# Research on Influencing Factors of High-Quality Development of Agricultural Trade between China and SCO Member States in the Context of Industrial Internet of Things

Authors: Ya-Ting Zhang; Buwajian Abula; Bin Wang
Journal: Mobile Information Systems
 (2022)

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## Abstract

The IoT applications in the agriculture industry are intertwined in many ways, creating a hybrid construct of traditional businesses with digital connectivity implementations. Strategic issues and principles on the assimilation of IoT solutions are prevailed through time: investment costs, data interconnectivity, management, and analysis, as well as data security. As the first international organization in which China is the founding country, the Shanghai Cooperation Organization provides strategic support for China on the international stage. At present, there are various issues on the bilateral trade in agricultural products. Realizing the high-quality development of agricultural products trade with SCO member states is of great significance in forming a new pattern of sustainable and healthy development of bilateral agricultural product trade cooperation during the “14th Five-Year Plan” period, promoting the joint construction of “a community of shared future for Shanghai Cooperation Organization.” On the basis of analyzing the status quo of bilateral agricultural trade from 2011 to 2019, the influence of various influencing factors on the high-quality development of bilateral agricultural trade is empirically analyzed based on the gravity model. The study found that the overall quality of agricultural trade between China and the SCO member countries has shown a fluctuating upward trend with a larger development space. The size of the market, the level of trade facilitation, and the degree of trade openness and trade structure in each country have a significant role in promoting export value of China and other countries.


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## Body

## 1. Introduction

As the first international organization in which China is the founding country, the Shanghai Cooperation Organization is of great strategic significance to China. It is not only a security barrier to stabilize the frontiers of mainland China, but also a strategic support for China on the international stage [1]. The SCO member states are all countries along the “Belt and Road” and are the key areas for the construction of the “Belt and Road” [2]. Since its establishment, it has made remarkable achievements in economic and trade cooperation with China, political diplomacy, regional security, etc. Promoting the construction of the “Belt and Road” and the development of the Eurasian Economic Union has become a new type of regional organization with important international influence and an important force in maintaining world peace and stability [3]. In the context of the “Belt and Road” initiative, the economic and trade cooperation between China and the SCO member states has been continuously enhanced. Agriculture, which is an important part of the bilateral national economy, has become a priority of trade cooperation. During the “14th Five-Year Plan” period, the economy of China has entered a stage of high-quality development [4]. As an important support for China’s economic development, agricultural product trade should also be transformed and upgraded to high-quality development. High-quality development is the only way to establish a modernized economic system and an inevitable requirement for comprehensive construction of a modern socialist country [5]. The high-quality development of agricultural trade is an important component of China’s high-quality economic development. In essence, high-quality development means high-quality supply and demand, allocation, input and output, income distribution, and economic circulation [6]. In the new era, all kinds of development in China should be fundamentally oriented towards innovation, coordination, greenness, openness, and sharing, and the high-quality development of agricultural trade should do the same.At present, the agricultural trade cooperation between China and the SCO member states has achieved certain results, but there are various problems such as low trade scale, irrational structure, weak complementarity, and concentrated types of trade. Deepening agricultural trade cooperation is an effective way to conform to the wave of globalization, keep pace with the times, and build a “community with a shared future for the SCO.” And it is also an important aspect of the need to promote the economic development of SCO member states and respond to international security crises. China is a country with a large population. With the improvement and enrichment of people’s living standards, the demand for overseas agricultural products has become more vigorous [6, 7]. Due to the unilateralism and trade protectionism implemented by the United States in the past two years, deepening agricultural cooperation with SCO member states is an effective way to get rid of the current negative impact.Scholars’ research on agricultural trade between China and SCO member countries mainly focuses on the competitiveness and complementarity of each country’s export products, trade facilitation, the analysis of the influencing factors of agricultural trade fluctuations, and the trade creation effect of the establishment of the free trade area on bilateral trade, etc. Someone applied the constant market share model to the research on the influencing factors of agricultural trade fluctuations between China and SCO member states from three aspects: demand, structure, and competitiveness. The study found that the SCO members’ increasing demand for agricultural imports is the primary factor to the growth of China’s agricultural exports [7]. Chinese experts conducted a qualitative and quantitative analysis of the trade in agricultural products between China and the Shanghai Cooperation Organization member states and concluded that the current bilateral trade is low in scale, unreasonable in structure, concentrated in types, low in added value, and weak in complementarity, with the lack of cooperation within the industry and other issues [5–7]. China has a large trade deficit by sorting out the overall import and export volume between China and the SCO member states from 2007 to 2017 [7]. Secondly, by calculating the RCA value of SCO members, respectively, and the Cab value of China’s import and export with these countries, as well as analyzing their competitive advantages and complementarity, it was found that China’s trade dominance of various agricultural products is at a disadvantage. Due to the high demand of work material, China has a high degree of integration with other countries [8]. And they deemed that China and the Shanghai Cooperation Organization member states should actively participate in activities such as trade cooperation, improvement of the trade environment, and optimization of agricultural products [9–11]. Scholars studied the impact of trade facilitation on the economies of SCO member states through the GTAP model and believed that the reduction of trade time will improve the terms of trade of all member states and drive economic growth [12]. Regarding the connotation of the current “high-quality development of agricultural trade between China and SCO member states,” there is no clear explanation yet [13]. It is basically believed that the high-quality development of agricultural trade covers reform and innovation, structural optimization, efficiency improvement, green and sustainable development, and competitiveness enhancement, etc. [14].There is a lack of in-depth research on the following three aspects in current academia: First one is the connotation of high-quality development of agricultural products. Only with an accurate understanding of the connotation of high-quality development can we scientifically measure the high-quality development of China’s agricultural trade and figure out the right path to achieve the high-quality development of agricultural products [15]. Secondly, the current literature mainly focuses on the empirical analysis of the overall development level of China’s agricultural product trade at the macro level and lacks research and analysis on the development level of a specific organization’s agricultural product trade from a high-quality perspective [16].Therefore, on the basis of the foregoing and an accurate understanding of the connotation of “high-quality development of agricultural trade,” this paper constructs a corresponding theoretical model to empirically test the influencing factors of high-quality development of agricultural trade between China and SCO member states. It is proposed to explore the path to achieve high-quality development of agricultural trade between China and the SCO member states from four aspects: optimization of trade structural, optimization of trade innovation, optimization of trade circulation, and optimization of trade coordination.


## 2. Methodology

### 2.1. Calculation Method of International Market Share

International market share (MS) measures the share and competitive position of a country’s agricultural products in the international market under equal market conditions [17]:(1)MSik=XikXwk,where Xik is, respectively, the export of commodity K by country I and the total exports of all commodities by country I; Xwk is, respectively, the world exports of commodity K and the total world exports.The higher the share of this indicator, the stronger the international competitiveness of agricultural products, and vice versa. The international market share reflects the international competitiveness of a certain product or industry of a country and the level of management, which may directly reflect the international competitiveness of agricultural products. However, this indicator has shortcomings; for example, its decline does not necessarily represent the decline of the international competitiveness of agricultural products, and sometimes factors such as adjustment of product structure and changes in consumption structure should be taken into consideration.


### 2.2. Setting Up the Gravity Model

In order to inspect the impact of relevant factors on the high-quality development of agricultural trade between China and SCO member states, this paper adopts the most widely used gravity model in international trade research, and its general form is as follows:(2)Fij=β0Miβ1Mjβ2Dijβ3Aijβ4.Among them,Fij is the bilateral trade volume between countries i and j; Mi and Mj represent the economic development level in countries I and j; Dij is the geographic distance between the two countries; Aij means other factors that affect trade flow. With in-depth study, more and more variables are integrated into the model. In order to facilitate regression, the gravity model is usually linearized, and the model transformation is as follows:(3)lnFij=β0+β1LnMi+β2LnMj+β3LnDij+β4LnAij+εijt.Based on the purpose of this research, variables such as trade facilitation index, trade openness, human capital, and technological innovation factors are included in the original model with details as below:(4)lnEXijt=β0+β1LnGDPjt+β2LnPOPjt+β3LnDISTij+β4LnOPENjt+β5LnHTPj+β6LnTFLit+β7LnHMjt+β8LnCRDjt++εijt.J is the importing country, i is the exporting country (China), and t is the year. EXij refers to the export volume of country i to country j, GDPjt represents the overall economic scale of country j in period of t, POPjt indicates the population of country j in period of t, DISTjt refers to the geographical distance between two countries, OPENjt represents the trade openness of country j in the year of t, HTPjt refers to the trade structure, which is the export volume of medium-high value-added agricultural products to country j in year of t, TFLjt represents the trade facilitation of country i in year of t, HMjt is the educational appropriations of country j in year of t, and CRD is the research and development (R&D) in country j in year of t. β0 is a constant term, β1, β2, β3, β4, β5, β6, β7, β8 are coefficients to be estimated, and εijt is a random error term.


### 2.3. Description of Variables in Gravity Model and Data Sources

EXijt is a variable; it refers to the export scale, which is represented by the total imports and exports of agricultural products from China to SCO member states. The data comes from the database of UN Comtrade (HS1996 six-digit code).GDPjt indicates the economic scale of country j, which is represented by GDP. It refers to the overall state of a country’s economy and reflects the economic development of the country. Generally, the higher the value, the greater the potential import demand, and the higher the demand for product diversification. Meanwhile, there is a positive impact on the expected scale of exports. The data comes from the World Bank’s “World Development Indicators.”POPjt represents the population of country j. Generally, the higher value, on the one hand, indicates a higher market demand of the country; on the other hand, it reflects a higher self-sufficiency and a lower demand for foreign products. Meanwhile, there is an uncertain impact on the expected scale of exports. The data comes from the World Bank’s “World Development Indicators.”DISTij refers to the geographic distance between the two countries, and it is represented by the absolute distance between the capital of China and country j in this paper. Generally, the farther the distance between the two countries, the higher the trade cost. However, with the improvement of infrastructure and trade facilitation in each country, the expected scale of exports is uncertain. The data comes from CEPII.OPENjt indicates the trade openness of country j, which is calculated by the ratio of total merchandise imports to GDP. Generally speaking, the higher the degree of trade openness of a country is, the more commodities it imports, and the expected scale of exports is positive. The data comes from the database of UN Comtrade and the World Bank’s “World Development Indicators.”HTPjt represents the trade structure, which is represented by the total export of medium-high-tech value-added agricultural products in country j. The foreign trade structure index is an important aspect of evaluating the quality of trade development. The larger the value, the better the trade structure of country j to a certain extent. And the expected scale of exports is positive. The data comes from the database of UN Comtrade.TFLit refers to the trade facilitation, which is represented by the preceding calculation results.The higher the trade facilitation, the lower the trade cost of China’s exports to it, and the expected scale of exports is positive. The data comes from the “Global Competitiveness Report” released by the World Bank.HMjt indicates the human capital, and the talent is the strategic resource to win the initiative in the international competition, and it is a strong driving force for social development. Chen Gonjun found that there is foot in the door effect of human capital to the development of agricultural trade. In this paper, human capital is also included in the analysis of influencing factors, which is represented by the human capital input of country j. The expected scale of exports is positive, and the data comes from the education expenditure in the “China Education Statistical Yearbook.”CRDjt means technological innovation, which is represented by the R&D expenditure of country j. Chang Fei assumed that the lack of financial support and innovation in China has seriously restricted the development of China’s agricultural trade. R&D expenditure indicates the expenses that enterprises invest in independent R&D in order to improve their techniques and the export competitiveness of their products. This paper holds that R&D intensity should be included in the influencing factors of trade quality improvement. The expected scale of exports is positive, and the data comes from the R&D expenditure in the “China Education Statistical Yearbook.”


## 2.1. Calculation Method of International Market Share

International market share (MS) measures the share and competitive position of a country’s agricultural products in the international market under equal market conditions [17]:(1)MSik=XikXwk,where Xik is, respectively, the export of commodity K by country I and the total exports of all commodities by country I; Xwk is, respectively, the world exports of commodity K and the total world exports.The higher the share of this indicator, the stronger the international competitiveness of agricultural products, and vice versa. The international market share reflects the international competitiveness of a certain product or industry of a country and the level of management, which may directly reflect the international competitiveness of agricultural products. However, this indicator has shortcomings; for example, its decline does not necessarily represent the decline of the international competitiveness of agricultural products, and sometimes factors such as adjustment of product structure and changes in consumption structure should be taken into consideration.


## 2.2. Setting Up the Gravity Model

In order to inspect the impact of relevant factors on the high-quality development of agricultural trade between China and SCO member states, this paper adopts the most widely used gravity model in international trade research, and its general form is as follows:(2)Fij=β0Miβ1Mjβ2Dijβ3Aijβ4.Among them,Fij is the bilateral trade volume between countries i and j; Mi and Mj represent the economic development level in countries I and j; Dij is the geographic distance between the two countries; Aij means other factors that affect trade flow. With in-depth study, more and more variables are integrated into the model. In order to facilitate regression, the gravity model is usually linearized, and the model transformation is as follows:(3)lnFij=β0+β1LnMi+β2LnMj+β3LnDij+β4LnAij+εijt.Based on the purpose of this research, variables such as trade facilitation index, trade openness, human capital, and technological innovation factors are included in the original model with details as below:(4)lnEXijt=β0+β1LnGDPjt+β2LnPOPjt+β3LnDISTij+β4LnOPENjt+β5LnHTPj+β6LnTFLit+β7LnHMjt+β8LnCRDjt++εijt.J is the importing country, i is the exporting country (China), and t is the year. EXij refers to the export volume of country i to country j, GDPjt represents the overall economic scale of country j in period of t, POPjt indicates the population of country j in period of t, DISTjt refers to the geographical distance between two countries, OPENjt represents the trade openness of country j in the year of t, HTPjt refers to the trade structure, which is the export volume of medium-high value-added agricultural products to country j in year of t, TFLjt represents the trade facilitation of country i in year of t, HMjt is the educational appropriations of country j in year of t, and CRD is the research and development (R&D) in country j in year of t. β0 is a constant term, β1, β2, β3, β4, β5, β6, β7, β8 are coefficients to be estimated, and εijt is a random error term.


## 2.3. Description of Variables in Gravity Model and Data Sources

EXijt is a variable; it refers to the export scale, which is represented by the total imports and exports of agricultural products from China to SCO member states. The data comes from the database of UN Comtrade (HS1996 six-digit code).GDPjt indicates the economic scale of country j, which is represented by GDP. It refers to the overall state of a country’s economy and reflects the economic development of the country. Generally, the higher the value, the greater the potential import demand, and the higher the demand for product diversification. Meanwhile, there is a positive impact on the expected scale of exports. The data comes from the World Bank’s “World Development Indicators.”POPjt represents the population of country j. Generally, the higher value, on the one hand, indicates a higher market demand of the country; on the other hand, it reflects a higher self-sufficiency and a lower demand for foreign products. Meanwhile, there is an uncertain impact on the expected scale of exports. The data comes from the World Bank’s “World Development Indicators.”DISTij refers to the geographic distance between the two countries, and it is represented by the absolute distance between the capital of China and country j in this paper. Generally, the farther the distance between the two countries, the higher the trade cost. However, with the improvement of infrastructure and trade facilitation in each country, the expected scale of exports is uncertain. The data comes from CEPII.OPENjt indicates the trade openness of country j, which is calculated by the ratio of total merchandise imports to GDP. Generally speaking, the higher the degree of trade openness of a country is, the more commodities it imports, and the expected scale of exports is positive. The data comes from the database of UN Comtrade and the World Bank’s “World Development Indicators.”HTPjt represents the trade structure, which is represented by the total export of medium-high-tech value-added agricultural products in country j. The foreign trade structure index is an important aspect of evaluating the quality of trade development. The larger the value, the better the trade structure of country j to a certain extent. And the expected scale of exports is positive. The data comes from the database of UN Comtrade.TFLit refers to the trade facilitation, which is represented by the preceding calculation results.The higher the trade facilitation, the lower the trade cost of China’s exports to it, and the expected scale of exports is positive. The data comes from the “Global Competitiveness Report” released by the World Bank.HMjt indicates the human capital, and the talent is the strategic resource to win the initiative in the international competition, and it is a strong driving force for social development. Chen Gonjun found that there is foot in the door effect of human capital to the development of agricultural trade. In this paper, human capital is also included in the analysis of influencing factors, which is represented by the human capital input of country j. The expected scale of exports is positive, and the data comes from the education expenditure in the “China Education Statistical Yearbook.”CRDjt means technological innovation, which is represented by the R&D expenditure of country j. Chang Fei assumed that the lack of financial support and innovation in China has seriously restricted the development of China’s agricultural trade. R&D expenditure indicates the expenses that enterprises invest in independent R&D in order to improve their techniques and the export competitiveness of their products. This paper holds that R&D intensity should be included in the influencing factors of trade quality improvement. The expected scale of exports is positive, and the data comes from the R&D expenditure in the “China Education Statistical Yearbook.”


## 3. Results and Discussion

China and the SCO member states are good strategic partners for each other. Since its establishment, it has made remarkable achievements in economic and trade cooperation, political diplomacy, and regional security with China. With the continuous expansion and deepening of bilateral cooperation, the trade in agricultural products between China and its member states has also achieved rapid development, showing new characteristics in terms of market scale, country distribution, and commodity structure.


### 3.1. Significant Rising Scale of Trade and Consistent Expanded Trade Deficit

With the proposal of the Belt and Road Initiative and the improvement of the overall level of the SCO states, the bilateral trade volume of agricultural products between China and its countries has continued to rise, and the trade status has been significantly improved. In terms of overall scale, the total trade volume of agricultural products between China and SCO member states has nearly doubled from US$ 4.995 billion in 2011 to US$ 9.033 billion in 2019, and bilateral agricultural trade in 2019 accounted for 4.2% of the total of China's foreign trade in agricultural products, and the trade status has been effectively improved. In the past 9 years, the bilateral trade volume has been on the rise in general, only decreased slightly in 2015, and then gradually increased since 2016. From 2011 to 2019, the average annual growth rate of bilateral agricultural foreign trade volume was about 9.67%. In 2017, as India and Pakistan are integrated into SCO, the cast of SCO and field have been expanded, and the scale has been increased. The Joint Statement of Heads of State of SCO on Trade Facilitation released by SCO Qingdao Summit in 2018 provided new guidance for bilateral trade facilitation. In 2018 and 2019, the annual growth rates of bilateral agricultural trade reached 31.79% and 22.09%, respectively.It cannot be ignored that China’s agricultural exports to SCO member states are far less than China’s agricultural imports from member states, and the trade deficit is on the rise. As shown in Figure1, the proportion of agricultural exports between China and the SCO member states was 43.1% in 2011, but in 2019 it narrowed to 24.4%, and the trade deficit changed from 0.689 to 4.631 billion US dollars, which is almost 8 times that of 2011. In terms of total imports and exports, in 2018, China’s imports of agricultural products from Shanghai Cooperation Organization member states reached US$5.032 billion, accounting for 3.98% of China’s total agricultural imports. The import value of agricultural products was 6.832 billion US dollars, which is about twice as much as in 2011, accounting for 4.87% of China’s agricultural imports. Meanwhile, China’s agricultural exports to the SCO member states are bleak, with a peak of 2.589 billion US dollars in 2014, accounting for less than 1% of China’s total agricultural exports. The ratio has always been less than 1%.Figure 1
Rising scale of trade between China and SCO countries.


### 3.2. Uneven Country Distribution

The trade in agricultural products between China and the SCO member states is quite different in terms of country distribution, showing a relatively concentrated feature. As shown in Table1, in terms of imports and exports, Russia and India are China’s main agricultural product trade partners among the SCO states, accounting for more than 70% of China’s agricultural trade volume with the BRICS countries. Among them, the share of China-India agricultural trade in 2019 reached 2.644 billion US dollars; the trade volume of agricultural products between China and India in that year accounted for 27% of that between China and other member states, with an increase of 3 percentage points compared with 23% in 2011. Russia accounts for a large proportion of the import and export trade of agricultural products between China and SCO members, which is mainly affected by the “dilution effect” caused by the expansion of the organization. In 2019, the total trade volume of agricultural products between China and the other five member states accounted for less than 20%, and the trade status has not changed significantly in recent years. Pakistan ranks first, accounting for about 8%; Kazakhstan ranks second, accounting for about 7%; Kyrgyzstan ranks third, accounting for about 3%; Uzbekistan ranks fourth, accounting for about 1%, and Tajikistan is the smallest, accounting for less than 1%.Table 1
Distribution of agricultural trade between China and SCO members in 2011–2019.
YearImport (%)Export (%)CNCNCNCNCNCNCNCNCNCNCNCNCNCNRUINKZPKTJKGUZRUINKZPKTJKGUZ201159.435.60.33.80.00.00.966.511.16.29.70.54.31.8201251.031.92.314.10.00.00.765.412.16.88.40.44.62.3201354.230.82.711.20.00.30.865.712.57.77.20.54.22.3201455.224.06.213.20.10.11.066.011.47.17.70.65.61.7201556.624.44.113.80.00.01.060.211.98.411.10.65.52.4201563.118.14.512.30.00.10.862.214.37.310.90.43.41.5201763.422.45.47.90.01.91.060.714.010.110.80.71.52.0201863.620.94.99.40.00.11.064.011.210.19.60.42.82.0201952.534.15.57.00.00.11.058.710.19.511.00.58.51.9As for the imports, although China’s imports from Russia have been severely reduced in the past two years, it has always been China’s largest source of agricultural imports among the member states and prevails the other six countries. Although the proportion of China’s imports from India dropped significantly in the past few years, it recovered in 2019. Since then, Russia and India are still China’s two important importing countries in the Shanghai Cooperation Organization member states. In terms of exports, Russia has remained the largest exporter among the member states, accounting for about 60%, while China’s exports to India, Kazakhstan, Pakistan, and Kyrgyzstan do not vary much, all of which are around 10%. The exports to Uzbekistan and Tajikistan are the least, both accounting for less than 2%.


### 3.3. Concentrated Commodity Structure

Similar to the characteristics of trade states, the commodity structure of agricultural trade between China and the SCO member states is relatively concentrated, and all types of agricultural products are quite different. Table2 shows that there are significant advantages for chapter 00 and chapter 01 products in the import and export trade of agricultural products between China and SCO member states. In general, they have accounted for more than 30% of China’s total agricultural trade with SCO member states, respectively, from 2011 to 2019, while there is a relatively low proportion of 02 and 04 products in the trade of agricultural products between China and SCO member states, and 04 product has shown a dramatic decline. Until 2019, they have accounted for 10.4% and 14.8% of China’s total agricultural trade with SCO member states, respectively.Table 2
Commodity structure of agricultural trade between China and SCO countries in 2011–2019.
YearImportExportImport & export000102040001020400010204201163.411.711.513.413.133.50.652.837.030.06.027.0201250.430.113.75.814.329.40.755.631.537.37.423.8201354.724.812.77.816.129.70.553.731.336.56.625.6201453.527.210.39.017.531.50.550.531.937.74.925.5201546.028.313.612.112.539.00.448.128.241.27.223.5201649.927.116.36.713.241.30.445.030.341.78.519.4201749.922.120.47.611.041.80.646.629.739.510.820.1201852.521.117.19.310.340.31.647.835.633.711.119.5201953.825.614.66.011.639.91.047.639.934.910.414.8In terms of imports, the most important import products of China from SCO member states are 00 products, whose trade status has declined slightly in recent years. In 2011, the 00 products accounted for 63.4% of China’s agricultural imports from SCO member states, which dropped to 53.83% in 2019, while the exports are contrary to it. The 00 products only accounted for less than 10% of China’s agricultural exports to SCO member states. The 04 products are the most competitive agricultural products in exports, and it accounted for about 50% of China’s agricultural exports to SCO member states.


### 3.4. Influencing Factors of High-Quality Development of Agricultural Trade

In this paper, Mixed Regression Model (MRM), Fixed Effects Model (FEM), and Random Effects Model (REM) are applied for regression analysis, respectively, in formula IV, with a time span of 9 years, which belongs to Balanced Panel Data. Before the regression analysis, log the data to deal with heteroscedasticity. At the same time, the F test is performed on the regression results of the MRM and FEM. Prob > F is infinitely close to 0, indicating that the null hypothesis is rejected and the FEM is superior to MRM. Meanwhile, Hausman test is performed on the regression results of the FEM and REM. The result is Prob = 0.1467, which does not reject the null hypothesis and does not mean that the FEM is superior to the REM. Thus, the REM was chosen to analyze the impact of various influencing factors on the high-quality development of agricultural trade between China and the SCO member states.It can be seen from Table3 that there is an ideal model obtained by the regression formula IV. The modulated coefficient of determination is 0.875, which represents that the extended gravity model formed in this paper has a good fitting effect on the sample data, and the explanatory of each variable is credible. Through a detailed analysis of the regression results of the model, it can be concluded that the regression coefficients of the importing country’s economic scale, trade facilitation, trade structure, and trade openness are significantly positive, which is consistent with the theoretical expectation of this paper. Moreover, the regression coefficient of the economic scale is 0.736, which indicates that, for every 1% increase in the GDP of the importing country, the trade volume between China and SCO member states will increase by 0.736%. The regression coefficient of trade facilitation is 0.174, representing that, for every 1% increase in the trade facilitation of importing countries, the export volume of China to SCO member states will increase by 0.174%. The regression coefficient of trade structure is 0.589, indicating that, for every 1% optimization of the trade structure, the volume of agricultural trade between China and SCO member states will increase by 0.589%. And the regression coefficient of trade openness is 0.478, which means that, for every 1% increase in the trade openness of importing countries, the export volume of agricultural products of China to it will increase by 0.478%. In the regression, due to the large number of dependent variables and the limited sample volumes, the t-statistics of the coefficients of some other dependent variables are not significant, which needs to be further explored in future study.Table 3
The results of regression analysis.
Variables123MRFeREx1GDPjt0.5130.5130.736∗∗∗(1.471)(1.266)(5.320)x2POPjt0.8180.818−0.142(0.567)(0.488)(−1.389)X3DISTij−0.628−1.628−1.326∗(−0.209)(−0.311)(−1.196)X4TFLit0.093∗∗∗0.0930.174∗∗∗(2.221)(1.051)(2.487)X5HTPjt0.211∗∗∗0.211∗∗∗0.589∗∗∗(4.479)(3.855)(8.778)X6OPENjt−0.389∗−0.3890.478∗∗∗(−1.770)(−1.523)(2.689)X7HMjt1.1731.1730.375(1.576)(1.356)(0.296)X8CRDjt−1.001−1.001−1.034(−1.386)(−1.192)(−0.644)_cons11.5499.42612.169(0.504)(0.379)(0.812)N545454adj. R20.9210.1880.875F test25.87 (p=0.001)Hausman test−34.37 (p=0.146)Notes:t-statistics in the brackets, and ∗, ∗∗, ∗∗∗ represent the significance level of 10%, 5%, and 1% respectively.


## 3.1. Significant Rising Scale of Trade and Consistent Expanded Trade Deficit

With the proposal of the Belt and Road Initiative and the improvement of the overall level of the SCO states, the bilateral trade volume of agricultural products between China and its countries has continued to rise, and the trade status has been significantly improved. In terms of overall scale, the total trade volume of agricultural products between China and SCO member states has nearly doubled from US$ 4.995 billion in 2011 to US$ 9.033 billion in 2019, and bilateral agricultural trade in 2019 accounted for 4.2% of the total of China's foreign trade in agricultural products, and the trade status has been effectively improved. In the past 9 years, the bilateral trade volume has been on the rise in general, only decreased slightly in 2015, and then gradually increased since 2016. From 2011 to 2019, the average annual growth rate of bilateral agricultural foreign trade volume was about 9.67%. In 2017, as India and Pakistan are integrated into SCO, the cast of SCO and field have been expanded, and the scale has been increased. The Joint Statement of Heads of State of SCO on Trade Facilitation released by SCO Qingdao Summit in 2018 provided new guidance for bilateral trade facilitation. In 2018 and 2019, the annual growth rates of bilateral agricultural trade reached 31.79% and 22.09%, respectively.It cannot be ignored that China’s agricultural exports to SCO member states are far less than China’s agricultural imports from member states, and the trade deficit is on the rise. As shown in Figure1, the proportion of agricultural exports between China and the SCO member states was 43.1% in 2011, but in 2019 it narrowed to 24.4%, and the trade deficit changed from 0.689 to 4.631 billion US dollars, which is almost 8 times that of 2011. In terms of total imports and exports, in 2018, China’s imports of agricultural products from Shanghai Cooperation Organization member states reached US$5.032 billion, accounting for 3.98% of China’s total agricultural imports. The import value of agricultural products was 6.832 billion US dollars, which is about twice as much as in 2011, accounting for 4.87% of China’s agricultural imports. Meanwhile, China’s agricultural exports to the SCO member states are bleak, with a peak of 2.589 billion US dollars in 2014, accounting for less than 1% of China’s total agricultural exports. The ratio has always been less than 1%.Figure 1
Rising scale of trade between China and SCO countries.


## 3.2. Uneven Country Distribution

The trade in agricultural products between China and the SCO member states is quite different in terms of country distribution, showing a relatively concentrated feature. As shown in Table1, in terms of imports and exports, Russia and India are China’s main agricultural product trade partners among the SCO states, accounting for more than 70% of China’s agricultural trade volume with the BRICS countries. Among them, the share of China-India agricultural trade in 2019 reached 2.644 billion US dollars; the trade volume of agricultural products between China and India in that year accounted for 27% of that between China and other member states, with an increase of 3 percentage points compared with 23% in 2011. Russia accounts for a large proportion of the import and export trade of agricultural products between China and SCO members, which is mainly affected by the “dilution effect” caused by the expansion of the organization. In 2019, the total trade volume of agricultural products between China and the other five member states accounted for less than 20%, and the trade status has not changed significantly in recent years. Pakistan ranks first, accounting for about 8%; Kazakhstan ranks second, accounting for about 7%; Kyrgyzstan ranks third, accounting for about 3%; Uzbekistan ranks fourth, accounting for about 1%, and Tajikistan is the smallest, accounting for less than 1%.Table 1
Distribution of agricultural trade between China and SCO members in 2011–2019.
YearImport (%)Export (%)CNCNCNCNCNCNCNCNCNCNCNCNCNCNRUINKZPKTJKGUZRUINKZPKTJKGUZ201159.435.60.33.80.00.00.966.511.16.29.70.54.31.8201251.031.92.314.10.00.00.765.412.16.88.40.44.62.3201354.230.82.711.20.00.30.865.712.57.77.20.54.22.3201455.224.06.213.20.10.11.066.011.47.17.70.65.61.7201556.624.44.113.80.00.01.060.211.98.411.10.65.52.4201563.118.14.512.30.00.10.862.214.37.310.90.43.41.5201763.422.45.47.90.01.91.060.714.010.110.80.71.52.0201863.620.94.99.40.00.11.064.011.210.19.60.42.82.0201952.534.15.57.00.00.11.058.710.19.511.00.58.51.9As for the imports, although China’s imports from Russia have been severely reduced in the past two years, it has always been China’s largest source of agricultural imports among the member states and prevails the other six countries. Although the proportion of China’s imports from India dropped significantly in the past few years, it recovered in 2019. Since then, Russia and India are still China’s two important importing countries in the Shanghai Cooperation Organization member states. In terms of exports, Russia has remained the largest exporter among the member states, accounting for about 60%, while China’s exports to India, Kazakhstan, Pakistan, and Kyrgyzstan do not vary much, all of which are around 10%. The exports to Uzbekistan and Tajikistan are the least, both accounting for less than 2%.


## 3.3. Concentrated Commodity Structure

Similar to the characteristics of trade states, the commodity structure of agricultural trade between China and the SCO member states is relatively concentrated, and all types of agricultural products are quite different. Table2 shows that there are significant advantages for chapter 00 and chapter 01 products in the import and export trade of agricultural products between China and SCO member states. In general, they have accounted for more than 30% of China’s total agricultural trade with SCO member states, respectively, from 2011 to 2019, while there is a relatively low proportion of 02 and 04 products in the trade of agricultural products between China and SCO member states, and 04 product has shown a dramatic decline. Until 2019, they have accounted for 10.4% and 14.8% of China’s total agricultural trade with SCO member states, respectively.Table 2
Commodity structure of agricultural trade between China and SCO countries in 2011–2019.
YearImportExportImport & export000102040001020400010204201163.411.711.513.413.133.50.652.837.030.06.027.0201250.430.113.75.814.329.40.755.631.537.37.423.8201354.724.812.77.816.129.70.553.731.336.56.625.6201453.527.210.39.017.531.50.550.531.937.74.925.5201546.028.313.612.112.539.00.448.128.241.27.223.5201649.927.116.36.713.241.30.445.030.341.78.519.4201749.922.120.47.611.041.80.646.629.739.510.820.1201852.521.117.19.310.340.31.647.835.633.711.119.5201953.825.614.66.011.639.91.047.639.934.910.414.8In terms of imports, the most important import products of China from SCO member states are 00 products, whose trade status has declined slightly in recent years. In 2011, the 00 products accounted for 63.4% of China’s agricultural imports from SCO member states, which dropped to 53.83% in 2019, while the exports are contrary to it. The 00 products only accounted for less than 10% of China’s agricultural exports to SCO member states. The 04 products are the most competitive agricultural products in exports, and it accounted for about 50% of China’s agricultural exports to SCO member states.


## 3.4. Influencing Factors of High-Quality Development of Agricultural Trade

In this paper, Mixed Regression Model (MRM), Fixed Effects Model (FEM), and Random Effects Model (REM) are applied for regression analysis, respectively, in formula IV, with a time span of 9 years, which belongs to Balanced Panel Data. Before the regression analysis, log the data to deal with heteroscedasticity. At the same time, the F test is performed on the regression results of the MRM and FEM. Prob > F is infinitely close to 0, indicating that the null hypothesis is rejected and the FEM is superior to MRM. Meanwhile, Hausman test is performed on the regression results of the FEM and REM. The result is Prob = 0.1467, which does not reject the null hypothesis and does not mean that the FEM is superior to the REM. Thus, the REM was chosen to analyze the impact of various influencing factors on the high-quality development of agricultural trade between China and the SCO member states.It can be seen from Table3 that there is an ideal model obtained by the regression formula IV. The modulated coefficient of determination is 0.875, which represents that the extended gravity model formed in this paper has a good fitting effect on the sample data, and the explanatory of each variable is credible. Through a detailed analysis of the regression results of the model, it can be concluded that the regression coefficients of the importing country’s economic scale, trade facilitation, trade structure, and trade openness are significantly positive, which is consistent with the theoretical expectation of this paper. Moreover, the regression coefficient of the economic scale is 0.736, which indicates that, for every 1% increase in the GDP of the importing country, the trade volume between China and SCO member states will increase by 0.736%. The regression coefficient of trade facilitation is 0.174, representing that, for every 1% increase in the trade facilitation of importing countries, the export volume of China to SCO member states will increase by 0.174%. The regression coefficient of trade structure is 0.589, indicating that, for every 1% optimization of the trade structure, the volume of agricultural trade between China and SCO member states will increase by 0.589%. And the regression coefficient of trade openness is 0.478, which means that, for every 1% increase in the trade openness of importing countries, the export volume of agricultural products of China to it will increase by 0.478%. In the regression, due to the large number of dependent variables and the limited sample volumes, the t-statistics of the coefficients of some other dependent variables are not significant, which needs to be further explored in future study.Table 3
The results of regression analysis.
Variables123MRFeREx1GDPjt0.5130.5130.736∗∗∗(1.471)(1.266)(5.320)x2POPjt0.8180.818−0.142(0.567)(0.488)(−1.389)X3DISTij−0.628−1.628−1.326∗(−0.209)(−0.311)(−1.196)X4TFLit0.093∗∗∗0.0930.174∗∗∗(2.221)(1.051)(2.487)X5HTPjt0.211∗∗∗0.211∗∗∗0.589∗∗∗(4.479)(3.855)(8.778)X6OPENjt−0.389∗−0.3890.478∗∗∗(−1.770)(−1.523)(2.689)X7HMjt1.1731.1730.375(1.576)(1.356)(0.296)X8CRDjt−1.001−1.001−1.034(−1.386)(−1.192)(−0.644)_cons11.5499.42612.169(0.504)(0.379)(0.812)N545454adj. R20.9210.1880.875F test25.87 (p=0.001)Hausman test−34.37 (p=0.146)Notes:t-statistics in the brackets, and ∗, ∗∗, ∗∗∗ represent the significance level of 10%, 5%, and 1% respectively.


## 4. Recommendation and Conclusion

### 4.1. Recommendation

The high-quality development of agricultural trade between China and SCO member countries can be optimized from four aspects. First, optimize the trade structure. Due to the different geographical locations and climates of each member country, the types and quality of agricultural products in each country are also different, and the quality and types of bilateral agricultural products are highly complementary. Therefore, China can expand the export scale of agricultural products to SCO member countries by pursuing common interests. At the same time, while consolidating the existing market, China can expand the trade market with other member states. Second, optimize the trade model. By implementing the “Internet +” development strategy and accelerating the establishment of a digital technology platform, China can innovate the bilateral trade cooperation model and gradually form a development pattern driven by trade innovation. Third, optimize trade channels. SCO has gradually enhanced the smooth functioning of bilateral supply chains and made trade channels more unimpeded. SCO members should make full use of the “One Belt, One Road” and other policy advantages, improve the infrastructure construction of China and the SCO member countries, and transform and upgrade ports, wharves, and port projects. Under the premise of effective supervision, SCO member countries can improve customs clearance efficiency and service levels. Finally, optimize the way of trade governance. SCO should adhere to the principle of “co-consultation, co-construction, and sharing” and work together to build a “Shanghai Cooperation Organization” community with a shared future. SCO should establish a good consultation mechanism, reduce trade frictions, and advocate SCO member countries to actively participate in trade facilitation cooperation and construction, so as to promote the better development of bilateral trade.


### 4.2. Conclusion

Based on the analysis of the characteristics of agricultural trade between China and SCO member states from 2011 to 2019, we may safely draw a conclusion that, first of all, the quality of agricultural trade between China and SCO member states is improving generally, representing an upward trend. Second, from what has been discussed above, and based on the gravity model, the impact of various influencing factors on the quality of agricultural trade between China and SCO member states was empirically analyzed; it turns out that the economic scale, trade facilitation, trade structure, and openness of importing countries have a significant role in promoting the export scale. And for every 1% increase in the GDP of the importing country, the trade volume between China and SCO member states will increase by 0.736%; for every 1% increase of the trade facilitation in the importing country, China’s exports to the SCO member states will increase by 0.174%; for every 1% improvement of the trade structure, the volume of agricultural trade between China and SCO member states will increase by 0.589%; for every 1% increase in the trade openness in the importing country, China’s agricultural export volume will increase by 0.478%.


## 4.1. Recommendation

The high-quality development of agricultural trade between China and SCO member countries can be optimized from four aspects. First, optimize the trade structure. Due to the different geographical locations and climates of each member country, the types and quality of agricultural products in each country are also different, and the quality and types of bilateral agricultural products are highly complementary. Therefore, China can expand the export scale of agricultural products to SCO member countries by pursuing common interests. At the same time, while consolidating the existing market, China can expand the trade market with other member states. Second, optimize the trade model. By implementing the “Internet +” development strategy and accelerating the establishment of a digital technology platform, China can innovate the bilateral trade cooperation model and gradually form a development pattern driven by trade innovation. Third, optimize trade channels. SCO has gradually enhanced the smooth functioning of bilateral supply chains and made trade channels more unimpeded. SCO members should make full use of the “One Belt, One Road” and other policy advantages, improve the infrastructure construction of China and the SCO member countries, and transform and upgrade ports, wharves, and port projects. Under the premise of effective supervision, SCO member countries can improve customs clearance efficiency and service levels. Finally, optimize the way of trade governance. SCO should adhere to the principle of “co-consultation, co-construction, and sharing” and work together to build a “Shanghai Cooperation Organization” community with a shared future. SCO should establish a good consultation mechanism, reduce trade frictions, and advocate SCO member countries to actively participate in trade facilitation cooperation and construction, so as to promote the better development of bilateral trade.


## 4.2. Conclusion

Based on the analysis of the characteristics of agricultural trade between China and SCO member states from 2011 to 2019, we may safely draw a conclusion that, first of all, the quality of agricultural trade between China and SCO member states is improving generally, representing an upward trend. Second, from what has been discussed above, and based on the gravity model, the impact of various influencing factors on the quality of agricultural trade between China and SCO member states was empirically analyzed; it turns out that the economic scale, trade facilitation, trade structure, and openness of importing countries have a significant role in promoting the export scale. And for every 1% increase in the GDP of the importing country, the trade volume between China and SCO member states will increase by 0.736%; for every 1% increase of the trade facilitation in the importing country, China’s exports to the SCO member states will increase by 0.174%; for every 1% improvement of the trade structure, the volume of agricultural trade between China and SCO member states will increase by 0.589%; for every 1% increase in the trade openness in the importing country, China’s agricultural export volume will increase by 0.478%.



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*Source: 9410605-2022-08-01.xml*