The potential for reducing China's carbon dioxide emissions: Role of foreign-invested enterprises

Global Environmental Change 35 (2015) 22–30

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The potential for reducing China’s carbon dioxide emissions: Role of foreign-invested enterprises§ X. Jiang a,*, K. Zhu b, S. Wang c a

School of Economics, Capital University of Economics and Business, Beijing, China University of International Business and Economics, Beijing, China c Center for Forecasting Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China b

A R T I C L E I N F O

A B S T R A C T

Article history: Received 30 October 2014 Received in revised form 7 July 2015 Accepted 19 July 2015 Available online

In this paper, we discuss the possible role of foreign-invested enterprises in the reduction of China’s carbon dioxide emissions by employing a time-series of unique input–output tables that distinguishes firm ownership and processing exports. The comparisons of carbon emission intensities indicate that although Chinese-owned enterprises experienced much faster upgrades of emission-intensity-related technologies since 1992, foreign-invested enterprises continued to outperform Chinese-owned enterprises by 10–110% in terms of sectoral emission intensities until 2010. Therefore, China may have reduced its carbon dioxide emissions by 1200–1400 million tonnes during 1992–2010, if Chineseowned enterprises could have duplicated the emission-intensity-related technologies of the corresponding foreign-invested enterprises. More specifically, China should prioritize the adoption of the thermal and wind power generation technologies of the U.S., the photovoltaic system and related national electric grid upgrading technologies of Germany, the mineral and metal production and carbon emissions control technologies of Japan, and the machinery and transportation technologies of Europe. In an ideal scenario that assumes Chinese-owned enterprises would have eventually adopted the world’s leading technologies, China could have reduced its total carbon dioxide emissions by about 50% in 2010. From a policy perspective, as foreign-invested enterprises are quite widespread; they can serve as very effective and efficient channels for technology transfer from developed to developing countries. ß 2015 Elsevier Ltd. All rights reserved.

Keywords: Carbon dioxide emissions Foreign-invested enterprises Technology transfer Input–output analysis China

1. Introduction Ever since the economic reforms in 1978, China has experienced unprecedented economic growth, with an annual GDP growth rate at 9.77%. Due to high dependency on coal for energy consumption, this economic growth also led to burgeoning growth of carbon dioxide (CO2) emissions in mainland China (abbr. as China in the following). In 1990, China emitted only 2178 million tonnes (Mt) of CO2, accounting for 10.12% of the world total. In 2010, these emissions have reached 7997 Mt, accounting for 25.38% of the world total (U.S. Energy Information Administration, 2014). Given China’s significant contribution to carbon emissions, climate change researchers have extensively studied potential ways to

§ The paper acknowledges funding from the National Natural Science Foundation of China (71103176 and 71473246). We are grateful for the comments and suggestions from the guest editors and two anonymous referees that help us to improve our paper significantly. * Corresponding author at: School of Economics, Capital University of Economics and Business, No. 121 Zhangjialu Kou, Fengtai District, Beijing, China. E-mail address: [email protected] (X. Jiang).

http://dx.doi.org/10.1016/j.gloenvcha.2015.07.010 0959-3780/ß 2015 Elsevier Ltd. All rights reserved.

reduce China’s CO2 emissions. Many studies have focused on the positive contributions of ‘‘backward’’ regions and sectors with high carbon intensities toward carbon intensity reductions as well as the role of renewable energy sources (Wu et al., 2005; Fan et al., 2007; Guan et al., 2008; Zhang and Cheng, 2009; Du et al., 2013; Liao and Cao, 2013; Xu et al., 2014a). Significant importance is given to reducing carbon intensity; as of 2011, in terms of purchasing power parities, China’s economy was still thrice as carbon intensive as those of Japan and Europe and twice as carbon intensive as that of the U.S. (Fig. 1a). Some international organizations and agreements, such as the Kyoto Protocol’s Clean Development Mechanism (CDM), the UN’s Global Environment Facility (GEF) under the UNFCCC framework, the World Bank’s Clean Technology Fund (CTF), have been focused on financing technology transfers from developed countries to developing countries (see Ghosh and Watkins (2009) for a thorough review). One often-neglected aspect, however, is the role of foreigninvested enterprises (FIEs) in technology transfer. Evidence from other developing countries suggests that FIEs, which are mostly sourced from developed countries, generally pollute less than their indigenous counterparts in developing countries for the same

X. Jiang et al. / Global Environmental Change 35 (2015) 22–30

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Fig. 1. (a) Comparisons of CO2 emission intensities between China, the main advanced economies, and the world average using purchasing power parities (in tonnes of CO2 per thousand year 2005 USD). (b) The performance of FIEs in China: FIEs’ share in China’s total industrial output of scale-above enterprises (+491% between 1990 and 2011), FIEs’ share in China’s gross goods trade volume (+240% between 1990 and 2011), and total assets of FIEs in China (in current trillion RMB). (a) Data are sourced from U.S. Energy Information Administration (http://www.eia.gov). (b) Data are sourced from the National Bureau of Statistics of China (http://www. stats.gov.cn).

output production (Mielnik and Goldemberg, 2002; Eskeland and Harrison, 2003; Peterson, 2008). This might also be the case in China, as over 80% of foreign direct investment (FDI) inflows to China come from advanced economies such as Japan, the U.S., the European Union, and the Special Administrative Regions of Hong Kong, Macau, and Taiwan. Due to the lack of official statistics on energy use and air pollution by firm ownership, however, to our knowledge, hardly any studies have discussed the CO2 reduction potentials of China from the viewpoint of the size of the emission intensity gap between FIEs and Chinese-owned enterprises (COEs). To fill the gap, numerous studies have analyzed the statistical relationship between China’s CO2 emissions and aggregate foreign direct investment (FDI) at the macro level. Based on econometric estimations, these studies suggest a positive spillover of FDI (and FIEs) on reducing energy consumption and CO2 emissions in China (Liang, 2008; Elliott et al., 2013; Lee, 2013; Zhang et al., 2014). Using unpublished firm-level data, Wang and Jin (2002) and Jiang et al. (2014a) found that FIEs in China polluted less than stateowned enterprises (SOEs) in 1999 and 2006–2007, respectively. All the above-mentioned studies, however, fail to capture temporal changes in the emission intensity gap between FIEs and COEs. In the 1990s, FIE-related outputs and trade continuously expanded in China, and in the 2000s, FIEs persistently accounted for more than 25% and 50% of China’s total industrial output of scale-above enterprises (that with annual main operating revenues above 5 million RMB) and goods trade, respectively (Fig. 1b). Meanwhile, China has significantly reduced its CO2 emission intensity, particularly in the 1990s. In contrast, the emission intensities of advanced economies remained relatively stable (Fig. 1a). Accordingly, there arise a series of questions: Did the CO2 emission intensity gap between COEs and FIEs decrease over the past two decades? If so, to what extent did it decrease, and did this rate vary from the 1990s to after 2000? Assuming the same decreasing rate persisted, when would China catch up with other developed countries in terms of emission intensity? More importantly, how does this relate to China’s potential for CO2 emissions reduction? By taking into account the heterogeneity of trade mode and firm ownership, this paper adapted the traditional environmental input–output (EIO) framework, to answer these questions. The use of the EIO model has been widely accepted to trace CO2 emissions along the production chain (Davis et al., 2011; Peters et al., 2012; Feng et al., 2013; Kanemoto et al., 2012, 2014; Lenzen et al., 2013). The traditional EIO model, however, fails to consider the heterogeneity of firm ownership and thus is unable to stress upon the role of FIEs in China’s emissions. In addition, China is characterized by a high proportion of processing exports (exceeding

50% of China’s gross exports), for which firms import parts and components from abroad under favorable tariff treatment and assemble them for export. As a result, the productions of processing exports have much lower energy consumption and CO2 emission intensities than normal productions. Without appropriate distinction of firm and trade type, the traditional EIO model may distort the picture of China’s CO2 emission patterns (Dietzenbacher et al., 2012; Su et al., 2013; Jiang et al., 2014b). Therefore, we adapted the traditional EIO model and compiled a time-series of ‘‘new’’ environmental input–output tables that distinguishes firm ownership (i.e., distinguishes FIEs from COEs) and trade mode (i.e., distinguishes processing exports from normal production), to study the issues pertaining to China’s emissions. To our knowledge, this is the first time that the heterogeneity of firm ownership has been considered in the EIO framework. To reflect the temporal changes in China’s context, our study covers the period 1992–2010, and we have compiled a series of ‘‘new’’ input–output tables for the benchmark years, namely the years when official survey-based input–output tables were available for China (1992, 1997, 2002, 2007 and 2010) (NBSC, 2014). The paper is organized as follows. In Section 2, we describe the new environment input–output (EIO) model and related data compilations. In Section 3, we analyze the emission intensity gap between FIEs and COEs and changes to it from 1992 to 2010. In Section 4, we introduce a series of scenarios that assume COEs could use technologies as advanced as that used by FIEs, to discuss the potential for CO2 emissions reductions in China. In the process, we also differentiate FIEs by their source country to provide more specific policy implications. In Section 5, we summarize our findings and conclude the paper. 2. Method and data Given that the production of processing exports emits relatively less CO2, previous studies have suggested that an input–output table distinguishing trade modes, rather than the traditional EIO model, should be employed when measuring CO2 emissions embodied in China’s exports (see, e.g., Dietzenbacher et al., 2012; Su et al., 2013; Weitzel and Ma, 2014). Considering the heterogeneity of firms, Ma et al. (2015) further distinguished firm ownership, in addition to trade mode, to measure the gross national income linked to China’s exports. In this paper, we adapt the table presented by Ma et al. (2015) and classify production in China into three classes: normal production by COEs (C), normal production by FIEs (F), and processing exports (P) (hereafter abbreviated as ‘‘CFP table’’). The adaption is mainly based on two

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X. Jiang et al. / Global Environmental Change 35 (2015) 22–30

considerations. First, the statistics on trade and firm ownership for China before 2000 are relatively weak. As a result, it is extremely hard to compile input–output tables regarding firm ownership and trade mode for the years 1992 and 1997. Second, processing exports mainly involve assembly and packaging activities, and therefore, the emission intensities of processing exports are quite similar for COEs and FIEs (Jiang et al., 2014b). We argue that not only does the distinction in the CFP table allow us to measure the heterogeneity of firm ownership, but it also eliminates the impacts of processing exports on the carbon intensities of COEs and FIEs in the sense that COEs and FIEs are involved in different proportions of processing exports and normal production. Table 1 explains the scheme of the environment CFP table. Like the traditional EIO table, in the CFP table, Z indicates intermediate delivery, Y indicates final use (including consumption, investment, and change in inventory), EX indicates exports, V indicates valueadded, X indicates outputs/inputs, and E indicates the CO2 emissions in the production process. The following superscripts are unique to the CFP table: C and F represent the normal production by COEs and FIEs, respectively, while P represents the lk processing exports. The element zlk i j in matrix z , for example, represents the intermediate inputs from sector i produced by type l (l = C, F) to sector j for production type k (k = C, F, P). It should be noted that Chinese regulations mandate that inputs imported into processing trades be tariff-free only if the goods they produce are exported. Therefore, we have XP = EXP and ZPk = 0(k = C, F, P). Based on the CFP table, the contributions of each production category to the value-added, final use, outputs, and production emissions of the entire economy can be quantified. Next, we turn to the compilation of the tables and the estimations of the CO2 emissions. Ma et al. (2015) provided very detailed description of how they compiled the Chinese input– output table distinguishing trade modes and firm ownership for year 2007. We aggregate Ma et al.’s (2015) table for processing trade modes into three production classes for 2007. We follow Ma et al. (2015) and start with the official benchmark input–output tables for 1992, 1997, 2002, and 2010. Given the information regarding trade by firm ownership and trade mode from China’s General Administration of Customs (CGAC), firm-level information on production by firm ownership from Annual Surveys of Industrial Production (ASIP), and industrial outputs/value-added data by firm

ownership at the industry level from the NBSC, we use a quadratic programming strategy to split the input–output blocks by firm type and processing exports. The aggregate numbers of the official ordinary input–output table and the balance condition are adopted as strict constraints. For the CO2 emission data, we first collect the energy use data in physical units from the corresponding Chinese energy statistics yearbooks, and then, we use the energy intermediate inputs in the CFP tables to proportionally allocate the energy use in physical units by sector and by production type (C, F, and P). Then, we follow IPCC’s (2006) framework and employ Peters et al.’s (2006) method to construct the Chinese CO2 emission satellite by sector and production type from the energy data. Please refer to Appendix A in Supplementary material for the details. 3. CO2 emission intensities of COEs and FIEs, 1992–2010 By summarizing the CO2 emissions and outputs of the CFP table and dividing them, we obtain the CO2 emission intensities by production type (C, F, and P). Note that, in this paper, we only focus on the CO2 emissions generated by the industry processes for normal production, which accounted for more than 90% of China’s total CO2 emissions (see e.g., Dietzenbacher et al., 2012). The CO2 emissions generated by processing exports and households are relatively small (together, they account for less than 10% of China’s total CO2 emissions) and are excluded from our analysis. From 1992 to 2010, the CO2 emission intensity per unit of COErelated outputs dropped from 3.35 tonnes/1000 RMB to 0.64 tonnes/1000 RMB, with a decreasing annual rate of 8.79% (Fig. 2c). The emission intensity per unit of FIE-related outputs also dropped during the same time period but at a much slower decreasing annual rate of 3.99% (from 0.86 tonnes/1000 RMB in 1992 to 0.41 tonnes/1000 RMB in 2010). As a result, the emission intensity gap between the COEs and FIEs has narrowed considerably. In 1992, the emission intensity of COEs was 289% higher than that of FIEs. In 2002, this gap decreased to 82.8%, and by 2010, it had further decreased to 54.8% (Fig. 2c). It is clear that, in general, COEs experienced faster upgrades of carbon intensity-related technologies than FIEs, especially from 1992 to 2002. The FIEs’ advantage over COEs in terms of emission intensity, however, might be caused by structural differences and may not be necessarily related to their being ‘‘cleaner’’ or less polluting than

Table 1 Framework of China’s environment input–output table distinguishing firm ownership and trade mode (CFP table).

X. Jiang et al. / Global Environmental Change 35 (2015) 22–30

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Fig. 2. Structures of normal production outputs for China: (a) Outputs of COEs in 1992, (b) outputs of FIEs in 1992; (c) CO2 emission intensities of normal production by COEs and FIEs for 1992–2010 in tonnes/1000 RMB, current prices; structures of normal production outputs for China: (d) Outputs of COEs in 2010, and (e) outputs of FIEs in 2010.

COEs within each sector. It is possible that COEs are mainly involved in carbon-intensive production (e.g., steel), whereas FIEs are mainly involved in ‘‘clean’’ production (e.g., high-tech products), resulting in an overall emission intensity advantage for FIEs (also known as the ‘‘Pollution Haven Hypothesis’’ in the literature; see e.g., Eskeland and Harrison, 2003; Cole, 2004; Javorcik and Wei, 2004; He, 2006; Levinson and Taylor, 2008). Therefore, we compare the structure of outputs by sector for normal production by COEs and FIEs at the beginning and the end of the time period of concern to our study (i.e., 1992, as shown in Fig. 1a and 1b, and 2010, as shown in Fig. 1d and 1e). For the sake of simplicity, the results are reported for the following nine sectors aggregated from the calculations based on 29 sector input–output tables: agriculture, labor-intensive products, machinery and electronic products, construction, mining, chemical and metal products, utilities, transportation, and other services (please refer to Appendix B in Supplementary material for the details of the aggregations of our CFP table). Several observations follow from the comparisons of the structures. First, for each 1000 RMB of production from 1992 to 2010, COEs persistently produced more services, and products in the agricultural, mining, and construction sectors than FIEs, while FIEs persistently produced more machinery, electronic, and laborintensive products than COEs. It is somewhat surprising that for each 1000 RMB of output, FIEs provided similar or even larger percentages than COEs of chemical, metal, and utilities products, all of which are very carbon-intensive (Guan et al., 2008; Dietzenbacher et al., 2012; Feng et al., 2013). This implies that the overall carbon intensity advantage of FIEs in normal production may not be attributed to the differences in the structures of outputs. Second, FIEs and COEs show quite different patterns of structural changes over time. For each 1000 RMB of production from 1992 to 2010, FIEs continuously expanded their shares of machinery and electronic products (+26 percentage points) and reduced their shares of labor-intensive products (20 percentage points). The structures of normal production outputs by COEs were

relatively stable. COEs slightly reduced their shares of agriculture (8 percentage points) and labor-intensive products (3 percentage points) and slightly expanded their shares for all the remaining sectors such as chemical and metal products (+3 percentage points) and utilities (+2 percentage points). Given that utilities and metal products are much more carbon-intensive than machinery and electronic products (Guan et al., 2008; Dietzenbacher et al., 2012; Feng et al., 2013), it seems that FIE production became cleaner than COE production over time. Since the structural differences are not the main drivers of the overall advantage enjoyed by FIEs, a subsequent question arises: To what extent do FIEs enjoy the advantage within each sector? Table 2 compares the CO2 emission intensities of COEs and FIEs in 1992 and 2010 for the 9 aggregated sectors, as well as 16 disaggregated manufacturing sectors. We place emphasis on the manufacturing sectors because they fall mainly in the FIE domain. Despite the variety across sectors, the gaps in sectoral emission intensities between FIEs and COEs have narrowed over time. This suggests that COEs experienced relatively faster upgrades in carbon emissions-related technologies across sectors than did FIEs during 1992–2010. Until 2010, however, FIEs still outperformed COEs by 10–110% in terms of sectoral emission intensities. 4. Potential for reducing China’s CO2 emissions from the perspective of the FIEs’ emission intensity advantage 4.1. Potential for reducing China’s CO2 emissions, 1992–2010 Given the emission intensity gap between COEs and FIEs, in this section, we quantify the extent of potential CO2 emissions reduction for China if COEs were to bridge this gap. If Ek (k = C, F, P) denotes the sectoral CO2 emissions for each production type, c 1 we have g k ¼ Ek ðX k Þ ðk ¼ C; F; PÞ as the CO2 emission intensity per unit of output, and the CO2 emitted from normal production of COEs is denoted by EC = gC  XC. Assuming that the average emission

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X. Jiang et al. / Global Environmental Change 35 (2015) 22–30

Table 2 CO2 emission intensities per unit output, in current prices (tonnes/1000 RMB). 2010 Emission intensities

1992 Emission intensities

Agriculture Mining Labor-intensive products Food products and tobacco Textile goods Wearing apparel, leather and furs Sawmill and furniture Pulp, paper and printing products Chemical and metal products Coke and refined petroleum Chemical industry Nonmetal mineral products Metals smelting and processing Metal products Machinery and electronic products Machinery and equipment Transport equipment Electric equipment and machinery Electronics and telecommunication Instruments and office machinery Crafts and other manufacturing Utilities Construction Transportation Other services Weighted Average

COEsFIEs

COEs

FIEs

Difference

228% 242% 382% 243% 259% 510% 554% 231% 121% 230% 255% 56% 261% 429% 238% 188% 283% 245% 548% 186% 394% 272% – 448% 290%

0.10 0.40 0.10 0.09 0.10 0.03 0.06 0.21 1.17 0.42 0.52 3.07 1.49 0.06 0.05 0.08 0.05 0.02 0.02 0.03 0.05 7.22 0.04 1.03 0.06

0.09 0.34 0.07 0.08 0.09 0.03 0.05 0.14 0.70 0.27 0.29 2.77 1.07 0.05 0.03 0.06 0.03 0.02 0.02 0.03 0.04 6.40 0.03 0.49 0.05

11% 18% 53% 13% 13% 18% 19% 56% 68% 55% 77% 11% 40% 24% 61% 28% 66% 10% 9% 9% 12% 13% 14% 110% 23%

289%

0.64

0.41

55%

COEs

FIEs

Difference

0.72 3.64 1.39 1.44 1.23 0.53 2.09 1.70 6.91 3.30 3.61 15.17 8.74 0.87 1.07 1.27 0.47 0.75 0.62 0.53 2.09 58.57 0.45 3.69 0.61

0.22 1.06 0.29 0.42 0.34 0.09 0.32 0.51 3.13 1.00 1.02 9.70 2.42 0.16 0.32 0.44 0.12 0.22 0.10 0.19 0.42 15.75 – 0.67 0.16

3.35

0.86

FIEs

COEsFIEs FIEs

*There were no FIEs in the construction sector in 1992. The average is weighted by the sectoral output.

intensities from the normal production by COEs mirror those of the normal production of FIEs across sectors, that is, g C1 ¼ g F , and that the sectoral outputs remain unchanged, we would have P EC1 ¼ g C1  X C . The change, ðEC  EC1 Þ, is the potential for China’s CO2 emissions reduction, provided the COEs would have duplicated the average emission-intensity-related technology levels attained by the FIEs. Fig. 3 gives the sectoral CO2 emissions from normal production of COEs and the corresponding reduction potentials through duplications. The results in Fig. 3 suggest that the chemical and metal products, utilities, and transportation sectors were the main sources of CO2 emissions, and thus, these sector groups presented the best potential for emission reduction in 1992–2010. Note that the COEs’ potential for CO2 emissions reductions through duplication of the emission intensities of the corresponding FIEs

is determined by both the size of COEs’ production and the degree of emission intensity gaps between the COEs and FIEs. In 1992– 2002, the sizes of COEs’ production were smaller than those in 2002–2010, but the emission intensity gaps between the COEs and FIEs were larger. In 2002–2010, the emission intensity gaps shrank, but the sizes of COEs’ production became much larger. As a result, the total potential for CO2 emissions reductions, which we calculated for 1992–2010, was relatively stable for China, at around 1200–1400 Mt. One of the advantages of the EIO model is that it can trace CO2 emissions along the value chain to the final uses. Many researchers have argued that consumption-based emissions rather than production-based emissions should be taken into account when assigning responsibilities for emission reduction. That is, consumers rather the producers should be responsible for the CO2

Fig. 3. (a) Sectoral CO2 emissions of COEs by normal production, 1992–2010, Business as usual (BAU) scenario and (b) reduction potentials of CO2 emissions of COEs by normal production, 1992–2010, under a scenario assuming that COEs duplicate the emission intensities of the corresponding FIEs at the sectoral level.

X. Jiang et al. / Global Environmental Change 35 (2015) 22–30

emissions embodied in exports (Peters et al., 2011; Davis et al., 2011; Kanemoto et al., 2012, 2014; Feng et al., 2013). Therefore, in the following statements, we discuss the potential for reducing China’s CO2 emissions linked to different final demand categories, that is, consumption, investment, and exports. Let Alk ¼ d1 Z lk ðX k Þ ðl ¼ C; F;

k ¼ C; F; PÞ be the input coefficients, g k ¼

1

d Ek ðX k Þ ðk ¼ C; F; PÞ be the CO2 emission intensities generated per unit of output, and B ¼ ðI  AÞ1 be the Leontief inverse. Then, the CO2 emissions embodied in the final demand categories are 0 CC 1 0 C 1 BCF BCP B YC þ YIC þ EX C FC FF FP F F F C (1) E˜ ¼ ð g gF gP Þ  @ B B B A  @ YC þ YI þ EX A 0 0 I EX P where YC, YI, and EX represent consumption, investment (and changes of inventory), and exports, respectively. Assuming that COEs duplicate the average carbon intensities of FIEs for normal cC ¼ g cF , the emissions embodied in the final production, that is, g 1

demand categories become 0 CC BCF B C E˜1 ¼ ð g 1 g F g P Þ  @ BFC BFF 0 0

1 0 C 1 YC þ YIC þ EX C BCP F FP A  @ F F A (2) B YC þ YI þ EX I EX P P Note the reduction in CO2 emissions ðE˜  E˜1 Þ is identical with P C the abovementioned term, ðE  EC1 Þ, since both assume the same duplications of the emission intensities of FIEs by COEs for normal production, and the other values (outputs/final demands) remain unchanged. While the total reduction potentials for CO2 emissions under each scenario are the same as those in Fig. 3, Fig. 4 provides an alternative way to understand the potential for CO2 emissions reductions, namely by final demand category. In 1992– 2002, the final demand category ‘‘consumption,’’ which showed the highest emission reductions, would have benefitted the most from the duplication of emission intensities, followed by investment and exports. Conversely, in 2002–2010, investment would have benefitted the most, followed by consumption and exports. 4.2. FIEs by source country and their roles in future CO2 emissions reduction in China Although the potential for CO2 emissions reductions would remain stable at 1200–1400 Mt in absolute terms, the pattern would be completely different if we were to evaluate the reduction potential as a share in total CO2 emissions (i.e., if we were to

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consider the ratio of the data in Fig. 3b and 3a, or that of the data in Fig. 4b to 4a). The duplications of FIEs’ emission intensities by COEs would have reduced China’s CO2 emissions by 65% in 1992. In 2010, however, this degree of emission reduction decreased to 20% only. This difference is caused by the narrowed advantage of FIEs over COEs in terms of emission intensities, as suggested in Section 3. In other words, COEs experienced a much more rapid decrease in emission intensities than did FIEs in 1992–2010. More specifically, the emission intensities of COEs for normal production decreased particularly rapidly in 1992–2002, and this rate continued to decrease at a slower speed since 2002 (see Fig. 2c). Assuming the decreasing rates of emission intensities for both COEs and FIEs remain the same as those of 2002–2010, COEs may catch up with the FIEs in terms of emission intensities within the next 20 years. This estimation regarding the time of 20 years, however, should be interpreted very carefully. On the one hand, it would become increasingly difficult to maintain the same decreasing rate of emission intensities for COEs, in the sense that COEs have already been adopting quite advanced technologies. On the other hand, catching up with FIEs does not necessarily translate into catching up with the developed countries. The issue is that the average emission intensities of FIEs do not represent the levels observed in developed countries; about half the FDI inflow into mainland China is from the Special Administrative Region of Hong Kong, while the ‘‘real’’ total FDI inflow from all the developed countries, such as the U.S., Europe, and Japan, only accounts for less than 25% of China’s total FDI inflow after China implemented its Open Door Policy in 1978 (NBSC, 2014). Therefore, in this section, we distinguish FIEs by their sources (i.e., home countries) and discuss to what extent China may have reduced its CO2 emissions if COEs were to duplicate the emission-intensity-related technologies of FIEs by source country. We take the year 2010, the most recent year in the CFP table, as the benchmark. We select five developed countries having the top five FDIs in China, namely the U.S., Japan, South Korea, Germany, and France, as our representative cases. Unfortunately, there are no available statistics on the emission intensities of FIEs in China by source country. Given the fact that FIEs are often endowed with best practice technologies in their home countries (Ramstetter, 2013; Sultan, 2013), in this paper, we rely on the emission intensities of these five countries as the approximation of the CO2 intensities of FIEs originating from these countries. Following similar procedures as shown in Section 4.1, we simulate COEs’ CO2 emissions under these five scenarios at both the sectoral and the aggregate levels (please refer to Appendix C in Supplementary material for detailed calculations). Table 3 summarizes our simulation results.

Fig. 4. (a) CO2 emissions embodied in final demand by final demand category, 1992–2010, Business as usual (BAU) scenario, (b) reduction potentials of CO2 emissions embodied in final demand, 1992–2010, under a scenario assuming that COEs duplicate the emission intensities of the corresponding FIEs at the sectoral level. Due to the discrepancy in the IO data (i.e., final demand + intermediate deliveries 6¼ total output), the sum of CO2 emissions for consumption embodied in the final demand categories mentioned differs slightly from the total CO2 emissions generated by normal production for all the sectors (as shown in Fig. 3).

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The results in Table 3 indicate that China’s total CO2 emissions from total normal production of COEs would have decreased by 1000–5000 Mt in 2010, if COEs could have adopted the emission intensity related technologies of FIEs sourced from different countries. The results by country provide some interesting insights. If the COEs had duplicated the emission intensity related technologies of the French FIEs, China’s total CO2 emissions would have reduced by 5190 Mt. This number is as high as 66.2% of China’s total CO2 emissions in 2010. For technology replications similar to the Japanese and German FIEs, China’s CO2 emissions would have reduced by around 4000 Mt, accounting for more than half of China’s total emissions in 2010. Even for the scenarios with smaller reduction potentials, such as the U.S. or South Korean cases, the CO2 emissions would have reduced by 1000–1500 Mt, accounting for 12–20% of China’s total emissions in 2010. By sector, chemical and metal products, utilities, and transportation present the most potential for reducing COEs’ carbon intensities close to international standards through the duplication of advanced technologies. Thus far, the measurements in this section are conducted from the perspective of CO2 emissions generated in the production process. Table 4 also provides the potential for CO2 emissions reductions by final demand category, that is, from the perspective of consumption-based emissions. While the patterns of emission reductions across scenarios are quite similar to those seen in Table 3, it is important to notice how technological duplications, for example, would influence the emissions embodied in exports. On the one hand, China has been criticized for its sizable emissions, both internal and external. On the other hand, China argues that a considerable proportion of these emissions is linked with exports, which are consumed by other (especially developed) countries. According to our calculations, the emissions embodied in Chinese exports are indeed considerable; for 2010, this number was 1624 Mt, accounting for 21.6% of China’s total CO2 emissions. The duplication of advanced technologies, however, would seriously reduce export-linked emissions. The duplications of French technologies, for example, could have cut China’s emissions

embodied in exports from 1624 Mt to 521 Mt in 2010. Thus, from the perspective of global climate change mitigation, discussing technology transfer is likely to be more constructive than debating about emissions reductions responsibilities across countries. 4.3. Discussion The extent of CO2 emissions reduction potentials suggested in Section 4.2 provided very useful policy implications for future CO2 emissions reduction in China. Before delving into the policies, it should be noted that, due to data limitations, our ‘‘new’’ input– output tables and the corresponding measurements were conducted at a relatively aggregated level. One of the consequences of this is that the advantage enjoyed by FIEs in terms of lower emission intensities might be caused by subsector structural differences (Su et al., 2010) and energy mix. For example, the potential for emission reductions in the electricity and steam sector from replications of Japanese or European technologies arises not only from the electricity generation technology (or efficiency) gaps between China and those countries, but also from the energy mix differences in electricity generation. In 2010, 74.1% of French electricity was generated from nuclear power, while the corresponding shares for Japan and Germany were around 30% (World Nuclear Association, 2014). In contrast, China relies to a great degree on thermal power for its electricity generation (79.2% in 2010), while ‘‘clean’’ nuclear power, hydropower, and wind power together only accounted for 20.8% of the total electricity generation in 2010. For thermal power generation, China may perhaps benefit the most from the experience of the U.S., as thermal power in the U.S. also accounted for around 70% of U.S. electricity generation (i.e., both countries shares a similar subsector structure). China’s emissions reduction potential due to duplication of technologies typically used in the U.S. electricity sector is estimated to be 348 Mt, accounting for 11.6% of CO2 emissions generated by the Chinese electricity sector in 2010. In addition to the thermal power plants, great importance should be attached to the possible role of technological upgrades in

Table 3 Potential for China’s CO2 emissions reductions by sector, 2010 (in million tonnes). Scenarios assuming that COEs duplicate the emission intensities of FIEs from

Agriculture Mining Labor-intensive products Food products and tobacco Textile goods Wearing apparel, leather, and furs Sawmill and furniture Pulp, paper, and printing products Chemical and metal products Coke and refined petroleum Chemical industry Nonmetal mineral products Metals smelting and processing Metal products Machinery and electronic products Machinery and equipment Transport equipment Electric equipment and machinery Electronics and telecommunication Instruments and office machinery Crafts and other manufacturing Utilities Construction Transportation Other services Total

U.S.

Japan

South Korea

Germany

France

19 23 48 0 32 7 17 9 807 21 70 240 519 2 11 5 3 0 0 0 2 349 6 29 20 1079

15 0 61 33 6 2 3 21 1340 67 187 525 552 8 42 29 17 2 1 0 0 2434 7 193 48 4141

48 0 30 30 8 7 1 16 825 52 212 245 308 8 39 24 15 1 0 0 1 1032 2 243 80 1557

7 5 60 20 13 0 3 24 1401 43 163 585 602 8 56 31 19 2 1 0 3 2100 15 274 56 3976

4 87 44 2 14 0 3 29 1816 45 262 720 781 8 38 28 17 1 0 0 6 2842 21 268 71 5190

X. Jiang et al. / Global Environmental Change 35 (2015) 22–30

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Table 4 CO2 emissions embodied in final demand by final demand category, 2010 (in million tonnes). Business as usual scenario

Consumption Investment Exports

2214 3999 1624

Scenarios assuming that COEs duplicate the emission intensities of FIEs from Average

U.S.

Japan

South Korea

Germany

France

1786 3262 1316

1988 3301 1417

901 1831 745

1766 3138 1283

1002 1865 793

631 1220 521

*Due to the discrepancy in the data, the sum of CO2 emissions embodied in consumption, investment, and exports does not equal the total CO2 emissions.

renewable energy power plants in future CO2 emissions reduction in China. Electricity generation has long been the major source of Chinese CO2 emissions. Take the instance of the year 2010, when the electricity and steam generation sectors emitted 3277 Mt CO2, accounting for 43.67% of China’s total emissions. Increasing the proportion of renewable energy will efficiently reduce China’s CO2 emissions, as renewables generally emit much less CO2 compared to conventional thermal-powered electricity generation. According to the German Wind Energy Association (2014), each kilowatt hour (kWh) of wind power emitted only 24 g of CO2 in 2014, while each kWh generated from natural gas, hard coal, and brown coal emitted 377, 810, and 1000 g of CO2, respectively. It is therefore not surprising that the Chinese government plans to significantly expand the share of renewable energy production, especially for electricity generation. According to the roadmap published by the China National Renewable Energy Center (CNREC, 2014), the share of installed renewable power capacity in China’s electricity generation capacity will reach around 40%, 50%, and 60% before 2020, 2030, and 2050, respectively. Among them, the installed wind power (including both onshore and offshore wind) capacity is expected to expand from 91 gigawatt (GW) in 2013 to 200 GW in 2020, whereas the installed photovoltaic (PV, especially distributed PV) power capacity is expected to expand from 6 GW to 70 GW during 2013–2020. In spite of the great importance attached by the central government to the renewable power sector, the technologies employed by China in this sector are still far behind those of the U.S. and Europe. In Germany, for example, PV-generated power totaled 35.2 terawatt hours (TWh) in 2013, covering approximately 7% of Germany’s electricity demand, or even 35% of the momentary electricity demand, on sunny weekdays. In 2013, wind energy contributes to about 9% of electricity consumption, supplying power to more than 15 million (three-person) households in Germany. In the U.S., lower wind speed turbines have become mainstream and have rapidly gained market share: in 2012, more than 50% of installations used IEC Class 3 and Class 2/3 turbines, while in 2013, based on the small sample of projects installed that year, the percentage increased to 90%. In brief, the thermal power generation technologies employed in the U.S. (e.g., the technologies used to convert coal into gas and remove impurities before burning coal; see Ghosh and Watkins (2009)), the wind power generation technologies employed in the U.S. (e.g., low wind speed turbines; see, Wiser and Bolinge (2014)), and the successful penetration of the PV system in German households (e.g., the decentralized PV electricity generation systems and their successful coupling with the national electricity grid; see Wirth (2014)) would help China make huge CO2 emissions reductions in electricity generation and further mitigate global climate change. In addition to electricity generation, cooperation among FIEs and their indigenous counterparts in other carbon-intensive sectors, such as cement, steel, and transportation, is also the need of the hour. In an ideal scenario, which assumes that COEs in carbon-intensive sectors other than electricity generation would have adopted the world’s leading technologies, China could have reduced its total CO2 emissions by as much as 1500–2000 Mt,

accounting for about 1/5th of its total CO2 emissions in 2010. Specifically, the mineral and metal production and carbon control technologies of Japan (e.g., clinker kiln substitution of shaft kilns to new dry rotary kilns with suspension pre-heaters or precalciners in the cement industry; see Xu et al. (2014b)) and the energy-saving technologies of Europe’s transportation sector (e.g., promotion of electric vehicles and improvements in battery technology; see He and Chen (2013)) could be priority acquisitions for China to help it meet its targets for CO2 emissions reduction. It should be noted that China has huge regional disparities in terms of economic development as well as CO2 emission intensities. With 40% of the country’s population, the eastern coastal region (including Beijing, Fujian, Guangdong, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, and Zhejiang), for example, accounted for about 60% of national GDP, 80% of FDI inflows, and 90% of trade volume in 2010. The eastern region is often endowed with quite advanced technologies, while the central and western regions still use old-fashioned technologies (see, e.g., Su and Ang, 2010; Feng et al., 2013; Weitzel and Ma, 2014). Therefore, higher priority should be assigned to technological upgradation in the central and western regions. Moreover, China is just one of the developing countries having lower emission intensities than developed countries. Although the importance of technology transfer from developed countries to developing countries has been widely recognized as a vital climate change mitigation measure (Ghosh and Watkins, 2009; Dimitrov, 2010; Watson and Byrne, 2011; Dechezlepretre et al., 2011; Ockwell and Mallett, 2012), our view on technology transfer is broader; we consider the potential role of numerous FIEs as a bridge for technology spillovers and transfers. In this context, mergers and acquisitions, technical exchanges, and labor pooling between foreign and domestic enterprises could assume added importance in discussions on technology transfers on future international climate change mitigation platforms. 5. Conclusions Based on a series of unique input–output tables that distinguish firm ownership and processing exports in China, this paper discussed the potential for China’s CO2 emissions reductions from technology exchange of emission-intensity-related technologies from FIEs to COEs. We first quantified the differences in production emission intensities by FIEs and COEs. The results indicate that from 1992 to 2010, COEs experienced fast upgrades in emissionrelated technologies and structural changes, which helped them reduce their respective emission intensities very rapidly. Thus, the emission intensity advantage enjoyed by FIEs over COEs was narrowed. However, in 2010, FIEs continued to outperform COEs by 10–110% across sectors in terms of emission intensities. Between 1992 and 2010, China had the potential to reduce its CO2 emissions by 1200–1400 Mt, provided COEs were able to fully duplicate the average emission-intensity-related technologies of FIEs. We further distinguished FIEs by source country (mainly developed countries) and discussed the reduction potentials for the most recent year in this study, 2010. We assumed that all COEs

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could duplicate the advanced technologies of FIEs from different developed countries. The results highlight the role of FIEs in reducing China’s CO2 emissions; in 2010, China may have reduced its emissions by about 25% (2000 Mt) if the COEs could have duplicated the best Japanese, American or European technologies in their respective sectors. If we take into account of the energy mix and duplication of technology in electricity generation industry (e.g., expansion of electricity generation from renewable energies), the emission reduction potential could even reach 50%. In our view, the numerous FIEs sourced from developed countries can play a significant role in global climate change mitigation, by serving as effective and efficient channels for the technology spillover and transfer from developed countries to their developing counterparts. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.gloenvcha.2015.07.010. References China National Renewable Energy Center (CNREC), 2014. China Wind, Solar and Bioenergy Roadmap 2050. Available at: http://www.cnrec.org.cn/cbw/zh/ 2014-12-25-456.html. Cole, M.A., 2004. Trade, the pollution haven hypothesis and the environmental Kuznets curve: examining the linkages. Ecol. Econ. 48, 71–81. Davis, S., Peters, G., Caldeira, K., 2011. The supply chain of CO2 emissions. Proc. Natl. Acad. Sci. U. S. A. 108, 18554–18559. Dechezlepretre, A., Glachant, M., Hascic, I., Johnstone, N., Meniere, Y., 2011. Invention and transfer of climate change-mitigation technologies: a global analysis. Rev. Environ. Econ. Policy 5, 109–130. Dietzenbacher, E., Pei, J.S., Yang, C.H., 2012. The environmental pains and economic gains of outsourcing to China. J. Environ. Econ. Manage. 64, 88–101. Dimitrov, R.S., 2010. Inside UN climate change negotiations: the Copenhagen conference. Rev. Policy Res. 27, 795–821. Du, K., Lu, H., Yu, K., 2013. Sources of the potential CO2 emission reduction in China: a nonparametric meta frontier approach. Appl. Energy 115 (15), 491– 501. Elliott, R.J.R., Sun, P.Y., Chen, S.Y., 2013. Energy intensity and foreign direct investment: a Chinese city-level study. Energy Econ. 40, 484–494. Energy Information Administration, 2014. International Energy Statistics. Available at: http://www.eia.gov/cfapps/ipdbproject/IEDIndex3. cfm?tid=91&pid=46&aid=31. Eskeland, G.S., Harrison, A.E., 2003. Moving to greener pastures? Multinationals and the pollution haven hypothesis. J. Dev. Econ. 70 (1), 1–23. Fan, Y., Liu, L.C., Wu, G., Tsai, H.T., Wei, Y.M., 2007. Changes in carbon intensity in China: empirical findings from 1980-2003. Ecol. Econ. 62, 683–691. Feng, K., Davis, S.J., Li, X., Sun, L., Guan, D., Liu, W., Zhu, L., Hubacek, K., 2013. Outsourcing CO2 within China. Proc. Natl. Acad. Sci. U. S. A. 110, 11654– 11659. German Wind Energy Association, 2014. Wind Moves Germany: Information on the Energy Transition. Online publication. Available at: http://www.windenergie.de/sites/default/files/download/publication/wind-movesenergiewende-wind-moves-germany/flyer_windbewegt_engl.pdf. Ghosh, A., Watkins, K., 2009. Avoiding Dangerous Climate Change: Why Financing for Technology Transfer Matters GEG Working Paper 2009/53. Guan, D., Hubacek, K., Weber, C.L., Peters, G.P., Reiner, D.M., 2008. The drivers of Chinese CO2 emissions from 1980 to 2030. Global Environ. Chang. 18, 626– 634. He, J., 2006. Pollution haven hypothesis and environmental impacts of foreign direct investment: the case of industrial emission of sulfur dioxide (SO2) in Chinese provinces. Ecol. Econ. 60 (1), 228–245. He, L.Y., Chen, Y., 2013. Thou shalt drive electric and hybrid vehicles: scenario analysis on energy saving and emission mitigation for road transportation sector in China. Transp. Policy 25, 30–40. IPCC, 1996. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual. Intergovernmental Panel on Climate Change. Javorcik, B.S., Wei, S.J., 2004. Pollution havens and foreign direct investment: dirty secret or popular myth? Contrib. Econ. Anal. Policy 3 (2), 1244. Jiang, L.L., Lin, C., Lin, P., 2014a. The determinants of pollution levels: firm-level evidence from Chinese manufacturing. J. Comp. Econ. 42, 118–142. Jiang, X., Zhu, K., Green, C., 2014b. The energy efficiency advantage of foreigninvested enterprises in China and the role of structural differences. China Econ. Rev., http://dx.doi.org/10.1016/j.chieco.2014.11.009 (forthcoming). Kanemoto, K., Lenzen, M., Peters, G., Moran, D., Geschke, A., 2012. Frameworks for comparing emissions associated with production, consumption, and international trade. Environ. Sci. Technol. 46 (1), 172–179.

Kanemoto, K., Moran, D., Lenzen, M., Geschke, A., 2014. International trade undermines national emission reduction targets: new evidence from air pollution. Global Environ. Chang. 24 (4), 52–59. Lee, J.W., 2013. The contribution of foreign direct investment to clean energy use, carbon emissions and economic growth. Energy Policy 55, 483–489. Lenzen, M., Dey, C., Foran, B., Widmer-Cooper, A., Ohlemu¨ller, R., Williams, M., Wiedmann, T., 2013. Modelling interactions between economic activity, greenhouse gas emissions, biodiversity and agricultural production. Environ. Model. Assess. 18 (4), 377–416. Levinson, A., Taylor, M.S., 2008. Unmasking the pollution haven effect. Int. Econ. Rev. 49 (1), 223–254. Liang, F.H., 2008. Does foreign direct investment harm the host country’s environment? Evidence from China. SSRN Working Paper. , http://dx.doi.org/ 10.2139/ssrn.1479864. Available at: http://ssrn.com/abstract=1479864. Liao, H., Cao, H., 2013. How does carbon dioxide emission change with the economic development? Statistical experiences from 132 countries. Global Environ. Chang. 23, 1073–1082. Ma, H., Wang, Z., Zhu, K., 2015. Domestic value-added in China’s exports and its distribution by firm ownership. J. Comp. Econ. 43 (1), 3–18. Mielnik, O., Goldemberg, J., 2002. Foreign direct investment and decoupling between energy and gross domestic product in developing countries. Energy Policy 30, 87–89. National Bureau of Statistics of China (NBSC), 2014. National Statistics. Available at: http://data.stats.gov.cn/ Ockwell, D.G., Mallett, A., 2012. In: Ockwell, D.G., Mallett, A. (Eds.), Low-carbon Technology Transfer: From Rhetoric to Reality. Routledge, Abingdon/New York. Peters, G., Marland, G., Le Quere, C., Boden, T., Canadell, J.G., Raupach, M.R., 2012. Rapid growth in CO2 emissions after the 2008-2009 global financial crisis. Nat. Clim. Change 2, 2–4. Peters, G.P., Weber, C., Liu, J., 2006. Construction of Chinese Energy and Emissions Inventory. Norwegian University of Science and Technology, Trondheim. Peters, G., Minx, J.C., Weber, C.L., Edenhofer, O., 2011. Growth in emission transfers via international trade from 1990 to 2008. Proc. Natl. Acad. Sci. U. S. A. 108 (45), 18554–18559. Peterson, S., 2008. Greenhouse gas mitigation in developing countries through technology transfer: a survey of empirical evidence. Mitig. Adapt. Strateg. Glob. Change 13, 283–305. Ramstetter, E.D., 2013. MNEs and energy efficiency in Southeast Asian manufacturing. Asian Econ. Pap. 12 (3), 120–147. Su, B., Ang, B.W., 2010. Input-output analysis of CO2 emissions embodied in trade: the effects of spatial aggregation. Ecol. Econ. 70, 10–18. Su, B., Ang, B.W., Low, M., 2013. Input-output analysis of CO2 emissions embodied in trade and the driving forces: processing and normal exports. Ecol. Econ. 88, 119–125. Su, B., Huang, H.C., Ang, B.W., Zhou, P., 2010. Input-output analysis of CO2 emissions embodied in trade: the effects of sector aggregation. Energy Econ. 32 (1), 166–175. Sultan, R., 2013. The determinants of energy intensity for the design of environmental strategies in the Mauritian textile sector. Int. J. Sustain. Econ. 5 (2), 140–156. Wang, H., Jin, Y., 2002. Industrial Ownership and Environmental Performance: Evidence From China. World Bank Policy Research Working Paper No. 2936. Watson, J., Byrne, R., 2011. China’s low carbon technology ambitions: the relationship between indigenous innovation and technology transfer. In: Paper for Research Network ‘‘Governance in China’’ and Association for Social Science Research on China (ASC) Joint International Conference, University of Hamburg, Germany, 9–11 December. Weitzel, M., Ma, T., 2014. Emissions embodied in Chinese exports taking into account the special export structure of China. Energy Econ. 45, 45–52. Wiser, R., Bolinge, M., 2014. 2013 Wind Technologies Market Report. U.S. Department of Energy. Available at: www.osti.gov/bridge. Wirth, H., 2014. Recent Facts about Photovoltaics in Germany. Working paper. The Fraunhofer Institute for Solar Energy Systems. Available at: http://www.ise. fraunhofer.de/en/publications/veroeffentlichungen-pdf-dateien-en/ studien-und-konzeptpapiere/recent-facts-about-photovoltaics-in-germany.pdf. World Nuclear Association, 2014. Nuclear share figures, 2003–2013. Available at: http://www.world-nuclear.org/info/Facts-and-Figures/ Nuclear-generation-by-country/ Wu, L., Kaneko, S., Matsuoka, S., 2005. Driving forces behind the stagnancy of China’s energy-related CO2 emissions from 1996 to 1999: the relative importance of structural change, intensity change and scale change. Energy Policy 33, 319–335. Xu, J.H., Fleiter, T., Fan, Y., Eichhammer, W., 2014a. CO2 emissions reduction potential in China’s cement industry compared to IEA’s Cement Technology Roadmap up to 2050. Appl. Energy 130, 592–602. Xu, S.C., He, Z.X., Long, R.Y., 2014b. Factors that influence carbon emissions due to energy consumption in China: decomposition analysis using LMDI. Appl. Energy 127 (15), 182–193. Zhang, C., Guo, B., Wang, J., 2014. The different impacts of home countries characteristics in FDI on Chinese spillover effects: based on one-stage SFA. Econ. Model. 38, 572–580. Zhang, X.P., Cheng, X.M., 2009. Energy consumption, carbon emissions, and economic growth in China. Ecol. Econ. 68 (10), 2706–2712.