The driving factors of China's embodied carbon emissions

Technological Forecasting & Social Change 153 (2020) 119930

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The driving factors of China's embodied carbon emissions A study from the perspectives of inter-provincial trade and international trade

T

Zhengning PU , Shujing YUE, Peng GAO ⁎

School of Economics and Management, Southeast University, 2 Si Pai Lou, Nanjing, Jiangsu Province 210000, China

ARTICLE INFO

ABSTRACT

Keywords: International trade Inter-provincial trade Embodied carbon emissions Driving factor Structural decomposition analysis

Based on China's regional extended input-output tables from 2002 to 2012, we built non-competitive inputoutput models in order to measure China's embodied carbon emissions (ECEs) from international trade and interprovincial trade, while also using a structural decomposition analysis (SDA) approach to examine the driving factors of variation in ECEs from international trade and inter-provincial trade. The findings were as follows. First, the ECEs of China's 30 provincial administrative regions all first increased and then decreased under four modes of trade. Second, there was notable carbon leakage in both international trade and inter-provincial trade, and this was particularly severe in terms of the transfer of ECEs in inter-provincial trade. Third, judging from both a consolidated perspective and using the SDA of driving factors of ECEs under four modes of trade, intermediate production technology, trade structure, and total trade volume all promoted the growth of ECEs. Specifically, total trade volume was the primary driving factor of the growth of ECEs, whereas the direct carbon emission coefficient had a significant inhibiting effect on the growth of ECEs.

1. Introduction With the rapid development of international trade and economic globalization, economic convergence between different countries continues to deepen. Consequently, the production of specific products is constantly subdivided and gradually separated from their consumption. As a result, this has promoted the allocation of global resources and the complementarity of advantages between different countries. In this process, their embedded pollution is transferred with their movement across different countries. The transfer of embodied carbon emissions (ECEs) is particularly severe, further aggravating the carbon leakage in international trade. This issue has subsequently become a focus of discussion in various conferences on global climate change (Kuik and Gerlagh, 2003; Babiker, 2005; Kuik and Hofkes, 2010; Aichele and Felbermayr, 2015; Zhang et al., 2017). Most previous studies on the ECEs produced by international trade have done so from the perspective of the above context. However, as far as China is concerned, resource endowment, economic development, and technological level vary significantly across different provincial administrative regions. As a result, a large-scale movement of resources and products exists between them. As the world's largest developing country, China is confronted with a range of economic, social, and environmental challenges,

⁎

and has both consumed energy and produced carbon emissions to extreme degrees in the process of industrialization and urbanization. On the contrary, China has expressed a clear commitment and taken a series actions to addressing global climate change, such as upgrading technology and promoting the use of new energy vehicles (Sun, Geng et al. 2018). In 2015, China made a solemn carbon reduction commitment within the framework of the Paris Agreement, pledging to peak its carbon emissions by 2030 and reduce its carbon emissions per unit GDP levels by 60% to 65% compared with those of 2005. Today, the question of how to achieve a win-win scenario between economic and social benefits within the established extent of carbon emissions represents a matter of urgency. It is, therefore, of particular importance to accurately measure the ECEs produced by international trade and inter-provincial trade, as well as to identify the driving factors behind ECEs. Considering China's actual conditions, this study mainly discusses the following questions: 1) Does carbon leakage exist in China's inter-provincial trade like it does in international trade; and 2) Which factors have affected the ECEs from China's international trade and inter-provincial trade? Based on the answers to the above questions, this study will further propose policy recommendations for China's ECEs reduction based on the analysis of driving factors. Regarding the ECEs from trade, existing studies mainly concentrate

Corresponding author. E-mail address: [email protected] (Z. PU).

https://doi.org/10.1016/j.techfore.2020.119930 Received 28 November 2019; Received in revised form 13 January 2020; Accepted 21 January 2020 0040-1625/ © 2020 Elsevier Inc. All rights reserved.

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on three aspects. First, using the input-output approach, existing studies have measured the ECEs from trade from production and consumption perspectives, while also analyzing inter-regional or inner-product ECEs (Wyckoff and Roop, 1994; Machado et al., 2001; Ahmad and Wyckoff, 2003; Chen and Chen, 2011; Zhong et al., 2018) and surprisingly, the carbon emissions embodied in China's bilateral trade is lower than the previous estimate (Liu, Meng et al. 2016; Liu, Chen et al. 2017). Second, using the statistical measurement or mathematical decomposition approach, existing studies have also investigated the driving factors behind ECEs and analyzed the effects of different driving factors on the embedded carbon emissions from trade (Dhakal et al., 2002; Dong et al., 2010; Zhao et al., 2016; Su et al., 2017; Zhou et al., 2018). Third, existing studies have introduced a number of methods or theories, such as DEA, global value chain, pollution haven, carbon tax,and Environment Kuznets Curve, and discussed their applicability in specific countries or regions (Cole, 2004; Peters and Hertwich, 2008; Liu et al., 2015; Liu and Lu 2015; Zhang et al., 2016;2017; Cai et al., 2018, Sun et al., 2019; Sun, Edziah et al. 2019). Regarding the decomposition of driving factors, existing studies mainly use Index Decomposition Analysis (IDA) and Structural Decomposition Analysis (SDA). IDA places low requirements on data, and merely requires summated data from different departments; SDA should be based on input-output tables. Hoekstra and Van den Bergh (2003) gave a comparison between SDA and IDA in terms of use conditions and decomposition effect, arguing that SDA places higher requirements on data and can thus be used to analyze various direct and indirect effects using the input-output approach. Therefore, this study used SDA to investigate the driving factors behind ECEs from China's international trade and inter-provincial trade sectors. The SDA approach based on input-output models has various advantages, including that the data quality is high, the theoretical basis is rigorous, and that both direct and indirect effects can be analyzed. For these reasons, it is the preferred approach of many scholars and has been widely applied in the fields of energy, economic development, and environmental protection (Su and Ang, 2012; Su and Thomson, 2016). In terms of studies on China-related issues, many scholars have studied China's economic development from the perspectives of energy intensity (Ma and Stern, 2008; Zeng et al., 2014), energy consumption (Zhao et al., 2012; Zhang and Lahr, 2014), and carbon emissions (Feng et al., 2012; Chang and Lahr, 2016). Many scholars have made great contributions to our understanding of ECEs. However, existing studies have universally ignored pollution in inter-provincial trade within the territory of large countries and have equally failed to analyze the influencing factors of pollution variation. Therefore, China, the world's largest developing country, was used as the subject of this study. Based on a distinction between international trade and inter-provincial trade, we built non-competitive input-output models in order to measure the ECEs produced by international trade and inter-provincial trade across China's 30 provincial administrative regions from 2002 to 2012. We then used SDA models to analyze the driving factors behind ECEs from international trade and inter-provincial trade. The aim was to identify the primary driving force behind the growth of China's ECEs, as well as to provide a reference for the development of China's low-carbon trade and formulation of lowcarbon economic policies by the relevant authorities. Compared with the existing studies discussed above, this study has the following innovative points. First, trade is distinguished into international trade and inter-provincial trade. Furthermore, international trade is then distinguished into export trade and import trade, while inter-provincial trade is distinguished into inter-provincial outflow trade and inter-provincial inflow trade. Non-competitive input-output models were built in order to measure the ECEs produced in international trade and inter-provincial trade across China's 30 provincial administrative regions. Previous studies have mostly concentrated on the ECEs from international trade, but scarcely discuss the ECEs produced by inter-provincial trade within the territory of large countries

and fail to distinguish between the ECEs produced under the respective, subdivided modes of trade. Second, the growth of ECEs from export trade, inter-provincial outflow trade, import trade, and inter-provincial inflow trade is broken down into four driving factors (the direct carbon emission coefficient, intermediate production technology, total trade volume, and trade structure). Accordingly, this study investigates the contribution of different driving factors to the growth of ECEs from international trade and inter-provincial trade, while also analyzing the differences between the driving factors across different regions. Compared with previous studies, both the modes of trade and the driving factors are classified in much more detail. For example, the classification of the driving factors incorporates intermediate production technology, which is scarcely mentioned in previous studies. The aforementioned study framework can highlight the variation of China's ECEs under four modes of trade, as well as the contribution of different driving factors to the growth of carbon emissions. Third, export trade and inter-provincial outflow trade are merged into the total outflow, while import trade and inter-provincial inflow trade are merged into total inflow. This study thus investigates the driving factors behind China's ECEs from the perspectives of total inflow and total outflow. The rest of this paper is organized as follows: Section 2 discusses the model building and data description, including non-competitive inputoutput models, measurement models for ECEs, and SDA models for driving factors of ECEs, as well as the sources and data processing methods used in this study. Section 3 relates to the analysis of the empirical results, analyzing the measurement results of ECEs from international trade and inter-provincial trade, SDA results regarding the driving factors of ECEs, and considering such SDA results from a consolidated perspective. Section 4 discusses the conclusions and policy implications and offers policy suggestions. 2. Model building and data description 2.1. Building non-competitive input-output models In an increasingly open economic system, production is gradually separated from consumption in a country or region, with products that are manufactured in one country or region very likely to be consumed in others. Hence, it is necessary to consider the carbon leakage arising from trade activities. Based on a distinction between intermediate input in the production of a country or region and the imports of other countries or regions, non-competitive input-output models were built in order to measure the ECEs from both the international trade and the inter-provincial trade of China's 30 provincial administrative regions from 2002 to 2012. The model is expressed as follows: The direct consumption coefficient matrix is expressed as Ai = Aid + Aim (i = 1, 2, 3) , where Aid denotes the direct consumption coefficient matrix of domestic or intra-provincial input and Aim denotes the direct consumption coefficient matrix of import trade or interprovincial inflow trade. Using the method for removing the intermediate input of import for reference ( Aim ) (Lau et al., 2007), it is assumed that Aim = Mi × Ai . Here, Mi is the import or inter-provincial inflow coefficient matrix, indicating the proportion of intermediate input of imported or inter-provincial inflowing products in the whole product sector. It is assumed that the intermediate input proportion of import or inter-provincial inflow is the same in the input in the product m11 0 0 0 m22 0 sector j by product sector i; then, Mi = is a diagonal

0 0 mnn matrix. From the perspectives of export trade and inter-provincial outflow trade, the diagonal matrix element is mij1 =

Tim + Timp

(Xi + Tim + Timp

Tie

Tiep)

(i , j = 1, 2,

, n; when i

j, mij1 = 0 ).

Specifically, Xi denotes the output of product sector i, Tim and Tie respectively denote the import trade volume and export trade 2

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volume of product sector i, and Timp and Tiep respectively denote the inter-provincial inflow trade volume and inter-provincial outflow trade volume of product sector i. From the perspective Tm of import trade, the diagonal matrix element is mij2 = (X + Tim T e) i

(i , j = 1, 2, provincial

mij3 =

i

inflowing products for final consumption, and Y denotes the sum of final consumption. 2.3. Building SDA models for the driving factors behind ECEs

i

, n; when i j, mij2 = 0 ). From the perspective of interinflow trade, the diagonal matrix element is

Timp

(Xi + Timp

Tiep)

(i , j = 1, 2,

, n; wheni

Based on the data regarding ECEs from international trade and the inter-provincial trade of different provincial administrative regions, we further built an SDA model for the driving factors behind ECEs. The aim was to discuss the contributions of four driving factors regarding the growth of ECEs under different modes of trade. Specifically, the four driving factors are the direct carbon emission coefficient (E1, E2, and E3), intermediate production technology (N1, N2, and N3), total trade volume(Fe, Fep, Fm, and Fmp), and trade structure (Se, Sep, Sm, and Smp). The SDA model is expressed as follows:

j, mij3 = 0 ). Therefore, the

direct consumption coefficient matrix of China's 30 provincial administrative regions is Aid = (I Mi ) Ai (i = 1, 2, 3). Then, the complete carbon emission coefficient (intensity) matrix of each product sector is as follows:

Fi = Ei (I

Aid )

1

(1)

2.2. Building measurement models for ECEs Based on both the input-output approach and the above model, we can build measurement models for ECEs under different modes of trade. 1. Measurement models for ECEs from export trade and inter-provincial outflow trade

Ce = E1 (I

1

Cep = E1 (I

Ce0 = E11 N11 Fe1 Se1

E10 N10 Fe0 Se0

(10)

(11)

(12)

To address the inaccuracy of the SDA approach, we used the method proposed by Dietzenbacher and Los (1998), which involves using the two-polar decomposition method to break down the specific ECEs from trade:

Ce = 1/2( E1 N10 Fe0 Se0 + E1 N11 Fe1 Se1) + 1/2(E11 N1 Fe0 Se0 + E10 N1 Fe1 Se1) + 1/2(E11 N11 Fe Se0+ E10 N10 Fe Se1) + 1/2(E11 N11 Fe1 Se + E10 N10 Fe0 Se ) = Ce ( E1) + Ce ( N1) + Ce ( Fe ) + Ce ( Se ) (13) Here, Ce(ΔE1), Ce(ΔN1), Ce(ΔFe), and Ce(ΔSe) respectively denote the effects of direct carbon emission coefficient, intermediate production technology, total export volume, and export structure on the variation of ECEs from export trade. Likewise, the ECEs from inter-provincial outflow trade can be decomposed as follows:

(4)

Where Cm denotes the ECEs from import trade, E2 is the direct carbon emission coefficient matrix of import trade, E2 (I A2d ) 1 [A2m (I A2d ) 1Y ] denotes the ECEs produced by imported products for intermediate input, E2 (I A2d ) 1Y m1 denotes the ECEs produced by imported products produce for final consumption, and Y denotes the sum of final consumption. Likewise, the measurement model for ECEs from inter-provincial inflow trade can be expressed as follows:

A3d ) 1Y + Y m2]

(9)

Ce = E1 N11 Fe1 Se1 + E10 N1 Fe1 Se1 + E10 N10 Fe Se1 + E10 N10 Fe0 Se

China needs to import foreign products in order to satisfy domestic needs. The ECEs from imported products imply that China saves the carbon emissions produced that would otherwise be added to domestic production. According to the non-competitive input-output model, and the complete carbon emission coefficient matrix, the measurement model for ECEs from import trade, can be expressed as follows:

A3d ) 1 [A3m (I

Cmp = E3 N3 F mpS mp

Decomposed from the reporting period:

2. Measurement models for ECEs from import trade and inter-provincial inflow trade

Cmp = E3 (I

(8)

Ce = E1 N10 Fe0 Se0 + E11 N1 Fe0 Se0 + E11 N11 Fe Se0 + E11 N11 Fe1 Se

(3)

A2d ) 1Y + Y m1]

Cm = E2 N2 F mS m

Superscripts 1 and 0 respectively denote the reporting period and the base period. Decomposed from the base period:

Where Cep denotes the ECEs from inter-provincial outflow trade, T denotes the column vector of the product value of inter-provincial outflow trade.

A2d ) 1 [A2m (I

(7)

Ce = Ce1

ep

Cm = E2 (I

Cep = E1 N1 F epS ep

Here, (I denotes the intermediate production technology and is replaced with Ni in this model. Take the ECEs from export trade as an example"

Where Ce denotes the ECEs from export trade, (I A1d ) 1 denotes the Leontief inverse matrix in which import trade and inter-provincial inflow trade are removed, E1 denotes the direct carbon emission coefficient matrix of export trade and inter-provincial outflow trade, and Te denotes the column vector of the product value of export trade. Likewise, the ECEs from inter-provincial outflow trade can be measured using the following equation: 1 A1d ) T ep

(6)

Aid ) 1

(2)

A1d ) T e

Ce = E1 N1 F eS e

0 0 1 1 Cep = 1/2( E1 N10 Fep Sep + E1 N11 Fep Sep) 0 0 1 1 0 + 1/2(E11 N1 Fep Sep + E10 N1 Fep Sep) + 1/2(E11 N11 Fep Sep +

E10 N10

1 1 0 Fep Sep ) + 1/2(E11 N11 Fep Sep + E10 N10 Fep Sep)

= Cep ( E1) + Cep ( N1) + Cep ( Fep) + Cep ( Sep)

(5)

(14)

Here, Cep(ΔE1), Cep(ΔN1), Cep(ΔFep), and CepΔ(ΔSep) respectively denote the effects of the direct carbon emission coefficient, intermediate production technology, inter-provincial outflow trade volume, and inter-provincial outflow trade structure on the variation of ECEs from inter-provincial outflow trade. The ECEs from import trade can be decomposed as follows:

Where Cmp denotes the ECEs from inter-provincial inflow trade, E3 denotes the direct carbon emission coefficient matrix of inter-provincial inflow trade, E3 (I A3d ) 1 [A3m (I A3d ) 1Y ] denotes the ECEs produced by inter-provincial inflowing products for intermediate input, E3 (I A3d ) 1Y m2 denotes the ECEs produced by inter-provincial 3

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administrative regions from 2002 to 2012. In order to make the results clearer, we showed exports and imports ECEs spaitial disturbution in Picture 1 of China's provinces from 2002 to 2012. In the picture, the reader can also clearly see the geographical location of the provinces in China. Overall, the ECEs from China's export trade, inter-provincial outflow trade, import trade, and inter-provincial inflow trade all tended to increase. From 2002 to 2012, the ECEs under the four modes of trade respectively increased by 7.37268, 1.09599, 10,14,824, and 10.7125 billion tons (increases of 88.66%, 49.25%, 343.86%, and 291.74%). Specifically, the ECEs under the four modes of trade tended to first increase, then decrease, reaching a peak in 2007. This implies that China's low-carbon trade policy has produced certain effects in recent years. The ECEs from export trade decreased marginally in Liaoning, Beijing, Jilin, and Heilongjiang, but increased marginally in the other 26 provincial administrative regions. Specifically, the incremental increases of Hebei and Inner Mongolia were ranked as the top two (specifically, 1.15547 and 1.01419 billion tons) and were far higher than those of the other provincial administrative regions. In addition, the two highest rate increases were those of Hainan and Ningxia, both of which continued to rise. In particular, the rate increase of Hainan was as high as 4228.76%. The ECEs from inter-provincial outflow trade decreased marginally in nine provincial administrative regions while increasing marginally in 21 provincial administrative regions. Specifically, the incremental increases of Shandong, Jiangsu, and Beijing ranked among the top three (specifically, 353.86 million, 280.88 million, and 231.39 million tons). In addition, the rate increases of Hainan and Shaanxi ranked among the top two (specifically, 2038.66% and 1951.05%). The ECEs from import trade increased marginally in all 30 provincial administrative regions. Specifically, the incremental increases of Shandong and Jiangsu ranked highest (specifically, 1.95595 and 1.04516 billion tons), while the rate increases of Hainan, Ningxia, and Qinghai ranked among the top three (specifically, 3518.71%, 1360.56%, and 1091.05%). Similar to the ECEs from import trade, the ECEs from inter-provincial inflow trade increased marginally in all 30 provincial administrative regions. Specifically, the incremental increases of Jiangsu, Shandong, and Guangdong were the highest (specifically, 1.36468, 1.19569, and 1.04601 billion tons), while the highest rate increases were those of Inner Mongolia, Guizhou, and Jiangxi (specifically, 997.27%, 595.17%, and 530.28%). There was notable carbon leakage in both international trade and inter-provincial trade, particularly regarding the severe transfer of ECEs from inter-provincial trade. Through international trade, environmental pollution was aggravated in Hebei and Inner Mongolia, but reduced in Shandong and Jiangsu. Through inter-provincial trade, environmental pollution was aggravated in Hainan and Shaanxi, but reduced in Jiangsu, Shandong, and Guangdong. Comparative analysis shows that there was a certain difference regarding carbon leakage in international trade and inter-provincial trade between provincial administrative regions. Through international trade, environmental pollution was aggravated in Hebei and Inner Mongolia, but reduced in Shandong and Jiangsu. Through inter-provincial trade, environmental pollution was aggravated in Hainan and Shaanxi, but reduced in Jiangsu, Shandong, and Guangdong. This phenomenon can be explained using the pollution haven hypothesis. Namely, economically developed regions bear higher environmental costs than economically underdeveloped regions, meaning they transfer the production of highly polluting products to economically underdeveloped regions; if things continue this way, this results in a transfer of ECEs between different regions. The above analysis leads to a conclusion similar to that of existing studies, namely that a transfer of ECEs exists in China's international trade. Compared with existing studies, the conclusions of this study show notable ECEs also exist in China's inter-provincial trade. In the context of a low-carbon economy, China must pay more attention to the ECEs produced by inter-provincial trade.

Cm = 1/2( E2 N20 Fm0 Sm0 + E2 N21 Fm1 Sm1 ) +

1/2(E21

N2 Fm0 Sm0

+

E20

N2 Fm1 Sm1 )

+

1/2(E21 N21

Fm Sm0 +

E20 N20 Fm Sm1 ) + + 1/2(E21 N21 Fm1 Sm + E20 N20 Fm0 Sm) (15)

= Cm ( E2 ) + Cm ( N2) + Cm ( Fm) + Cm ( Sm)

Here, Cm(ΔE2), Cm(ΔN2), Cm(ΔFm), and Cm(ΔSm) respectively denote the effects of the direct carbon emission coefficient, intermediate production technology, total import volume, and import structure on the variation of ECEs from import trade. The ECEs from inter-provincial inflow trade can be decomposed as follows:

Cmp= 0 0 1 1 1/2( E3 N30 Fmp Smp + E3 N31 Fmp Smp ) 0 0 1 1 0 ) + 1/2(E31 N31 Fmp Smp + 1/2(E31 N3 Fmp Smp + E30 N3 Fmp Smp + 1 1 0 ) + 1/2(E31 N31 Fmp E30 N30 Fmp Smp Smp + E30 N30 Fmp Smp)

= Cmp ( E3) + Cmp ( N3) + Cmp ( Fmp)+ (16)

Cmp ( Smp)

Here, Cmp(ΔE3), Cmp(ΔN3), Cmp(ΔFmp), and Cmp(ΔSmp) respectively denote the effects of the direct carbon emission coefficient, intermediate production technology, inter-provincial inflow trade volume, and inter-provincial inflow trade structure on the variation of ECEs from inter-provincial inflow trade. 2.4. Data sources and processing In order to ensure the reliability of the data sources, the data used in this study were mainly cited from China's regional input-output tables of 2002, 2007, and 2012 (prepared by Li et al.) ,1 China Statistical Yearbook, China Energy Statistical Yearbook, and the statistical yearbooks of different provincial administrative regions. Using a method specified in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, the equation for the carbon emission coefficient of fossil energy can be expressed as follows:

k = NCVk × CEFk × COFk ×

44 (k = 1, 2, 3, 12

, 8)

(17)

Where NCVk denotes the average lower heating value, CEFk denotes the carbon emission coefficient, COFk denotes the carbon oxidation factor, 44 and 12 respectively denote the molecular weight of CO2 and C, and k denotes one of the eight selected heavily consumed fossil fuels.2 The energy consumption of power generation is large, so we added the energy consumption of power generation in addition to the eight fossil fuels. 3. Analysis of empirical results 3.1. Measurement results of ECEs from international trade and interprovincial trade Table 1 lists the measurement results of ECEs from international trade and inter-provincial trade across China's 30 provincial 1

We selected all regional extended input-output tables after China's accession to the WTO. Thus far, the latest regional extended input-output table is China's regional extended input-output tables from 2012 (released in 2018). This regional extended input-output table comprises the input-output tables of China's 31 provincial administrative regions. For lack of data regarding Hong Kong, Macao, and Taiwan, as well as energy consumption data of Tibet, we selected China's 30 provincial administrative regions (excluding Hong Kong, Macao, Taiwan, and Tibet) as the subject of this study. 2 We selected eight heavily consumed fossil fuels, including coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, and natural gas. 4

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Table 1 Measurement results of ECEs under different modes of trade in China's 30 provincial administrative regions from 2002 to 2012 (unit: 10,000 tons). Export ECEs

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

Inter-provincial outflow ECEs

Import ECEs

Inter-provincial inflow ECEs

2002

2007

2012

2002

2007

2012

2002

2007

2012

2002

2007

2012

2626 22,406 91,814 97,786 34,682 106,967 26,099 38,433 22,425 35,604 28,924 28,261 6486 9220 44,467 36,674 14,937 12,648 19,131 8834 237 9457 11,057 24,984 8216 19,656 34,424 4547 3115 27,422

5991 40,772 265,969 188,656 126,164 187,448 29,664 60,785 37,667 88,741 69,705 54,771 16,373 12,321 137,043 129,875 26,938 33,604 74,859 25,218 8622 9579 23,297 78,719 44,126 69,678 62,132 4342 34,047 46,738

2257 30,378 207,361 149,752 136,101 96,793 16,868 24,637 32,111 82,415 54,058 62,320 17,133 30,551 64,350 95,174 23,357 33,769 36,942 20,866 10,252 16,512 20,465 48,240 33,547 64,848 50,078 13,986 45,628 48,059

14,911 9650 5712 28,088 1305 25,415 929 1777 18,459 18,364 13,648 1366 5996 635 20,296 3055 1366 1347 31,798 993 24 581 2007 1504 1425 68 9022 1253 244 1296

22,439 12,631 20,088 53,355 8281 35,272 1476 5464 30,378 57,557 40,960 2952 16,939 2725 81,724 8415 5021 5072 54,521 2690 3015 1652 4050 5202 1910 5869 7633 4386 6056 2311

38,050 7521 18,821 5275 1738 13,262 1051 925 13,561 46,452 31,710 2052 15,341 3323 55,682 4440 3781 1645 48,076 1569 509 1774 5289 998 944 1404 778 454 1518 4193

5573 7054 12,973 14,634 3429 22,008 1740 8902 17,718 23,793 18,452 6262 3843 3739 21,897 8562 6583 4310 25,360 2656 148 3423 23,695 2883 15,973 5062 7593 785 790 15,289

12,900 13,094 42,611 27,027 12,263 30,181 63,297 13,680 108,949 57,437 38,433 11,919 9407 9007 213,065 70,598 36,810 10,746 48,637 6588 2046 4823 10,180 13,649 11,823 9338 9239 2358 6935 30,147

31,126 29,330 105,564 44,057 26,483 64,485 18,581 18,697 58,599 128,309 85,966 26,788 35,655 20,921 217,492 40,095 36,868 21,710 96,928 17,813 5341 12,547 31,470 18,302 31,260 19,935 21,358 9353 11,539 23,381

12,826 7799 18,268 4136 2427 19,005 6072 8783 23,339 41,547 39,472 7791 8926 6462 34,068 13,590 9398 7884 47,116 5618 1395 5668 10,366 1818 5282 6871 5911 783 1300 3267

21,930 15,923 44,845 8674 7249 24,390 12,703 12,313 38,869 77,546 68,067 15,094 15,986 14,664 57,784 25,754 11,630 14,749 84,071 9532 1677 9237 16,138 5906 8772 9912 5600 1350 2262 5243

42,516 38,461 74,708 23,825 26,634 63,127 21,223 24,941 72,628 178,015 123,752 40,734 23,015 40,731 153,637 67,623 42,591 39,896 151,717 18,769 4903 23,434 43,441 12,639 19,051 28,022 13,352 4253 6555 14,248

Data source: generated according to the equations above and China's regional extended input-output tables in specific years.

regions were positive. Specifically, the contribution values of Jiangsu and Beijing ranked among the top two (specifically 265.89 and 229.79 million tons). From 2002 to 2007, only the contribution values of Jilin and Gansu were negative, while those of the other 28 provincial administrative regions were positive. Specifically, the contribution values of Shandong and Jiangsu ranked among the top two (specifically, 587.39 and 372.98 million tons). From 2007 to 2012, the contribution values of just five provincial administrative regions were positive, while those of the 25 other provincial administrative regions were negative, implying that the inter-provincial transfer of ECEs was marginally reduced. There are, respectively, 20, 10, 8, and 14 provincial administrative regions in which the contribution rate of the four driving factors was negative. Among the four driving factors, the direct carbon emission coefficient inhibited the growth of ECEs from inter-provincial outflow trade most significantly, inter-provincial outflow trade volume promoted the growth of such ECEs most significantly, and intermediate production technology and inter-provincial outflow trade structure also promoted the growth of such ECEs. Table 4 describes the SDA results regarding the driving factors behind the growth of ECEs from import trade. From 2002 to 2012, the contribution values of all 30 provincial administrative regions were positive. Specifically, the contribution values of Hebei, Inner Mongolia, and Henan ranked among the top three (specifically, 1.19665, 1.07240, and 1.05768 billion tons). From 2002 to 2007, the contribution values of just four provincial administrative regions were negative, while those of the other 26 provincial administrative regions were positive. Specifically, the contribution values of Jilin, Hebei, and Henan ranked among the top three (specifically, 1.52893, 1.25067, and 1.17421 billion tons). From 2007 to 2012, the contribution values of eight provincial administrative regions were negative, while those of the 22 other provincial administrative regions were positive. Specifically, the contribution values of Inner Mongolia, Jiangsu, and Anhui ranked among the top three (specifically, 866.29, 636.54, and 622.93 million

3.2. SDA results regarding the driving factors behind ECEs from international trade and inter-provincial trade Based on the data regarding ECEs from China's international trade and inter-provincial trade, the following section analyzes the driving factors behind these ECEs (as described in Tables 2–5). The aim is to identify which factors affected the growth of ECEs under different modes of trade (export trade, inter-provincial outflow trade, import trade, and inter-provincial inflow trade) from 2002 to 2012. Table 2 describes the SDA results regarding the driving factors behind the growth of ECEs from export trade. From 2002 to 2012, only the contribution values of four provincial administrative regions were negative, while those of the other 26 provincial administrative regions were positive. Specifically, the contribution values of Hebei and Inner Mongolia ranked among the top two (specifically, 1.11218 and 1.00357 billion tons). From 2002 to 2007, only the contribution values of Qinghai and Jilin were negative, while the contribution values of the other 28 provincial administrative regions were positive. Specifically, the contribution value of Hebei ranked first (specifically, 1.72528 billion tons). From 2007 to 2012, the contribution values of Jiangxi and Ningxia ranked among the top two (merely 182.31 and 111.05 million tons), while those of 20 provincial administrative regions were negative, implying that most provincial administrative regions obtained good results from low-carbon development during this period. Among the four driving factors, the direct carbon emission coefficient affected the variation of ECEs from export trade most significantly and inhibited the growth of such ECEs overall. By contrast, intermediate production technology, export trade volume, and export trade structure promoted the growth of such ECEs overall. Table 3 describes the SDA results regarding the driving factors behind the growth of ECEs from inter-provincial outflow trade. From 2002 to 2012, the contribution values of nine provincial administrative regions were negative, while those of 21 other provincial administrative 5

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Table 2 Growth structure of ECEs from export trade in China's 30 provincial administrative regions from 2002 to 2012 (unit: 10,000 tons). 2002–2007

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

2007–2012

2002–2012

Ce(ΔE1)

Ce(ΔN1)

Ce(ΔFe)

Ce(ΔSe)

Ce(ΔE1)

Ce(ΔN1)

Ce(ΔFe)

Ce(ΔSe)

Ce(ΔE1)

Ce(ΔN1)

Ce(ΔFe)

Ce(ΔSe)

−3950 −31,103 −81,366 −93,893 −30,667 −80,609 −43,930 −42,566 −23,164 −26,602 −20,919 −24,723 −3470 −4991 −26,518 −40,802 −13,306 −6748 −31,685 −7157 4018 −6083 −9851 −23,143 −5729 −27,548 −27,673 −2768 12,420 −27,940

1017 3259 32,017 23,853 17,729 7118 −3223 9099 −3987 −956 383 5113 2697 988 21,966 5345 2399 6436 4507 5767 607 5032 1267 19,733 −4705 44 −19,567 3650 3042 7334

6724 41,258 224,671 158,317 122,274 158,414 17,402 49,913 38,825 78,151 39,360 39,432 13,708 8358 60,185 125,203 17,457 25,113 59,771 14,926 837 1751 19,158 49,721 33,751 72,128 67,363 −1464 11,647 41,730

−645 5539 −2795 544 −15,917 −2034 3698 6896 3701 240 21,871 7329 −3275 −831 33,181 8672 5612 −4875 23,215 3686 2854 114 2901 8149 12,154 5241 8329 305 3454 −1197

−3192 −46,777 −207,867 −340,659 −204,585 −203,293 −24,802 −58,597 −34,548 −88,515 −64,139 −71,049 −14,515 −26,747 −98,586 −139,477 −20,547 −40,172 −55,456 −18,354 −15,910 −14,266 −23,085 −98,432 −39,748 −110,384 −70,173 −5525 −62,970 −44,361

−4148 18,637 85,273 −32,342 5200 −1405 15,487 −11,716 −10,561 −2455 10,168 −22,105 5676 5218 7790 −19,615 3751 −8805 2817 −39 −2816 −9772 6276 −5042 3766 −10,773 3374 −2469 −11,085 12,847

2628 22,371 38,906 347,928 212,077 148,152 4375 45,293 42,245 101,252 46,518 97,797 11,781 34,464 30,347 145,831 23,444 48,091 −6623 15,142 22,614 27,130 15,092 86,540 26,589 92,070 74,230 15,612 75,542 43,266

820 −5906 22,378 −10,619 −5753 −36,411 −10,729 −12,176 −2854 −14,830 −8577 −60 −1536 5296 −9278 −26,828 −9724 1769 21,760 −1537 −2387 2896 −2247 −11,842 −1162 26,066 −19,961 2007 9618 −10,482

−4539 −88,371 −266,837 −573,115 −247,980 −290,723 −45,336 −96,807 −69,701 −126,047 −85,950 −125,156 −18,278 −49,797 −101,826 −183,419 −38,322 −50,372 −73,866 −23,793 1782 −29,589 −36,968 −119,029 −37,646 −154,556 −115,615 −14,048 −5844 −84,096

−6681 37,066 127,665 25,662 51,067 −7268 18,914 −8072 −27,906 −7678 12,445 −16,461 13,169 18,504 25,521 −20,192 12,842 8616 4447 9154 239 305 9272 31,214 −3010 −12,157 −38,116 10,207 2277 19,682

10,339 60,082 246,370 611,741 318,966 314,367 21,867 96,675 106,437 191,389 88,701 165,599 20,274 49,605 76,124 267,594 38,036 66,088 64,563 25,425 4783 32,818 35,209 114,380 56,138 198,164 177,187 12,916 36,831 93,418

135 −1499 4020 −11,160 −21,696 −26,445 −4844 −5649 827 −11,380 9468 7751 −4099 3443 19,267 −5653 −3471 −3523 23,162 1648 3013 3268 1998 −880 9432 15,395 −7533 273 8403 −7806

Data source: calculated according to the equations above and China's regional extended input-output tables in specific years.

Table 3 Growth structure of ECEs from inter-provincial outflow trade in China's 30 provincial administrative regions from 2002 to 2012 (unit: 10,000 tons). 2002–2007

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

2007–2012

2002–2012

Cep(ΔE1)

Cep(ΔN1)

Cep(ΔFep)

Cep(ΔSep)

Cep(ΔE1)

Cep(ΔN1)

Cep(ΔFep)

Cep(ΔSep)

Cep(ΔE1)

Cep(ΔN1)

Cep(ΔFep)

Cep(ΔSep)

−15,476 −8387 −5839 −30,318 −1567 −16,027 −1780 −2295 −20,696 −15,892 −12,303 −1371 −3384 −863 −15,376 −2660 −1987 −1030 −25,710 −732 1255 −804 −1627 −1571 −341 −1155 −3958 −1423 2062 −958

1063 1040 2473 4080 1271 −944 −228 1341 −3898 −1323 1839 575 2865 280 7330 445 813 1056 5435 550 45 558 234 1059 −382 202 −2592 2885 391 725

17,665 9603 15,976 48,240 5829 28,011 830 3506 36,524 51,325 34,139 2502 10,906 2460 61,603 7304 4455 3886 45,105 1839 532 1271 3040 3726 1321 6099 9677 2104 4373 1797

3926 786 2132 3637 1343 −3813 267 1173 −218 3188 3344 −32 230 197 5182 31 363 −252 −8 77 1147 50 402 413 −89 562 −4579 −441 −1073 −713

−40,826 −11,860 −17,276 −28,326 −4522 −28,130 −1328 −3187 −19,212 −47,378 −32,398 −2416 −13,172 −3055 −65,040 −6617 −3304 −3050 −45,247 −1560 −1712 −1372 −4619 −3072 −1252 −3652 −3147 −973 −3510 −3048

−59,350 5201 9246 −2469 −95 1134 989 −530 −5455 5617 9894 −1155 4311 879 11,824 −3430 1005 −606 16,642 151 −260 −1932 516 −137 63 −440 128 −756 −441 1269

84,919 4080 8880 −1594 −1203 7566 200 −60 6935 28,183 15,304 2520 8101 3196 35,234 9112 1402 578 20,855 942 348 4460 7103 −245 704 186 −2762 −1643 −587 2421

31,057 −2894 −2081 −15,994 −648 −72 −411 −702 896 2870 −2468 121 −700 −398 −5423 −2697 −334 −299 124 −688 −870 −1042 −1941 −659 −477 −462 −903 −545 53 1267

−101,643 −22,208 −23,155 −30,281 −3360 −42,756 −2215 −2922 −35,010 −64,374 −43,894 −3991 −15,935 −4919 −101,826 −7254 −5530 −2736 −78,226 −1806 90 −2675 −8042 −2751 −1431 −2760 −4910 −709 −198 −5316

−31,124 9827 13,529 205 901 63 1131 −6 −12,190 7344 14,557 −396 8649 1955 25,521 −1378 2683 421 29,803 642 3 −610 1291 623 −195 −210 −1081 375 204 2709

132,735 11,516 22,344 16,027 2697 31,913 1086 2002 41,914 80,286 46,427 5280 17,148 5787 76,124 14,746 5377 2903 65,854 2114 235 5284 10,773 1818 1676 4418 664 −217 1589 5198

23,012 −1566 792 −8697 170 −1497 101 172 163 3334 261 −148 −705 −127 19,267 −1751 −118 −306 −236 −371 158 −810 −914 −177 −505 −108 −2810 −241 −326 169

Data source: calculated according to the equations above and China's regional extended input-output tables in specific years. 6

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Table 4 Growth structure of ECEs from import trade in China's 30 provincial administrative regions from 2002 to 2012 (unit: 10,000 tons). 2002–2007

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

2007–2012

2002–2012

Cm(ΔE2)

Cm(ΔN2)

Cm(ΔFm)

Cm(ΔSm)

Cm(ΔE2)

Cm(ΔN2)

Cm(ΔFm)

Cm(ΔSm)

Cm(ΔE2)

Cm(ΔN2)

Cm(ΔFm)

Cm(ΔSm)

−2926 −13,479 −37,452 −14,397 −5649 −16,533 56,727 −6806 14,735 −9999 −14,413 −12,128 −1582 −3150 50,552 44,900 11,231 −3080 −12,852 −2144 832 −4731 −33,801 −6241 −29,281 −9021 −7110 −615 5418 −18,240

743 6075 39,069 6900 6250 6488 15,368 3714 −135 1446 5854 4770 680 1043 7971 5259 100 3171 6203 1462 −17 1026 3381 9872 1841 3081 −4718 229 1635 12,052

4272 19,385 113,626 17,362 17,383 28,512 58,027 10,674 28,626 26,427 31,832 23,066 6415 3576 28,995 58,544 6420 10,028 25,783 4740 −297 1734 19,360 12,021 23,994 26,046 9828 24 5614 33,632

1223 2671 9824 3715 2626 2692 22,771 −50 −2896 4474 9822 1534 434 2661 −1418 8718 1100 −948 4247 1227 −95 991 3000 4088 3671 −1470 895 −551 −126 1420

852 −3409 11,198 −3341 −8690 −11,437 −125,504 −9451 −80,954 −307 −2724 −2459 5317 −402 −116,766 −208,138 −48,932 −5975 1419 6795 272 996 1069 −12,880 13,974 −6863 371 2931 −5164 −73,863

−1038 2999 −12,647 −69,858 5249 655 −19,218 2524 7191 −1063 −4129 3238 −57 3681 −59 476 12,071 7530 1537 −1344 61 −536 2130 223 3143 2045 5323 3849 −1011 20,657

4996 15,123 7532 48,042 91,137 60,017 32,235 35,238 66,689 60,111 22,995 60,571 −9452 21,274 58,777 215,623 25,638 27,373 8270 12,388 9675 25,345 12,513 33,784 42,272 35,729 22,379 8017 20,983 65,228

31 636 −11,485 1781 −1066 −5017 −17,132 −1887 3532 4912 −4555 944 3770 −3303 5400 −19,615 10,842 5149 10,046 2572 1280 3031 −2286 921 −2536 5080 1174 720 2226 −2433

−4265 −25,210 −22,951 −86,859 −33,714 −41,095 −17,793 −27,827 −23,733 −19,876 −17,808 −33,942 1712 −8868 −11,846 −25,544 −12,129 −14,692 −17,952 1842 5388 −15,472 −66,392 −23,818 −81,839 −27,636 −15,100 −271 8816 −116,156

−987 15,935 27,551 35,833 33,109 11,501 3140 12,139 1929 2239 3602 15,632 336 8492 8380 12,110 6003 16,177 7368 2528 33 4195 15,384 22,443 20,108 10,814 −7831 5331 1857 54,710

12,664 35,545 110,650 123,060 102,214 92,300 35,874 50,168 57,360 95,337 54,315 95,785 717 24,411 39,611 122,718 18,162 38,602 46,093 16,712 6023 33,805 54,406 37,667 110,952 70,694 49,961 8016 17,980 98,544

742 3732 4415 11,684 5630 2672 2051 −525 1232 8301 4571 2060 2762 1345 −2694 −3516 6434 3162 9145 4613 266 5328 1968 5497 7858 755 1111 1529 922 1357

Data source: calculated according to the equations above and China's regional extended input-output tables in specific years.

Table 5 Growth structure of ECEs from inter-provincial inflow trade in China's 30 provincial administrative regions from 2002 to 2012 (unit: 10,000 tons). 2002–2007

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

2007–2012

2002–2012

Cmp(ΔE3)

Cmp(ΔN3)

Cmp(ΔFmp)

Cmp(ΔSmp)

Cmp(ΔE3)

Cmp(ΔN3)

Cmp(ΔFmp)

Cmp(ΔSmp)

Cmp(ΔE3)

Cmp(ΔN3)

Cmp(ΔFmp)

Cmp(ΔSmp)

−9215 −1834 −647 −502 −430 −3303 −76 −641 −16,411 −14,189 −4495 −364 −3052 −529 −6606 −677 −676 −414 −27,880 −322 −801 −177 −580 −151 −547 −450 −544 −41 −80 −306

3321 99 228 271 181 357 −12 273 1251 3136 638 99 3017 148 3513 230 289 250 −5340 231 −13 165 −300 87 −417 174 −343 42 56 26

11,798 1505 1104 1243 801 3213 −230 741 27,059 23,351 7285 543 3486 1294 8060 1536 1006 577 37,466 541 1422 230 991 384 1764 930 1552 112 101 146

−1188 621 73 −236 −51 846 11 9 4046 −66 −213 −78 90 92 215 −148 −46 −79 3077 269 75 −27 12 −95 96 −41 73 −39 41 −167

−850 342 663 159 7 519 89 69 411 2434 914 227 263 233 183 214 237 125 3104 49 42 157 173 34 224 20 52 0 2 87

−25,956 3568 2073 −313 18 −311 585 −189 −10,744 −2526 1328 −151 4049 420 6128 −517 −22 −117 4691 −14 −149 −360 359 −48 245 −110 12 4 −32 98

42,644 7181 5298 545 −58 5881 281 1208 15,178 15,542 7743 398 14,159 250 37,586 2345 196 201 8328 2709 434 1034 2277 −215 −568 −6 84 −63 36 1319

11,900 −1098 −186 326 370 147 −137 −166 −4101 −192 −341 82 −2490 405 1821 −526 99 110 −9621 −388 −226 −101 −459 95 −77 −119 216 21 −63 −202

−22,136 −6427 −3843 −630 −456 −7670 −530 −2160 −17,095 −19,268 −10,043 −380 −9591 −875 −31,677 −1097 −650 −403 −28,307 −1919 −1068 −239 −1377 −75 −375 −347 −578 −26 −51 −1954

−12,508 6135 3814 186 160 764 568 12 −10,227 155 2157 −25 9410 785 22,147 −88 405 186 −1067 775 −229 10 592 25 −43 −44 −656 29 6 302

59,141 10,366 8253 1907 912 12,592 975 3304 42,836 47,151 20,734 1100 19,495 2120 57,013 4280 1354 824 48,070 3827 1874 1348 3708 145 961 888 2115 39 100 3645

7957 310 382 31 220 1661 −74 149 1176 −549 10 61 −1820 284 3419 −639 −26 46 −4871 392 208 −199 −232 −3 178 −100 221 −4 5 −993

Data source: calculated according to the equations above and China's regional extended input-output tables in specific years. 7

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tons). There were, respectively, 26, 2, 0, and 3 provincial administrative regions in which the contribution rate of the four driving factors was negative. The direct carbon emission coefficient inhibited the growth of ECEs from import trade, whereas intermediate production technology, import trade volume, and the import trade structure promoted the growth of such ECEs overall. Table 5 describes the SDA results regarding the driving factors behind the growth of ECEs from inter-provincial inflow trade. From 2002 to 2012, the contribution values of all 30 provincial administrative regions were positive. Specifically, the contribution values of Shandong, Beijing, and Jiangsu ranked among the top three (specifically, 509.01, 324.54, and 274.90 million tons). From 2002 to 2007, only the contribution values of Xinjiang and Jilin were negative, while those of the 28 other provincial administrative regions were positive. Specifically, the contribution values of Shanghai, Jiangsu, and Guangdong ranked among the top two (specifically, 159.45 and 122.32 million tons). From 2007 to 2012, the contribution values of five provincial administrative regions were negative, and those of the other 25 provincial administrative regions were positive. Specifically, the contribution values of Shandong and Beijing ranked among the top two (specifically, 457.19 and 277.37 million tons). There were, respectively, 30, 9, 0, and 12 provincial administrative regions in which the contribution rate of the four driving factors was negative. Direct carbon emission coefficient inhibited the growth of ECEs from inter-provincial inflow trade, while inter-provincial inflow trade volume promoted the growth of such ECEs. In addition, intermediate production technology and inter-provincial inflow trade structure also promoted the growth of such ECEs significantly. Among the driving factors behind ECEs under the four modes of trade, the direct carbon emission coefficient inhibited the growth of ECEs overall, while intermediate production technology, trade volume, and trade structure promoted the growth of ECEs overall (specifically, the promoting effect of trade volume was most significant). Evidently, during the years from 2002 to 2012, China's level of production technology continued to improve, and the number of direct carbon emissions produced in the manufacturing process gradually decreased. However, carbon emissions from indirect consumption increased marginally, with unreasonable trade structures and an increasing volume of total trade constituting the essential driving forces behind the growth of ECEs.

international trade, the contribution values regarding the driving factors behind the growth of ECEs from international trade were all positive in all 30 provincial administrative regions. Specifically, the contribution values of Hebei and Inner Mongolia ranked among the top two (specifically, 2.30883 and 2.07597 billion tons). Judging by the contribution rate of the four driving factors, there are, respectively, 28, 7, 0, and 10 provincial administrative regions in which the contribution rate of the four driving factors was negative. The direct carbon emission coefficient significantly inhibited the growth of ECEs from international trade, international trade volume significantly promotes the growth of such ECEs, and both intermediate production technology and international trade structure promoted the growth of such ECEs overall, but not significantly. From the perspective of inter-provincial trade, the contribution values regarding the driving factors behind the growth of ECEs from inter-provincial trade were negative in Guizhou, Qinghai, Liaoning, Gansu, and Shanxi, but positive in the 25 other provincial administrative regions. Specifically, the contribution values of Shandong, Beijing, and Jiangsu ranked among the top three (specifically, 699.88, 554.33, and 540.79 million tons). Judging by the contribution rate of the four driving factors, there are, respectively, 25, 12, 4, and 14 provincial administrative regions, in which the contribution rate of the four driving factors was negative. The direct carbon emission coefficient inhibited the growth of ECEs from inter-provincial trade, interprovincial trade volume promoted the growth of such ECEs, and both intermediate production technology and inter-provincial trade structure affected the growth of such ECEs slightly but had a certain promoting effect on the growth of such ECEs overall. From the perspectives of total outflow trade and total inflow trade, ECEs are analyzed as follows. Figs. 1 and 2, respectively show the SDA results regarding the driving factors behind ECEs from total outflow trade and total inflow trade across China's 30 provincial administrative regions from 2002 to 2012. From the perspective of total outflow trade, the contribution values regarding the driving factors behind the growth of ECEs from total outflow trade are negative in Jilin, Heilongjiang, and Liaoning, but positive in the 27 other provincial administrative regions. This implies that most of China's provincial administrative regions have a net outflow of ECEs. Specifically, the contribution values of Hebei and Inner Mongolia ranked among the top two (specifically, 1.24729 and 1.00764 billion tons). Judging by the contribution rate of the four driving factors, there are, respectively, 26, 10, 3, and 11 provincial administrative regions in which the contribution rate was negative. The direct carbon emission coefficient significantly inhibited the growth of ECEs from total outflow trade, total outflow trade volume significantly promoted the growth of such ECEs, and both intermediate production technology and total outflow trade structure also promoted the growth of such ECEs significantly. From the perspective of total inflow trade, the contribution values regarding the driving factors behind the growth of such ECEs were all positive in China's 30 provincial administrative regions. The contribution values of Hebei, Jiangsu, Henan, and Inner Mongolia ranked among the top four (specifically, 1.2827, 1.13491, 1.08225, and 1.08076 billion tons). Judging by the contribution rate of the four driving factors, there are, respectively, 28, 4, 0, and 2 provincial administrative regions in which the contribution rate of the four driving factors was negative. Much like the variation of ECEs from total outflow trade, the direct carbon emission coefficient significantly inhibited the growth of ECEs from total inflow trade, total inflow trade volume significantly promoted the growth of such ECEs, and both intermediate production technology and total inflow trade structure promoted the growth of such ECEs overall.

3.3. SDA results regarding the driving factors behind ECEs from a consolidated perspective From a consolidated perspective, this section analyzes the SDA results regarding the driving factors behind ECEs. First, ECEs from export trade and import trade are merged into ECEs from international trade, while ECEs from inter-provincial outflow trade and inter-provincial inflow trade are merged into ECEs from inter-provincial trade. Accordingly, the driving factors of ECEs are analyzed from the perspectives of international trade and inter-provincial trade. Second, ECEs from export trade and inter-provincial outflow trade are merged into ECEs from total outflow trade, while ECEs from import trade and interprovincial inflow trade are merged into ECEs from total inflow trade. Accordingly, the driving factors of ECEs are analyzed from the perspectives of total inflow trade and total outflow trade. In other words, we analyzed the SDA results regarding the driving factors behind ECEs at two levels: (1) international trade and inter-provincial trade; and (2) total outflow trade and total inflow trade. We comparatively analyzed the variations regarding the driving factors behind ECEs from a consolidated perspective in order to reach more valuable conclusions. From the perspectives of international trade and inter-provincial trade, ECEs are analyzed as follows. Figs. 3 and 4 respectively show the SDA results regarding the driving factors behind ECEs from both international trade and inter-provincial trade across China's 30 provincial administrative regions from 2002 to 2012. From the perspective of

4. Conclusions and policy implications 4.1. Conclusions Based on a distinction between international trade and inter-provincial trade, we built non-competitive input-output models and SDA 8

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10,00,000 8,00,000 6,00,000 4,00,000 2,00,000

-2,00,000 -4,00,000 -6,00,000 -8,00,000

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Niangxia Xinjiang

0

Direct carbon emission coefficient

Intermediate production technology

Total international trade

International trade structure

Fig. 1. SDA results regarding the driving factors behind ECEs from international trade in China's 30 provincial administrative regions from 2002 to 2012 (unit: 10,000 tons).

models for the driving factors behind ECEs according to China's regional extended input-output tables of 2002, 2007, and 2012. Using the SDA approach, we measured the ECEs from four modes of trade (export trade, inter-provincial outflow trade, import trade, and inter-provincial inflow trade) and analyzed their driving factors. The conclusions are summarized as follows. First, across China's 30 provincial administrative regions, the ECEs under the four modes of trade tended to first increase and then decrease, reaching a peak in 2007. This implies that China's low-carbon trade policy has been effective to some extent in recent years. Through international trade, environmental pollution has been aggravated in Hebei and Inner Mongolia but reduced in Shandong and Jiangsu. Compared with existing studies, this study finds that a massive transfer of ECEs exists in inter-provincial trade. Through interprovincial trade, environmental pollution has been reduced in Jiangsu, Shandong, and Guangdong, but aggravated in provincial administrative regions, such as Hainan and Shaanxi. Second, according to the SDA results regarding the driving factors behind ECEs under four modes of trade, the direct carbon emission coefficient was shown to significantly inhibit the growth of ECEs overall, total trade volume was shown to significantly promote the growth of ECEs overall, and intermediate production technology and trade structure were shown to promote the growth of ECEs overall, but not significantly. Evidently, during the

years 2002 to 2012, China's level of production technology continued to improve, and direct carbon emissions produced in the manufacturing process gradually decreased. However, carbon emissions from indirect consumption increased marginally, while unreasonable trade structures and an increasing total trade volume constituted essential driving forces behind the growth of ECEs. Third, the SDA results regarding the driving factors behind ECEs from a consolidated perspective were found to be similar to those under subdivided modes of trade. Judging from the perspective of whether you are dealing with total outflow trade and total inflow trade or international trade and inter-provincial trade, the direct carbon emission coefficient was shown to significantly inhibit the growth of ECEs, while intermediate production technology, total trade volume, and trade structure were shown to promote the growth of ECEs overall. Furthermore, the total trade volume is the primary driving factor behind the growth of ECEs. 4.2. Policy implications Based on both the above conclusions and the development status of China's international trade and inter-provincial trade, we offer a few policy suggestions. First, it is necessary to introduce state-of-the-art low-carbon production technologies. The conclusions above imply that

2,50,000 2,00,000 1,50,000 1,00,000 50,000

-50,000 -1,00,000 -1,50,000

Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

0

Direct carbon emission coefficient

Intermediate production technology

Total international trade

International trade structure

Fig. 2. SDA results regarding the driving factors behind ECEs from inter-provincial trade in China's 30 provincial administrative regions from 2002 to 2012 (unit: 10,000 tons). 9

Technological Forecasting & Social Change 153 (2020) 119930

8,00,000 6,00,000 4,00,000 2,00,000 0 -2,00,000 -4,00,000 -6,00,000 -8,00,000

Beijing Tianjin Hebei Shanxi Inner… Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

Z. PU, et al.

Direct carbon emission coefficient

Intermediate production technology

Total international trade

International trade structure

Fig. 3. SDA results regarding the driving factors behind ECEs from total outflow trade in China's 30 provincial administrative regions from 2002 to 2012 (unit: 10,000 tons).

the direct carbon emission coefficient significantly inhibits the growth of ECEs from trade, while total trade volume significantly promotes the growth of ECEs from trade. Theoretically, a reduction in total trade volume can substantially reduce the ECEs from trade. However, in the long term, it is not feasible to reduce trade volume and economic growth substantially. Therefore, it is advisable to improve production technologies further. In particular, regions such as Hebei and Inner Mongolia should reasonably introduce state-of-the-art low-carbon production technologies both domestically and abroad, increase lowcarbon levels and energy utilization efficiency in the production process, and further promote the development of low-carbon trade. Second, the improvement of intermediate production technology should become the focus of China's next step. The results of the research in Section 3.3 show that intermediate production technology has become a positive driver of ECEs in China's provinces, both inflow, and outflow. The above result shows that, on the one hand, compared with the terminal technology represented by the direct carbon emission coefficient, China's intermediate production technology still has a high room for improvement, on the other hand, it also shows that intermediate flows including warehousing and logistics, etc. In the link, technological constraints are constantly promoting the leakage of hidden carbon. Therefore, China should gradually consider tilting the center of gravity of upgrading its production technology and lowcarbon technology from terminal technology to intermediate production technology in order to reduce hidden regions. Carbon emissions. Third, it is necessary to widen the carbon emission trading market. Empirical analysis shows that there exists a transfer of ECEs from international trade and inter-provincial trade. To further support the

common but differentiated responsibility for carbon reduction, it is necessary to admit the carbon leakage in international trade and interprovincial trade, gradually perfect the carbon trading market, and guarantee the carbon emission permit trade between different regions. Regions with substantial carbon emissions or transfers of carbon emissions should purchase a certain amount of carbon emission permits from regions with low levels of carbon emissions, thus promoting synergistic development between economies and environments across different regions. On this basis, China should further establish a national carbon trading market and seek domestic carbon trading price parity to reduce the ECEs Interprovincial transfers within China also. Fourth, in China, there are many industrial transfers from developed regions to less developed areas. Although these transfers support the development of China's underdeveloped regions to a certain extent, from a time-varying perspective, these transfers often contain medium to high levels of hidden carbon. Therefore, in the implementation of China's relevant regional transfer policies in the future, policymakers should include factors such as low carbon as pre-decision conditions for project transfers to ensure that all regions in China can achieve joint sustainable development. CRediT authorship contribution statement Zhengning PU: Conceptualization, Writing - original draft, Visualization, Writing - review & editing, Funding acquisition. Shujing YUE: Methodology, Project administration, Data curation. Peng GAO: Methodology, Writing - original draft, Visualization, Software.

2,00,000 1,50,000 1,00,000

0 -50,000 -1,00,000 -1,50,000

Beijing Tianjin Hebei Shanxi Inner… Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

50,000

Direct carbon emission coefficient

Intermediate production technology

Total international trade

International trade structure

Fig. 4. SDA results regarding the driving factors behind ECEs from total inflow trade in China's 30 provincial administrative regions from 2002 to 2012 (unit: 10,000 tons).

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Technological Forecasting & Social Change 153 (2020) 119930

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Picture 1. Export (Left) and Import (Right) ECEs from 2002 to 2012.

Acknowledgments

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