficiency in China’s high-tech manufacturing industry after theimplementation of environmental regulations? What differences are therebetween the estimation results of green innovation efficiency with consideringenvironmental pollution and those of conventional innovation efficiencywithout considering environmental pollution? These questions are still waitingto be answered [23, 24].In this paper, a two-stage network DEA model with undesirable outputs is usedto estimate green innovation efficiency of China’s provincial high-techmanufacturing industry between 2006 and 2015; the volatility in greeninnovation efficiency in the high-tech manufacturing industry before and afterthe implementation of the Circular Economy Promotion Law of the People’sRepublic of China is examined, confirming that the method for estimatinginnovation efficiency without considering environmental pollution can resultin bias. This paper is divided into six sections. The first section is theintroduction. In the second section, a framework for the concept of greeninnovation in the high-tech manufacturing industry is established from theperspective of innovation value chain. The third section outlines theestimation method, indicators, and data employed in this paper. In the fourthsection, the network DEA method considering environmental pollution isemployed to estimate green innovation efficiency in China’s high-techmanufacturing industry, and estimation results of conventional innovationefficiency without considering environmental pollution are also provided. Thefifth section presents a discussion of research results, including thesignificance and limitations of the research, as well as a direction forfuture research. The sixth section provides the conclusion and implications.#### 2. Green Innovation Process in the High-Tech Manufacturing IndustryTo carry out innovation activities, an organization must first acquirerelevant knowledge, convert it into new products and processes, and thenachieve added value through these innovation outputs. For such a circularprocess from knowledge acquisition, conversion to utilization constitutes aninnovation value chain (IVC) [25, 26]. The IVC represents a comprehensiveanalytical framework that decomposes innovation into multiple stages in orderto specify its operating processes. Using IVC, managers are able to identifyorganizational weaknesses and select suitable innovation tools and approaches[25, 27]. Based on IVC, the innovation process of an industry can be dividedinto two stages: the R&D stage and the achievement transformation stage [28].In the first stage, universities, research institutions, and firms put intechnological resources and convert them into patent achievements throughresearch and development; firms further transform some of the patentsgenerated in the first stage into economic and societal benefits in the secondstage, during which the inputs usually include other factors like capital andlabor [28, 29]. Drawing on studies on the two-stage innovation process ofChina’s high-tech manufacturing industry [29] and considering the factor ofenvironmental pollution in the innovation process [30], this paper proposesthe conceptual framework of green innovation in the high-tech manufacturingindustry from the IVC perspective (see Figure 1).Based on the IVC perspective, green innovation efficiency in the high-techmanufacturing industry can be decomposed into the R&D efficiency in the firststage and the achievement transformation efficiency in the second stage. Theformer reflects the level to which scientific and technological resources areconverted into technological achievements by research actors, which ismeasured by the ratio of the output to the input of R&D; meanwhile the latterreflects the capability of firms to transform technological achievements intoeconomic outputs and environmental benefits, which is measured by the ratio ofthe output to the input of achievement transformation [17, 31]. Drawingreferences from existing studies on the input and output indicators pertainingto the two-stage green innovation efficiency in the high-tech manufacturingindustry [28, 29] and considering the availability of data, this paperconsiders R&D capital and R&D personnel as inputs of the R&D stage [9], andthe number of patent applications and the number of invention patents areowned as intermediate outputs (the outputs in the R&D stage are also theinputs in the achievement transformation stage) [23]; capital and labor areselected as the supplementary inputs in the achievement transformation stage[29, 32]; and new product sales revenue, prime operating revenue, andpollutant emissions are used as the final outputs [30, 33, 34].#### 3. Materials and Methods##### 3.1. A Two-Stage Network DEA Model with Undesirable OutputsThe innovation process in the high-tech manufacturing industry entailsmultiple inputs and outputs. Data envelopment analysis (DEA) is highlyapplicable to estimate the efficiency of such a process [35, 36]. Whenestimating innovation efficiency in the high-tech manufacturing industry, theconventional DEA method views innovation as a “black box,” failing to considerits internal operations. The network DEA approach emerging in recent years isable to accurately portray the operating process of decision-making units(DMUs) and provide well-targeted improvement solutions [37–40]. Due to thefact that green innovation process in the high-tech manufacturing industry isdivided into two stages under the IVC perspective, there is a necessity to usethe network DEA model to estimate the green innovation efficiency [30, 41].When assessing innovation efficiency, the conventional network DEA approacheither fails to consider undesirable outputs or fails to utilize the weakdisposability assumption when addressing undesirable outputs [42, 43].Maghbouli et al. [44] assumed variable returns to scale and the weakdisposability of undesirable outputs, proposing a two-stage network DEA modelwith undesirable outputs which has a considerably high applicability toestimate the green innovation efficiency in China’s high-tech manufacturingindustry.Assume that there are K DMUs and each DMU consists of two stages. In the firststage, the initial inputs into the k-th are , and the desirable intermediateoutputs are . In the second stage, the inputs are twofold: first, thedesirable intermediate outputs in the first stage and the external inputs ,the desirable and undesirable outputs of which are and , respectively. TheDMU pending to be evaluated is ; under the assumptions of variable returns toscale and weak disposability, the global efficiency of the two-stage networkDEA with undesirable final outputs can be solved using the following model[44]:In the object function, is Russell’s measure of the R&D efficiency in thefirst stage, is Russell’s measure of the achievement transformationefficiency in the second stage.##### 3.2. Indicators and DataUnder the IVC perspective, green innovation in the high-tech manufacturingindustry can be divided into two stages: R&D stage and achievementtransformation stage. Inputs in the R&D stage include R&D capital and R&Dpersonnel. R&D personnel is measured by the full-time equivalents of R&Dpersonnel (FTERDP), while R&D capital is measured by the R&D capital stock(RDS) [9, 30]. The outputs from the R&D stage are the number of patentapplications (PA) and the number of patents owned (NP) [23]. The supplementaryinputs in the achievement transformation stage are physical capital and labor;the former is measured by physical capital stock (K), while the latter ismeasured by the number of employees in the high-tech manufacturing industry(L) [29, 32]. The desirable outputs in the achievement transformation stageare new product sales revenue (YNP) and prime operating revenue (Y) [30]. Theundesirable outputs of the achievement transformation stage are pollutantemissions. Considering the fact that SO2 as one of the major environmentalpollutants has a considerably high homogeneity, SO2 emission is selected asthe indicator of undesirable outputs [34].Data on full-time equivalents (FTERDP), number of patent applications (PA),number of patents owned (NP), number of employees (L), new product salesrevenue (YNP), and prime operating revenue (Y) are obtained from ChinaStatistics Yearbook on High Technology Industry. The data used to estimate R&Dcapital stock (RDS) and physical capital stock (K) are also from ChinaStatistics Yearbook on High Technology Industry. Data on SO2 emission (BSO2)are obtained from China Statistical Yearbook on Environment, and the methodused to calculate SO2 emissions in China’s provincial high-tech manufacturingindustry is derived from Peng and Zhou [45].Statistical data of 28 provinces in mainland China (data of other provincesare missing) spanning from 2006 to 2015 were selected to measure the greeninnovation efficiency in the high-tech manufacturing industry (see Table 1).|* * * — Variable| Unit| Mean| Std. dev.| Min| Max * * * R&D capital stock (RDS)| 100 million yuan| 106.535| 231.823| 0.178| 2044.384 Full-time equivalents (FTERDP)| Man-year| 16932.630| 33175.270| 11.800|224334.000 Number of patent applications (PA)| Piece| 3307.543| 7647.005| 1.000|58119.000 Number of patents owned (NP)| Piece| 3196.386| 11378.510| 1.000| 125471.000 Physical capital stock (K)| 100 million yuan| 988.718| 1219.791| 22.949|9514.534 Number of employees (L)| Person| 391334.700| 713679.400| 4739.000| 3890108.000 New product sales revenue (YNP)| 100 million yuan| 695.091| 1544.006| 0.019|12396.770 Prime operating revenue (Y)| 100 million yuan| 2668.986| 5048.211| 11.033|33491.550 SO2 emission (BSO2)| 10,000 tons| 3708.555| 3129.999| 381.206| 15379.350 * * * #### 4. Results##### 4.1. Measurement of Green Innovation EfficiencyBased on the panel data of Chinese provinces spanning from 2006 to 2015,equation (1) was used to calculate the green innovation efficiency of China’sprovincial high-tech manufacturing industry, the results of which are shown inTable 2.|* * * — Area| Green innovation efficiency| R&D efficiency| Achievement transformationefficiency * * * Beijing| 1.000| 1.000| 1.000 Tianjin| 0.839| 0.699| 0.979 Hebei| 0.443| 0.286| 0.600 Shanxi| 0.412| 0.465| 0.359 Inner Mongolia| 1.000| 1.000| 1.000 Liaoning| 0.399| 0.297| 0.502 Jilin| 0.677| 0.550| 0.804 Heilongjiang| 0.269| 0.129| 0.409 Shanghai| 0.866| 0.732| 1.000 Jiangsu| 0.949| 0.897| 1.000 Zhejiang| 0.511| 0.346| 0.677 Anhui| 0.333| 0.266| 0.400 Fujian| 0.662| 0.489| 0.835 Jiangxi| 0.365| 0.270| 0.460 Shandong| 0.589| 0.479| 0.699 Henan| 0.635| 0.493| 0.776 Hubei| 0.332| 0.216| 0.448 Hunan| 0.457| 0.408| 0.506 Guangdong| 1.000| 1.000| 1.000 Guangxi| 0.519| 0.414| 0.623 Hainan| 1.000| 1.000| 1.000 Chongqing| 0.635| 0.547| 0.723 Sichuan| 0.462| 0.429| 0.495 Guizhou| 0.276| 0.159| 0.392 Yunnan| 0.536| 0.476| 0.597 Shaanxi| 0.181| 0.095| 0.266 Gansu| 0.569| 0.499| 0.640 Ningxia| 0.877| 0.753| 1.000 Eastern region| 0.751| 0.657| 0.845 Middle region| 0.435| 0.350| 0.520 Western region| 0.562| 0.486| 0.637 Overall| 0.600| 0.514| 0.685 * * * The eastern region includes Beijing, Tianjin, Hebei, Liaoning, Shanghai,Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, and Hainan; the middle regionincludes Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, and Hunan;the western region includes Guangxi, Inner Mongolia, Chongqing, Sichuan,Guizhou, Yunnan, Shaanxi, Gansu, and Ningxia. Other provinces were notincluded in the sample due to serious data missing. As can be known from Table 2, when measuring the green innovation efficiencyin China’s provincial high-tech manufacturing industry from the IVCperspective, the green innovations of 4 provinces, namely, Beijing, Guangdong,Inner Mongolia, and Hainan, are effective, all of which are 1.000, while thoseof other provinces are ineffective. Among the ineffective provinces, Jiangsu,Ningxia, Shanghai, and Tianjin have relatively higher green innovationefficiencies in their high-tech manufacturing industry, with annual meanvalues between 2006 and 2015 being 0.949, 0.877, 0.866, and 0.839,respectively. The province having the lowest green innovation efficiency inits high-tech manufacturing industry is Shaanxi, with an annual mean valuebetween 2006 and 2015 being only 0.181. Heilongjiang, Guizhou, Hubei, andAnhui also showed relatively lower green innovation efficiencies in theirhigh-tech manufacturing industry, with annual mean values being 0.269, 0.276,0.332, and 0.333, respectively. As such, the green innovation efficiencyvaries significantly across China’s provincial high-tech manufacturingindustries, with the majority of provinces having a relatively low greeninnovation efficiency.Based on China Statistics Yearbook on High Technology Industry, theseprovinces are divided into three regions, namely, the eastern, central, andwestern regions. Regional differences in green innovation efficiency inChina’s high-tech manufacturing industry are shown in Figure 2.As can be seen, between 2006 and 2015, the eastern region has an annual meanvalue in terms of green innovation efficiency in the high-tech manufacturingindustry higher than those of the central and western regions. The year 2009represents a “turning point” whereby green innovation efficiencies of theeastern and western regions in their high-tech manufacturing industry shiftfrom growth to decline. Green innovation efficiency in the high-techmanufacturing industry exhibited a declining trend between 2009 and 2014 inthe eastern region, while it showed a climbing trend between 2010 and 2015 inthe western region. For the central region, green innovation efficiency in thehigh-tech manufacturing industry came to its first “turning point,” shiftingfrom growth to decline, in 2008; then it exhibited a continuous drop for twoyears in a row from 2009 to 2010, followed by an S-shaped growth after 2010.In general, green innovation efficiency in China’s high-tech manufacturingindustry showed a rapid growth from 2006 to 2009, arriving at a “turningpoint” in 2009 and then exhibiting a slow growing trend between 2010 and 2015.On the one hand, green innovation efficiency in China’s high-techmanufacturing industry shows considerable provincial differences, with themajority of provinces having a relatively low efficiency, which explains therelatively low mean value of green innovation efficiency in the high-techmanufacturing industry. On the other hand, green innovation efficiencies inthe high-tech manufacturing industry across the eastern, central, and westernregions experienced major volatilities between 2006 and 2015, but the threeregions exhibited significantly different variation trends in terms of theirgreen innovation efficiencies.Based on the IVC perspective, this paper decomposes green innovationefficiency into R&D efficiency and achievement transformation efficiency forfurther analysis.As can be known from Table 2, when taking environmental pollution intoconsideration, only Beijing, Guangdong, Inner Mongolia, and Hainan areeffective in the R&D stage in their high-tech manufacturing industries, withefficiencies being invariably 1.000 between 2006 and 2015; meanwhile the other24 provinces are ineffective. Among the ineffective provinces, Jiangsu,Ningxia, and Shanghai showed relatively higher R&D efficiencies in their high-tech manufacturing industries, with annual mean values being 0.897, 0.753, and0.732, respectively between 2006 and 2015. In some provinces, the R&Defficiency in the high-tech manufacturing industry varies significantly acrossdifferent periods. For example, Ningxia had R&D efficiency lower than 0.500between 2006 and 2008, but it reached 1.000 between 2011 and 2015. From 2006to 2015, the province having the lowest R&D efficiency in its high-techmanufacturing industry is Shaanxi, with an annual mean value of only 0.095. Inaddition, Heilongjiang, Guizhou, Hubei, Anhui, Jiangxi, Hebei, and Liaoningalso have an annual mean value lower than 0.300 in their R&D efficiencies.Figure 3 shows the regional differences in terms of R&D efficiency in China’shigh-tech manufacturing industry while considering environmental pollution.When taking environmental pollution into consideration, the annual mean valueof R&D efficiency in the high-tech manufacturing industry in the easternregion was higher than those in the central and western regions between 2006and 2015, while the annual mean value of the central region is lower than thatof the western region. In general, the annual mean value of R&D efficiency inChina’s high-tech manufacturing industry is only 0.514. The year 2009represents a “turning point” for eastern region’s R&D efficiency in the high-tech manufacturing industry, shifting from growth to decline. From 2009 to2015, R&D efficiency in the high-tech manufacturing industry in the easternregion fluctuated in the range of [0.600, 0.800]. In the central region, R&Defficiency in the high-tech manufacturing industry experienced a rapid growthbetween 2006 and 2008, followed by a continuous drop for two consecutive yearsfrom 2009 to 2010, and then fluctuated in the range of [0.300, 0.500] from2011 to 2015. In the western region, R&D efficiency in the high-techmanufacturing industry exhibited a climbing trend from 2006 to 2015, with aminor volatility only in 2014. In overall, when taking environmental pollutioninto consideration, R&D efficiency in China’s high-tech manufacturing industryexhibited an “S”-shaped growing trend from 2006 to 2015.As can be known from Table 2, when taking environmental pollution intoconsideration, there are 7 effective provinces in the achievementtransformation stage in their high-tech manufacturing industries, which areBeijing, Inner Mongolia, Shanghai, Jiangsu, Guangdong, Hainan, and Ningxia;other provinces, in comparison, are ineffective. Among the ineffectiveprovinces, Tianjin, Fujian, and Jilin have relatively higher achievementtransformation efficiencies, with annual mean values being 0.979, 0.835, and0.804, respectively. The province having the lowest achievement transformationefficiency in the high-tech manufacturing industry is Shaanxi, which has anannual mean value of only 0.266. In addition, Shanxi and Guizhou also showedrelatively low achievement transformation efficiencies in their high-techmanufacturing industry, with annual mean values being 0.359 and 0.392,respectively. Figure 4 displays the regional differences in terms ofachievement transformation efficiency in China’s high-tech manufacturingindustry while considering environmental pollution.When taking environmental pollution into consideration, the achievementtransformation efficiency in the high-tech manufacturing industry in theeastern region between 2006 and 2015 was significantly higher than those ofthe central and western regions. However, the eastern region’s achievementtransformation efficiency in the high-tech manufacturing industry constantlyfluctuated within the range of [0.800, 0.900]. In the central region,achievement transformation efficiency in the high-tech manufacturing industrypeaked at 0.746 in 2008 and then exhibited a declining trend for twoconsecutive years from 2009 and 2010, followed by an “S”-shaped growing trendbetween 2010 and 2015. In the western region, achievement transformationefficiency in the high-tech manufacturing industry exhibited an “S”-shapedgrowing trend from 2006 to 2015, with a downward volatility only in 2010 andin 2013. Generally, while considering environmental pollution, achievementtransformation efficiency in China’s high-tech manufacturing industryexhibited an upward trend from 2006 to 2015 and then showed an inverted“U”-shaped change between 2006 and 2010, followed by an “S”-shaped growingtrend from 2011 to 2015.##### 4.2. Measurement of Conventional Innovation EfficiencyTo analyze the differences between the green innovation efficiency andconventional innovation efficiency estimation results, equation (1) isutilized to calculate the conventional innovation efficiency of China’sprovincial high-tech manufacturing industry without considering environmentalpollution, as shown in Table 3.|* * * — Area| Conventional innovation efficiency| R&D efficiency| Achievementtransformation efficiency * * * Beijing| 1.000| 1.000| 1.000 Tianjin| 0.870| 0.748| 0.991 Hebei| 0.276| 0.200| 0.351 Shanxi| 0.444| 0.608| 0.280 Inner Mongolia| 0.987| 0.979| 0.995 Liaoning| 0.390| 0.287| 0.493 Jilin| 0.515| 0.452| 0.578 Heilongjiang| 0.208| 0.114| 0.301 Shanghai| 0.915| 0.830| 1.000 Jiangsu| 0.958| 0.915| 1.000 Zhejiang| 0.507| 0.244| 0.770 Anhui| 0.336| 0.232| 0.440 Fujian| 0.695| 0.489| 0.900 Jiangxi| 0.284| 0.233| 0.334 Shandong| 0.607| 0.489| 0.726 Henan| 0.411| 0.412| 0.409 Hubei| 0.311| 0.203| 0.419 Hunan| 0.356| 0.304| 0.408 Guangdong| 1.000| 1.000| 1.000 Guangxi| 0.479| 0.541| 0.416 Hainan| 0.945| 0.922| 0.969 Chongqing| 0.605| 0.517| 0.694 Sichuan| 0.415| 0.375| 0.456 Guizhou| 0.284| 0.161| 0.407 Yunnan| 0.526| 0.448| 0.603 Shaanxi| 0.182| 0.095| 0.268 Gansu| 0.456| 0.447| 0.465 Ningxia| 0.886| 0.772| 1.000 Eastern region| 0.742| 0.648| 0.836 Middle region| 0.358| 0.320| 0.396 Western region| 0.535| 0.482| 0.589 Overall| 0.566| 0.501| 0.631 * * * As can be known from Table 3, when conventional innovation efficiency inChina’s provincial high-tech manufacturing industry is estimated from the IVCperspective, Beijing and Guangdong are found to be effective, while otherprovinces are ineffective. Among the ineffective provinces, Inner Mongolia,Jiangsu, Hainan, and Shanghai showed relatively higher conventional innovationefficiencies in their high-tech manufacturing industries, with annual meanvalues being 0.987, 0.958, 0.945, and 0.915, respectively. The province havingthe lowest conventional innovation efficiency in high-tech manufacturingindustry is Shaanxi, with an annual mean value of only 0.182. In addition,Heilongjiang and Hebei also showed relatively low conventional innovationefficiencies in their high-tech manufacturing industries, the annual meanvalues of which are 0.208 and 0.276, respectively. Figure 5 shows the regionaldifferences in terms of conventional innovation efficiency in China’s high-tech manufacturing industry without considering environmental pollution.From a regional perspective, the eastern region has a conventional innovationefficiency in the high-tech manufacturing industry higher than those of thecentral and western regions and higher than the nationwide average level.However, conventional innovation efficiency of the eastern region constantlyfluctuates within the range of [0.697, 0.769]. From 2006 to 2015, the centralregion’s conventional innovation efficiency in the high-tech manufacturingindustry was constantly lower than those of the western region and nationwideaverage; in the meantime, both the central and western regions exhibited agrowing trend in terms of their conventional innovation efficiency in thehigh-tech manufacturing industry. In general, conventional innovationefficiency in China’s high-tech manufacturing industry exhibits a growingtrend.The conventional innovation efficiency is further decomposed into R&Defficiency and achievement transformation efficiency from the IVC perspective.As can be known from Table 3, when not taking environmental pollution intoaccount, only Beijing and Guangdong are effective in the R&D stage in theirhigh-tech manufacturing industries, while other provinces are ineffective.Among the ineffective provinces, Inner Mongolia, Hainan, Jiangsu, and Shanghaihave a relatively higher R&D efficiency in their high-tech manufacturingindustries, with annual mean values being 0.979, 0.922, 0.915, and 0.830,respectively. The province having the lowest R&D efficiency in China’s high-tech manufacturing industry is Shaanxi, which has an annual mean value of only0.095. In addition, Heilongjiang, Guizhou, Hebei, Hubei, Anhui, Jiangxi,Zhejiang, and Liaoning also showed relatively lower R&D efficiencies in theirhigh-tech manufacturing industries, with annual mean values being invariablylower than 0.300. Figure 6 presents the regional differences in terms of R&Defficiency in China’s high-tech manufacturing industry without consideringenvironmental pollution.When not taking environmental pollution into account, the eastern region hasR&D efficiency in its high-tech manufacturing industry higher than those ofthe central and western regions. The eastern region’s R&D efficiency in thehigh-tech manufacturing industry grew rapidly between 2007 and 2010 and thenexhibited an “S”-shaped volatility from 2011 to 2015, without showing asignificantly growing trend. In the central region, R&D efficiency in thehigh-tech manufacturing industry gained a rapid growth between 2007 and 2010,followed by an “S”-shaped growth from 2011 to 2015. In the western region, R&Defficiency in the high-tech manufacturing industry achieved a rapid growthfrom 2006 to 2009, followed by an “S”-shaped growing trend between 2010 and2015. In general, when not taking environmental pollution into account, R&Defficiency in China’s high-tech manufacturing industry also exhibits a growingtrend.As can be known from Table 3, when not taking environmental pollution intoaccount, Beijing, Shanghai, Jiangsu, Guangdong, and Ningxia are effective inthe achievement transformation stage in the high-tech manufacturing industry,while other provinces are ineffective. Among the ineffective provinces, InnerMongolia, Tianjin, and Hainan have a relatively higher achievementtransformation efficiency in their high-tech manufacturing industries, withannual mean values being 0.995, 0.991, and 0.969, respectively. The provincehaving the lowest achievement transformation efficiency in China’s high-techmanufacturing industry is Shaanxi, with an annual mean value of only 0.268. Inaddition, Shanxi, Heilongjiang, and Jiangxi also showed relatively lowerachievement transformation efficiencies in their high-tech manufacturingindustries, with annual mean values being 0.280, 0.301, and 0.334,respectively. Figure 7 presents regional differences in terms of achievementtransformation efficiencies in China’s high-tech manufacturing industrywithout considering environmental pollution.When not taking environmental pollution into account, the eastern region’sachievement transformation efficiency in the high-tech manufacturing industryis far higher than those of the central and western regions from 2006 to 2015.The eastern region’s achievement transformation efficiency in the high-techmanufacturing industry experienced a “downward-upward-downward” process, withthe efficiency constantly fluctuating within the range of [0.796, 0.884]. Inthe central region, achievement transformation efficiency in the high-techmanufacturing industry declined slowly from 2006 to 2007 and then exhibited agrowing trend between 2008 and 2015. In the western region, achievementtransformation efficiency in the high-tech manufacturing industry exhibited adeclining trend from 2006 to 2010, followed by a growing trend from 2011 to2015. In general, when not taking environmental pollution into account,achievement transformation efficiency in China’s high-tech manufacturingindustry exhibited an “S”-shaped downward trend first and then an “S”-shapedupward trend.##### 4.3. Comparison of Estimation Results between Green InnovationEfficiency and Conventional Innovation EfficiencyFrom 2006 to 2015, both the green innovation efficiency and conventionalinnovation efficiency of China’s high-tech manufacturing industry wereconsiderably low (which were 0.600 and 0.566, respectively). From the IVCperspective, it can be attributed to the phenomenon where the majority ofprovinces simultaneously have low efficiencies during the R&D stage andachievement transformation stage (when taking environmental pollution intoconsideration, the mean values of R&D efficiency and achievementtransformation efficiency are 0.514 and 0.685; when not taking environmentalpollution into account, the mean values are 0.501 and 0.631), and theinefficient phenomenon is especially salient during the R&D stage. Both thegreen innovation efficiency and conventional innovation efficiency in China’shigh-tech manufacturing industry have significant regional differences, whichare higher in the eastern region than in the central and western regions.During the R&D stage, such regional differences are more salient.When taking environmental pollution into consideration, green innovationefficiency in China’s high-tech manufacturing industry started to declineafter 2009, followed by a slow growth; provincially, Beijing, Shanghai, InnerMongolia, and Hainan have a green innovation efficiency of 1.000 in theirhigh-tech manufacturing industries. When not taking environmental pollutioninto account, conventional innovation efficiency in China’s high-techmanufacturing industry still exhibited a growing trend in 2009; provincially,only Beijing and Shanghai reached a 1.000 conventional innovation efficiencyin their high-tech manufacturing industries.#### 5. Discussion##### 5.1. Theoretical ImplicationsFew existing studies focusing on innovation efficiency in China’s high-techmanufacturing industry have considered environmental pollution, and thefindings of these studies often highlight the economic benefits of innovationbut ignored its environmental benefits. Luo et al. [32] proposed theinnovation value chain model for China’s high-tech manufacturing industry butfailed to consider the environmental pollution. Luo et al. [33] proposed thegreen innovation value chain model for the industrial sector withoutconsidering supplementary inputs. In this paper, environmental pollution andsupplementary inputs are both incorporated into the analytical framework forinnovation efficiency in the high-tech manufacturing industry, and aconceptual model for green innovation in the high-tech manufacturing industryis established based on the IVC perspective. In addition, green innovationefficiency in the high-tech manufacturing industry is further decomposed intoR&D efficiency and achievement transformation efficiency, deepening theunderstanding of variations and regional differences in green innovationefficiency in China’s high-tech manufacturing industry. Using the network DEAmodel, this paper also analyzes the differences of estimation results betweengreen innovation efficiency and conventional innovation efficiency, whichconfirmed that the estimation method for conventional innovation efficiencycan produce bias, thereby providing a reference for improving estimationmethods for innovation efficiency.##### 5.2. Practical ImplicationsThis research shows that both the green innovation efficiency and conventionalinnovation efficiency in China’s high-tech manufacturing industry arerelatively low. Despite substantial technological advancement in areas ofmanned space programs and the BeiDou Navigation Satellite System, the overalllevel of China’s research and development is still considerably low and Chinarelies heavily on foreign countries in terms of core technologies, asmanifested by chip importation. On the one hand, China’s inputs in basicresearch and R&D programs with original innovations are lower thaninternational levels. On the other hand, China’s high-tech manufacturingindustry is less concentrated, as manifested by a large number of small-sizedmanufacturing firms with no proprietary core technologies and limited R&Dinputs that are distributed to numerous firms, leading to a severe shortage ofR&D inputs for original innovations. Regionally, the eastern region in Chinaenjoys more abundance technological resources and has a relativelysophisticated mechanism to transform technological achievements in comparisonwith the central and western regions. As such, the eastern region produceshigher technological outputs and economic benefits.With in-depth implementation of the Circular Economy Promotion Law of thePeople’s Republic of China, more stringent requirements pertaining topollutant discharges will be imposed on manufacturing firms in China. To meetenvironmental regulations, part of the high-tech manufacturing firms may adoptmeasures like production cut and technological reformation in order to satisfythe requirement of emission reduction. Although environmental regulation mayhave caused reduced economic outputs from the high-tech manufacturing industryin the short term, in the long run, there has been no sign of declinedinnovation outputs from the high-tech manufacturing industry with theimplementation of environmental regulations. In addition, Inner Mongolia andHainan have also gained certain economic and ecological benefits throughadjusting industrial structure and protecting the ecological environment,despite their low technological inputs. This also provides a reference forother provinces seeking green innovations in their high-tech manufacturingindustries.##### 5.3. Limitations and Future ResearchIn this paper, a conceptual framework of green innovation in the high-techmanufacturing industry is constructed based on the IVC perspective; thenetwork DEA model with undesirable outputs is utilized to measure the greeninnovation efficiency in China’s provincial high-tech manufacturing industryfrom 2006 to 2015, in combination with an in-depth analysis of periodicvariations and regional differences. However, due to problems with dataavailability, this paper fails to consider factors such as energy consumption,soil and water pollution, and the use of chemicals when measuring greeninnovation efficiency in China’s high-tech manufacturing industry. In themeantime, as data on emissions of environmental pollutants by industry was nolonger provided by the China Statistical Yearbook on Environment after 2015,this paper also does not estimate green innovation efficiency in China’s high-tech manufacturing industry after 2016. In addition, this paper examines thevariations in green innovation efficiency in China’s high-tech manufacturingindustry before and after the implementation of Circular Economy Promotion Lawof the People’s Republic of China but fails to analyze the relationshipbetween environmental regulation and variations in green innovationefficiency. Subsequent studies will be carried out to establish a theoreticalanalytical framework aimed at analyzing the effect of environmental regulationon green innovation efficiency in the high-tech manufacturing industry and aneconometric model to empirically analyze the relationship betweenenvironmental laws and regulations and the variations in green innovationefficiency, thereby providing policy recommendations for improving greeninnovation efficiency in China’s high-tech manufacturing industry.#### 6. ConclusionsIn this paper, the green innovation process in the high-tech manufacturingindustry is divided into two stages, namely, the R&D stage and the achievementtransformation stage, based on the IVC perspective; a network DEA model withundesirable outputs is introduced to measure green innovation efficiency inChina’s provincial high-tech manufacturing industry; a comparative analysis ofestimation results pertaining to the green innovation efficiency andconventional innovation efficiency is carried out. The research shows that,between 2006 and 2015, except for 4 provinces of Beijing, Guangdong, InnerMongolia, and Hainan, the majority of provinces have ineffective greeninnovation process in their high-tech manufacturing industries. The annualmean value of green innovation efficiency in China’s high-tech manufacturingindustry is only 0.600; the green innovation efficiency dropped in 2009 butexhibited an upward trend in general. Regionally, the eastern region has agreen innovation efficiency in the high-tech manufacturing industry greaterthan those of the central and western regions, and regional differences in R&Defficiency are more salient than those in achievement transformationefficiency. From the perspective of innovation value chain, both the R&Defficiency and achievement transformation efficiency of China’s high-techmanufacturing industry are inefficiencies, with the low efficiency phenomenonbeing more salient in the R&D stage. Compared with the method of conventionalinnovation efficiency without considering environmental pollution, theestimation method for green innovation efficiency can not only avoid bias ofestimation results of provinces producing low pollution emissions like InnerMongolia and Hainan but also reflect the volatility in efficiency of the high-tech manufacturing industry before and after the implementation of theenvironmental law.To improve green innovation efficiency in China’s high-tech manufacturingindustry, efforts should be first concentrated on improving the R&Defficiency. Regional distribution of technological resources should beoptimized to promote coordinated development in terms of technologicalresources across the eastern, central, and western regions. Oriented towardsmarket demands, active guidance should be provided to steer the capital factortowards the R&D area and support high-tech manufacturing firms to conduct R&Dactivities. Second, equal importance should also be attached to improving theachievement transformation efficiency. The mechanism of technologicalachievement transformation should be perfected and construction ofintermediary service institutions and intermediate platforms should beadvanced in order to provide high-precision services for achievementtransformation. Communications associating with technological achievementtransformation between the eastern, central, and western regions should bepushed forward, and resource distribution pertaining to regional achievementtransformation should be optimized through measures like industry-universitycollaboration and technology transfer. Finally, the high-tech manufacturingindustry should pay equal attention to economic and environmental benefitswhile carrying out innovation processes to avoid sacrificing environmentalbenefits in exchange for short-term economic gains. Although environmentalregulations may result in a short-term reduction in economic outputs, in thelong run, environmental regulations will promote high-quality development ofthe high-tech manufacturing industry. Local governments should further improvetheir assessment systems over the circular economy and urge high-techmanufacturing firms to adopt clean technologies to reduce pollutant dischargesand thereby achieve coordinated development between technology, economy, andecology.#### Data AvailabilityThe data of this study are obtained from China Statistics Yearbook on HighTechnology Industry, China Industry Statistics Yearbook, and China StatisticalYearbook on Environment. The data can be downloaded from the website ofNational Bureau of Statistics.#### Conflicts of InterestThe authors declare that there are no conflicts of interest regarding thepublication of this paper.#### AcknowledgmentsThis study was supported by the Project of Humanities and Social Sciences ofMinistry of Education of China (18YJC630131) and the Project of Humanities andSocial Sciences Key Research Base of Ministry of Education of China(17JJD790012).

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