a case study on export of china

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Original research article, embodied energy in export flows along global value chain: a case study of china’s export trade.

• Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education, School of Energy and Power Engineering, Dalian University of Technology, Dalian, China

Energy issues are closely related to the development of human society and economy. Embodied energy is the total direct and indirect energy consumption required for the production of goods and services. In the context of the intensifying development of economic globalization and prosperity of international trade, embodied energy is considered as a better indicator to comprehensively reflect the nature of a country’s energy use than the direct energy use. The development of trade in value added (TiVA) accounting and global value chain theory has brought new ideas to embodied energy research. This study applies TiVA accounting to the study of embodied energy and establishes a complete framework to decompose the sources, destinations, and transfer routes of embodied energy in a country’s exports, and comprehensively depicts the embodied energy flows in China’s exports at the country and sector levels as an instance. The results show that China exports large amounts of embodied domestic energy use, and export is an important factor for the rapid growth of China’s energy and emissions. At the country level, the United States and EU28 are traditional major importers of China, and developing countries, such as Brazil, India, and Indonesia, are emerging markets. China’s embodied energy flows to different importers vary in terms of trade patterns, flow routes, and the embodied domestic energy intensities. At the sector level, the light industry and the services create more benefits, whereas manufacturing, such as chemicals and metal products, consumes more energy, and there is a mismatch between the main sectors that create economic benefits from exports and the main sectors that consume energy for exports. These results indicate that embodied energy of China’s exports has a great impact on global energy consumption and carbon emission, and the optimizing of China’s export embodied energy structure is conducive to global energy conservation and emission reduction. This article strongly suggests the importance of the global value chain decomposition framework in embodied energy research.

Introduction

Energy is a basic element of the social economy, and the available energy both limits and governs the structure of human economies ( Costanza, 1980 ). The huge economic growth and human welfare improvements are coupled with ever-increasing energy depletion. The primary world energy consumption rose sharply from 361.52 EJ in 1995 to 583.90 EJ in 2019 ( BP, 2019 ). On the other hand, owing to the rapid development of the global economy and industrialization, the massive emissions resulting from the combustion of fossil energy have become a leading cause of global climate change. Sustainable energy development and combating climate change have become key issues of global concern. Meanwhile, the emerging economic globalization has accelerated the spatial separation of production and consumption in global supply chains, connecting economic development in one country with energy use in another country through good flows in international trade. Such separation no longer limits the energy import (by countries) to the context of direct import of energy products and can also improve the import of energy-intensive intermediate and final products to achieve the goal of reducing domestic energy consumption ( Wiedmann et al., 2015 ). Simultaneously, this kind of spatial separation occurs to carbon emissions related to fossil fuel energy embodied in the products. Such carbon emission flows related to the embodied fossil fuel energy may result in carbon leakage if only the carbon emitted domestically is taken into account, without considering carbon embodied in imported goods and emitted in the exporting countries ( Wyckoff and Roop, 1994 ). And many studies on international trade, embodied energy, and emissions demonstrated that the embodied flows of energy and emissions may cause the consequence of carbon leakage ( Mongelli et al., 2006 ; Lin and Sun, 2010 ; Cui et al., 2015 ). With the development of international trade and increasing production globalization, energy flows among countries are becoming increasingly intricate, as are the carbon emissions related to these energy flows. Direct domestic energy consumption can no longer completely delineate the nature of a country’s energy use, and therefore, “embodied energy” is considered to be a more appropriate measure. In the context of global action to tackle climate change and carbon emission reduction, trade embodied energy (especially embodied fossil fuel energy) is closely related to the transfer and flows of carbon emissions, and may even cause carbon leakage, which is not conducive to global emission reduction. Therefore, it is of great significance to study the flows of energy embodied in trade, especially fossil fuel energy.

Embodied energy is the total direct and indirect energy consumption required for the production of goods and services ( Bullard and Herendeen, 1975 ). Input-output analysis is the main method used to measure embodied energy. In recent decades, research on embodied energy using input-output analysis has been developed in a spurt with the continuous improvement of input-output technology, along with the growing enrichment of energy statistics in various countries and different input-output databases. The related literature can be divided according to the research scale, including the global level ( Bortolamedi, 2015 ; Chen and Wu, 2017 ; Jiang et al., 2020 ), multilateral country level ( Wu and Chen, 2019 ; Zhang et al., 2019 ), bilateral country level ( Yang et al., 2014 ; Tao et al., 2018 ), single-country level ( Costanza, 1980 ; Lenzen, 1998 ; Machado et al., 2001 ; Lam et al., 2019 ; Wang and Yang, 2020 ), region level ( Sun et al., 2017 ; Guo et al., 2020a ; Zheng et al., 2020 ), city level ( Chen and Chen, 2015 ; Guo et al., 2015 , 2020b ), and sector level ( Liu et al., 2012 , Liu et al., 2020a , b ; Sun et al., 2016 ; Guo et al., 2019 ). There are also numerous studies regarding the research of embodied energy of different energy varieties, such as coal ( Xia et al., 2017 ; Wu and Chen, 2018 ), oil ( Tang et al., 2012 ; Wu and Chen, 2019 ; Wang and Yang, 2020 ), natural gas ( Kan et al., 2019 , 2020 ), biomass ( Ji et al., 2020 ), and nuclear energy ( Cortés-Borda et al., 2015 ). “Complex network analysis” is also commonly used in embodied energy research ( An et al., 2015 ; Chen and Chen, 2015 ; Yang et al., 2015 ; Sun et al., 2016 ; Chen et al., 2018 ; Liu et al., 2019 ; Tang et al., 2019 ). Chen conducted a comprehensive analysis of the abundance of literature related to embodied energy and concluded that embodied energy can provide a well-integrated perspective on energy consumption and demand, and as embodied energy has been used in academics, issues related to China have been holding a high level of attention ( Chen et al., 2019 ).

China is the largest energy consumer and CO 2 emitter in the world, and its energy issue has attracted worldwide attention. China is also the largest trading country and the primary trading partner of many countries. In recent years, China’s huge international trade surplus has received unprecedented attention, and it has become a reason for bilateral trade friction. Compared with imported products from developed countries and regions, China’s export products have lower value added and higher energy consumption and emissions per unit export trade volume, which will inevitably lead to the imbalance of energy consumption and emission flows. On the one hand, it has brought great pressure on domestic energy resources and the environment; on the other hand, it has also aroused international concern and even criticism about the growth of China’s energy demand and emissions, and various “China threat theories” have emerged in an endless stream. In recent years, with respect to the context of global action to tackle climate change, “China’s climate threat theory” is also on the rise. However, regarding the studies on embodied energy, the fact that China is a net exporter of energy contradicts this “threat theory” to some extent in terms of energy use ( Tang et al., 2012 ; Cui et al., 2015 ; Wu and Chen, 2017 ; Jiang et al., 2020 ; Wang and Ge, 2020 ). Many empirical analyses on carbon emission embodied in China’s export also suggested that the scale of carbon emission embodied in China’s foreign trade is very large ( Shui and Harriss, 2006 ; Weber et al., 2008 ; Lin and Sun, 2010 ; Su and Ang, 2013 ). A large amount of energy and carbon emissions embodied in China’s exports meet the consumption of other countries and regions (especially developed countries), which has changed the pattern of global energy consumption and carbon emissions to a certain extent. The embodied fossil fuel energy in China’s exports is not only closely related to embodied carbon emissions, but also an important factor driving China’s energy consumption. Studies on the energy (especially fossil fuel energy) embodied in China’s exports are significant for China’s energy conservation and emission reduction, as well as the global emission reduction and combating climate change. Conversely, previous research on China’s embodied energy is mostly limited to the gross value measure at the national, regional, or sector levels, as well as the net energy transfer in China’s international or domestic regional trade ( Gao et al., 2018 ; Tang et al., 2019 ; Guo et al., 2020a ; Liu et al., 2020a ; Zheng et al., 2020 ).

Studies on detailed energy flows embodied in China’s international trade are as important as the amount of embodied energy in international trade; however, previous studies have seldom depicted the detailed energy flow routes, except for the gross value and the net flow value and directions. In addition, there is still a lack of comprehensive analyses at various levels. Moreover, most of the existing research is based on gross value accounting. Due to the deepening of the international division of labor in production and the in-depth development of intermediate goods trade, intermediate goods may cross borders back and forth. Therefore, the energy embodied in export goods is not limited to domestic sources and can come from foreign countries. Imports may also include the domestic energy that was previously exported. Nevertheless, the analysis based on gross value accounting cannot separate these parts from the total embodied energy. Moreover, previous studies often use the domestic energy use coefficient to replace the coefficients of other countries when it comes to import and export issues and to deduct the embodied energy of imported intermediate products in exports, resulting in large inaccuracies in the results. It is difficult to analyze the source of embodied energy in a country’s imports and exports, nor to ascertain the real destinations of the energy embodied in the exported intermediates because of the restrictions on energy data and corresponding input-output data, as well as the limitation of gross value accounting itself. With the advent of trade in value added (TiVA) accounting and the development of the global value chain theory, the sources, destinations, and transfer routes of the value added in international trade can be completely decomposed; these harbor new ideas for embodied energy research.

In 2012, the WTO (World Trade Organization) and OECD (Organization for Economic Co-operation and Development) launched the “Measurement of Trade in Value Added” joint research project. Several international organizations, such as the European Union and United Nations Conference on Trade and Development (UNCTAD) have also conducted statistical studies on TiVA ( OECD and WTO, 2011 ). This work has promoted the mainstreaming of TiVA statistics and made it a permanent part of the official international statistical system. The measurement of the global value chain based on TiVA accounting has been widely adopted. “Global value chain” is also called “vertical specialization,” and it has many related labels (such as “value chain cutting,” “outsourcing production,” “production non-integration,” “production fragmentation,” “multi-level production,” and “product internal specialization”) ( Hummels et al., 2001 ). Balassa proposed a kind of continuous production process in which product is divided into a vertical trade chain, which extends to many countries, and the interconnectivity of this production process is gradually enhanced. Each country focuses on a specific stage in the production process and adds value according to its comparative advantage. This global division phenomenon is defined as vertical specialization ( Balassa, 1965 ). However, because of the restrictions on data and calculation methods, the research on vertical specialization remained at the case study level until Hummels defined a narrow concept of vertical specialization and put forward a quantitative index of systematic measurement, which made it possible to measure the global value chain ( Hummels et al., 2001 ). Since then, the methodology has been developed continuously ( Koopman et al., 2008 , 2010 , 2012 , 2014 ; Wang et al., 2009 ; Daudin et al., 2011 ; Johnson and Noguera, 2012 ; Stehrer, 2012 ; Timmer et al., 2014 ). Finally, Wang et al. compared the TiVA accounting method (with the gross value accounting system) from the perspective of gross exports and decomposed the total exports into 16 terms (consisting of 12 value added items and 4 double-counting items), thereby, realizing the complete decomposition of gross exports ( Wang et al., 2014 ); the decomposition framework of the global value chain accounting is thus complete.

The TiVA accounting and global value chain decomposition framework have brought new ideas to embodied energy research, and in particular, because of the abundance of the global input-output data, some studies have adopted this measure to analyze embodied energy ( Liu et al., 2019 , 2020b ); however, these analyses were only conducted at the country aggregate level and focused on the construction sector. There is still a lack of detailed analyses of China’s export embodied energy flows. In addition, in the process of completing the final decomposition framework, the importance of the forward linkage and backward linkage measures at the sector level has been stressed, owing to the fact that forward linkage focuses on the source sectors that initially consume the energy, whereas the backward linkage focuses on the sectors that finally export products ( Wang et al., 2014 ). However, this difference has not been considered in previous studies at the sector level, and the analysis of embodied energy at the sector level was mostly conducted for a certain sector, with relatively little analysis of the differences between the various sectors. Following the global value chain decomposition framework, this study decomposes a country’s gross export into 17 terms and energy embodied in the gross exports into 13 terms (respectively, according to the sources and final destinations of value added and energy consumption). Then, using the decomposed components, this study provides a detailed analysis of China’s export embodied energy at both country and sector levels according to the comprehensive aspects of gross value, trade patterns, sources, and destinations. Furthermore, a new indicator of energy intensity is proposed in this study to evaluate the real domestic energy cost of economic benefits in exports. Overall, considering China’s exports as an example, this article shows the detailed routes (from sources to the final destinations) to depict the embodied energy flows along the global value chain. On one hand, it is conducive to analyze the impact of China’s export embodied energy on global energy consumption; on the other hand, it can describe the flow of energy-related carbon emissions. Moreover, this study is the basis and conducive to describe the real energy transfer between countries, and the research framework of this study is also applicable to the study of embodied carbon emissions and other embodied flows.

The remainder of this article is organized as follows. In section “Methodology and Data,” the methodology and data sources are introduced. In section “Empirical Result,” the empirical results are analyzed in detail. Section “Discussion and Implications” presents a discussion of the relevant results, and the conclusions and outlooks are presented in section “Conclusion and Outlooks.”

Methodology and Data

Methodology.

The multi-region input-output (MRIO) analysis is commonly used to measure the embodied resource and environmental flows across regions. Almost all the decomposition methods in the recent vertical specialization and TiVA literature are rooted in the work of Leontief (1936) . Table 1 provides a fundamental framework for the MRIO table. According to the basic input-output model, all gross output of country s must be used as either intermediate goods or final goods at home (or abroad):

Table 1. Basic framework of a multi-region input-output table.

where X s ( X r ) denotes the gross output of country s (country r ), and A sr ( A ss ) is the direct input coefficient matrix, which gives the intermediate use in country r (country s ) of goods produced in country s , and each element of it equals the corresponding intermediate use divide gross input, i.e., a ij = z ij / x j . Y sr ( Y ss ) denotes the final use in country r (country s ) of goods produced in country s . It can be expressed in matrix form as follows:

After rearranging equation (2), we can obtain the following:

where B sr is the total requirements matrix, which gives the total requirement to produce a unit of gross output of country r needed from country s (similar for B ss , B Gs , B sG , and B GG ). Y s ( Y G ) is the gross final goods produced in country s (country G ), including domestic use Y ss ( Y Gs ) and abroad use ∑ r ≠ s G Y s r ( ∑ r ≠ G G - 1 Y G r ).

Subsequently, we denote the direct energy use coefficient matrix as F , and F = [ F 2 ⋯ 0 ⋮ ⋱ ⋮ 0 ⋯ F G ] ; each submatrix in it is the diagonal matrix of the sectoral direct energy use coefficient. For instance, F s = [ f 1 s ⋯ 0 ⋮ ⋱ ⋮ 0 ⋯ f N s ] ; its elements are denoted by f j s = e u j s / x j s . f j s is the direct energy use coefficient of sector j in country s , and e u j s is the direct energy use of sector j in country s . Then, we can obtain the gross direct energy use vector (i.e., E U = [ E U s ⋮ E U G ] ; each element represents the gross direct energy use of the corresponding country) as follows:

The gross exports of country s , E s , includes the intermediate exports ( ∑ r ≠ s G A s r X r ) and final goods exports ( ∑ r ≠ s G Y s r ), that is, E s = ∑ r ≠ s G A s r X r + ∑ r ≠ s G Y s r . The corresponding energy use embodied in the gross exports of country s ( GEEX s ) can be denoted as: G E E X s = ∑ r ≠ s G F s A s r X r + ∑ r ≠ s G F s Y s r .

As a significant contribution to the global value chain and TiVA accounting, a country’s exports to another country are completely decomposed into 16 components ( Wang et al., 2014 ). This study follows the decomposition framework proposed by Wang et al. (2014) and further splits the fifth term of their decomposition framework into two parts according to the final destination, and the process is detailed in Appendix . Thus, this study decomposes a country’s bilateral exports into 17 terms in line with the sources, absorbed destinations, and flow routes of value added. The final decomposition equation is expressed as follows:

where E sr is the total exports of country s to country r , and V s is the value added coefficient diagonal matrix of country s , in which each element v j s is the value added coefficient of sector j in country s , and v j s = v a j s / x j s , v a j s is the value added of the corresponding sector. L ss is the local Leontief inverse matrix; L s s = [ l 11 s s ⋯ 0 ⋮ ⋱ ⋮ 0 ⋯ l N N s s ] = [ 1 - a 11 s s ⋯ - a 1 N s s ⋮ ⋱ ⋮ - a N 1 s s ⋯ 1 - a N N s s ] - 1 , and E r * is the gross export of country r . And the meaning and acronym of each term in equation (5) are as follows:

(1) DVA_FIN is the domestic value added in the final exports and absorbed in the direct importer.

(2) DVA_INT is the domestic value added in intermediate exports and absorbed in the direct importer.

(3) DVA_INTrexI1 is the domestic value added in the intermediate exports and re-exported by the direct importer to a third country to produce domestic final use.

(4) DVA_INTrexF is the domestic value added in the intermediate exports and used by the direct importer to produce final goods and used in the third country.

(5) DVA_INTrexI2 is the domestic value added in intermediate exports and used by the direct importer to produce intermediate goods and re-exported to the third country to produce their exports to the fourth country.

(6) DVA_INTrexI3 is the domestic value added in intermediate export and used by the direct importer to produce intermediate goods and re-exported to the third country to produce their exports to the direct importer.

(7) RDV_FIN is the domestic value added in intermediate exports and returns home via final imports.

(8) RDV_Final2 is the domestic value added in intermediate exports and returns home via final imports from the third country.

(9) RDV_INT is the domestic value added in intermediate exports and returns home via intermediate imports.

(10) DDC_FIN is the pure double-counting from domestic source due to final exports production.

(11) DDC_INT is the pure double-counting from domestic source due to intermediate exports production.

(12) MVA_FIN is the value added from the direct importer used in the final exports.

(13) MVA_INT is the value added from the direct importer used in intermediate exports.

(14) MDC is the pure double-counting sourced from the direct importer.

(15) OVA_FIN is the value added from the third country used in the final exports.

(16) OVA_INT is the value added from the third country used in the intermediate exports.

(17) ODC is the pure double-counting sourced from the third country.

Following this decomposition framework, we decompose the gross energy embodied in the exports of country s to country r ( GEEX sr ) into 13 parts as follows:

The decomposition framework of the embodied energy in exports is presented in Figure 1 , and the terms in equation (6) correspond to each term in the figure. Thus, we can measure the domestic content of energy and value added embodied in a country’s gross exports.

Figure 1. Decomposition framework of gross energy embodied in exports.

To analyze the real situation of domestic energy use and economic gains, this study defines a new measure of energy intensity (export embodied domestic energy intensity, EMDEI), representing a country’s domestic energy consumption for creating a unit of domestic value added through export; the formulas for the country aggregate and bilateral country levels are as follows:

Country aggregate level:

Bilateral country level:

where DEU and DVA are the sums of the first to the ninth term in equations (6) and (5), respectively, denoting the gross value of the domestic content embodied in the country’s exports at the corresponding level; the superscript s denotes the exporter and r denotes the direct importer.

As mentioned in the previous section, the exports (accounting based on forward and backward linkages) are equal at the country level but differ at the sector level; this distinction has been disregarded in previous embodied energy studies. A sector’s gross export embodied energy (or value added) based on the forward linkage focuses on the source sector, including the energy (or value added) of a given sector and embodied in the gross exports of all sectors in this country, whereas the measure based on backward linkage focuses on the final export sector, including the gross energy (or value added) from all sectors in the country embodied in a given sector’s gross exports. For instance, in a two-sector country case, the export value of sector 1 is 60 units and the value of sector 2 is 40 units based on the forward linkage measure, and the values change to 35 and 65 units, respectively, based on the backward linkage measure. The country aggregation is the same (100 units) (An illustration refers to Supplementary Figure 1 ). These two measures are expressed in the following equations:

where d e u i s and d v a i s are the sector aggregations of the first to ninth terms in equations (6) and (5); “ fw ” and “ bw ” in the subscript represent the forward and backward linkage measures, respectively. The forward linkage measure is the sum across the columns along the row, whereas the backward linkage measure is the sum across the rows along the column. Thus, the EMDEI at the sector level can be calculated as follows:

Forward linkage based:

Backward linkage based:

Data Sources

The MRIO tables used in this study were derived from the world input-output Database (WIOD) ( Timmer et al., 2015 ) and were released in 2016. The series of world input-output tables (WIOTs) cover 28 EU (European Union) countries and 15 other major economies, along with the “rest of the world region” (ROW) for the period 2000-2014 (for specific countries and regions, refer to Supplementary Table 1 ). The corresponding energy data were derived from the Joint Research Center of the European Commission ( Corsatea et al., 2019 ). The Joint research center provides two sets of data, including total energy use and emission-related energy use, and the emission-related energy use data were used in this study. This article decomposes the gross export of China to other countries and regions covered in the WIOT, along with the gross energy embodied in the export for the period 2000–2014. In this study, 18 sectors (merged of the 56 sectors in the original WIOTs) in China were analyzed at the sector aggregate level, the details are listed in Table 2 .

Table 2. Sectors and numbers.

Empirical Results

Analysis at country aggregate level.

Based on equations (5) and (6), this study decomposes China’s exports to the 43 importers covered in the WIOT for the period 2000–2014, and the results at country aggregate level are presented in this section.

The decomposition results of China’s gross exports (17 terms) and gross exports embodied energy (13 terms) are shown in the stacked bars in Figure 2 , along with the domestic value added (DVA) (gross exports embodied domestic energy use, DEU) proportion of China’s total GDP (total energy use) shown in the line charts. China’s gross export increased from 262 billion dollars in 2000 to 2,425 billion dollars in 2014, with only a slight dip caused by a global recession in 2009. DVA_FIN and DVA_INT (the domestic value added in final exports and intermediate exports and absorbed in the direct importer) dominated the gross exports, DVA_INTrexI1 (the domestic value added in the intermediate exports and re-exported by the direct importer to a third country to produce domestic final use), DVA_INTrexF (the domestic value added in the intermediate exports and used by the direct importer to produce final goods and used in the third country), OVA_FIN (the value added from the third country used in the final exports), and OVA_INT (the value added from the third country used in the intermediate exports) also accounted for significant proportions, along with the growing pure double-accounting part [that included DDC_FIN (the pure double-counting from domestic source due to final exports production), DDC_INT (the pure double-counting from domestic source due to intermediate exports production), MDC (the pure double-counting sourced from the direct importer), and ODC (the pure double-counting sourced from the third country)]. What should be noted is that the pure double counting in the exports is due to the intermediates across borders back and forth in international trade. This part grows as the intermediate trade and processing trade are increasing in recent decades. And the traditional gross value accounting, these double countings are also calculated as part of a country’s gross exports, although they do not create any value added (whether domestic or foreign). The DVA proportion of China’s total GDP reflects, to some extent, the dependence on the export of China’s economic development, which showed an increasing trend before 2007 and a decreasing trend thereafter. The proportion reached a peak of 28.8% in 2006 and stabilized at approximately 20% in 2013 and 2014, equivalent to the level in 2002.

Figure 2. Decomposed terms of gross export and corresponding embodied energy and the domestic content share in total China’s GDP and energy use. The graphs are provided for (A) gross export and (B) gross export embodied energy.

The gross energy use embodied in China’s exports (GEEX) showed the same tendency as the gross exports before 2008 and increased from 8,600 PJ in 2,000 to 29,600 PJ in 2007, while showing a different trend since 2008. After the global financial crisis, the GEEX picked up slightly in 2010; after 2011, it stabilized at 30,000 PJ. DEU_FIN and DEU_INT (domestic energy use embodied in the final exports and intermediate exports and absorbed in the direct importer) accounted for over 70% of the GEEX, whereas DEU_INTrexI1 (the domestic energy use embodied in the intermediate exports and re-exported by the direct importer to a third country to produce domestic final use), DEU_INTrexF (the domestic energy use embodied in the intermediate exports and used by the direct importer to produce final goods and used in the third country), OEU_FIN (the energy use from the third country used in the final exports), and OEU_INT (the energy use from the third country used in the intermediate exports) dominated the remainder. The DEU proportion of China’s gross energy use showed the same trends as the economic results throughout the study period, peaking in 2006 at 40% and stabilizing in 2013 and 2014 at approximately 26%. The proportion of DEU each year was slightly higher than the corresponding DVA proportion, implying that China’s exports are more domestic energy-intensive than its domestic consumed products.

According to the trade pattern, the gross export can be classified as final trade [including DVA_FIN, MVA_FIN (the value added from the direct importer used in the final exports), and OVA_FIN] and intermediate trade [including the remainder 14 terms in equation (5)]. Correspondingly, the GEEX can be divided into GEEFX [gross energy embodied in final exports, including DEU_FIN, MEU_FIN (the energy use from the direct importer used in the final exports), and OEU_FIN] and GEEIX [gross energy embodied in intermediate exports, including the remaining 10 terms in equation (6)]; the decomposition results by trade pattern can be found in Supplementary Figure 2 . The final trade accounted for more than half of China’s total exports, and at the end of the study period, the share decreased to approximately 50%. The GEEFX accounted for over half of the GEEX before 2013, and the GEEIX exceeded 50% in 2013 and 2014.

In terms of the sources, the value added and energy embodied in China’s exports can be divided into domestic and foreign contents, namely, DVA and FVA [foreign value added embodied in export, including MVA_FIN, MVA_INT (the value added from the direct importer used in intermediate exports), OVA_FIN, and OVA_INT]; DEU and FEU [foreign energy embodied in exports, including MEU_FIN, MEU_INT (the energy use from the direct importer used in intermediate exports), OEU_FIN, and OEU_INT]; the pure double-counting terms of exports are excluded, and the details can be found in the Supplementary Figure 3 . Domestic sources dominated throughout the study period, with the DVA accounting for over 80% and DEU accounting for over 90% in most years. The proportion of domestic energy in China’s exports was higher than that of the DVA, which indicates that the role of China’s exports in creating economic benefits was less than that of stimulating energy consumption. The share of FVA was larger than the share of FEU, implying that China’s exports drove more foreign economy compared to its dependence on foreign energy, especially during the period 2002-2008, in which China’s exports showed extensive growth.

The EMDEI (export-embodied domestic energy intensity) of China can be calculated using equation (7). In contrast to the commonly used energy intensity in exports (gross energy consumption divided by gross exports), the EMDEI used in this study measures the domestic energy use when creating a unit of domestic value added through exports and is supposed to be a more effective index to evaluate the relationship between energy use and economic growth driven by a country’s exports. The results of China’s EMDEI for the period 2000-2014 are shown in Figure 3 . The EMDEI of gross export decreased from 35.8 kJ/dollar in 2000 to 13.4 kJ/dollar in 2014. The energy intensity of the export products was higher than that of products consumed domestically, which further reflected that China’s export product structure tended to be highly energy-dependent; although this phenomenon has improved since 2007, the export product structure still needs to be improved. The embodied domestic energy intensity of China’s exports has been declining, mainly due to the general improvement in the energy efficiency of domestic production and partly due to the structural optimization of export products. The most striking finding is that China’s EMDEI in gross exports continuously declined after 2003, and the EMDEI of the intermediate exports was always higher than that of the final exports, indicating that China needs to pay more energy cost to obtain economic benefits through the export of intermediate goods more than that of final goods.

Figure 3. Embodied domestic energy intensity of China’s exports.

Analysis at Bilateral Country Level

Analysis at the country aggregate level can only present an overview of energy flows embodied in China’s exports. The analysis of the energy embodied in China’s exports to different economies can provide further information based on spatial heterogeneity. The distribution of China’s GEEX is presented in Figure 4 , along with the proportions of the energy embodied in the final exports. 28 EU countries covered in the WIOT are analyzed as one region in this study, that is, EU28. According to the figure, the rest of the world (ROW), the United States, EU28, Japan, and Korea were the top five importers of China in terms of the gross export embodied energy, accounting for over 60% of China’s GEEX calculated during the whole study period; the United States is the largest individual importer except for the country groups, such as ROW and EU28. In addition, developing countries, like Brazil, India, and Indonesia, accounted for a growing proportion.

Figure 4. Energy embodied in China’s export to each importer and the corresponding proportion of energy embodied in final exports; graphs are provided for (A) gross export embodied energy and (B) proportion of final export embodied energy.

Trade patterns of embodied energy varied widely between the importers of China’s exports, and according to Figure 4 , the GEEFX proportions of the GEEX in China’s exports to each importer (hereinafter referred to as “the proportion” in this paragraph) varied significantly. The proportion of China’s exports to Russia was the largest, followed by the exports to the United States and Norway. In addition, except for the top three countries, the proportions of exports to Japan, EU28, Australia, and Canada were higher than the proportion of China’s aggregate exports. The proportion of exports to India was the lowest before 2004, and since then, South Korea has occupied the last rank. Except for these two importers, the proportions of exports to the Czech Republic, Turkey, Mexico, Brazil, Indonesia, and ROW were lower than the proportion of China’s aggregate exports in most years during the study period. To sum up, the embodied energy is exported to developed economies, mainly through final goods, and to developing economies, mainly through intermediates.

The analysis of the GEEX sources can reveal the dependence of China’s exports on different energy sources. The GEEX sources of export to the importers are shown in Figure 5 , and three types of energy sources were analyzed in this study, including domestic energy, energy from the direct importer, and energy from the third-party (energy from all countries and regions except for China and the direct importer). Domestic energy supplied the vast majority of export energy demand, over 87% as calculated, and foreign sources were mainly from a third-party, with a small part also from direct importers themselves. The domestic share was higher in 2000 as a whole and lower in 2007 and 2014. The source mix varied for different importers; the DEU share of exports to the United States was the lowest, whereas the share of exports to Korea was higher than the share of exports to other economies. The DEU share of exports to India ranked third in 2000 and 2005 and was replaced by Mexico in 2010 and 2014. The share of energy from the importer itself was larger in China’s exports to Russia and ROW than to other importers, while the share of energy from the third-party in exports to Brazil and the United States was the highest.

Figure 5. Embodied energy sources of China’s export to each importer; graphs are provided for (A) 2000, (B) 2005, (C) 2010, and (D) 2014.

The GEEX does not come from one source, and the DEU is not completely absorbed by the direct importers. Figure 6 shows the destinations where the DEU was finally absorbed, and three types of final destinations were analyzed in this study, including the direct importer, the third-party (energy absorbed in all countries and regions except for China and the direct importer), and returned home (absorbed in China), and the energy absorbed in the former two destinations is the part of domestic energy that is really exported to foreign countries and regions. In general, over 70% of domestic value added was absorbed in the direct importers for export to most countries. The share that is absorbed in direct importer was higher, the corresponding value chain of the export products was shorter. The share of DEU absorbed by the direct importer in exports to the United States was the highest, followed by the export to Russia, Brazil, Australia, and Japan. The share of DEU returned home in exports to Korea was evidently higher than those of exports to other importers. The share of DEU absorbed by the third-party in exports to Mexico was the highest, followed by the exports to Korea.

Figure 6. Final destination of embodied domestic energy in China’s export to each importer; graphs are provided for (A) 2000, (B) 2005, (C) 2010, and (D) 2014.

The EMDEI varies among importers, and differences exist in final exports and intermediate exports. The details of EMDEI in the gross exports, final exports, and intermediate exports (to each importer) can be found in Supplementary Figure 4 . The EMDEI generally showed a significant decline in the gross exports and final exports, and the range of difference among importers in the final export was the smallest, whereas in intermediate exports it was the largest. The EMDEI in the gross exports decreased from 30 kJ/dollar to 48 kJ/dollar to less than 20 kJ/dollar in the exports to all the importers. In 2014, the EMDEI in the gross exports to India was the highest, followed by the EMDEI in the gross exports to Korea and Turkey; the EMDEI in gross exports to Russia was the lowest, followed by the EMDEI in the gross exports to ROW, Norway, the Czech Republic, and EU28. The EMDEI in the final exports decreased from 27 kJ/dollar to 38 kJ/dollar to less than 15 kJ/dollar in exports to all the importers. In 2014, the EMDEI in the final exports to Mexico was the highest, followed by the EMDEI in the final exports to Turkey, India, Brazil, Australia, EU28, and the United States, and the EMDEI in final export to Russia was the lowest, followed by the EMDEI in the final exports to ROW, the Czech Republic, Korea, Japan, and Norway. For EMDEI in intermediate exports, the value varied in a wide range among economies, and the value in the intermediate exports to India (in 2001 and 2002) and Korea were largest, whereas the value in the intermediate exports to Russia was lower than that in the intermediate exports to other economies. In 2014, the EMDEI in the intermediate exports to Korea was much higher than that of others, whereas in the intermediate exports to Russia it was the lowest, followed by the United States, Norway, and Japan. Except for Korea, both China’s intermediate and final exports to developing economies have higher export-embodied domestic energy intensities, while its exports to developed economies have lower embodied domestic energy intensities. However, the domestic energy intensity of China’s intermediate and final export to the United States varies highly; the final export to the United States has a higher domestic energy intensity than the final export of other countries. However, in the case of intermediate export, the United States has a lower domestic energy intensity than the intermediate export of other countries; this is because the final products that the United States imports from China are mainly clothing, textiles, and other manufacturing products having a high energy intensity, whereas the intermediate imports include services having a low energy intensity.

Analysis at Sector Aggregate Level

Analysis at the sector level can provide further information about the energy flows embodied in China’s exports according to sector heterogeneity. In this study, we have segregated the decomposition results based on equations (5) and (6) according to 18 sectors, and the results at the sector aggregate level are presented in this section. As mentioned in the previous section, the analysis at the sector level shows a distinction between the forward and backward linkage measures. The forward linkage measure focuses on the source sector, including the energy (or value added) from this sector, and is embodied in the gross exports of other sectors, whereas the backward linkage measure focuses on the export sector, including all the energy (or value added) from other sectors, and is embodied in this given sector’s gross exports.

The domestic value added and energy embodied in each sector’s gross exports are shown in Figure 7 . The sector mix differed widely between the forward and backward linkage measures. As for DVA (the embodied domestic value added) based on the forward linkage measure, the top three sectors were S.18 (other service activities), S.16 (wholesale and retail trade, accommodation, and food service activities), and S.10 (manufacture of electrical equipment and products); S.10, S.04 (manufacture of textiles, wearing apparel, and leather products), and S.16 (wholesale and retail trade, accommodation, and food service activities) were the top three sectors based on the backward linkage measure. For DEU (the gross embodied domestic energy) based on the forward linkage measure, S.14 (electricity, gas, steam, and water supply) was undisputedly the largest, followed by S.09 (Manufacture of basic metals and metal products) and S.07 (chemical industry), whereas the top three sectors based on the backward linkage measure were S.10, S.09, and S.07. Except for that S.10 ranks top three in terms of both its contribution to DVA and DEU based on the two measures; there is a mismatch between the main sectors that create the economic benefits from the exports and the main sectors that consume energy for the exports. In general, the light industry and the services create more benefits, whereas manufacturing, such as chemicals and metal products, consumes more energy.

Figure 7. Export embodied domestic value added and domestic energy at sector aggregate level based on forward and backward linkages; graphs are provided for (A) domestic value added embodied in each sector’s export and (B) domestic energy embodied in each sector’s export.

The export patterns of the sectors differ greatly, and Figure 8 presents the GEEFX (energy use embodied in gross final exports) proportion of the GEEX (energy use embodied in gross exports) in each sector (hereinafter referred to as “the proportion” in this section) based on the backward linkage and forward linkage measures. When using the forward linkage measure, the proportions of the sectors can be divided into four grades: the first grade includes S.13 (other manufacturing) and S.04 (manufacture of textiles, wearing apparel, and leather products), of which the proportions were much higher than other sectors (over 75%), implying that the energy in these sectors was almost exported embodied in the final goods; the second grade includes S.03 (manufacture of food products, beverages, and tobacco products), S.10 (manufacture of electrical equipment and products), S.01 (agriculture, forestry, fishing, and related service activities), S.11 (manufacture of machinery and equipment n.e.c), and S.12 (manufacture of transport equipment), of which the proportions ranged between 55% and 70%; the third grade includes S.05 (manufacture of products of wood and cork), S.14 (electricity, gas, steam, and water supply), S.16 (wholesale and retail trade, accommodation, and food service activities), S.06 (manufacture of article, printing, and reproduction), S.18 (other service activities), S.07 (chemical industry), S17 (transportation, warehousing, postal, and telecommunications), S.09 (manufacture of basic metals and metal products), S.02 (mining and quarrying) after 2003, and S.08 (manufacture of other non-metallic mineral products) before 2010, of which the proportions ranged between 40% and 55%; and the last grade included S.15 (construction), S.02 before 2003 and S.08 after 2010, of which the proportions were lower than 40%. The situation differed when using the backward linkage measure, and the proportions of sectors can be clearly divided into three grades: the first grade includes S.13, S.03, and S.04, of which the proportions were higher than 80%, implying that the energy exported through these sectors almost embodied in the final goods; the second grade includes S.11, S.10, S.12, and S.01 for most years, of which the proportions were higher than 50%; the third grade includes the other sectors, of which the proportions were almost lower than 40%, and the proportion of construction sector (S.15) in this grade was approximately 0 for most years because this sector scarcely directly exported products.

Figure 8. Share of final export embodied energy in the sector’s gross export embodied energy; graphs are provided for (A) forward linkage and (B) backward linkage.

Figure 9 shows the sources of the GEEX in each sector. The domestic source generally dominated each sector’s GEEX, followed by the energy from the third-party, and the share of energy from the direct importer was the least. Based on the forward linkage measure, the sector ranks by domestic source share differed in various years; for example, the domestic energy share of S.02 (mining and quarrying) and S.15 (construction) ranked as the lowest two in 2005 and 2010, respectively, whereas S.13 (other manufacturing) had the lowest domestic source share in 2014. However, when using the backward linkage measure, the sector ranks of the domestic source share were relatively stable, and S.14 (electricity, gas, steam, and water supply), S.02, S.08 (manufacture of other non-metallic mineral products), and S.09 (manufacture of basic metals and metal products) ranked in the top four, and S.10 (manufacture of electrical equipment and products) occupied the bottom position.

Figure 9. Embodied energy source of each sector’s export; graphs are provided for forward linkage-based measure in (A) 2000, (B) 2005, (C) 2010, and (D) 2014 and backward linkage-based measure in (A1) 2000, (B1) 2005, (C1) 2010, and (D1) 2014.

Figure 10 presents the share of destinations where the DEU of each sector is finally absorbed. Based on the forward linkage measure, over 75% of the DEU in each sector was absorbed by the direct importers, and the proportions in S.13 (other manufacturing), S.04 (manufacture of textiles, wearing apparel, and leather products), S.03 (manufacture of food products, beverages, and tobacco products), and S.01 (agriculture, forestry, fishing, and related service activities) ranked in the top four. In general, the share that finally returned home grew over time. The share that was finally absorbed by the third-party was higher in S.02 (mining and quarrying), S.07 (chemical industry), and S.09 (manufacture of basic metals and metal products) than in other sectors. When using the backward linkage measure, the share of DEU that was absorbed by the direct importer in S.13, S.03, S.04, and S.01 ranked in the top four, similar to the results obtained using the forward linkage measure; the shares in S.02 and S.09 ranked the lowest in 2000 and 2005, whereas S.07 took the place of S.09 in 2010 and 2014. The share that finally returned home also grew over time in most of the sectors, and this share in S.02, S.07, and S.09 was relatively larger than in other sectors. The share that was finally absorbed by the third-party in S.02, S.07, and S.09 ranked in the top three, whereas the share in S.03, S.04, and S.13 ranked in the bottom three.

Figure 10. Embodied domestic energy destination of each sector’s export; graphs are provided for forward linkage-based measure in (A) 2000, (B) 2005, (C) 2010, and (D) 2014 and backward linkage-based measure in (A1) 2000, (B1) 2005, (C1) 2010, and (D1) 2014.

The sector EMDEI measures the domestic energy use for creating a unit of domestic value added through exports, that is, the energy cost of economic income derived from the exports for each sector, and the result is shown in Figure 11 . Moreover, the forward linkage-based EMDEI of a sector actually means the energy use in this sector for the country to create a unit domestic value added through exports. Based on this measure, the EMDEI of S.14 (electricity, gas, steam, and water supply) was much higher than the value of other sectors, and the value in 2014 was approximately 230 kJ/dollar, equivalent to 40% of the value in 2000. The EMDEI of S.08 (manufacture of other non-metallic mineral products) ranked second, declining from 102 kJ/dollar in 2000 to 40 kJ/dollar in 2014, and different from the tortuous downward trend of other sectors, the value of this sector smoothly ascended to the peak in 2004 to later decline. The EMDEIs of S.09 (manufacture of basic metals and metal products) and S.07 (chemical industry) ranked third and fourth, respectively, and that of S.06 (manufacture of article, printing, and reproduction) stably ranked fifth after 2005. In 2014, except for the top five sectors, S.13 (other manufacturing) had the highest value, followed by S.02 (mining and quarrying), S.04 (manufacture of textiles, wearing apparel, and leather products), and S.05 (manufacture of products of wood and cork). The value of S.16 (wholesale and retail trade, accommodation, and food service activities) was the lowest, followed by those of S.15 (construction) and S.10 (manufacture of electrical equipment and products).

Figure 11. Export embodied domestic energy intensity of each sector; the graphs are provided for (A) forward-linkage based measure and (B) backward-linkage based measure.

The backward linkage-based EMDEI of each sector calculates the energy use to create a unit domestic value added in the whole country through exports in this sector. Based on this measure, the EMDEI of S.14 was much higher than the value of other sectors, but much lower than the value based on the forward linkage measure. The EMDEIs of S.08, S.09, and S.07 ranked second, third, and fourth, respectively, the same as the forward linkage-based measure, and the EMDEIs of other sectors showed a downward trend and were generally higher than those based on the forward linkage measure. In 2014, except for the mentioned top four sectors, the EMDEI of S.06 was the highest, followed by that of S.02 and S.15, whereas the value of S.16 was the lowest, followed by that of S.18 (other service activities), S.01 (agriculture, forestry, fishing, and related service activities), and S.03 (manufacture of food products, beverages, and tobacco products). In addition, the EMDEIs of the chemical industry and metal and non-metallic equipment manufacturing industries were the highest (based on the forward and backward-linkage measures), implying that these sectors need to pay the greatest cost of energy when China creates domestic economic benefits through exports. The costs of energy were also higher for these sectors to create economic income through exports than as compared to other sectors. Therefore, improving the export structure and reduce the proportion of these sectors’ products in total exports, as well as fundamentally improving the technical level of these sectors and promote their energy efficiency in production are conducive for China’s energy conservation.

Analysis at Sector-Country Level

The results at the country aggregate and bilateral country levels are presented in the previous subsections, along with the results obtained at the sector aggregate level. Figure 12 shows the energy flows embodied in China’s export at the sector-country level in 2000 and 2014. In each chord diagram, the nodes in the upper half (proceeding clockwise from “S.01” to “S.18”) represent the DEU in each sector’s export (based on backward linkage measure), and “Foreign energy” represents the foreign energy embodied in China’s export; these nodes are source nodes, representing the energy sources of China’s GEEX (energy embodied in gross exports). The nodes proceeding clockwise from “CHN” to “Third-party” in the lower half represent the destinations where the embodied energy is finally absorbed. The destination node of “CHN” represents the DEU (domestic energy embodied in exports) that is exported at first and finally returns home and is absorbed by China; the node of “Third-party” represents the gross value of the DEU that was absorbed by the third-party. The lines from the source nodes to the destination nodes represent the scales of the embodied energy flows.

Figure 12. Embodied energy flows in China’s export at the sector-country level; graphs are provided for (A) 2000 and (B) 2014.

As shown in Figure 12 , the details of the results over these two years vary significantly; a few examples are given below. Comparing the results of 2014 to 2000, the scale of China’s GEEX increased, and the foreign energy share also slightly increased. The share of the DEU that returned home also increased significantly; the main sources were S.09 (manufacture of basic metals and metal products) and S.10 (manufacture of electrical equipment and products). The sector share decreased evidently in S.01 (agriculture, forestry, fishing, and related service activities), S.02 (mining and quarrying), S.03 (manufacture of food products, beverages, and tobacco products), S.04 (manufacture of textiles, wearing apparel, and leather products), S.16 (wholesale and retail trade, accommodation, and food service activities), S.17 (transportation, warehousing, postal, and telecommunications), and S.18 (other service activities), while increased obviously in S.08 (manufacture of other non-metallic mineral products), S.09, S.10, S.11 (manufacture of machinery and equipment n.e.c), and S.12 (manufacture of transport equipment). The destinations of sectors also varied, and some sectors showed their unique distribution of destinations, while for other sectors, the distribution of importers was ranked by their gross import volume from China. For instance, the GEEX of S.13 (other manufacturing) was mainly absorbed by the United States and EU28, while the ROW was China’s largest importer in 2000. The share of S.13 to ROW increased evidently. In general, the GEEX was mainly absorbed by the EU28, ROW, and third-party in both years.

Overall, the United States and EU28 have been China’s largest energy importers, except for the ROW and Japan (whose share fell sharply in 2014). Therefore, this study further analyzes these two key importers more specifically. The detailed energy flows embodied in China’s export to the United States in 2014 are shown in Figure 13 , and the energy flows embodied in China’s exports to the United States in 2000 and exports to EU28 in 2000 and 2014 can be found in Supplementary Figures 5 – 7 .

Figure 13. Energy flows embodied in China’s export to the United States in 2014.

As shown in Figure 13 , from left to right, the first column represents t, which is the total energy embodied in China’s export to the United States in 2014 from different sources, including domestic energy (that is, from China), energy from third-party (that is from other countries and regions except for China and the United States), and energy from the United States itself. The second column represents the forward linkage sectors that initially consume domestic energy (or foreign energy in intermediates) to produce products. The line between domestic energy and each sector is the domestic energy directly used for each sector’s production, while the other two sources are the foreign intermediate goods directly used for each sector’s production. The third column represents the backward linkage sectors that finally export products to the United States, and the line from the second column to the third column is the flow of the total energy embodied in China’s exports to the United States in the domestic value chain stage. The fourth column shows the export patterns, that is, the energy embodied in the final products and embodied in the intermediate products. The fifth column contains 13 parts, which are the 13 terms in GEEX decomposition framework of this article, and represents the transfer routes of the embodied energy flows. The last column represents the final destination where the embodied energy of China’s exports is finally absorbed, including energy that is absorbed in the United States, energy that is absorbed by third parties (other countries and regions except for China and the United States), and energy that returns to China and is absorbed.

Comparing the results of exports to the United States for the year 2014 (referring to Figure 13 ) with that of 2000 (referring to Supplementary Figure 5 ), the overall size of the embodied energy flows increased by a factor of 1.5, whereas the share of domestic sources decreased slightly and the share of third-party sources increased. The sector mix based on both the forward and backward linkages changed in a way. The top five source sectors were S.14 (electricity, gas, steam, and water supply), S.07 (chemical industry), S.09 (manufacture of basic metals and metal products), S.08 (manufacture of other non-metallic mineral products), and S.02 (mining and quarrying) in 2000, while changed slightly to S.14, S.09, S.07, S.02, and S.08 in 2014. The top five export sectors were S10 (manufacture of electrical equipment and products), S.04 (manufacture of textiles, wearing apparel, and leather products), S.13 (other manufacturing), S.07, and S.09 in 2000, while S.10, S.07, S.04, S.09, and S.11 (manufacture of machinery and equipment n.e.c) ranked in the top five in 2014. In addition, no energy was exported to the United States through S.15 (construction) and S.16 (wholesale and retail trade, accommodation, and food service activities). Furthermore, the share of energy embodied in the intermediate exports increased obviously in 2014. As for the final destination, the share of domestic energy absorbed by the United States decreased slightly, whereas that of the other two increased slightly.

At the sector level, the structures of energy source sectors and export sectors were relatively different. In 2014, as for the source sector, S.14 (electricity, gas, steam, and water supply), S.09 (manufacture of basic metals and metal products), and S.07 (chemical industry) contributed the most, whereas S.10 (manufacture of electrical equipment and products), S.07, and S.04 (manufacture of textiles, wearing apparel, and leather products) exported the most. There were also significant differences in the transfer between the forward and backward sectors. For example, the energy used in S.04 was mainly exported to the United States through S.04 itself, but the energy used in S.14 was mainly exported through other sectors. In addition, the energy used in S.15 (construction) and S.16 (wholesale and retail trade, accommodation, and food service activities) was exclusively exported to the United States through other sectors. Trade patterns also differed among the sectors, as the exports of S.04, S.10, and S.13 (other manufacturing) were mainly through final goods while intermediate export was the main way for the export of S.07, S.12 (manufacture of transport equipment), and S.17 (transportation, warehousing, postal, and telecommunications).

Comparing the results of exports to the EU28 for the year 2014 (referring to Supplementary Figure 7 ) with those obtained for 2000 (referring to Supplementary Figure 6 ), it can be found that the overall scale of the embodied energy flows increased by 2.4 times, whereas the share of domestic source decreased, and the share of the third-party source and energy from EU28 itself both increased. There were no significant changes in the source sector mix, except for an obvious increase in S.13 (other manufacturing) and a decrease in S.08 (manufacture of other non-metallic mineral products) and S.17 (transportation, warehousing, postal, and telecommunications). The top five export sectors were S.10 (manufacture of electrical equipment and products), S.07 (chemical industry), S.04 (manufacture of textiles, wearing apparel, and leather products), S.09 (manufacture of basic metals and metal products), and S.18 (other service activities) in 2000, but changed to S.10, S.07, S.09, S.04, and S.11 (manufacture of machinery and equipment n.e.c) in 2014. The share of the energy embodied in the final exports increased obviously, whereas that of the intermediate exports decreased. As for the final destination, the share of the domestic energy that was absorbed by the EU28 decreased evidently, and the shares that returned home and was finally absorbed by the third-party both increased.

In 2014, as for the source sectors, S.14 (electricity, gas, steam, and water supply), S.09 (manufacture of basic metals and metal products), and S.07 (chemical industry) contributed the most, while S.10 (manufacture of electrical equipment and products), S.07, and S.09 exported the most as the backward sectors. Notably, the embodied energy flows of China’s exports to EU28 differed considerably from those of China’s exports to the United States in various details. For instance, the source sector structures in both cases were similar; although the backward sector mix was similar in 2014, it differed widely in 2000. S.18 (other service activities) accounted for a significant share of the gross embodied energy flows from China to EU28 as a backward sector, sourcing from S.14 and S.18 itself, while the embodied energy of China’s exports to the United States seldom flows through S.18 directly. Furthermore, there were some energy flows exported to EU28 through S.15 (construction) and S.16 (wholesale and retail trade, accommodation, and food service activities) despite a decrease in the share in 2014, whereas in the case of United States, there was no direct export through these two sectors. Another difference is the trade pattern of S.10 (in the case of EU28), the export pattern of S.10 was represented by a 50/50 split between the final and intermediate exports in 2014. Additionally, the share of intermediate exports in 2014 increased evidently compared with the share in 2000, which is inconsistent with the evolution of the export trade pattern distribution of China’s total exports to EU28. However, in the case of exports to the United States, the evolution of the export trade pattern of S.10 was consistent with the trend of China’s total exports to the United States, showing the trend of an increase in the intermediate export exports.

Discussion and Implications

At the country aggregate level, China exported a large amount of embodied energy, while other countries avoid a large amount of domestic energy use by consuming China’s products. Since the energy data used in this study are emission-related energy use, it suggests that China also exported large amounts of carbon emissions to meet the demand for other countries and regions, which shows that China’s exports have changed the global energy consumption and carbon emission pattern to a certain extent. Taking both the embodied energy and the economy into consideration, the role of exports in China’s economic development and energy consumption showed an initial rise followed by a decline, while peaking in 2006. Moreover, according to the decomposition of China’s gross exports, there is a growing share of pure double countings, which cannot be recognized and excluded in the traditional gross value accounting and lead to an overestimation of the gross exports volume. Thus, the TiVA accounting and global value-chain theory used in this study do perform better in international trade analysis. The share of domestic energy in the two patterns of trade, that is, the final exports and intermediate exports, was similar to the trade pattern share of value added. Each of these two export routes accounted for approximately half of the gross value of the exports, which was inconsistent with the trend that the share of intermediate trade was in a rapid expansion globally, due to China’s downstream position in the global value chain and its participation in global production through the processing and assembly of supplied materials. The ratio of domestic content in gross exports indicates the ability to create value added per unit of energy consumption. The ratio rising means that there are some local market advantages, such as a low cost of transportation or low cost of high-skilled labor ( Liu et al., 2020a ). The ratio of domestic content in China’s gross export showed a decreasing trend before 2007 and an increasing trend thereafter, reflecting the growing competitiveness of China’s exports in the later study period. The higher the proportion of domestic components of embodied energy, on the one hand, it can be considered that exports are more dependent on domestic energy supply, or that the domestic self-sufficiency rate of export embodied energy is relatively high. But from another point of view, about 90% of the fossil energy embodied in China’s exports is supplied domestically, indicating that China mainly acts as a supplier of fossil energy in its export production and its participation in the global value chain. The energy intensity of the export products was higher than that of products consumed domestically, which further reflected that China’s export product structure tended to be highly energy-dependent; the domestic energy intensity of the intermediate exports was higher than that of the final exports, indicating that China needs to pay more energy cost to obtain economic benefits through the export of intermediate goods more than that of final goods. This result suggests that China’s participation in global production is more inclined towards high-energy consumption, even upstream of the production chain, and is more inclined to be an energy supplier with high-energy consumption and low income, rather than to be in the research and development stage of low-energy consumption and high income. Previous literature has shown that China’s energy intensity and carbon intensity are higher than those of many developed countries. Developed countries imported products with high energy intensity and carbon intensity from China to reduce their domestic energy use and carbon emissions and to reach their carbon emission reduction targets. However, from a global point of view, the imbalances of energy and carbon intensities between China and developed countries may lead to a growth of global carbon emissions, which increases the risk of global carbon leakage. Thus, optimizing the energy structure and declining the energy intensity of China’s exports are conducive to energy conservation and emission reduction in China and the whole world.

At the bilateral country level, the United States and EU28 are the top two importers, and the shares of Brazil, India, and Indonesia are also increasing. The embodied energy is exported to developed economies mainly through final goods, and to developing economies mainly through intermediates. Russia is a special case because China’s exports to Russia are mainly concentrated in the fields of clothing, instruments, mechanical and electrical transport equipment, raw materials, chemical products, and agricultural products; these are mainly final goods. Compared with China’s exports to other importers, China’s export structure to South Korea is more in favor of energy-intensive intermediates. The exports to Korea are mainly concentrated in low-end mechanical and electrical products, base metals, and chemical products, and the export structures can be further improved. The embodied domestic energy intensities of China’s intermediate exports differ significantly among importers, whereas the energy intensities of the final exports differ slightly, which is due to the greater difference in the importer distribution of China’s intermediate exports. Except for Korea, both China’s intermediate and final exports to developing economies, such as Mexico, India, Indonesia, and Turkey, have higher export-embodied domestic energy intensities, while its exports to developed economies, such as Norway, Japan, EU28, and the Czech Republic have lower embodied domestic energy intensities. Therefore, the international trade between China and emerging markets needs to be emphasized and embodied flows of fossil fuel energy and carbon emissions in these trades may be an increasingly important factor of global emission reduction.

At the sector aggregate level, there is a big difference based on the forward and backward linkage measures, whether for embodied value added analysis or embodied energy analysis. In general, the light industries and the services create more benefits, whereas manufacturing sectors, such as chemicals and metal products, consume more energy. There is a mismatch between the main sectors that create the economic benefits from the exports and the main sectors that consume energy for the exports. In fact, the transfer process between the forward-linkage sectors and backward-linkage sectors of energy flows is the energy transfer process in domestic value chains. The imbalances between the forward linkage and backward linkage measures are the effects of domestic value chains. Thus, the differences between the forward and backward linkage measures at the sector level deserve more attention. S.14 (electricity, gas, steam, and water supply) is the upstream sector of the production chain, and most of the energy embodied in exports is initially consumed in this sector. The energy in S.15 (construction) is almost exported embodied in the export of other sectors, and thus is easily neglected in the research of energy export. The construction sector is a special sector that needs attention in the study of export embodied energy. The domestic energy embodied in the exports of S.02 (mining and quarrying), S.07 (chemical industry), and S.09 (manufacture of basic metals and metal products) are more often absorbed by the third-party than the domestic energy embodied in other sectors’ exports, because the production chains related to these sectors are longer, and thus, it is easier for these sectors to participate in global production. Therefore, enhancing the competitiveness of these sectors in global trade and promoting the energy efficiency of their productions are conducive to improving China’s position in the global value chain and achieve the country’s goal of energy reduction. The EMDEI of S.14 is significantly higher than that of other industries, and the energy consumption of this sector is mainly due to the production of electricity; therefore, optimizing the power supply structure of China may effectively improve China’s export embodied energy intensity, and is conducive to China’s energy conservation and emission reduction. In addition, the EMDEIs of the chemical industry and metal and non-metallic equipment manufacturing industries are the highest (based on the forward and backward-linkage measures), implying that these sectors need to pay the greatest cost of energy when China creates domestic economic benefits through exports. Additionally, the costs of energy are also higher for these sectors to create economic income through exports than as compared to other sectors. Therefore, improving the export structure and reducing the proportion of these sectors’ products in total exports, as well as fundamentally improving the technical level of these sectors and promoting their energy efficiency in production are conducive to China’s energy reservation and emission reduction, as well as to the global emission reduction.

At the sector-country level, the domestic energy embodied in some sectors showed a unique distribution of destinations, although, for many sectors, the distribution of importers was ranked by their total import volume from China. In addition, the energy source and export sectors have extremely different structures, and the energy flows from the source sector to the export sector reflect the embodied energy transfer routes in the domestic supply chain. The embodied energy flows of China’s exports to the EU28 are relatively different from those in exports to the United States. Although EU28 and the United States are both major importers of China, the flow routes of the export embodied energy vary significantly, and even in their domestic value chain stage, the flow routes between the source and export sectors are different.

Conclusion and Outlooks

This study presents a detailed analysis of the energy embodied in China’s exports at the country aggregate, bilateral country, sector aggregate, and sector-country levels in terms of the gross value, trade patterns, energy sources, and domestic energy destinations. The export embodied domestic energy intensity is calculated to show the energy cost required to create a unit domestic value added through exports. The major conclusions are as follows.

(1) In terms of total volume, China’s export embodied energy is very large, and the domestic energy use proportion of China’s gross energy use was high (although declining after 2006, but still at 26% at the end of the study period), and to a certain extent, China’s embodied fossil fuel energy exports have changed the pattern of world energy consumption, as well as the carbon emission. This finding well confirms China’s unique position as a “world processing plant” in international trade, and proves that export is an important factor that cannot be ignored to promote the rapid growth of China’s energy and emissions. Optimizing the energy structure of China’s exports is conducive to energy conservation and emission reduction in China and the whole world.

(2) The proportion of domestic energy in China’s exports was higher than that of the domestic value added; China’s exports in creating economic benefits were less than that of stimulating energy consumption. The energy intensity of the export products was higher than that of products consumed domestically, and China’s export structure is still characterized by high-energy consumption and needs to be continuously optimized. China’s participation in global production is more inclined to high-energy consumption, even in the upstream of the production chain, and is more in the initial energy-input stage of high-energy consumption and low-income than in the research and development stage of low-energy consumption and high-income. Optimizing the structure of export products in China, can not only improve China’s position in the global production chain, and enhance its competitiveness in high-end industries with low energy intensity, but is also conducive to global energy reservation and emission reduction.

(3) At the country level, the United States and EU28 are traditional major importers of China, and developing countries, such as Brazil, India, and Indonesia, are emerging markets. Embodied energy exported to developed importers is mainly through final goods, and to developing regions, mainly through intermediates. In general, the domestic energy cost per unit of economic income (through exports) to developed countries is lower than that of exports to developing countries. Therefore, the international trade between China and emerging markets needs to be emphasized, and embodied flows of fossil fuel energy and carbon emissions in these trades may be an increasingly important factor of global emission reduction.

(4) At the sector level, the light industry and the services create more benefits, whereas manufacturing, such as chemicals and metal products, consumes more energy, and there is a mismatch between the main sectors that create economic benefits from exports and the main sectors that consume energy for exports. The differences between the forward and backward linkage measures at the sector level require more attention. The sectors of electrical equipment manufacturing and products, mining and quarrying, chemical industry, manufacture of basic metals and metal products, and construction sector are the key sectors that need more attention in the study of export embodied energy. The costs of domestic energy to benefit from the exports of chemical, metal, and non-metal equipment manufacturing sectors are the highest among all sectors, and China should not only improve the export structure and reduce the proportion of products from these sectors in total exports but also fundamentally improve the technical level of these sectors and promote their energy efficiencies.

(5) The domestic energy embodied in some sectors showed a unique distribution of destinations. There is a large gap in the flow routes of the energy embodied in China’s exports to different importers, and even in their domestic value chain stages, the flow routes between the domestic sectors are different. Therefore, it is of great significance and importance to conduct detailed studies on the sources, destinations, and transfer routes of energy flows embodied in China’s international trade from the perspectives of the trade-in value added method and the global value chain.

This article provides a basic framework for the study of embodied flows based on the global value chain theory and trade in value added accounting. As a stage achievement, this article makes a detailed analysis of the embodied energy flows of China’s exports. The main deficiency of this article lies in the lag of the research period caused by data limitation. The world input-output tables have a strong lag and cannot reflect the latest information, and it is necessary to improve the timeliness of research. Besides, this study can be further deepened, such as switching importers and exporters, in order to analyze the net energy transfer in bilateral trade. Moreover, the research framework of this article is also applicable to the study of carbon emissions embodied in international trade and has an important reference role for the flow and transfer of carbon emissions embodied in international trade.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Author Contributions

BZ, SB, and YN made substantial contributions to the conception of the work, and to its drafting and/or critical revision, and approved for publication. All authors contributed to the article and approved the submitted version.

This study was supported by the National Natural Science Foundation of China (71873021 and 71573029).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

Financial support from the National Natural Science Foundation of China is deeply acknowledged. The authors would like to thank the comments and suggestions of reviewers for their important help in improving the quality of this article.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenrg.2021.649163/full#supplementary-material

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In Wang et al. (2014) , the gross exports of country s to country r were decomposed into 16 terms using equation (A1):

where E sr is the total exports of country s to country r , and V s is the value added coefficient diagonal matrix of country s , in which each element v j s is the value added coefficient of sector j in country s (similar for V r and V T ). L ss is the local Leontief inverse matrix. E r^* is the gross export of country r . The meaning and acronym of each term in equation (A1) are as follows:

(T1) is the domestic value added in the final exports and absorbed in the direct importer.

(T2) is the domestic value added in intermediate exports and absorbed in the direct importer.

(T3) is the domestic value added in the intermediate exports and re-exported by the direct importer to a third country to produce domestic final use.

(T4) is the domestic value added in the intermediate exports and used by the direct importer to produce final goods and used in the third country.

(T5) is the domestic value added in intermediate exports and used by the direct importer to produce intermediate goods and re-exported to the third country to produce their exports to other countries (except for the original exporter, country s).

(T6) is the domestic value added in intermediate exports and returns home via final imports from country r .

(T7) is the domestic value added in intermediate exports and returns home via final imports from the third country.

(T8) is the domestic value added in intermediate exports and returns home via intermediate imports.

(T9) is the pure double-counting from domestic source due to final exports production.

(T10) is the pure double-counting from domestic source due to intermediate exports production.

(T11) is the value added from the direct importer used in the final exports.

(T12) is the value added from the direct importer used in intermediate exports.

(T13) is the pure double-counting sourced from the direct importer.

(T14) is the value added from the third country used in the final exports.

(T15) is the value added from the third country used in the intermediate exports.

(T16) is the pure double-counting sourced from the third country.

What should be noted in this decomposition framework is that, in the fifth terms (T5), only export from the third country (country t ) to the original exporter (country s ) are excluded from country t ’s total export. However, there is still a part of exports are consumed by country r , and related value added from country‘ s embodied in this part of exports are absorbed in country r , and it should be split from (T5). In the formula, when u = r , ( T 5 ) ′ = ( V s L s s ) T # ( A s r ∑ t ≠ s , r G B r t Y t r ) , represents the domestic value added in intermediate export and is used by the direct importer to produce intermediate goods and re-exported to the third country to produce their exports back to the direct importer. From the perspective of the final absorbed destination of the domestic value added embodied in country s , this part should be calculated separately, since it represents the domestic value added of the exporter, country s , and is absorbed in the direct importer, country r , through the third country, country t . This transfer route is different from other flows. Thus, in this study, the fifth term (T5) in Wang et al. (2014) is divided according to the final absorbed destination. Namely, we divided (T5) into two parts, ur in one part and u = r in the other as follows:

Thus, this article decomposes the gross exports of a country into 17 terms according to the original sources, final destinations, and transfer routes of the value added embodied in the exports.

Keywords : embodied energy, global value chain, export, input-output analysis, trade in value added

Citation: Zhang B, Bai S and Ning Y (2021) Embodied Energy in Export Flows Along Global Value Chain: A Case Study of China’s Export Trade. Front. Energy Res. 9:649163. doi: 10.3389/fenrg.2021.649163

Received: 04 January 2021; Accepted: 29 March 2021; Published: 04 May 2021.

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Copyright © 2021 Zhang, Bai and Ning. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yadong Ning, [email protected]

This article is part of the Research Topic

Sustainable Energy Production and Consumption: System Accounting, Integrated Management, Policy Responses

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Heavy subsidies for industry, together with weak sales in China, have set the stage for an export boom, raising fears of factory job losses elsewhere.

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By Keith Bradsher

Reporting from Beijing

For decades, China has moved methodically to dominate ever more industries, from toys and clothing in the 1980s to semiconductors and renewable energy today. China now produces a third of the world’s manufactured goods — more than the United States, Germany, Japan, South Korea and Britain combined. Its trade surplus in these goods is equal to a tenth of the entire Chinese economy.

And those exports keep increasing, causing alarm about China’s manufacturing “overcapacity” among its biggest trading partners. Top leaders in the United States and Europe have begun calling on China to dial back how much it sells to the world, and to increase its imports. On Tuesday, President Biden raised U.S. tariffs sharply on imports from China of electric cars , solar panels and other high-tech manufactured goods.

China’s industrial policies had a consistent focus.

Almost a decade ago, China launched an ambitious program called Made in China 2025 . The plan was for China to replace key imports in 10 advanced manufacturing industries by making its own products. The state-controlled banking system directed loans to those key sectors.

Fast forward 10 years, and China’s domestic economy is hurting mostly because of a housing market crash. Leaders in Beijing have ordered increased lending for many of the same manufacturing sectors to compensate for slower consumer spending, and are stepping up exports.

For China’s economic policymakers, the strategy is familiar.

It works like this: Regulators restrict the investment options of Chinese households, which have little choice but to deposit enormous sums of money into banks at low interest rates. The banks then lend the money at low rates to start-ups and other businesses. According to China’s central bank, net lending for industry swelled to $670 billion last year from $83 billion in 2019.

Beijing instructs local governments to help the chosen industries. The assistance takes the form of cheap land for factories, new highways for freight trucks, bullet train lines and other infrastructure.

The Kiel Institute for the World Economy in Kiel, Germany, calculated in a study that more than 99 percent of Chinese companies whose stock traded publicly received direct government subsidies in 2022.

China keeps factory wages low, which helps the competitiveness of its manufacturers. Residence permits limit the ability of rural families to move permanently to cities, where they would qualify for better job benefits. Independent labor unions are barred, and would-be organizers are detained by the police.

Those programs have helped China grow in many industries, fanning fears in the United States and elsewhere that factory jobs could be lost. American tariffs are now targeting exports in some of China’s largest and fastest-growing industries.

Car exports rose fast.

The auto sector is a prime example of how China has been able to move so fast to gain manufacturing dominance.

Just four years ago, China was a weakling in car exports, shipping one million low-priced cars a year mainly to less affluent markets in the Mideast and elsewhere. China has since surpassed Japan and Germany by a wide margin to become the world’s largest car exporter. Shipments are running at an annual pace of nearly six million cars, sport utility vehicles, pickups and vans.

Three-quarters of these exports, particularly to Russia and to developing countries, are cars with gasoline engines, which fewer buyers in China want. Battery electric cars are cheaper to buy in China, and electricity to charge them is cheaper than gasoline.

China’s top leaders have heavily subsidized the research and production of battery electric cars for the past 15 years .

Companies are ramping up their manufacturing of battery electric cars and building a fleet of ships to export them to distant markets, particularly in Europe. Automakers are introducing 71 models of electric cars in China this year, many of them loaded with advanced features and selling for less than comparably equipped cars in the West.

China now leads in producing electric car batteries.

China started off far behind the West in electric car batteries — and Chinese officials knew it.

By 2011, Beijing had begun requiring Western companies to transfer key technologies to operations in China if they wanted consumers in China to receive the same subsidies for imported electric cars that were being offered for cars made in China. Without the subsidies, automakers like General Motors and Ford Motor could not compete with electric cars made in China.

Multinational automakers responded by pressuring their South Korean suppliers, which at the time led the electric car battery industry to build factories in China. Beijing went further in 2016 and declared that even electric cars made in China would qualify for consumer subsidies only if they used batteries from factories owned by Chinese companies . Even automakers like South Korea’s Hyundai abandoned the Chinese factories of South Korean battery manufacturers and switched their contracts to Chinese battery companies like CATL.

Chinese companies now produce the majority of the world’s electric car batteries. Technological breakthroughs over the last several years have meant the cars can achieve greater range.

According to a new report from the Atlantic Council, a research group in Washington, China’s exports of lithium-ion batteries leaped to $65 billion last year from $13 billion in 2019. Nearly two-thirds of these exports went to Europe and North America. Much of the rest went to East Asia, where the batteries are often assembled into products that end up being sold to Europe or North America.

China turned to solar to reduce reliance on oil imports.

China has long made solar panels a top priority to limit its dependence on imports of oil and other fossil fuels along sea lanes controlled by the United States or India, another geopolitical rival. A tenfold expansion of China’s solar panel manufacturing capacity from 2008 to 2012 caused the world price of solar panels to drop about 75 percent. Many American and European factories closed.

Three of China’s largest solar panel producers suffered financial collapses of their own as prices plunged, saddling banks with losses on loans. Smaller rivals in China were able to buy their factories for fractions of the original construction cost. This second generation of companies was then able to make panels more cheaply and invest in cutting-edge research.

Chinese companies make almost all of the world’s solar panels. The country’s exports of solar cells, which the Biden administration is raising tariffs on, have more than doubled in the past four years, to $44 billion last year. China is ramping up twice as fast its exports of solar wafers, a key component.

U.S. limits on chips promoted a China shift.

Export controls by the United States have limited the sale to China of the most advanced semiconductors, which make up about 5 percent of the market, and the technologies to manufacture them. But Chinese companies, benefiting from enormous government subsidies, have become more competitive in the other 95 percent of the market.

The chips made by China are used in a range of equipment in the West, including many cars. Even gasoline engines in cars are controlled by semiconductor often made in China.

Why is the West acting now?

The November election has put political pressure on President Biden to show that he is taking a tough stand toward China .

Trade issues have also become enmeshed with security concerns. Russia’s war in Ukraine is showing that wars may be decided in part by which side can make more drones, artillery shells and vehicles.

China contends that its rising trade surpluses are the legitimate result of the competitiveness of Chinese companies.

Jorge Toledo Albiñana, the European Union’s ambassador to China, disagreed. “In Europe,” he said in a speech last week, “there is increasing pressure to react to what is widely seen as a worsening lack of level playing for our companies and investors.”

Keith Bradsher is the Beijing bureau chief for The Times. He previously served as bureau chief in Shanghai, Hong Kong and Detroit and as a Washington correspondent. He has lived and reported in mainland China through the pandemic. More about Keith Bradsher

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Industry linkage, spatial correlation, and city exports: case study of the textile and clothing export industry in China

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• Published: 18 July 2020
• Volume 66 , pages 91–112, ( 2021 )

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• Shulin Wan 1 ,
• Weixin Luan 1 &
• Qiaoqiao Lin 1  

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Many studies focus on economic growth in cities, but few investigate urban export growth. This paper discusses city export differences from the perspective of industry linkage and spatial correlation. The study employs a spatial simultaneous equation to investigate these issues using 2007–2016 customs data obtained from the Chinese customs database and Chinese prefecture-level city panel data. We examine three sub-industries of the textile and clothing export industry as an example: chemical fiber exports, textile exports, and clothing exports. Our findings show that the demand linkage is greater than the cost linkage within a city, the intra-industry spatial interactions across cities lead to export agglomeration, and the inter-industry spatial interactions across cities lead to export dispersion. Further, in general, promoting the development of only the downstream export industry will drive the expansion of the upstream export industry. This study provides a new explanation to better understand the spatial distribution of China’s exports, thereby offering important policy implications for stabilizing national development.

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The choice of the textile and clothing export industry is explained in Sect.  3 .

In this paper, the inter-city industry linkage is regarded as a spatial effect. When only industry linkage is mentioned hereafter, it refers to industry linkage within the city.

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Acknowledgements

This work was supported by the Youth Program of National Natural Science Foundation of China under Grant No. 41701134; the National Natural Science Foundation of China under Grant No. 41671117; and the Fundamental Research Funds for The Central Universities under Grant No. 3132019363.

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Wan, S., Luan, W. & Lin, Q. Industry linkage, spatial correlation, and city exports: case study of the textile and clothing export industry in China. Ann Reg Sci 66 , 91–112 (2021). https://doi.org/10.1007/s00168-020-01011-4

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• Published: 20 August 2019

Twin deficit hypothesis and reverse causality: a case study of China

• Umer Jeelanie Banday 1 &
• Ranjan Aneja 1  

Palgrave Communications volume  5 , Article number:  93 ( 2019 ) Cite this article

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This paper analyses the causal relationship between budget deficit and current account deficit for the Chinese economy using time series data over the period of 1985–2016. We initially analyzed the theoretical framework obtained from the Keynesian spending equation and empirically test the hypothesis using autoregressive distributed lag (ARDL) bounds testing and the Zivot and Andrew (ZA) structural break for testing the twin deficits hypothesis. The results of ARDL bound testing approach gives evidence in support of long-run relationship among the variables, validating the Keynesian hypothesis for the Chinese economy. The result of Granger causality test accepts the twin deficit hypothesis. Our results suggest that the negative shock to the budget deficit reduces current account balance and positive shock to the budget deficit increases current account balance. However, higher effect growth shocks and extensive fluctuation in interest rate and exchange rate lead to divergence of the deficits. The interest rate and inflation stability should, therefore, be the target variable for policy makers.

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Introduction.

Fiscal and monetary strategies, when executed lucidly, assume a conclusive part in general macroeconomic stability. The macroeconomic theory which assumes an ideal connection between budget (or fiscal) deficit and trade balance is known as twin deficit hypothesis. The growing literature on twin deficit hypothesis (TDH) has been theoretically and empirically researched by researchers like Kim and Roubini ( 2008 ), Darrat ( 1998 ), Miller and Russek ( 1989 ), Lau and Tang ( 2009 ), Abbas et al. ( 2011 ), Bernheim and Bagwell ( 1988 ), Lee et al. ( 2008 ), Corestti and Muller ( 2006 ), Altintas and Taban ( 2011 ), and Banday and Aneja ( 2016 ).

A study by Tang and Lau ( 2011 ) found that a 1% increase in budget deficit causes 0.43% increase in current account deficit in the US from 1973 to 2008. After the currency crises and Asian financial crises, various countries simultaneously experienced budget deficit and current account deficit. Lau and Tang ( 2009 ) confirmed the twin deficits hypothesis for Cambodia based on cointegration and Granger causality testing. Banday and Aneja ( 2016 ) and Basu and Datta ( 2005 ) confirmed the twin deficits hypothesis for India by applying cointegration and Granger causality testing. Kulkarni and Erickson ( 2011 ) found that, in India and Pakistan trade deficit was driven by the budget deficit. Thomas Laubach ( 2003 ) found that for every one percentage increase in the deficit to GDP ratio, long-term interest rates increase by roughly 25 basis points. The recent study by Engen and Hubbard ( 2004 ) found that when debt increased by 1% of GDP, interest rate would go up by two basis points. China needs to take care of lower interest rate rather than higher interest rate especially on home loans which is very low in real estate market and has created a serious trouble in the economy.

The literature identifies that the FDI “crowding in effect” as an important source of China’s current account surpluses. In fact, the World Developmental Report (1985) demonstrated that a country can promote growth using FDI and technology transfers. China employed such strategies to a remarkable effect on foreign exchange reserves which increased from US$ 155 billion in 1999 to US$ 3.8 trillion by the end of 2013. Since then, there has been a sharp decline in the capital account surplus. The data from the State Administration of Foreign Exchange (SAFE) showed that in 2015, China recorded a deficit of US$142.4 billion on the capital and financial account. However, it posted a current account surplus of US$ 330.6 billion.

However, majority of the countries are facing both budget deficit and current account deficit. The increasing budget deficit alongside with current account deficit has been an imperative issue for policy makers in China. Besides, given the importance on free trade, decentralization and development there is a need to understand the association between budget deficit and trade imbalance in Chinese economy. Few researchers like Burney et al. ( 1992 ), Banday and Aneja ( 2015 ), Lau and Tang ( 2009 ), Corsetti and Müller ( 2006 ) and Ghatak and Ghatak ( 1996 ) have emphasized the issue emerged because of growing budget deficit and its relationship with macroeconomic factors likes interest rate, exchange rate, inflation, and consumption.

In the economic literature there are two distinct theories that show the linkage between budget deficit and current account deficit. The first theory is based on the traditional Keynesian approach, which postulates that current account deficit has a positive relationship with budget deficit. This means that an increase in budget deficit will lead to a current account deficit and a budget surplus will have a positive impact on the current account deficit. The increase in budget deficit will lead to domestic absorption and, thus, increase domestic income, which will lead to an increase in imports and widen the current account deficit. The twin deficits hypothesis is based on the Mundell-Fleming model (Fleming, 1962 ; Mundell, 1963 ), which asserts that an increase in budget deficit will cause an upward shift in interest rate and exchange rate. The increase in interest rates makes it attractive for foreign investors to invest in the domestic market. This increases the domestic demand and leads to an appreciation of the currency, which in turn causes imports to be cheaper and exports costlier. However, the appreciation of domestic currency will increase imports and lead to a current account deficit (Leachman and Francis, 2002 ; Salvatore, 2006 ). Conversely, the Ricardian Equivalence Hypothesis (REH) disagrees with the Keynesian approach. It states that, in a setting of an open economy, there is no correlation between the budget and current account deficits and hence the former would not cause the latter. In other words, a change in governmental tax structure will not have any impact on real interest rate, investments or consumption (Barro, 1989 ; and Neaime, 2008 ). The assumption here is that consumption patterns of consumers will be based on the life cycle model, formulated by Modigliani and Ando in 1957 , which suggests that current consumption depends on the expected lifetime income, rather than on the current income as proposed by the Keynesian model. Furthermore, the permanent income hypothesis developed by Milton Friedman in 1957, states that private consumption will increase only with a permanent increase in income. This means that a temporary rise in income fueled by tax cuts or deficit-financed public spending will increase private savings rather than spending (Barro, 1989 , p. 39; Hashemzadeh and Wilson, 2006 ). As private savings rise, the need for a foreign capital inflow declines. In this situation a current account deficit will not occur (Khalid and Guan, 1999 , p. 390).

This paper attempts to find out how strong is the relationship between budget and current account deficits in China? Addressing this question is relevant for both policy and academic perspectives. Policymakers would like to know to what extent fiscal adjustment contributes to addressing external disequilibria, especially in the case of increasing external and fiscal imbalances. Secondly, we examined the effects of budget deficit on the current account in the multivariate framework, and also have derived the general relationship among those variables. The study is based on ARDL model to investigate twin deficit hypothesis for china both in short-run and long-run. Further, we analyze the direction of causality if such a relationship exists. We utilize econometric methods for example, ARDL bound testing approach and Granger causality test to achieve these goals. In section “Theoretical Background and Literature Review” we explore the theoretical foundation of the twin deficits hypothesis. Section “Macroeconomics Aspects of the Chinese Economy” will give Macroeconomics Aspects of the Chinese Economy. Section “Data and Empirical results” describes data and empirical results. Section “Results and Discussion” provides empirical results and section “Conclusions”, provides conclusion to the study.

Theoretical background and literature review

The theoretical relationship between budget deficit and current account deficit can be represented by the national income accounting identity:

where IM stand for imports, EX for exports, S p for private savings, I for real investments, G for government expenditure, T for taxes, and TR for transfer payments. When IM is greater than EX, the country has current account deficit. From the right-hand side of the equation, when (G + TR − T) is greater than 0, the country is running a budget deficit. The difference between budget deficit and current account deficit must equal the private savings and investments which are shown below.

In the literature, various studies attempt to find the relationship between budget deficit and current account deficit for different countries or groups of countries using various methods depending upon the sample size and pre-testing of the data. The researchers have generally focused on the U.S economy because of its simultaneous budget and current account deficit since 1980s (Miller and Russek, 1989 ; Darrat, 1998 ; Tallman and Rosensweig, 1991 ; Bahmani-Oskooee, 1992 ). However, researchers from different countries have studied twin deficit hypothesis and obtained different results for different countries (Like India, U.S and Brazil) ((Bernheim, 1988 ; Holmes, 2011 ; Salvatore, 2006 ; Mukhtar et al. 2007 ; Kulkarni and Erickson, 2011 ; Banday and Aneja, 2016 ; Ganchev, 2010 ; Lau and Tang, 2009 ; Rosensweig and Tallman, 1993 ; Fidrmuc, 2002 ; Khalid and Guan, 1999 )). These studies do not support the REH but rather accept the Keynesian traditional theory, finding that budget deficit does have an impact on current account deficit in the long run. There are studies that support the Ricardian equivalence theorem which denies any correlation between budget deficit and current account deficit (Feldstein, 1992 ; Abell, 1990 ; Kaufmann et al. 2002 ; Enders and Lee, 1990 ; Kim, 1995 ; Boucher, 1991 ; Nazier and Essam, 2012 ; Khalid, 1996 ; Modigliani and Sterling, 1986 ; Ratha, 2012 ; Kim and Roubini, 2008 ; Algieri, 2013 ; Rafiq, 2010 ). These contradictory results may be due to differences in the sample period and the methods of measuring variables, which use different econometric techniques.

Khalid ( 1996 ) researched 21 developing countries from the period of 1960–1988, taking three variables into consideration: real private consumption, real per capita gross domestic product (GDP) as a proxy of real disposable income and real per capita government consumption expenditure as a proxy of real public consumption. This was done using Johansen cointegration (1988) and full information maximum likelihood (FIML) for parameter estimates. The model gives us restricted and unrestricted parameter estimations when restricted parameters are used, meaning that parameter estimation is non-linear when testing the REH for the sample countries. The results did not reject the REH for twelve of the countries, but the remaining five countries do diverge from the REH. The rejection of Ricardian equivalence in the latter group of countries demonstrates lack of substitutability between government spending and private consumption. Ghatak and Ghatak ( 1996 ) studied variables such as private consumption, government expenditure, income, taxes, private wealth, government bonds, government deficits, investments, government spending and interest on bonds to test the Ricardian Equivalence hypothesis for India for the period from 1950 to 1986. The study employed multi-cointegration analysis and rational expectation estimation, and both the tests rejected the REH, finding evidence that tax cuts induce consumption. Thus, the results invalidate the REH for India.

Ganchev ( 2010 ) rejected the Ricardian equivalence theorem for the Bulgarian economy using monthly time series data for the period 2000–2010. The long-run results of the vector error correlation model (VECM) showed evidence of the structural gap theory, which states that fiscal deficit influences current account deficit. However, vector autoregression (VAR) results did not show any evidence of a short-run relationship between budget deficit and current account deficit. The study, therefore, found that fiscal policy should not be used, in the Bulgarian economy, as a substitute for monetary policies in maintaining the internal equilibrium.

Nazier and Essam ( 2012 ) studied the Egyptian economic data from 1992 to 2010. The data included five variables: GDP, government budget deficit (as primary deficit), current account deficit, real lending interest rate (RIR) and real exchange rate (RER). The study used structural vector autoregression (SVAR) analysis, which also gave an impulse response function (IRF) to capture the impact of budget deficit on current account deficit and real exchange rate. The findings support the twin divergence hypothesis. This contradicts the theoretical framework, which finds that a shock given to a budget deficit leads to an improvement in the current account deficit and exchange rate. Sobrino ( 2013 ) investigated the causality between budget deficit and current account deficit for the small open economy of Peru for the period 1980–2012 using the Granger causality and Wald tests, generalized variance decomposition and the generalized impulse-response function. The study found no evidence of a causal relationship between budget deficit and current account in the short run.

Goyal and Kumar ( 2018 ) investigates the connection between the current account and budget deficit, and the exchange rate, in a structural vector autoregression for India over the period 1996Q2 to 2015 Q4. The impact of oil stuns and the differential effect of consumption and venture propose compositional impacts and supply stuns rule the conduct of India's CAD, directing the total request channel. Ricardian hypothesis is not supported.

Afonso et al. ( 2018 ) studied 193 countries over the period of 1980–2016 using fixed effect model and system GMM model. The results find the existence of fiscal policy reduces the effect of budget deficit on current account deficit. When there is an absence of fiscal policy rule twin deficit hypothesis exists.

Bhat and Sharma ( 2018 ) examines the association between current account deficit and budget deficit for India over the period of 1970–1971 to 2015–2016 using ARDL model. The results accepts Keynesian proposition and rejects Ricardian equivalence theorem. The results find long-run relationship between current account deficit and budget deficit. Rajasekar and Deo ( 2016 ) find the long-run relationship and bidirectional causality between the two deficits in India. Garg and Prabheesh ( 2017 ) investigate the twin deficit for India by using ARDL model and confirm the twin deficit hypothesis. Badinger et al. ( 2017 ) investigated the role of fiscal rules in the relationship between fiscal and external balances for 73 countries over the period of 1985–2012. Their results confirm the twin deficits hypothesis. Litsios and Pilbeam ( 2017 ) investigates Greece, Portugal and Spain using ARDL model. The empirical results suggest negative relationship between saving and current account deficit in all three countries.

It is clear that the research so far has not yielded any concrete evidence regarding the causal relationship between budget deficit and current account deficit. As such there is a disagreement on the causality between the two deficits. In this paper we test the theory with the support of autoregressive distributed lag (ARDL) bounds testing approach using data for the emerging country China.

Macroeconomics aspects of the Chinese economy

The Chinese economy has experienced an unparalleled growth rate over the past few decades, with increases in exports, investments and free market reforms from 1979 and an annual GDP growth rate of 10%. The Chinese economy has emerged as the world’s largest economy in terms of purchasing power parity, manufacturing and foreign exchange reserves.

In 2008, the global economic crisis badly affected the Chinese economy, resulting in a decline in exports, imports and foreign direct investments (FDI) inflow and millions of workers losing their jobs. It is visible from the Fig. 1 that China’s current account surplus has dropped significantly from its boom during 2007–2008 financial crises. After the financial crisis, China’s exports fell by 25.7% in February 2009. Further, the exclusive demand for Chinese goods in the international market pushed up current account surplus to 11% of the GDP in 2007, in a global economy (Schmidt and Heilmann, 2010 ). It is said that China responded to the 2008 crisis with a greater fiscal stimulus in terms of tax reduction, infrastructure and subsidies when compared to the Organization for Economic Co-operation and Development (OECD) countries (Herd et al., 2011 ; Morrison, 2011 ).

Behavior of Macroeconomic variables. Overview of Budget Deficit (BD), Current Account Balance (CAB), Interest Rate (INT), Inflation (INF), Money Supply (MS) and Real effective Exchange Rate (REER) from 1985 to 2017. Source: Compiled based on data taken from the World Bank. BD and CAB are expressed as percentage of GDP

However, we draw an important inference from the Fig. 1 , when the budget deficit was lowest (−0.41% of GDP in 2008), the current account surplus increased to 9.23% of the GDP. The negative shock to the budget deficit reduces current account surplus and positive shock to the budget deficit increases current account surplus.

The deprecation of the exchange rate is directly related to the elasticity of demand which improves the exports of the country and finally enhances current account surplus. However, the negative shift in the current account is due to structural and cyclical forces. The cyclic forces can be seen from the trade prospective: the increasing prices of Chinese imports like oil and semiconductors, pulls the current account balance downwards. However, the structural shift can be seen from the financial side, which affects Chinese saving and investments. The investments got reduced to 40% and household savings has reduced from 50% to 40% of GDP.

Data and empirical results

For undertaking the empirical analysis, we use the World Bank data for the following variables. The study covers the period from 1985–2016 and is based secondary data.

Current account deficit (CAD) indicates the value of goods, services and investments imported in comparison with exports on the basis of percentage of the GDP

Budget deficit (BD) indicates the financial health in which expenditure exceeds revenue as a percentage of the GDP

Deposit interest rate (DIR) as a proxy of interest rate (INT) the amount charged by lender to a borrower on the basis of percentage of principals

Inflation (INF) is measured on the basis of consumer price index which reflects annual percentage change in the cost of goods and services

Broad money (MS) a measure of the money supply that indicates the amount of liquidity in the economy. It includes currency, coins, institutional money market funds and other liquid assets based on annual growth rate and real effective exchange rate (REER)

The basic model to find out the relationship among BD, CAD, INF, INT, REER, and MS is as follows:

where INF is inflation and DIR is direct interest rate.

Based on Fig. 2 we will estimate the two models in which CAD and BD is dependent variables and others are independent variables which is as below:

Relationship of the dependent variables (CAD and BD) with other macroeconomic variables

To estimate the above models we have employed various econometric techniques to achieve the objective of the study are as below:

Unit root test to check stationarity of the variables.

ARDL bound testing for long-run and short-run relationship among the variables.

Granger causality to check the causal relationship among the variables.

Empirical results

Unit root test.

As we are using time series data, it is important to check the properties of the data; otherwise, the results of non-stationary variables may be spurious (Granger and Newbold, 1974 ). To assess the integration and unit root among the variables, numerous unit root tests were performed. Apart from applying the unit root test with one structural break (Zivot and Andrews, 1992 ), we also employed a traditional Augmented Dickey-Fuller (ADF) unit root test ( 1981 ) and Phillips-Perron test (PP 1988 ).

To ascertain the order of integration, we first applied the ADF and PP unit root tests. The results of the unit root test suggest that the RER and INF series is integrated of order zero I(0), and other variables are integrated of order I(1). The Zivot and Andrews (ZA) unit root test begins with three models based on Perron ( 1989 ). The ZA model is as follows:

From the above equations, DU t ( λ ) = 1, if t  >  Tλ , 0 otherwise: DT t ( λ ) =  t  − T λ if t  > T λ , 0 otherwise. The null hypothesis for Eqs. 4 – 6 is α  = 0, which implies that ( Yt ) contains a unit root with drift and excludes any structural breakpoints. When α  < 0 it simply means that the series having trend-stationary process with a structural break that happens at an unknown point of time. DT t is a dummy variable which implies that shift appears at time TB, where DT t  = 1 and DT t  =  t  − TB if t  > TB; 0 otherwise. The ZA test (1992) suggests that small sample size distribution can deviate, eventually forming asymptotic distribution. The results of the unit root tests are given below.

The first step in ascertaining the order of integration is to perform ADF and PP unit root tests. Table 1 , above, provides the results of the ADF and PP tests, which suggest that four out of six variables are non-stationary at level and two variable is stationary at level. However, it is important to check the structural breaks and their implications, and the ADF and PP tests are unable to find the structural break in the data series. To avoid this obstacle, we applied a ZA (1992) test with one break, the results of which are given in Table 2 . The structural break test reveals four breaks in Model A. The first, in 1992, may be due to inflation caused by privatization; the second, in 1994 may be due to higher inflation which caused the consumer price index to shot up by 27.5% and imposition of a 17% value-added tax on goods; the third, in 2003, may be due to a 48% decline in state owned enterprises at the same time as a reduction on trade barriers, tariffs and regulations was put in place and the banking system was reformed; the fourth, in 2008, is probably due to the global financial crisis

The ZA test with one structural break gives different results, as all six variables are non-stationary at a 1% level.

ARDL bounds testing approach

We applied the ARDL bounds testing approach to check the cointegration long-run relationship by comparing F -statistics against the critical values for the sample size from 1985 to 2016. The bounds testing framework has an advantage over the cointegration test developed by Pesaran et al. ( 2001 ), in that the bounds testing approach can be applied to variables when that have different orders of integration. Thus, it is inappropriate to apply a Johansen test of cointegration, and we applied ARDL bounds testing to determine the long-run and short-run relationships. The F -statistics are compared with the top and bottom critical values, and if the F -statistics are greater than the top critical values, it means there is a cointegrating relationship among the variables Table 3 .

All values are calculated by using Microfit which defines bounds test critical values as “ k ” which denotes the number of non-deterministic regressors in the long-run relationship, while critical values are taken from Pesaran et al. ( 2001 ). The critical value changes as ‘ k ’ changes. The null hypothesis of the ARDL bound test is Ho: no relationship. We accept the null hypothesis when F is less than the critical value for I(0) regressors and we reject null hypothesis when F is greater than critical value for I(1) regressors. For t-statistics we accept the null hypothesis when t is greater than the critical value and reject the null hypothesis when t is less than the critical value.

As seen in Table 4 , the calculated F -statistic F  = 9.439 is higher than the upper bound critical value 3.99 at the 5% level. The results over the period of 1985 to 2016 suggest that there is a long run relationship among the variables and the null-hypothesis of no cointegration is rejected meaning acceptance of traditional Keynesian approach for China. The bounds test results conclude that there is strong cointegration relationship among budget deficit, current account deficit, interest rate, exchange rate, inflation and money supply. Since the F-statistic was greater than the upper bound critical value, we performed a diagnostic testing based on auto-correlation, normality and heteroskedasticity, the results of which were insignificant based on the respective P -values. The Fig. 3 gives the results of CUSUM and CUSUMSQ tests which check the stability of the coefficient of the regression model. The CUSUM test is based on the sum of recursive residuals, and the CUSUMSQ test is based on the sum of the squared recursive residual. Both the graphs are stable, and the sum does not touch the red lines. Hence there were no issue of serial corelation, Heteroscedasticity and normaility in this model see Table 5 .

CUSUM and CUSUMSQ stability tests. Plot of cumulative sum of recursive residuals and plot of cumulative sum of squares of recursive residuals

Long-run and short-run relationship

After discovering the cointegrating relationship among the variables, it is important to determine the long-run and short-run relationships using the ARDL model.

The above Eqs. 7 , 8 of the ARDL model capture the short and long-run relationship among the variables. The model is based on Schwarz Bayesian Criteria (SBC) optimized over 20,000 replications. The lagged error correction term (ECM) is estimated from the ARDL model. The coefficient ECM t −1 should be negative and significant to yield the evidence of a long run relationship and speed of equilibrium (Banerjee et al. 1993). The results of Eqs. 7 , 8 are provided in Table 6 . The results find a strong long-run relationship among the variables for the period from 1985 to 2016.

At 5% level of significance the long-term estimates of the ARDL model find evidence of the twin deficits hypothesis for China, as all the variables are found to be significant at this level. Thus, our study upholds the empirical validity of the Keynesian proposition for China, while rejecting the Ricardian equivalence hypothesis.

The short-run results are significant; the coefficients of BD, CAD, INF, and INT are significant at a 5% level. This shows that a small change in the budget deficit has a significant impact on the current account deficit; similarly most of the macroeconomic variableshave a significant impact on the current account deficit. As we found evidence of a long-run relationship, we calculated the lagged ECM from the long-run equations. The negative (−0.95348) ECM coefficient and the high speed of adjustment brought equilibrium to the economy, with the exogenous shocks and endogenous shocks restoring it after a long period.

The Chinese economy is one of the most integrated economies in the world and has emerged as a powerful actor in the global economy. The pace of growth in china’s economy has accelerated since the decades in which there was a higher export promotion due to market liquidity and flexible governmental policies. In the early 1990s, it was reported in the Chinese print media that although the Chinese economy was expanding rapidly, the deficits still existed. These were called ‘hard deficits’ primarily because they were financed by expanding money supply and hence exerted inflationary pressure on the economy (People’s Daily, March 28, 1993). Deficit financing may also increase interest rates because once the monetary accommodation of the deficit is ruled out, the government has to incentivize consumers and firms to buy more government bonds. If the purchase of government bonds does not increase in direct proportion to the rise in deficit, government must borrow more money. This, in turn, crowds out private investment. Such a reduction in the private sector's demand for capital has been summarized by Douglas Holtz–Eakin, the director of the U.S Congressional Budget Office, as a “modestly negative” effect of the tax cut or budget deficits on long - term economic growth.

This study finds that a higher interest rate significantly affects the budget deficit and current account deficit in both the short-run and long-run. Chinese banks are increasing interest rates on home loans that had previously been very low; however, because of its economic bubble, especially in the real estate market, this is creating serious trouble in the economy. This is similar to the American bubble, in which most people were unable to repay loans, creating a financial crisis. Still, China must work within the existing bubble, even though it creates a lack of investments and can cause trouble for economies worldwide. The debt bubble is due to the elimination of loan quotas for banks in an attempt to increase small business. These companies are still struggling to repay that debt, which is almost half the amount of GDP of both private and public debt (The Economist, 2015).

While empirical research shows a statistically significant relationship between higher budget deficits and long-term interest rates, there are differences on the magnitude of the impact. Chinese needs to take care of lower interest rate rather than higher interest rate especially on home loans which was very low; in real estate market which has created a serious trouble in the economy.

It is also important to note that the concept of the deficit includes both hard and soft deficits. The “hard” part of the deficit, being financed by printing money, is inflationary. The “soft” part, being financed by the government's domestic and foreign debts, is non-inflationary. However, they are not reported by the Chinese government.

Moreover, hard deficits and consequent inflation increases capital inflows (to prevent interest rates from rising) and causes current account deficit. In China, deficits occurred as a result of overestimating revenues or underestimating expenditures (Shen and Chen, 1981 ) and contributed to the development of bottle-necks in sectors such as energy, transportation, and communications sectors that are of vital importance for the long-term growth of China. However, given the reluctance of the non-government investors to venture into such low pay-back sectors, the Chinese government’s policy to balance budgets by cutting its capital expenditure can severely restrict the development of these sectors (Luo, 1990 , and Colm and Young, 1968 ).

However, the natural explanation for the external surplus is that the economy follows an export-driven growth model by increasing FDI inflows. In the rising tide of economies, China’s demographic boom has created an economic miracle, allowing them to invest in education and skilled workers that will accordingly benefit the economy.

During the global financial crisis of 2008, the Chinese government combined active fiscal policy and loose monetary policy and introduced a stimulus package of $580 billion (around 20% of China’s GDP) for 2009 and 2010. The package partially offset the drop-in exports and its effect on the economy, so that the economic growth of China remained well above international averages. During this period (2008–2009) the current account surplus, as a percentage of GDP was at its highest and the budget deficit was at its lowest point of the entire decade. Our findings suggest that a strong long-run and short-run relationship exists among the variables. The empirical findings support the Keynesian proposition for Chinese economy.

Granger causality

In this section we attempt to estimate the causality from X t to Y t and vice versa. We apply Granger causality to check the robustness of our results and to detect the nature of the causal relationship among the variables based on Eqs. 9 , 10 .

Table 7 describes the results of Granger causality test. The reverse causality holds in light of the fact that budget deficit cause Granger to the current account deficit. For all the variables, the reverse causality from budget deficit to macro variables and current account deficit to macro variables holds at 5% level of significance. Thus, the results of Granger causality gives us more evidences in support of the Keynesian proposition for China in the light of above data. The reverse causality was not apparent because Chinese economy is one of the most integrated economies with higher capital outflows, export-led growth and export promotion due to market liquidity and flexible governmental policies. While the capital inflow determines a tight fiscal policy to avoid overheating of economy (see author Castillo and Barco ( 2008 ) and Rossini et al. ( 2008 ).

Results and discussion

The ambiguous verdict on the twin deficits hypothesis based on two competing theories: the Keynesian preposition and the Ricardian Equivalence theorem. In this paper we investigated the link between budget deficit and current account deficit with an emphasis on the impact of macro-variables shock on current account balance and budget deficit in the Chinese economy over the period 1985–2016.

The conclusion of this study is based on ARDL bound testing approach. The model is based on Schwarz Bayesian Criteria (SBC) optimized over 20,000 replications. With this data and analysis, we conclude that there is a long-run and short-run relationship among the variables. We accept the Keynesian hypothesis, which means we did not find evidence in support of the Ricardian Equivalence theorem in the Chinese economy. This was surprising because higher governmental expenditure and flexible governmental policies could have pushed up the current account deficit. We further applied Granger causality test the results concluded that there is a strong inverse causal relationship between budget deficit and current account deficit meaning that Keynesian preposition is more plausible than Ricardian Equivalence theorem in the light of above data. Our results are consistent with Banday and Aneja, ( 2019 ); Banday and Aneja, ( 2016 ); Ganchev, 2010 ; Bhat and Sharma ( 2018 ); Badinger et al. ( 2017 ), and Afonso et al. ( 2018 ).

The findings of the Granger causality test revealed that there exists bidirectional causality running from budget deficit to current account balance and vice versa in China. A similar movement of the budget deficit and the current account balance is the thing that one would expect when there are cyclic shocks to output. Detailed empirical findings highlight that the interest rate bubble and higher inflated housing prices are becoming a challenge for the Chinese economy, as they are now nearing the prices of the US bubble before the financial crisis popped it. If the US increases interest rates, the money will flow out of the Chinese market, which could cause a similar crisis in China. It will be a challenge for the monetary authority to bring stability in China where inflation, interest rate bubble and exchange rate volatility are of primary concern. Expenditure reduction for low payback sectors, such as energy and communication, could also be a worry in the future. Rising inflation can significantly increase the inflow of capital due to an increase in domestic demand; this can lead to a current account deficit, as it is now more than half of the GDP. The indebtedness in the economy is at its peak, and this can crush the financial cycle and cause financial crisis.

Conclusions

The cointegration investigation confirms the presence of a long-run relationship between the variables. The results propose that there is a positive connection between the current account deficit and fiscal deficit. Our outcomes show that increase in money supply and the devaluation of the exchange rate increases current account balance. The effects of the current account deficit and the exchange rate have been taken as exogenous. A long-run stationary association between the fiscal deficit, current account deficit, interest rate, inflation, money supply, and the exchange rate has been found for China. Granger causality tests reveal bidirectional causality running from budget deficit to current account balance and macroeconomic variables to budget deficit and current account balance.

A genuine answer for the issue of budget deficit and current account deficit lies with a coherent package comprising of both fiscal and monetary approaches. It must concentrate on stable interest rate, inflation target and monetary position will supplement the budget-cut policies. The findings of the paper will be helpful for future empirical models, which attempt to further highlight the transmission mechanism.

Data availability

For undertaking the empirical analysis, we use the World Bank data for the following variables. The study covers the period from 1985 to 2016 and is based on secondary data which are available at: https://data.worldbank.org/country/china .

Change history

01 october 2019.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Banday, U.J., Aneja, R. Twin deficit hypothesis and reverse causality: a case study of China. Palgrave Commun 5 , 93 (2019). https://doi.org/10.1057/s41599-019-0304-z

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Examining the determinants and efficiency of China’s agricultural exports using a stochastic frontier gravity model

Roles Conceptualization, Data curation, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliations College of Economics and Management, Northwest A&F University, Shaanxi, China, School of Rural Technology and Entrepreneurship Development Rano, Kano State Polytechnic, Kano, Nigeria

Roles Data curation, Writing – original draft, Writing – review & editing

Affiliation College of Economics and Management, Northwest A&F University, Shaanxi, China

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Affiliation School of Humanities and Social Sciences, North South University, Bashundhara, Dhaka, Bangladesh

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Affiliation Department of Economics, University of Jhnag, Jhang, Pakistan

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* E-mail: [email protected]

• Nazir Muhammad Abdullahi, 
• Qiangqiang Zhang, 
• Saleh Shahriar, 
• Muhammad Saqib Irshad, 
• Abdullahi Bala Ado, 

• Published: September 9, 2022
• https://doi.org/10.1371/journal.pone.0274187
• Reader Comments

This paper aims to examine the key determinants and efficiency of China’s agricultural exports with its 114 importing countries by applying the Stochastic Frontier Analysis (SFA) on an augmented gravity model for the period of 2000–2019. The Poisson Pseudo Maximum Likelihood (PPML) and the fixed effect models were also estimated simultaneously to confirm the robustness of our findings. The results reveal that China’s economic size (GDP) and its importing countries, the Belt and Road Initiative (BRI), common border, and the Chinese language positively determine China’s agricultural export flows. The results, on the other hand, also reveal that China’s agricultural export is adversely influenced by the income (per capita GDP) of China and its trade partners, currency depreciation, distance, and landlocked. On an average account, China has untapped the potential of 51% in its agriculture export with the countries used in this study. We provide policy suggestions as part of our study.

Citation: Abdullahi NM, Zhang Q, Shahriar S, Irshad MS, Ado AB, Huo X (2022) Examining the determinants and efficiency of China’s agricultural exports using a stochastic frontier gravity model. PLoS ONE 17(9): e0274187. https://doi.org/10.1371/journal.pone.0274187

Editor: Carlos Alberto Zúniga-González, Universidad Nacional Autonoma de Nicaragua Leon, NICARAGUA

Received: September 10, 2021; Accepted: August 23, 2022; Published: September 9, 2022

Copyright: © 2022 Abdullahi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information file.

Funding: Xuexi Huo This work is Supported by the Earmarked Fund for China Agricultural Research System (Grant No. CARS-28). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Liberalization of international trade and globalization of the global economy has substantially aided the expansion of agricultural trade during the last decade [ 1 , 2 ]. For several years, China’s economy has been rapidly expanding, with an annual growth rate of more than 10% [ 3 ]. The country’s consistent and steady economic development has made it an important worldwide economic growth engine. China, being the world’s second-largest economy, has a significant impact on global trade patterns and the global economy [ 4 , 5 ]. Since its transformation into the global fastest rising economy, China has made remarkable changes in its export structure [ 6 – 8 ]. China’s structural changes began in 1978, when the country started its economic reform and opening-up program. China’s agriculture industry has seen a substantial transformations during the last three decades.

Chinese agricultural exports to the global market have climbed from US$2.4 billion in 2001 to US$11.27 billion in 2019, nearly two decades after China joined the World Trade Organisation (WTO). China has established itself as a major player in the world agriculture market [ 9 ]. Recent statistics show that China’s agricultural exports share accounted for a proportion of 4.33%. Although the share seems to be tiny, but China’s agricultural exports had increased considerably from 2.19% in 2001 to 3.49% in 2011 and then rose to 4.33% in 2019 [ 10 ]. Revealing that China’s exports have profited from the reduction of trade barriers such as tariffs, quantitative limitations, and other non-tariff trade measures have decreased [ 11 , 12 ]. Table 1 shows that China’s agricultural export is just behind the largest exporters such as the USA, the Netherlands, and Canada, comparable with those of Germany, Brazil, Russia, and Thailand and much higher than Australia Belgium, France, and Indonesia. With profound engagement in the global market, the export performance of China did not only has a deep impact on the country’s agriculture industry, but also has a significant impact on other nations’ challenging areas. The Chinese agricultural industry contributes roughly 10% of the country’s Gross Domestic Product (GDP) and absorbs about 220 million people agricultural workers [ 13 ].

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Due to the unrealised trade potential, particularly trade in agriculture, China, like other emerging countries, will benefit from more foreign trade. Since the policy of opening up, this has piqued the interest of decision-makers. Export, according to certain research, is the most important avenue for economic integration and growth [ 14 ]. Other studies, on the other hand, have found that agricultural exports are critical to the economic success of emerging countries, including China, since the allocation of scarce resources may provide China with a competitive edge in the agricultural sector [ 15 , 16 ]. As a result, a rise in agricultural revenue would directly contribute to China’s economic growth as well as the advancement of the country’s rural revitalization strategy and the war against poverty [ 17 – 19 ]. Xiong [ 20 ] documented that an increase in export would lead to a decrease in income inequality.

Therefore, the importance of examining the factors influencing China’s agricultural product exports become necessary, taking into account the country’s recent success in global trade as well as identifying countries where China should concentrate its efforts, countries where there is a possibility to expand agricultural export that can continue to lead the sector towards maintaining its substantial growth path. Identifying new opportunities can boost the sector’s contribution to the national economy.

Furthermore, the present study also attempts to analyze China’s agricultural sector’s exports’ efficiency. Thus, the study’s objectives are to: First, look at the factors that influence China’s bilateral agricultural export flows and how China’s BRI affects export performance. Second, estimate the efficiency of China’s agricultural exports with its importing partners. In this case, the term efficiency describes a level at which an economy or entity can no longer export additional amounts of a good, whereas, inefficient in export means an economy exports are blow the maximum level, showing a gap between the observed and maximum level of exports. Afterwards accomplishing the present study’s objectives, this study offers a significant lucrative contribution in four ways. First, our study fills a research gap in China’s agricultural export trade literature by estimating the major determinants of agriculture exports. Second, this study applies the stochastic frontier gravity model to capture China’s agriculture trade efficiencies. Third, we contribute to the growth of export research that use the Stochastic Frontier Gravity Model (SFGM), which is uncommon in the literature. Fourth, we use a panel of 114 countries to forecast China’s main trading partners over a 20-year timeframe (2000–2019).

The remainder of the paper is divided into sections: The second section of the paper presents an overview of China’s agricultural exports. We detail the methodological framework of the study in section 3. In section 4, we discuss the results of the study. Section 5 wraps up the research and makes policy suggestions.

2. China’s agricultural sector: An overview

A substantial majority of the population, particularly those who reside in rural areas, engages in agriculture. The agricultural sector is an important contributor to China’s economy as it contributes a considerable share of the country’s GDP and provides employment. Over the 20 years (2000–2019), the percentage share of agricultural export in total GDP was decreasing. However, on average, Fig 1 depicts the increasing trend of China’s agricultural exports during the study period. In contrast, the pattern indicates a considerable decline at times, most notably in the year after the global financial crisis of 2008. China announced a mix of macroeconomic and industrial policies to cope with the financial crisis [ 21 ]. In 2010, China’s agricultural products export rebounded with nearly 34% ( Fig 1 ). Similarly, in 2013, Chinese President Xi Jinping introduced the Belt and Road Initiative (BRI) to the world, with the goal of encouraging deeper regional cooperation in trade and investment across Asia, Europe, and Africa, as well as their adjacent oceans [ 22 ]. Since its inception, the BRI has piqued the interest of many nations across the world, resulting in many trade prospects.

https://doi.org/10.1371/journal.pone.0274187.g001

In 1998, China exported agricultural products valued at only USD 2.606 billion to the BR countries. By 2017, the exports had increased to USD 22.683 billion, nearly 10 times the export volume of 20 years earlier [ 23 ]. The BRI significantly enhances the trade network connectivity of the African countries [ 24 ].

Fig 2 shows the primary agricultural produced in China and the major exportable agricultural commodities. In 2018, China produced 2.57 billion tons of maize, 2.14 billion tons of paddy rice, 1.75 billion tons of vegetables, and 1.43 billion tons of paddy (milled rice). These commodities are the top four produced in China in 2018. Similarly, the top exportable commodities are food pre, fruits prepared, vegetables, and tea with the export values of US$ 4.24 billion, US$ 2.10 billion, US$ 2.1 billion, and US$ 1.79 billion respectively.

(a) Production Quality (tons), (B) Expert Value (1000 USD).

https://doi.org/10.1371/journal.pone.0274187.g002

2.1. The structure of China’s agricultural products market

Fig 3 shows China’s export destinations based on the average export share of agricultural commodity between 2000 and 2019, indicating that China’s top ten agricultural product destinations are as follows: Japan (13.83%), the USA (12.52%), South Korea (6.44%), Germany (5.96%), Italy (5.76%), India (4.36%), Viet Nam (4.13%), Indonesia (2.80%), Turkey (2.77%), and Thailand (2.75%). Similarly, China was responsible for the imports of Japan (8.33%), the USA (5.58%), South Korea (7.61%), Germany (3.11%), Italy (4.81%), India (5.97%), Viet Nam (6.61%), Indonesia (5.35%), Turkey (4.88%), and Thailand (4.43%) during the same period. Fig 3 also reveals that China’s agricultural product exports to these ten nations accounted for almost 61% of the country’s overall agricultural product exports to the international market. China’s agricultural goods have the largest foreign markets in Japan and the United States. Fig 4 reveals that these two countries together with South Korea, Germany, Italy, India, and Viet Nam imported agricultural products from China worth US$ 693 million from 2000 to 2019 annually, accounting for 53% of the total market share.

https://doi.org/10.1371/journal.pone.0274187.g003

https://doi.org/10.1371/journal.pone.0274187.g004

2.2. Literature review

This sub-section gives a detailed review of some existing literature on the determinants of exports using the gravity model. A literature review provides a complete overview of the related studies to strengthen the basis of our knowledge and to put our study in the right direction [ 25 ]. Moreover, we also divide this section into two parts. In the first part, we discuss literature on agricultural and agri-food export that uses the gravity model and or the SFGM. Meanwhile, the second part is limited to literature on commodity specific that uses the gravity model.

Recently, Ya and Pei [ 26 ] investigated the determinants of agricultural trade between China and the 58 African states using dataset from 2010 to 2019. The study explored how some factors (economic scale, geographical and demographic factors, natural resource endowment, the level of agricultural science and technology, political factors, and exchange rate factors) affect the country’s trade flows. Abdullahi, Huo [ 27 ] studied the factors and potential of Nigeria’s agri-food using the SFGM. The study shows that Nigeria’s agri-food trade potential is mostly with the big economics. Abdullahi, Aluko [ 28 ] examined the factor that influence agri-food exports from Nigeria to its importing nations (the EU), along with its efficiency and potential. The study uses a panel dataset spanning 1995 to 2019 to show that Nigerian food exports are influenced by economic size, per capita income, EU new members and distance. In addition, the study also reveals that the country’s agri-food exports to the EU are less efficient and there is a large untapped potential for trade with the EU in this sector.

Nguyen [ 29 ] observed Vietnam’s agricultural exports determinants using the SFGM on a panel dataset covering 2000 to 2018. The study revealed that the influence of “behind-the-border” constraints prevent the country from realizing its full potential. The study further revealed that ASEAN is the primary market for Viet Nam. Similarly, Hajivand, Moghaddasi [ 30 ] provided the case study of the Iranian agricultural exports. Their study indicated that almost 70% of the country’s potential has been realized during the study period.

Barma [ 31 ] investigated the Indian agricultural export with its 112 importing countries, from 2000 to 2013. The study showed that importing countries’ GDP, population, and common language increase Indian agricultural export, whereas, distance and landockedness decrease export flows. Barma proposed that the export efficiency scores can be used to develop future export diversification policies. The SFGM was used by, Atif, Haiyun [ 32 ] to investigated the key factors impacting Pakistan agricultural export. Their study confirmed the fact that the bilateral exchange rate along with tariff impact Pakistan’s agricultural export positively. Colonial history, a shared border, and a common language, on the other hand, have a detrimental impact on Pakistan’s agricultural exports. The study concluded that Pakistan has significant export potential with its neighbors and recommended that political and economic issues be resolved with nations that share a similar border.

Using a panel dataset spanning 1994 to 2008, Assem, Romstad [ 33 ] investigated the factors affecting Egypt’s agricultural exports. The study employed fixed effects and random effects to show that the GDP positively determines Egypt’s agricultural export. In contrast, GDP per capita and distance are negatively associated with agricultural export flows. Shuai [ 34 ] used a gravity model with a fixed effect method to explore the Sino-US agriculture trade potential. According to the study, China and the United States have varied agri-export potentials due to geographical differences.

Furthermore, many researchers have used the gravity model to investigate a commodity-specific model to determine the factors affecting a single commodity’s exports. For instance, Abdullahi, Shahriar [ 35 ] studied the gravity equation for Nigeria’s cocoa exports with its 36 major importing partners from 1995–2018. The study confirmed the consistence of the gravity model assumptions in the context of the Nigerian cocoa industry. The study also highlighted the needs to expand export.

Nasrullah, Chang [ 36 ] provided a case study of China’s forest product exports to the global market. Their study showed that GDP and GDP per capita positively impact Chinese export whereas distance has a negative effect on export. The study further suggested promoting the Chinese language to importing countries as a tool for increasing export flows to China’s partners.

Shahriar, Lu [ 6 ] explored the determinants of exports in the Chinese meat industry using a panel dataset spanning 1996 to 2016, for China and its importing partners. Their empirical findings showed that the GDP, Chinese language, exchange rate, and China’s land area are positively affecting the export flow of China’s pork. The study also observed the impact of the WTO and the BRI on China’s pork export flows.

Kea, Li [ 37 ] studied the dynamic form of the gravity model of Cambodian rice exports. The results revealed that the EU, ASEAN, and China are the major destinations of Cambodian rice exports. The findings also showed that the exchange rate policy, agricultural land reform, and historical ties, promote rice exports. To deal with zero trade problems, the authors applied the PPML and Heckman selection.

Using the gravity model’s static and dynamic forms, Nsabimana and Tirkaso [ 38 ] observed coffee export performance in Eastern and Southern African. The researchers found that the population size of both exporting and importing countries, geographical distances, and income are the major determinants of coffee exports. They indicated a room for expansion of coffee export by extorting countries in these regions as their finding revealed that the countries are underperforming in terms of their full potential on the international market.

In China’s context, several studies were carried out to examine the factors affecting China’s exports through trade analysis (see [ 4 , 39 , 40 ]). Nonetheless, just a few research on China’s agricultural exports have been discovered [ 26 , 41 – 43 ]. To the best of our abilities, no academic study found in the existing literature that examines the factors affecting China’s agricultural product exports at a bilateral level, as well as take other time-variant and invariant metrics, to explain China’s agricultural export performance. As a result, the current study attempts to address this gap in the literature by employing a stochastic frontier gravity model.

3. Methodology and data

The gravity model, which revealed the relevance of spatial dynamics in international trade theory, sparked renewed interest among academics in developing a theoretical basis for the gravity model. Anderson [ 45 ], for example, used the product differentiation model to first replicate the gravity equation. Bergstrand [ 46 ] used monopolistic competition models to investigate the theoretical foundations of bilateral trade. By assuming rising returns to scale and a differentiated product market, Helpman and Krugman [ 47 ] vindicated the gravity model. The gravity model, according to Deardorff [ 48 ] describes a wide range of models and may be validated using conventional trade theories.

To scheme the determinants of trade for trading countries, the gravity model is often estimated. The estimate approach, however, is not without flaws. Imports and exports are frequently regarded as the sample’s average rather than the trading nations’ best potential values. It’s possible that estimating the gravity model will be difficult if the sample contains highly varied values [ 52 ]. Similarly, the model has been criticized by Anderson and Van Wincoop [ 53 ], who claims it fails to account for the influence of Multilateral Trade Resistance [MTR] on bilateral trade. Distance, tariff rate, colonial connection, and common language are all included in the MTR [ 54 ]. The standard gravity estimations, according to Ravishankar and Stack [ 52 ], are derived from the data set of nations with regular trade ties. Stochastic frontier analysis (SFA) is a para-metric approach to estimate productivity and efficiency. SFA assumes that businesses fail completely to utilize existing technology and resources, or they suffer from diseconomies of scale, leading to inevitable inefficiencies in production.

The primary drawback of the SFA is that the prospects are set using sample averages rather than the greatest potential boundaries. As a result, the stochastic frontier technique performs better in gravity model estimation. To explain trading partner variance, Kalirajan [ 55 ] added a stochastic frontier into the gravity equation. The trade frontier calculated using this method provides flexibility in determining the best amount of trade among the nations under consideration. The stochastic error term produced inside the model impacted the trade frontier positive or otherwise. This permits the randomly generated frontier to change based on the influential of the equation. The magnitude of trade observed can be compared with the projected frontier of the importing nation, in order to determine the maximum size of a trade. This method is thoroughly explained in the next sections.

3.1. The stochastic frontier gravity model (SFGM)

The SFA has been used extensively to evaluate firm efficiency, developed separately by Aigner, Lovell [ 56 ] and Meeusen and Van Den Broeck [ 57 ] using Stochastic Frontier Analysis (SFA) in production economics. The SFA approach argues that a given amount of inputs can yield estimates of the highest level of output and a Production Possibility Frontier (PPF). An inefficient firm/industry is one that operates on the blow frontier output, showing a gap between the observed and maximum potential levels of output. Technically efficient, on the other hand, uses the PPF to match observed and frontier output levels. As a result, the former refers to the potential for increased productivity. As a result, the technically inefficient production function describes how far the actual output does not match the potential output. The exports frontier can also be used to define the SFA approach, where inefficient export performance refers to how far actual export falls short of maximum potential export.

3.2. Sample size and data sources

The panel data set consists of bilateral agricultural products export flows from China to its 114 importing partners over 20 years, spanning from 2000–2019. During the period, the exported value of Chinese agricultural products to these 114 destinations accounted for almost 92% of China’s total agricultural products exported value. These countries were chosen based on the value of China’s agricultural exports on a yearly basis. As a result, the total number of observations in this study’s dataset is 2280 ( N = 114 × T = 20). Both in terms of time and importing partners, the highest feasible frequency of data was employed. Except for dummy variables, all variables are transformed into natural log form to evaluate the relationship. Table 2 lists the data sources and descriptions of the variables utilized, whereas Table 3 gives the descriptive statistics for our model’s variables. The parameters were estimated using the Stata version 15.1 software program.

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4. Empirical results and discussion

4.1. factors affecting china’s agricultural products export.

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https://doi.org/10.1371/journal.pone.0274187.t005

In the gravity literature GDP is used as a proxy for market size, in our case for the market size of China and its partners. China’s market size is reflecting the export capacity of agricultural commodities while the market size of the importers’ is reflecting the demand for agricultural commodities. According to the empirical estimates of the parameter (GDP), the positive and significant estimates for GDP it . GDP jt indicate that countries with high-income levels traded more. This result is in line with the economic theory and most prior studies on gravity equations [ 37 , 66 – 68 ]. The coefficients of this variable are 1.06 and 0.89 from SFGM and PPML respectively. This indicates that a 1% increase GDP it . GDP jt will increase China’s agricultural exports by approximately 0.98%. China’s income and its importing partners are supporting the adequate demand and supply circumstances of agricultural commodities. We found the pcGDP it . pcGDP jt to be highly significant at a 1% level but with negative coefficients, indicating that the negative sign of GDP per capita variable reveals that as the pcGDP it . pcGDP jt increases, China agricultural exports less with it.

Prior international trade literature considered distance as a trade resistance factor and is anticipated to decrease China’s agricultural trade [ 35 , 69 ]. The distance between Beijing and importing partners’ capital city is taken as a proxy for trade costs. In our model Dis ij has a negative highly significant impact on bilateral trade. This shows that a 1% increase in distance will reduce agricultural export volume by 0.81%. This suggests that to increase its agricultural exports, China should look for a nearby market. This result is in line with the classical findings of the gravity model. Similar findings were also recorded by previous studies of agricultural product exports such as Atif, Haiyun [ 32 ]; Barma [ 31 ].

The Ex ijt is used as a proxy to the exchange rate policy and it is positively significant at the 5% level in the SFGM, but insignificance in the other models. The coefficient of Ex ijt shows that a 1% increment of (Yuan Chinese currency) may drop the agricultural commodities exports by 0.03%. Therefore, China needs to maintain or depreciate the exchange rate because a rise in “Yuan” is expected to drop the export revenue because of very low elasticity. China’s exchange rate policy is such a highly argumentative and extensively discussed issue that it often generates heated debate around the globe [ 70 – 72 ]. The United States of America has criticized China’s exchange rate policy for the two country’s bilateral trade deficit [ 73 – 75 ]. Evidence indicates that exchange rate volatility harms exports both in the short-and-long run [ 8 , 76 ]. Thus, our result is supportive of prior studies.

The average tariff rates (1 + Imp jt ) are an explicit measure of trade cost and higher tariff rates imposed by importing country tends to daunt export [ 77 , 78 ]. In other words, the export of a country is significantly determined by the tariff rate charged by the importing country. The negative coefficients confirm that China’s agriculture exports are negatively associated with tariff rates and a 1% increase in tariff rate by importing country will decrease the agricultural exports by 1.57% and vice versa. Prior studies of agricultural exports such as Ghazalian [ 79 ] and Olper and Raimondi [ 80 ] have also reported a negative impact of this variable.

This study has also added four dummy variables to examine the effect of the BRI, border, common language, and landlocked on agricultural commodities exports. The BRI jt membership of importing countries is positively significant at 1% in all models. China is building cross-border ties employing the six economic corridors under the BRI’s structure [ 81 ]. Our study found a policy variable of the BRI jt to be positively significant, indicates the significance of the BRI on export flows of China agricultural commodities. The prior studies such as Shahriar, Lu [ 6 ], Kea, Li [ 37 ], Chen [ 82 ] also documented the positive impact of the BRI on China and its partners’ export flows. Under the Umbrella of the BRI China is building the global trading networks and thus regional economic incorporation through regional trade agreements, foreign direct investments, special economic zones, and infrastructural connectivity [ 4 , 22 ].

The Chinese language (CN ij ) variable is strongly significant at 1% with coefficients of 1.62 and 0.41, revealing that sharing a common language with an importing country promotes agricultural export flows by nearly 1.02%. Our study affirms other studies that recommended using the Chinese language as a tool for networks and communications, as language stimulates trade flows [ 83 , 84 ]. Similar results were reported in the Chinese meat industry’s Shahriar, Lu [ 6 ] and the Chinese forest products export Nasrullah, Chang [ 36 ].

Regarding the Landlocked ij variable, we found a negative coefficient of about 0.97 in the SFGM and the PPML, indicating that agricultural export flows decrease by 0.97% with the landlocked countries. As a result of being landlocked, there are additional trading expenses. Similarly, trade costs are found to have an impact on trade composition and a country’s comparative advantage in exports [ 85 ].

4.2. Efficiency of China’s agricultural products export

Table 6 shows the average technical efficiency of China’s agricultural export drawn from the SFGM from 2000 to 2019. It demonstrates the average export performance with major importers. The estimates show that China’s agricultural commodities export has not reached its optimum level (100% efficient) with any importing partners. Although, the majority of the importing partners have attained a relatively high efficiency. Yet, there is an excellent opportunity for improving agricultural exports’ performance with trading partners. In general, the average efficiency of the trading partners over the study period was 48.52%. While some countries are more efficient that others, for instance, the least efficient countries are Bahrain at 9.34%, Afghanistan at 16.93%, Qatar at 21.78%, and Cameroon at 26.44%. Meanwhile, higher export efficiencies are recorded for Germany 62.8%, Viet Nam 62.12%, New Zealand 61.62%, and Denmark 61.39%.

https://doi.org/10.1371/journal.pone.0274187.t006

5. Conclusion

This study uses SFA to investigate the gravity equation of China’s agricultural exports for a panel of 114 countries between 2000 and 2019. Using the SFGM technique, we estimate the main determinants and efficiency of China’s agricultural exports with its major trading partners. However, to overcome the zero trade selection bias and the presences of heteroskedasticity and to confirm the robustness of the findings, we also employ the FE and the PPML.

The results are as follows: first, we find that China’s GDP (economic size) and its importing countries stimulate agricultural export flows. Second, we find that the BRI, common border and the Chinese language positively determine China’s agricultural export. Third, we find that China’s income (per capita) and that of China’s importing nations and exchange rates discourage agricultural exports from China. Fourth, we also observe the negative impact of geographical distance, importer’s tariff, and landlocked of importing countries. Fifth, we find that China’s agricultural exports to its 114 selected partners are largely inefficient.

We have drawn up some recommendations based on our findings. First, the research insights would improve the agricultural export flows around the globe. China introduced a number of economic and fiscal reforms. It also liberalized the trade to facilitate its global and regional integration. Therefore, China is likely to promote its agricultural exports to the global market. Second, the depreciation of China’s currency would improve agricultural export flows and its low magnitude indicates that the policymakers should give special attention to the exchange rate policy. Third, China’s agricultural exports have a satisfying effect on the BRI, therefore, China should continue to build a strong relationship with the current members and develop a policy that will attract non-members. Countries engage in agricultural trade for a variety of reasons. History shows that nations engage in agri-food trade because it yields several benefits. In the economic realm, a food exporting country may have a comparative advantage and is able to sell its food at an attractive price, thereby earning foreign currency by meeting demand in another country. Or perhaps a country wants to reduce its food surplus and support domestic prices. As a large and populous country, China enjoys comparative advantages in agricultural exports. Exports may be used to bind other nations to the exporter; in this respect agricultural exports are useful for maintaining alliances and empires. China is likely to develop its trade network under its BRI framework. To this end, the issue of the Coronavirus pandemic might hinder the growth of the Chinese agricultural exports.

Fourth, the negative effect of the geographical distance and positive effect of the Chinese language suggests that China should give more attention to the nearby countries’ markets and countries with a common language to boost agricultural commodities exports. Fifth, China’s agricultural export would benefit more from increasing its exports to countries with observed low efficiency.

Last but not the least, this study can be used as a yardstick for future researchers as it is the first piece of research that estimates the efficiency and potential of China’s agricultural exports. Further research may add novelty to the existing body of knowledge focusing on the BRI and non-BRI countries for cross-country comparisons. Future researchers may consider to include both import and export models in a single study. For comparative analysis of major agricultural exporting countries, further research efforts are needed to chain the gravity models’ findings, shown competitiveness and comparative advantage measurements of agricultural commodities in the global markets.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0274187.s001

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December 08, 2020

Rethinking Export Controls: Unintended Consequences and the New Technological Landscape

By: Martijn Rasser

Executive Summary

Export controls can be a formidable tool to protect technological advantage and to deter illiberal and dangerous use of technologies. Used properly for their intended purpose—to affect the military potential of a foreign country, to advance major foreign policy objectives, or to fulfill international obligations—export controls can bolster U.S. national security. 1 Their utility is beginning to erode, however. U.S. export controls were designed for an era when the United States enjoyed overwhelming technological dominance. U.S. policymakers often wield export controls as if that is still the case. As a result, current export control implementation often compounds unintended consequences that harm U.S. economic and technological competitiveness.

The United States must change its approach to export controls to reestablish their power as an effective instrument in foreign and national security policymaking. While the United States is still the world’s technology leader, it is no longer the global juggernaut. Global research and development (R&D) spending is distributed more evenly than even 20 years ago. Technological know-how is also more diffused, with middle powers having significant prowess in select areas of technology, opening up alternative technology acquisition pathways. Concerningly, China is a near-peer in many technology areas, and perhaps at parity or ahead in some, meaning that opportunities for effective export controls are growing scarcer.

The United States must change its approach to export controls to reestablish their power as an effective instrument in foreign and national security policymaking.

These trends have important implications—applying export controls with inadequate consideration of the shifts in the global technology landscape means amplifying unintended consequences that can cause lasting damage to U.S. firms and industries and pose avoidable hurdles to technology cooperation with allies and partners. To be sure, export controls still function reasonably well as it pertains to the movement of dual-use goods, and when applied multilaterally, such as through the Wassenaar Arrangement. In the case of a rising, technologically capable China, however, U.S. export controls would be more effective if wielded as part of a comprehensive national security and economic statecraft that safeguard U.S. technological advantages, rather than as a traditional non-proliferation tool. End-use controls, such as for human rights violations, remain an important exception and should continue.

To revamp export controls for this new era—a global strategic competition with technology at its core—U.S. policymakers should focus on three tenets for implementation.

• Work with allies. Export controls should be crafted and upheld in concert with relevant tech-leading democracies in most cases. The United States rarely has the clout to act unilaterally and be fully effective.
• Raise the threshold. The most effective export controls are ones tailored to undermining a foreign actor’s technology indigenization efforts. This requires understanding American technological advantages—and how to sustain and amplify them—and knowing where the technological chokepoints are. Export controls should then only be used if no viable alternative technology acquisition pathways exist.
• Reframe the goal. The Trump administration has increasingly used export controls as a haphazard and blunt tool of economic statecraft; any results are likely to prove tactical and ephemeral. For strategic and enduring impact, export controls need to be considered in the context of national technology and economic statecraft strategies. In the context of a rising China and a global technology-centered strategic competition, this means protecting select U.S. areas of technological advantage.

An Export Control Approach Designed for a Different Era

Export control authorities have not kept pace with rapidly evolving technologies—and their growing uses in ways that may threaten U.S. national security interests—nor do they reflect the current conditions. Three main trends underscore how the tech landscape has changed since the mid-20th century.

• The United States doesn’t dominate global research and development (R&D) as it did in decades past. Technology is an enabler of economic, military, and political power. American technological leadership is increasingly significant to national security but also increasingly fragile—two trendlines that are concerning. Today’s science and technology landscape is a more level playing field internationally than in the past. The U.S. share of global R&D has fallen from 69 percent in 1960 to 28 percent in 2018. 2 This shift happened despite overall U.S. spending on R&D holding steady at around 3 percent of GDP over this time period; other countries, China in particular, greatly increased overall spending. Year-over-year R&D spending growth rates are also higher in many countries, meaning that the U.S. global share will continue to shrink. 3 Defense-related R&D is on the same track. In 1960, the United States accounted for 36 percent of global spending. By 2016, U.S. defense R&D was down to 3.7 percent of the world total. 4 Another key shift occurred in the makeup of U.S. R&D spending, with U.S. government investments increasingly taking a back seat to private industry. U.S. business-funded R&D rose from 33 percent of total U.S. R&D spending to 70 percent in 2018. 5 This is a concerning development because the U.S. government remains the largest funder of basic research, the efforts where major technological breakthroughs are most likely to happen. Technological achievements that are the backbone of the modern economy and foundational to U.S. military power—semiconductors, the global positioning system (GPS), and the early internet—are rooted in federally-funded research efforts in the 1960s and 1970s. 6 Inattention to basic research today could shortchange American technological prowess for decades, with major implications for U.S. national security.
• Technologies and their associated know-how are increasingly diffused. Rarely does any single country hold all the keys for any specific technology area. Nor does any one company have full control over the necessary raw materials and components. Highly globalized supply chains mean that U.S. multinational firms increasingly act as coordinators of networks, orchestrating a complex set of elements such as R&D, design, manufacturing, and distribution, usually in multiple countries on several continents. 7 While such arrangements typically result in higher economic productivity and efficiency, they also introduce vulnerabilities such as dependence on foreign sources for key supply chain inputs and, particularly with China, increased susceptibility to economic coercion. For example, the U.S. technology company Apple is headquartered in California, which is home to most of its R&D functions and where one of its most iconic products—the iPod portable music device—was designed. Japanese firm Toshiba, however, designed the iPod’s hard drive and built it in factories in China and the Philippines. Another U.S. firm designed the device’s controller chip of which production was outsourced to third parties in Taiwan and the United States. Other iPod components were made in Japan, Thailand, Taiwan, Korea, and Singapore. Final assembly was in China by Taiwan-owned firms. 8 This supply chain is relatively simple compared to those of semiconductors. The semiconductor industry offers a high-level view of the intricate complexity of globalized supply chains. The United States continues to lead the world in semiconductor design and R&D. It also dominates sales of logic and analog semiconductors. South Korea, on the other hand, has two-thirds of global market share for memory semiconductors, while Europe accounts for almost half of discrete semiconductors. 9 A handful of firms in the United States, Japan, and the Netherlands control nearly two-thirds of global market share for semiconductor manufacturing equipment. 10 Asia accounts for 80 percent of the world’s semiconductor foundries and assembly/test operations. China provides 16 percent of the world’s microchip supply and is also an important customer for semiconductors. 11 Defense supply chains also have critical dependencies on foreign entities. A report by data analytics firm Govini found that the Department of Defense has dozens of Chinese companies in its IT supply chains. 12 The defense industrial base is in many cases completely dependent on foreign sources for specialty chemicals, critical minerals, and unique carbon fibers that are essential raw materials for defense items. 13 For example, each F-35 fighter aircraft requires 425 pounds of rare earth elements, minerals with unique properties used in applications such as missile guidance systems. China has a near lock on the mining and processing of rare earths. 14
• Licit and illicit transfers often effectively circumvent export controls. The fact that most technologies have civilian and military end uses makes entity-specific export controls difficult to enforce. That technologies and related know-how are often available from multiple sources makes unilateral export controls less effective. The broader context in which such actions occur must also be considered. A country seeking to attain high-end technological capabilities often does so using a techno-nationalist strategy, which comprises three thrusts: “indigenizing foreign technology and related processes; diffusing that know-how throughout [its] economy; and boosting its capacity for innovation and manufacturing.” 15 Japan pursued a techno-nationalist strategy in the decades after World War II; South Korea, Singapore, and Taiwan from the 1960s–1990s; and China continues to do so today, with particular emphasis on technology transfer methods such as industrial espionage, cyber theft, academic exchanges, and open source data mining. 16 China’s military aviation industry took a “buy, build, or steal” approach, which included co-production and reverse engineering, to attain technological capabilities in areas subject to export controls including avionics, airframe design, fire control radars, and composite materials. Many gains were the result of successful “spin-on” of dual-use technologies to military applications. 17 More broadly, China’s military-civil fusion strategy supports and encourages “illegal transfers of dual-use technology … from the commercial and academic sectors to the [People’s Liberation Army].” 18

Unintended Consequences of Export Controls

Export controls have downsides. By definition they constrain U.S. competitiveness, a cost that policymakers theoretically accept when they implement these policies. More often than not, and especially in the way these policies have been applied over the last several years, however, there are unintended effects that harm national competitiveness. While this impact has been studied for decades, the trajectories of waning U.S. clout in global R&D, greater technology diffusion, more effective licit and illicit tech acquisition pathways, and U.S. attempts to control new types of technologies, are amplifying the severity of unintended consequences of export controls. 19 Understanding their growing impact is essential to revising export control policy to simultaneously promote U.S. technological advantages and ensure the competitiveness of U.S. companies in an era of technological competition. There are six major consequences to consider.

• Eroding U.S. company competitiveness and market share. Export control compliance can be onerous both in terms of the cost associated with navigating the process successfully and the time it takes to do so. The waiting period and complex administrative procedures required to receive approval for a sale can be such that opportunities are lost to foreign competitors, putting downward pressure on employment growth. 20 Smaller companies may forego producing export-controlled goods altogether because the cost of doing business is too high. 21 The impact of export controls can extend to entire industries. The U.S. space industry was likely impaired due to export controls. Before the establishment of new rules in 1998 pertaining to satellites in the U.S. International Traffic in Arms Regulations (ITAR), a regulatory regime that restricts the export of military- and defense-related technologies, U.S. global market share was about 75 percent. By 2012, the U.S. share was less than half the world total. 22 A survey that the Department of Commerce’s Bureau of Industry and Security conducted found that 35 percent of nearly 1,000 respondents reported lost sales of space-related products and services due to export control regulations between 2009 and 2012. 23 Respondents specifically noted that ITAR regulations “ensur[ed] that U.S. companies cannot compete” effectively in the global marketplace and that “it is unlikely that the European space industry would have grown so significantly, so quickly.” 24
• Avoiding U.S.-origin items in the supply chain. Foreign entities impacted by U.S. export controls often find ways to sidestep U.S origin restrictions or seek ways to design out U.S. content altogether. Some U.S. export controls hinge on the de minimis rule, where only a certain percentage (typically no more than 25 percent) of a product’s overall fair market value can consist of U.S. controlled technology. Export controls can incentivize end users to manipulate the value of non-U.S. inputs, such as by increasing the cost of foreign labor or materials. It also could prompt U.S. companies to move operations abroad. 25 A more drastic step is to design out U.S. technology or components altogether. Chinese telecommunications firm Huawei set out to build smartphones without American semiconductors after the company was targeted with export controls. 26 Companies in allied and partner countries, while not directly targeted, are also affected by U.S. export controls as part of globalized supply chains. They too are considering ways to reduce or eliminate U.S. technology inputs to decrease the risk of collateral damage, although this can be a significant challenge given the complexity of chip manufacturing processes. 27 For example, during an earnings call on October 14, 2020, Peter Wennink, CEO of Dutch photolithography manufacturer ASML, noted that the firm was looking at non-U.S. alternatives for metrology process tools to sidestep export restrictions. 28
• Capitalizing on U.S. export controls. Unilateral U.S. actions to restrict technology present opportunities to foreign competitors. In a 2002 study, the Government Accountability Office determined that U.S. policy on restricting sales of semiconductor manufacturing equipment to China was ineffective, in large part because European and Japanese companies continued selling equipment that was less than two generations behind the commercial state-of-the-art. 29 Similarly, high-performance computer vendors in China, Japan, South Korea, and Taiwan gained market share when U.S. manufacturers were constrained by export control regulations. 30 In the space industry, non-U.S. entities promote “ITAR-free” space-related products and services, including specially designed satellites. 31 Such spacecraft are so successful that they often command higher prices than a U.S. equivalent, underlining the competitive headwinds ITAR restrictions create. 32 Similarly, in response to semiconductor-related sanctions, Huawei is seeking to set up a chip plant that would not use American technology. 33
• Posing barriers to joint R&D. Export controls can hinder collaborative research efforts with allies and partners and constrain routine academic activity. ITAR again provides illustrative examples of such hurdles. ITAR’s definition of “defense services” is broad and vague. It also encompasses information out in the public domain. In practice, this means, “U.S. university researchers with prior knowledge of applicable export-controlled technical data” cannot work with foreign counterparts on defense-related projects without securing a license or exemption. 34 Even visits by foreign scholars to a research lab or presenting previously unpublished research at a conference where foreigners may be in attendance requires prior governmental approval. 35 The American-led multi-nation Joint Strike Fighter program, which developed the F-35 aircraft, was plagued by technology transfer problems. The United Kingdom, the United States’ foremost partner in the effort, complained of difficulties in transferring data and technical information due to ITAR restrictions. British access to the plane’s software source code was also an issue. In 2005, frustration mounted such that UK officials seriously considered withdrawing from the program altogether before an eventual agreement was reached. 36
• Accelerating tech indigenization efforts. Restricting access to technologies is likely to increase the urgency for the affected entity to try to end its reliance on foreign inputs. Chinese chipmakers, for example, are doubling down on methods to manufacture semiconductors with homegrown or non-U.S. foreign equipment. 37 While successful technology indigenization is a herculean task—China has spent decades trying to indigenize the manufacture of semiconductors and turbofan engines with limited progress—the drive to do so has spinoff effects that can make China more competitive over the long run. Reacting to U.S. policies provides greater focus to technology investment and development. China’s leaders are taking cues from American technology restrictions to guide the country’s technology indigenization strategy. 38 Chinese leaders, for example, point to U.S. policies toward Huawei as the impetus for doubling down on its technology indigenization goals. 39 This strategy likely entails greater spending on STEM education and R&D, greater competition for talent, the potential for more aggressive IP theft and industrial espionage, and ancillary scientific discoveries and engineering capabilities that have broader utility. These developments pose challenges and risks to the United States.
• Generating uncertainty for U.S. companies. Unclear and unpredictable export control policies can hinder a company’s ability to conduct long-term planning in a range of areas including R&D, mergers and acquisitions, capital expenditures, and supply chain management. 40 In one case, Micron, a U.S semiconductor firm, noted that the lack of decisions on its license applications for exports to China was hurting long-term sales. At the same time, several of Micron’s U.S. competitors determined their products were not subject to the same export controls and continued supplying products. 41 How a corporation’s lawyers interpreted the vague federal guidelines thus placed certain companies at an immediate competitive advantage.

Tenets to Inform Future U.S. Export Control Policies

Formulating successful 21st-century export control policies will require a rethink of current policy implementation practices. The shifts in the global technology landscape and mounting unintended consequences have wide-reaching implications for how and when to apply export controls as part of a broader economic statecraft strategy. Three overarching principles should guide policymakers in crafting export control regulations that fit the current strategic technology competition.

Tenet 1: Work with allies. Export controls in most cases will require coordination with and participation of one or more allied and partner countries. The United States rarely has sufficient dominance in a technology area to go it alone. In areas where it does, unilateral action puts major burdens on U.S. companies and friendly foreign entities that are part of their supply chains.

• Export controls, in most circumstances, will have to be implemented on a multilateral basis to have the desired impact. They are increasingly prone to ineffectiveness because the global science and technology landscape is a more level and diffuse playing field. Furthermore, application of unilateral sanctions is likely to encourage and accelerate de-Americanization of supply and value chains, putting U.S. technology sector competitiveness at risk.

Tenet 2: Raise the threshold. Export controls should be applied first and foremost as a strategic imperative to promote and preserve an edge in technological competitiveness, not as a tactic to achieve concessions. Efforts to restrict access to technologies will be most effective if it targets a country’s tech indigenization efforts. Nonetheless, controls with the goal of proscribing specific end uses are feasible when crafted in concert with other relevant countries.

• Export controls against a single or small number of entities are difficult and costly to enforce. The global diffusion of technological advancements and know-how, and the generally multi-use nature of foundational and emerging technologies, makes it challenging to prevent specific entities from acquiring a capability, or to curtail specific end uses.

Tenet 3: Reframe the goal. Export controls as economic statecraft are generally overused and, under the Trump presidency, wielded as a blunt instrument. These policy approaches increasingly impair the economic competitiveness of U.S. companies and technology sectors. These tactics are also unnecessarily polarizing toward allied and partner countries. A more strategic and sensible approach to export controls is necessary.

• Export controls are not an end in and of themselves. Investments in R&D of next-generation technologies need to happen concurrently to lay the foundation for continued competitiveness. More broadly, export controls need to be crafted as part of an overarching national strategy for technology and a broader economic statecraft strategy, not as scattershot policy actions.

Conclusion and Questions for Further Consideration

Current export controls are increasingly counterproductive to U.S. national security. They are in need of reevaluation and redesign. The implications of global technology-related trends and growing impact of unintended consequences as a result of export controls mean that changes are in order.

Policymakers in the United States will be well-served by initiating a formal, multi-stakeholder process to evaluate a series of questions, the answers to which should guide export controls of the future. These questions should include:

• How much economic harm to U.S. technology developers and manufacturers is tolerable in the service of U.S. national security?
• At what point would such harm undermine U.S. national security by fundamentally eroding U.S. technological advantages?
• In what ways can the United States work with like-minded nations to coordinate investments in, and regulatory, tax, and policy support for, technology entrepreneurship and competitiveness?
• What are the particular technology areas or types of technology services that the United States should target with proactive investments in order to increase U.S. competitiveness?
• How should the United States coordinate with like-minded tech-leading countries to manage technology transfers that might threaten their shared security interests?
• What new legal arrangements and compliance architecture will such coordination require and how should the United States best support its partners in its development?

In answering these questions, U.S. policymakers and stakeholders will craft a contemporary approach to collaborative technology statecraft and update what international technology and security partnerships are essential to U.S. national security.

Regardless of the specific outcomes of this inquiry, policymakers must bear in mind that crafting multilateral approaches to technology restrictions should be their first priority. Furthermore, export controls should also be used more sparingly and more strategically. By adapting U.S. export control policy to the current geopolitical context and to the realities of today’s technology landscape, policymakers can assure that export controls remain an effective tool to achieve U.S. foreign policy goals and to safeguard U.S. national security.

About the Author

Martijn Rasser is a Senior Fellow in the Technology and National Security Program at the Center for a New American Security (CNAS). Mr. Rasser served as a senior intelligence officer and analyst at the Central Intelligence Agency. Upon leaving government service, he was Chief of Staff at Muddy Waters Capital, an investment research firm. More recently, he was Director of Analysis at Kyndi, a venture-backed artificial intelligence startup. Mr. Rasser received his BA in anthropology from Bates College and his MA in security studies from Georgetown University.

Acknowledgments

I am indebted to Elizabeth Rosenberg for her substantive contributions, to Kevin Wolf for his invaluable feedback, and to Ainikki Riikonen and JJ Zeng for their research assistance. Thank you to my CNAS colleagues Maura McCarthy, Melody Cook, and Emily Jin for their role in the review, production, and design of this commentary. Any errors or shortcomings that remain are my responsibility alone.

As a research and policy institution committed to the highest standards of organizational, intellectual, and personal integrity, CNAS maintains strict intellectual independence and sole editorial direction and control over its ideas, projects, publications, events, and other research activities. CNAS does not take institutional positions on ​policy issues and the content of CNAS publications reflects the views of their authors alone. In keeping with its mission and values, CNAS does not engage in lobbying activity and complies fully with all applicable federal, state, and local laws. CNAS will not engage in any representational activities or advocacy on behalf of any entities or interests and, to the extent that the Center accepts funding from non-U.S. sources, its activities will be limited to bona fide scholastic, academic, and research-related activities, consistent with applicable federal law. The Center publicly acknowledges on its website annually all donors who contribute.

About This Commentary Series

CNAS launched a new project in 2020 on export controls that will run through the beginning of 2021. This project examines the policy goals of U.S. export controls, their effectiveness, the role of allies and partners, and the potential unintended consequences of America’s growing use of export controls. As a part of this project, CNAS asked a group of experts and policymakers to offer their perspectives on the policy goals that U.S. export controls should serve, and how and under what circumstances U.S. export controls can effectively achieve those policy goals. This paper is published as one of the project’s commentaries.

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• Frank Tang, “US technology embargo list gives China a blueprint for home-grown innovation over the next decade, top science official says,” South China Morning Post , September 17, 2020, https://www.scmp.com/economy/china-economy/article/3101948/us-technology-embargo-list-gives-china-blueprint-home-grown?mc_cid=e4d53b3d67&mc_eid=c1f9a346a7 . ↩
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• Jenny Leonard and Ian King, “Five months after Huawei export ban, U.S. companies are confused ,” Los Angeles Times , October 24, 2019, https://www.latimes.com/business/story/2019-10-24/huawei-export-ban-us-companies-confusion . ↩

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China’s exports improvement: neither what the economy wants nor needs.

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Containers stacked at a port in Lianyungang in China's eastern Jiangsu province. (Photo by AFP) / ... [+] China OUT (Photo by STR/AFP via Getty Images)

A rebound in exports during this year’s first quarter offers just about the only bright spot in China’s economic picture these days. The improvement might take the edge off the worst economic disappointments of 2024, but this kind of support certainly offers no long-run answer. Fundamentally, this source of strength is bad for China. Indeed, it risks distracting policy makers from the economic emphases they must cultivate.

On the surface the data look encouraging. After months of decline, China’s overall export volumes picked up 1.5% during the first three months of the year, the most recent period for which complete data are available. Growth in value terms came in at 4.4%. This quarterly gain, however, disguises the fact that the entire advance occurred in January. In February and March, the direction was down from the January high so that March exports were 9 percent below the January levels. This monthly pattern leaves room to doubt how much the quarterly pattern signals an improved export picture.

Patterns of buying reported by China’s major trading partners also raise questions about the durability of this improvement. The U.S. Department of Commerce , for instance, reports that American imports of Chinese goods in March, the most recent month for which data are available, were 26% lower than only six months ago. The European Commission reports that European Union (EU) imports from China for 2023, the most recent period for which complete data are available, were 18% lower than in 2022. For Japan, the figure for the same period was 11% lower than in the prior year. Data comparisons of this sort seldom agree without any one more correct than another, but the picture strongly suggests that the export improvement in China is neither as strong nor as secure as appears on the surface.

Probable reasons for any improvement in exports detract still more from any enthusiasm they might engender. If the surge has indeed occurred, it is likely to have come from a pricing edge generated by the combination of yuan depreciation on foreign exchange markets and China’s tendency toward deflation. According to calculations done recently by The Wall Street Journal , the combined effect of both these trends has brought down the cost of Chinese goods on global markets some 14% from where they were two years ago. According to the report that accompanied these calculations, this price advantage has served as a kind of “rocket fuel” for Chinese exports. But for all this, it should be apparent from the data quoted above that the price advantage has yet to move the American, European, and Japanese businesses away from their ongoing efforts to diversify sourcing away from China.

Aside from reason to doubt whether this recent price advantage offers “rocket fuel,” there is good reason to doubt whether this kind of help is what China wants or needs. After all, China has aimed to move its economy toward higher-value products, whereas it is simpler items that China wants to leave behind that are most sensitive to pricing. The recent pricing edge, if it is working, will tend to pull Chinese production away from where China wants to go and back toward the country’s less developed past when it specialized in simple products such as clothing, shoes, and toys. No doubt this pull is why China’s export gains have occurred at the expense of emerging Asian economies. it might produce good numbers but it interferes with the next step in Chinese economic development, a step that International Monetary Fund (IMF) has long recommended for China and that Beijing holds out as the country’s long-term goal.

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Even if China were to accept such a retrograde “solution” for its economy, it is not apparent that the ongoing currency depreciation and deflation will go on indefinitely, as it must to sustain this kind of export growth. More significantly, this is neither the direction China needs to go nor should go in its efforts to restore prosperity to its economy. It is definitely not an answer for the nation’s huge economic problems.

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US China Tech Controls Face Problematic Diagnosis

Recent reports highlight the negative impacts of US tech export controls targeting China. But the US is determined to march forward — and to force allies to follow.

Although US export controls are designed to widen Washington’s lead over Beijing in the race for advanced semiconductors, two new studies suggest the crackdown is boomeranging, hurting US business while helping Chinese companies.

Analysis from the New York Federal Reserve shows the new controls wiped “out $130 billion in market capitalization” for US firms and caused them to “experience a drop in bank lending, profitability, and employment.” Another study from US and Chinese financial experts claims controls on dual-use tech from 2007-2019 caused Chinese tech manufacturing or assembly firms to produce “more high-quality innovations.”

In short: US pressure appears to be accelerating Chinese innovation while depriving US firms of the revenue needed to keep their lead. Despite this evidence, the Biden Administration defends the chip crackdown and vows to continue expanding the export controls.

These studies are among the first to outline the costs of China-focused export controls imposed by the Trump and Biden administrations. President Joseph Biden expanded Trump-era efforts to hamper Chinese telecommunications champion Huawei in October 2022 imposing new export controls on advanced AI chips and chip-making tech.

The US continues to tighten those restrictions. On May 7, the US Department of Commerce revoked licenses for Intel and Qualcomm to sell chips to Huawei. In April, the US forced Dutch chip equipment giant ASML to stop servicing tools made with US tech sold to Chinese customers. The Commerce Department is reportedly considering new controls on selling the software that powers AI services such as ChatGPT. A proposed bipartisan House bill would provide the Department of Commerce with new powers to block exports of advanced AI systems.

The new independent studies portray past US export controls as provoking a wide range of unintended consequences. China “boosted domestic innovation and self-reliance, and increased purchases from non-US firms that produce similar technology.” Beijing points to new Huawei phones powered by Chinese-made advanced chips as early evidence of China out-innovating the US controls.

Admittedly, US experts and officials draw the opposite conclusion. They see the new Huawei phones as evidence the controls are working, as the phones’ chips are low quality, costly to produce, and may have been made with illegally obtained US tech. As US Commerce Secretary Gina Raimondo puts it : “The export controls are working because that chip is years behind what we have in the United States. We’ve out-innovated China.”

US officials outline a two-pronged strategy to maintain the West’s global lead in tech innovation. The export controls are one prong. The other is pouring cash from the respective US and European Chips Acts . The DC-based Semiconductor Industry Association forecasts success for the joint cash-and-control strategy. It projects the US and Europe will jointly produce 34% of the world’s advanced chips by 2032, with China producing only 3%.

But the carrot and stick program faces challenges.

Start with the public funding of chip manufacturing. Taiwan Semiconductor Manufacturing Company’s new chip factory in Phoenix, Arizona — bolstered by $6.6 billion from the US CHIPS Act — is plagued with labor issues. Intel, in line to receive $8.5 billion , claims it needs even more money and wants a second CHIPS Act, which the Biden administration supports , but a frugal Congress may not.

Enforcement of existing rules is spotty. Congress has failed to increase the US export control agency’s budget since 2010 despite expanding its responsibilities. Chinese firms can skirt the regulations by smuggling US tech through shell companies. The Entity List , a central part of US export controls, is address-based , making it prone to evasion by moving restricted tech from a sanctioned chip fab to an unsanctioned one located in the same facility.

US controls also depend on allies in Europe and Asia enacting similar restrictions in a timely manner. The Netherlands and Japan are partially matching US efforts — under intense pressure from Washington — but their controls lack US-style enforcement capabilities such as end-use and personnel restrictions. The United States imposed its controls unilaterally, and it took months to convince the Dutch and Japanese to adopt similar measures. This bought time for China to stockpile Dutch and Japanese tech.

Time gaps like this reduce the efficacy of US controls, potentially facilitating China’s jump to the next tier of advanced chips.

Although the European Union is exploring ways to strengthen its export controls regime, its emphasis on “de-risking” supply chains from China is increasingly focused on China’s “unfair” trade practices, not on limiting China’s technological advancement.

Another challenge is the EU’s unwieldy structure. EU governments and industry groups remain skeptical of ceding certain national security powers to the Brussels-based European Commission.

So far, Chinese retaliation has been limited. Beijing is approving export licenses after slapping controls on minerals critical to chipmaking in September 2023. Chinese officials are cheering US chipmaker Micron’s construction of a new plant in Xi’an despite a purported sales ban. President Xi Jinping is ramping up efforts to subsidize Chinese chipmakers and remove US parts from Chinese tech.

The US rationale for imposing export controls is to prevent China from using Western tech to modernize its military. But the strategy risks dangerous side effects. While US controls may be widening the innovation window — at great cost to US and European industries — they are creating a necessity for China to close the gap.

As the US cracks down, European and Asian allies could revolt. China could start weaponizing its considerable leverage in other areas such as legacy chips , solar panels , or critical minerals . The tech Cold War would intensify, fracturing the global tech ecosystem — and hurting everyone.

Matthew Eitel is Special Assistant to the President & CEO at the Center for European Policy Analysis (CEPA).

Bandwidth is CEPA’s online journal dedicated to advancing transatlantic cooperation on tech policy. All opinions are those of the author and do not necessarily represent the position or views of the institutions they represent or the Center for European Policy Analysis.

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Joe Cash reports on China’s economic affairs, covering domestic fiscal and monetary policy, key economic indicators, trade relations, and China’s growing engagement with developing countries. Before joining Reuters, he worked on UK and EU trade policy across the Asia-Pacific region. Joe studied Chinese at the University of Oxford and is a Mandarin speaker.

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