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Published: 25 December 2024
Citation: Mai, Q.; Wang, X.
Time-Varying Effects of China’s
Agricultural OFDI on the
Sustainability of Bilateral Agricultural
Trade Within RCEP Countries:
Multiple Analyses Based on the
Model of Trade Gravity and TVP-VAR.
Sustainability 2025,17, 26. https://
doi.org/10.3390/su17010026
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Article
Time-Varying Effects of China’s Agricultural OFDI on the
Sustainability of Bilateral Agricultural Trade Within RCEP
Countries: Multiple Analyses Based on the Model of Trade
Gravity and TVP-VAR
Qiangsheng Mai and Xiaoyan Wang *
School of Economics and Management, Southwest Forestry University, Kunming 650224, China;
maiqiangsheng@sohu.com
*Correspondence: 15059870073@163.com
Abstract: China’s agricultural OFDI has achieved a new promotion of regional opening
to the RCEP agricultural field, and the bilateral agricultural trade cooperation has great
potential. China’s agricultural OFDI has been steadily increasing in recent years. As
significant trade partners for Chinese agricultural products, RCEP countries represent
an important region for China’s agricultural OFDI initiatives. However, existing studies
have paid little attention to the trade effect and dynamic impact of China’s agricultural
OFDI on agricultural products of RCEP countries. Therefore, based on the panel data
of agricultural trade between China and RCEP countries from 2004 to 2019, this paper
uses the trade gravity model and TVP-VAR model to investigate the time-varying effects
of China’s agricultural OFDI on agricultural trade of RCEP countries. The findings are
as follows:
(1) The
impact of China’s agricultural OFDI on bilateral agricultural trade
between China and RCEP countries has significant time-varying characteristics, and there
are short-, medium-, and long-term positive impacts most of the time. (2) When the bilateral
agricultural trade reaches a certain level, the role of China’s agricultural OFDI is limited,
but the overall positive and significant impact remains, showing a strong trade effect.
(3) The
positive impact on China’s agricultural OFDI has a heterogeneous impact on RCEP
countries with different income levels and geographical connections, but the overall positive
externality effect is significant. The research conclusions expand the research perspective on
the trade effect of agricultural OFDI to a certain extent and provide management and policy
implications for agricultural enterprises to “move fast” and promote the sustainability of
bilateral agricultural trade between China and RCEP countries through government efforts.
Keywords: agricultural OFDI; RCEP; trade effect; TVP-VAR model
1. Introduction
Outward foreign direct investment (OFDI) is an investment made by an investment
entity in one country or region to an enterprise in another country or region for the
purpose of gaining operational control and sharing long-term profits. For a long time, the
interaction between OFDI and international trade has been a hot topic in academic circles.
It is generally believed that there are significant trade effects. Early studies have found
that if the assumption that factors of production cannot flow internationally is relaxed,
OFDI will have a transfer effect on international trade; that is, OFDI will cause changes
in bilateral trade patterns [
1
]. With the development of global economy and in-depth
Sustainability 2025,17, 26 https://doi.org/10.3390/su17010026
Sustainability 2025,17, 26 2 of 24
research in the field of investment and trade, OFDI’s transfer theory of international trade
has been unable to fully explain the evolution law of international economic activities.
OFDI will bring the transfer of production factors in management experience, professional
technology, and other aspects, further promote the sustainability of international trade
development, and thus produce the trade creation effect of OFDI [
2
]. Relevant empirical
tests also find that not only will monopolistic enterprises’ OFDI produce trade effects
(including trade creation and trade transfer effects), but also other enterprises will produce
trade effects in the marginal expansion of the entire industry [
3
]. With China’s accession to
the WTO, the “going out” regulation has been relaxed, and the research on the impact of
China’s OFDI on international trade has received more attention. Some scholars believe
that China’s OFDI belongs to the trade creation type. After China’s economy enters the
stage of transformation and upgrading, traditional labor-intensive industries must transfer
to meet the requirements of industrial upgrading and other economic growth and quality
improvement, and the most important way is foreign investment [
4
]. Some scholars also
believe that China’s OFDI will indeed promote trade exchanges, but this effect is the
joint result of trade creation and trade transfer, because investment activities increase the
participation rate of Chinese enterprises in international trade and international division of
labor [5].
Trade in agricultural products accounts for a large proportion among RCEP member
countries, and the RCEP agreement has a far-reaching impact on trade in agricultural
products. The RCEP will provide a broader platform for the construction and development
of agricultural internationalization, help promote the high-quality agricultural development
of economies in the region [
6
], and accelerate the pace of China’s transformation from a
major agricultural country to an agricultural power. OFDI for agriculture can effectively
alleviate the problems of China’s agricultural development, consolidate and promote
the political, diplomatic, and economic and trade relations between China and other
countries, and is an important part of China’s peaceful development strategy [
7
]. China
has maintained long-term and close investment cooperation with RCEP state parties in
agricultural trade and agriculture. China’s advantages in market, capital, technology, and
business environment will lead the overall agricultural economic and trade cooperation
among RCEP countries to a larger scope and a higher level [8].
After the RCEP takes effect, Chinese agricultural enterprises’ active overseas invest-
ment activities can promote the import and export trade of agricultural products, and even
have an impact on the national economy of the host countries, which is the so-called trade
creation effect. China takes advantage of geopolitics and deeply participates in the regional
agricultural value chain, which helps to improve China’s position in the value chain of the
“Belt and Road” countries, that is, the so-called trade diversion effect. Studies have proved
that in the short term, China’s agricultural OFDI has an import transfer effect and presents a
long-term equilibrium relationship [
9
]. Long-term analysis shows that China’s agricultural
OFDI has a driving effect on the host countries, especially in ASEAN countries, where
there are a large number of investment enterprises, a large amount of investment, a wide
range of cooperation fields and industrial chains, and the local agricultural and economic
development has been promoted significantly [
10
]. After reviewing the existing literature,
it can be found that the academic circle has established a relatively complete and mature
theoretical system for the research on the connection between OFDI and international
trade, but in terms of the research content, developing countries are rarely involved. Most
domestic scholars study the trade effects of OFDI and foreign trade based on their own
national conditions, or explore the impact of direct investment on trade in some regions or
sectors from the perspective of a certain field or sector, etc., but there are few studies in the
field of agriculture.
Sustainability 2025,17, 26 3 of 24
In the context of the rapid development of China’s OFDI, what is the relationship
between China’s agricultural direct investment in RCEP countries and agricultural trade?
From the analysis of empirical evidence, does China’s agricultural OFDI bring about
a time-varying impact of “only good and win-win” or “mutual benefit and win-win”?
Existing studies have not given a full explanation. As China rapidly develops from a large
trading and agricultural country to an investment and agricultural power, it is particularly
important to discuss the impact of China’s agricultural OFDI on the agricultural trade of
RCEP member countries, which will help better optimize the structure of OFDI and provide
certain strategic guidance for Chinese agricultural enterprises to enter the international
market, promoting the sustainability of bilateral economic and trade cooperation. Outward
investment in agriculture and trade in agricultural products have an important impact
on sustainability, involving multiple economic, environmental, and social dimensions.
Outward investment in agriculture contributes to technology transfer and productivity
gains, especially in developing countries, by introducing advanced technologies such as
precision agriculture and water-saving irrigation that not only increase crop yields but
also reduce resource consumption, thereby contributing to environmental sustainability.
At the same time, agricultural trade improves the efficiency of agricultural production
in various regions by optimizing the allocation of global resources and promotes the
development of agriculture through changes in market demand. Therefore, studying
the impact of agricultural investment on trade at different points in time is helpful for
promoting long-term agricultural trade cooperation between countries, paying attention
to green investment, promoting fair trade mechanisms, strengthening international policy
coordination and technical cooperation, and guiding the rational allocation of agricultural
resources, so as to achieve the long-term sustainability of agricultural systems.
The possible marginal contributions of this paper are as follows: Based on the current
economic and trade development trend and geo-economic relations between China and
RECP countries, China’s agricultural OFDI will have a profound trade impact on RECP
member countries, and this study will help to deepen the research in this field. At the
same time, it helps to analyze the economic and trade cooperation between China and
RECP countries, provide an empirical basis for optimizing international trade policies, and
effectively deal with the possible international impact of China’s agricultural OFDI.
2. Theoretical Analysis and Research Hypothesis
Agricultural OFDI is an important way [
8
] for a country to participate in the global
agricultural value chain based on comparative advantages such as resources, technology,
and market, and realize cross-border cooperation and international status promotion of the
agricultural industry chain. The technology spillover effect generated by agricultural OFDI
is reflected in the export of raw materials, agricultural machinery, technology, and other
products required by the host country from the mother country for agricultural activities,
thus promoting the trade creation effect [
9
]. Since the beginning of the 21st century, China
has accelerated its participation in the agricultural value chain, and the scale of agricultural
OFDI has increased significantly. China has entered a new development stage [
10
] of
actively participating in the allocation of global agricultural resources. China’s agricultural
OFDI has a positive effect on agricultural imports by promoting product diversification
rather than simply raising prices [
11
]. In addition, it promotes trade liberalization and
enhances the ability of developing countries to import and export in global trade [
12
].
Agricultural OFDI by Chinese enterprises not only increases agricultural investment in
host countries and helps alleviate poverty, but also expands supply in local markets [
13
].
China’s agricultural OFDI has not only solved the problem of overcapacity and promoted
the transfer of agricultural technology, but also improved the agricultural production
Sustainability 2025,17, 26 4 of 24
efficiency [
14
] in developing countries, provided new development impetus for agriculture
and rural areas in less-developed countries, and effectively ensured food security [
15
]. At
the same time, the scale of China’s agricultural imports continues to expand, and the rate
of food self-sufficiency continues to decline, resulting in a long-term trade deficit in foreign
trade. The fundamental reason is that compared with the major agricultural producers
in the world, China’s agricultural competitive advantage is generally declining [
16
]. By
exporting technical advantages such as breeding, cultivation, and processing, China’s
agricultural OFDI produces agricultural products in the host country, which has a trade
transfer effect and can make up for the deficiency of China’s food self-sufficiency rate to
a certain extent. On the other hand, enterprises in the host country can rapidly improve
their technology and productivity by learning and imitating the production management
experience of foreign-funded enterprises. This spillover effect is finally reflected in the
total amount of agricultural foreign direct investment, the increase in production, and the
improvement of the agricultural import and export trade structure, that is, the technological
spillover effect of agricultural OFDI. According to “dry learning” and the “inter-industry
spillover effect”, China’s agricultural OFDI is conducive to promoting the development
of agricultural planting, management, and technology levels of RCEP countries, thus
generating a trade creation effect. Therefore, the first hypothesis is proposed:
H1. There is a trade effect between China’s agricultural OFDI and agricultural trade between
China and RCEP countries.
The trade effect of OFDI is complicated, and OFDI with different investment motives
has different [
17
] effects on trade. In theory, OFDI can promote the upgrading of product
structure between the home country and the host country, so that both sides can produce
more products that meet the needs and preferences of consumers and improve the added
value and market share [
18
] of products. At the same time, OFDI can also improve the prod-
uct quality of the two countries through technology transfer, brand effect, certification effect,
and other ways, and further enhance the competitive advantage [
19
] of both sides in the
international market. In fact, the position of Chinese agriculture in the Asia-Pacific produc-
tion network is rising steadily, but the participation in the value chain is relatively low [
20
].
The technical complexity of China’s agricultural exports is constantly improving, and the
technical complexity of agricultural exports varies greatly [21] among different categories
and regions. By providing funds and using agricultural technology to stimulate agricul-
tural production potential, China has promoted the development of agricultural sectors
in developing countries [
22
]. Studies have shown that outward foreign direct investment
(OFDI) generally has a positive impact on trade development in developing countries [
23
].
Taking China as an example, it has shown a significant trade creation effect on the OFDI of
countries along the “Belt and Road”, especially in promoting medium and low technology
exports [
24
]. However, the relationship between OFDI and trade may vary depending
on a country’s income level. Low-income countries may face substitution effects, while
upper-middle-income countries tend to exhibit complementary effects [
25
]. China’s OFDI
can have a positive impact on export trade by expanding market scale, improving product
competitiveness, optimizing industrial structure, etc. Huawei’s investment in Africa is
a typical case. In general, incorporating OFDI into the framework of international trade
theory contributes to a more comprehensive understanding of its interrelationship [
26
].
The main body of China’s agricultural OFDI is agricultural enterprises. In addition to land
leasing, Chinese investors also implement the order contract model to [
20
] connect overseas
bases, farmers, and markets, promote commercial investment, and develop the agricultural
development potential of host countries. At the same time, it will bring capital, equipment,
and experience to host countries, accelerate technology promotion and innovative applica-
Sustainability 2025,17, 26 5 of 24
tion, and significantly improve the agricultural production conditions of RCEP member
countries. Therefore, this paper proposes the second hypothesis:
H2. China’s agricultural OFDI has a positive impact on the bilateral agricultural trade effect
between China and RCEP countries.
3. Basic Situation of Bilateral Agricultural Trade
3.1. China’s Agricultural OFDI Has Developed Rapidly
In recent years, China’s agricultural OFDI has shown a rapid growth trend. According
to the statistics of the Ministry of Commerce, from 2006 to 2016, China’s agricultural OFDI
increased by 8.5 times and reached USD 4.680 billion in 2019, an increase of 9.80% over
2018. In 2020, affected by the COVID-19 epidemic, China’s agricultural OFDI decreased
to USD 4.20 billion. However, from the long-term trend, the scale of China’s agricultural
OFDI continues to expand and still maintains a steady growth momentum. In 2022, the
investment stock reached USD 30.2 billion, creating 180,000 local jobs in host countries.
From the perspective of investment region, geographical relationship is the main
factor when considering the regional distribution of China’s agricultural OFDI, and more
than 50% of the investment flows into Asian countries. Political relations are also a key
factor in the regional distribution of China’s agricultural OFDI. In Asia, Africa, and Latin
America, most of the countries in these regions maintain good diplomatic relations with
China and have abundant land, water resources, and labor force, which is in line with
China’s agricultural development needs. In terms of the scale of investment, it has covered
eight major fields: grain, cotton, oil, sugar, glue, livestock, fishery, agricultural materials,
and agricultural machinery, and the industrial categories are constantly enriched. From the
perspective of investment methods, the main ones are leasing land overseas and establish-
ing overseas agricultural production bases, or implementing the contract model of order,
providing seeds, fertilizers, and agricultural machinery to farmers in the host country, and
purchasing agricultural products. There are also some agricultural enterprises participating
in cross-border agricultural investment activities through mergers and acquisitions [27].
3.2. Trade in Agricultural Products Between China and RCEP Countries Has
Expanded Significantly
RCEP countries are important agricultural trading partners of China, with trade in
agricultural products accounting for more than 30 percent of China’s total agricultural trade.
Figure 1shows that, from 2004 to 2023, the import and export volume of China’s agricultural
trade with RCEP countries increased from USD 19.34 billion to USD
103.33 billion
, a
year-on-year
increase of 12.8 percent. Among them, the value of exports increased from
USD 11.909 billion to USD 416.03 billion, and the value of imports increased from USD
7.430 billion to USD 617.72 billion, further strengthening the agricultural trade relations
between China and RCEP member countries.
China and RCEP countries are complementary and synergistic in the trade of agri-
cultural products. China’s major agricultural exports include fruits, aquatic products, tea,
and flowers, which have a high demand and competitiveness in RCEP countries. The
major agricultural products imported from RCEP countries include soybeans, palm oil,
rubber, and rice, which are important raw materials for China’s agricultural production
and processing. Through trade in agricultural products, China and RCEP countries can
optimize resource allocation and improve economic returns. With the implementation of
the RCEP agreement, there will be more opportunities and space for cooperation between
China and RCEP countries in agricultural trade. The RCEP will provide more convenient
and favorable trade terms for both sides, reduce tariff barriers, standardize trade rules, and
promote trade facilitation [28].
Sustainability 2025,17, 26 6 of 24
Sustainability 2025, 17, x FOR PEER REVIEW 6 of 24
RCEP agreement, there will be more opportunities and space for cooperation between
China and RCEP countries in agricultural trade. The RCEP will provide more convenient
and favorable trade terms for both sides, reduce tariff barriers, standardize trade rules,
and promote trade facilitation [28].
Figure 1. Trade in agricultural products between China and RCEP countries (2004–2023). Units: per
billion USD.
The ratio of total agricultural imports and exports of one country to another country’s
gross agricultural product can be used as a measure of dependence on agricultural trade.
As shown in Figure 2, the average dependence of RCEP countries on China’s agricultural
products is greater than the average dependence of China on its agricultural products,
indicating that China is in a relatively active position and occupies a dominant position in
RCEP countries’ agricultural products trade. From the analysis of the evolution trend of
dependency degree, the dependency degree of RCEP countries on China’s agricultural
products trade shows an increasing trend year by year, and the growth rate is greater than
that of non-RCEP countries in China’s agricultural products trade. It is expected that, in
the future, the trade volume of agricultural products between China and RCEP countries
will continue to maintain a steady growth, bringing more benefits and impetus to the eco-
nomic and social development of both sides.
Figure 2. Trade dependence between China and RCEP member countries (2004–2021).
-20%
-10%
0%
10%
20%
30%
40%
50%
0.00
1,000,000.00
2,000,000.00
3,000,000.00
4,000,000.00
5,000,000.00
6,000,000.00
7,000,000.00
2004 2006 2008 2010 2012 2014 2016 2018 2020 2022
Import Value
Export Value
Import Growth Rate
Export Growth Rate
0
0.0002
0.0004
0.0006
0.0008
0.001
0.0012
0.0014
0.0016
0.0018
2004 2006 2008 2010 2012 2014 2016 2018 2020
RCEP countries' trade dependence on China
China's trade dependence on RCEP countries
Unit: million/USD Unit: %
Figure 1. Trade in agricultural products between China and RCEP countries (2004–2023). Units: per
billion USD.
The ratio of total agricultural imports and exports of one country to another country’s
gross agricultural product can be used as a measure of dependence on agricultural trade.
As shown in Figure 2, the average dependence of RCEP countries on China’s agricultural
products is greater than the average dependence of China on its agricultural products,
indicating that China is in a relatively active position and occupies a dominant position
in RCEP countries’ agricultural products trade. From the analysis of the evolution trend
of dependency degree, the dependency degree of RCEP countries on China’s agricultural
products trade shows an increasing trend year by year, and the growth rate is greater than
that of non-RCEP countries in China’s agricultural products trade. It is expected that, in the
future, the trade volume of agricultural products between China and RCEP countries will
continue to maintain a steady growth, bringing more benefits and impetus to the economic
and social development of both sides.
Sustainability 2025, 17, x FOR PEER REVIEW 6 of 24
RCEP agreement, there will be more opportunities and space for cooperation between
China and RCEP countries in agricultural trade. The RCEP will provide more convenient
and favorable trade terms for both sides, reduce tariff barriers, standardize trade rules,
and promote trade facilitation [28].
Figure 1. Trade in agricultural products between China and RCEP countries (2004–2023). Units: per
billion USD.
The ratio of total agricultural imports and exports of one country to another country’s
gross agricultural product can be used as a measure of dependence on agricultural trade.
As shown in Figure 2, the average dependence of RCEP countries on China’s agricultural
products is greater than the average dependence of China on its agricultural products,
indicating that China is in a relatively active position and occupies a dominant position in
RCEP countries’ agricultural products trade. From the analysis of the evolution trend of
dependency degree, the dependency degree of RCEP countries on China’s agricultural
products trade shows an increasing trend year by year, and the growth rate is greater than
that of non-RCEP countries in China’s agricultural products trade. It is expected that, in
the future, the trade volume of agricultural products between China and RCEP countries
will continue to maintain a steady growth, bringing more benefits and impetus to the eco-
nomic and social development of both sides.
Figure 2. Trade dependence between China and RCEP member countries (2004–2021).
-20%
-10%
0%
10%
20%
30%
40%
50%
0.00
1,000,000.00
2,000,000.00
3,000,000.00
4,000,000.00
5,000,000.00
6,000,000.00
7,000,000.00
2004 2006 2008 2010 2012 2014 2016 2018 2020 2022
Import Value
Export Value
Import Growth Rate
Export Growth Rate
0
0.0002
0.0004
0.0006
0.0008
0.001
0.0012
0.0014
0.0016
0.0018
2004 2006 2008 2010 2012 2014 2016 2018 2020
RCEP countries' trade dependence on China
China's trade dependence on RCEP countries
Unit: million/USD Unit: %
Figure 2. Trade dependence between China and RCEP member countries (2004–2021).
Sustainability 2025,17, 26 7 of 24
4. Analysis Based on Trade Gravity Model
4.1. Model Setting
The idea and concept of the gravitational model originated from the law of grav-
itation in physics proposed by Newton, and the gravitational model has been continu-
ously expanded and improved after the introduction of studies to measure bilateral trade
flows. After the 1980s, some scholars added factors such as adjacent borders and cultural
convergence into the model, mainly because these factors have a strong impact on the
characteristics of the agricultural outbound investment network, and then have an impact
on a country’s position in the division of labor in the global agricultural value chain. Con-
versely, the rise of a country’s position in the global agricultural value chain also affects the
distribution of a country’s foreign investment in agriculture on a global scale. When setting
the model, although different scholars made small adjustments to the explanatory variables
of the gravity model, the core explanatory variables generally chose objective factors with
short-term stability. Therefore, in this paper, referring to Anderson’s practice [
29
], OFDI
flow, which is not easy to change in the short term, is selected as the core explanatory
variable. The specific model is as follows:
ln EXi=α0+α1ln OFDIi+α2ln CGDP +α3ln GDPi+α4ln disti+α5coml angi+α6contigi+
α7FTAi+α8Marketi+∑j=15
i=1year +εi
(1)
ln IMi=β0+β1ln OFDIi+β2ln CGDP +β3ln GDPi+β4ln disti+β5comlangi+β6contigi+
β7FTAi+β8Marketi+∑j=15
i=1year +φi
(2)
4.2. Description of Variables and Data Sources
1. Core explanatory variable: China’s agricultural OFDI flow. According to Chen and
Guo’s research [
26
], agricultural OFDI flow directly reflects the outflow of funds related
to agriculture in a country. At the same time, considering the interference of the novel
coronavirus epidemic, the regression results of the trade gravity model may be unstable,
so the data from 2004 to 2019 are selected in this paper, all of which are taken from the
Statistical Bulletin of China’s OFDI in previous years.
2. Explained variables: China’s imports and exports of agricultural products to RCEP
countries. According to the research [
30
], the trade volume of agricultural products can
reflect the direct trade of agricultural products between the two countries. The relevant
agricultural product data used in this paper are from the UN COMTRADE database and
the 3-digit data under the third edition of the United Nations Standard Trade Classification
(STIC). According to the WTO definition, agricultural products include categories 0, 1, 2,
and 4 of STIC, but do not include categories 27 and 28 of Category 2.
3. Control variables: The geographical distance between China and RCEP capitals,
GDP, common official language, territorial border, accession to the WTO, and market share
of agricultural products trade were selected as the model-influencing factors in this paper.
The above data came from CEPII of the French Institute of International Economics. In
order to prevent absorption by fixed effects, DIST is multiplied by Brent international crude
oil price in the current year as the measurement data. In the MARKET, the proportion
of the trade volume of agricultural products between RCEP countries and China in the
trade volume of their own agricultural products is measured, reflecting China’s position
in the trade volume of agricultural products between RCEP countries. The larger the
value, the greater the possibility for China to carry out trade in agricultural products with
RCEP countries.
Due to the lag effect of China’s accession to the World Trade Organization in 2001
and the outlier impact of the epidemic on the data after 2019, the data time span in this
Sustainability 2025,17, 26 8 of 24
paper is from 2004 to 2019. The natural logarithm is taken for all the above data, and the
influence of seasonal factors and heteroscedasticity is excluded by using the censusX12
seasonal method. The names and meanings of the specific variables involved in this study
are shown in Table 1:
Table 1. Variable names and their meanings.
Variables Meaning
IMPORT The annual value of China’s agricultural imports to RCEP members.
EXPORT China’s annual agricultural exports to RCEP member countries.
OFDI Annual flow of China’s agricultural direct investment in RCEP member countries
GDP
GDP of RCEP member countries, reflecting the level of economic development of the host country.
DIST
The straight-line distance between Beijing, China, and the capitals of RCEP member countries.
Distance is generally considered to reflect the cost of trade. It is calculated by multiplying the
straight-line distance between two capitals by the price of Brent crude oil in each year.
CONTIG
Dummy variable, indicating whether China borders the territory of the host country. Yes = 1, no = 0.
COMLANG Dummy variable that indicates whether the host country shares an official language with China.
Yes = 1, no = 0.
MARKET
The proportion of RCEP member countries’ agricultural trade with China in the member countries’
total agricultural trade.
FTA Dummy variable, indicating whether an RCEP member has joined the WTO. Yes = 1, no = 0.
EXi
represents China’s agricultural exports to country i;
IMi
represents the amount of
China’s agricultural imports to i. The core explanatory variable,
OFDIi
, represents the flow
of China’s agricultural foreign direct investment to country i. In addition,
DISTi
represents
the straight-line distance between China and the capital of country i, and
GDPi
represents
the gross domestic product of country i. The control variables include YEAR, which refers
to the annual dummy variable;
COMLANGi
, which refers to whether China and country i
share the same official language;
CONTIGi
, which refers to whether China and country i
share the same territory;
FTAi
, which refers to whether country i has joined the WTO; and
Marketi
, which refers to the proportion of the agricultural trade volume between country i
and China in the total agricultural trade volume of country i.
4.3. Baseline Regression Results
According to the sample data, Pooled OLS was selected for analysis in this paper,
and the results were shown in Table 2. Model (1) to model (3) tested the import effect of
agricultural OFDI, and model (4) to model (6) tested the export effect of agricultural OFDI.
The results show that China’s agricultural OFDI has a significant creative effect on the
import and export trade of agricultural products in RCEP countries. First, it can be seen
from model (3) that the import coefficient is 0.723 and significant at the 1% level, which
indicates that after controlling other factors and annual effects, when China’s agricultural
OFDI to RCEP countries increases by 1 percentage point, China’s agricultural imports to
RCEP countries will increase by 0.723%. Secondly, it can be seen from model (6) that the
export coefficient is 0.273 and significant at the 1% level, which means that when other
factors remain unchanged, every 1% increase in China’s agricultural OFDI will increase
China’s agricultural exports to RCEP countries by 0.273%. This indicates that China’s
agricultural OFDI improves the agricultural infrastructure level of RCEP countries, which in
turn stimulates the agricultural exports of RCEP countries to China, and ultimately leads to
the agricultural trade surplus of RCEP countries with China. Thirdly, the distance between
the capitals of the two countries has positive and negative effects on the import and export
Sustainability 2025,17, 26 9 of 24
trade of agricultural products between China and RCEP countries.
Models (2) and (3)
show
that the distance between the capitals of the two countries will have a significant positive
impact on the import volume of agricultural products between China and RCEP countries.
It can be seen from models (5) and (6) that the greater the distance between the two capitals,
the lower the trade volume of China’s agricultural exports to RCEP countries. This indicates
that China has a strong preference for obtaining overseas agricultural production resources
and is more willing to import agricultural production raw materials from faraway countries
with strong resource endowments. However, compared with the export of agricultural
products, the long distance will inevitably increase the export cost. If the territory borders
have a significant positive impact on the import and export volume of agricultural products,
this indicates that China and the RCEP member countries with territorial borders have
a more significant trade creation effect, which validates the research conclusion [
31
] of
most scholars. Compared with model (3) and model (6), it can be seen that the increase in
the proportion of agricultural trade volume between RCEP member countries and China
in their total agricultural trade volume has a significant positive impact on the import of
agricultural products from RCEP member countries to China, while it has a significant
negative impact on the export of agricultural products from RCEP member countries to
China. This is consistent with the fact that China has shown a trade deficit in agricultural
products in recent years. Finally, by comparing the coefficients of model (2) with
model (5)
and model (3) with model (6), it can be found that after controlling other factors, the
import transfer effect of agricultural products of China’s agricultural OFDI is greater
than the export creation effect of agricultural products. Hypothesis 1 and Hypothesis 2
are confirmed.
Table 2. The results of baseline regression.
Model (1) Model (2) Model (3) Model (4) Model (5) Model (6)
Variables Import Export
lnOFDI 0.799 *** 0.718 *** 0.723 *** 0.532 *** 0.263 *** 0.273 ***
(0.076) (0.066) (0.067) (0.061) (0.049) (0.048)
InGDP 0.982 *** 1.007 *** 1.144 *** 1.134 ***
(0.071) (0.072) (0.052) (0.052)
lnDIST 0.483 *** 0.719 *** 0.883 *** 1.136 ***
(0.134) (0.157) (0.099) (0.113)
CONTIG 1.048 *** 1.145 *** 0.306 * 0.193
(0.233) (0.236) (0.173) (0.170)
COMLANG 0.607 * 0.741 ** −0.380 −0.312
(0.360) (0.369) (0.270) (0.266)
MARKET 14.06 *** 13.09 *** 4.630 *** 4.149 ***
(1.747) (1.811) (1.295) (1.307)
FTA 2.214 *** 2.157 *** 1.156 *** 1.291 ***
(0.249) (0.253) (0.184) (0.183)
year no no yes no no yes
Constant 1.247 55.14 *** 53.80 *** 4.506 *** 1.010 −4.910
(0.8880) (9.1600) (11.6900) (0.7180) (6.7870) (8.4310)
Observations 224
R-squared 0.3320 0.8520 0.8610 0.2520 0.8610 0.8760
Note: ***, **, and * indicate a significance level of 1%, 5%, and 10%, respectively.
4.4. Robustness Analysis
Considering that there are many zeros in the zero-value trade and dummy variables,
this paper further selects the PPML (pseudo-maximum likelihood estimation) model for
regression, which can effectively correct the zero-value problem and heteroscedasticity
problem [
32
,
33
]. Table 3shows the estimation results of the PPML model. From the results
Sustainability 2025,17, 26 10 of 24
of model (7) to model (12), the following can be seen: First, the core explanatory variables
are significantly positive at the level of 1%, showing that China’s agricultural OFDI can
promote the bilateral agricultural trade between China and RCEP member countries and
stimulate the intra-regional agricultural trade circulation. At the same time, by comparing
model (9) and model (12), it can be seen that the reverse import-induced effect of OFDI is
greater than the export-created effect, which is consistent with the result of the benchmark
regression model; that is, China’s agricultural OFDI can promote China’s imports of
agricultural products from RCEP countries to a greater extent, possibly because China has a
long-term problem of insufficient self-sufficiency rate of agricultural products. In addition,
RCEP member countries have always been important agricultural trade partners of China,
and the reverse import-induced effect can improve China’s agricultural consumption
structure and establish a long-term and stable supply base of agricultural raw materials.
Second, for the control variables, consistent with the benchmark regression model, the
economic level of RCEP member countries has a significant promoting effect on bilateral
trade at the 1% level. Both distance and market have the same effect at a significance
level of at least 5%. Both distance and market have a driving effect on China’s import of
agricultural products from RCEP member countries, while they have an inhibiting effect
on China’s export of agricultural products from RCEP member countries. It can be seen
that China imports more agricultural products from member countries that are far away
from each other. The possible reason is that RCEP member countries such as Australia,
Singapore, and New Zealand have more complementary agricultural products, so China
imports the most. At the same time, whether the territory borders or not and whether the
RCEP is a member of the WTO will have a significant trade effect. In short, the results of
the OLS model and PPML model show the same direction of influence, but the degree of
influence is different, which further verifies the robustness of baseline regression.
Table 3. Results of robust regression.
Model (7) Model (8) Model (9) Model (10) Model (11) Model (12)
Variables Import Export
LNOFDI 0.081 *** 0.090 *** 0.090 *** 0.052 *** 0.029 *** 0.030 ***
(0.012) (0.015) (0.015) (0.005) (0.005) (0.004)
LnGDP 0.109 *** 0.110 *** 0.113 *** 0.112 ***
(0.008) (0.008) (0.004) (0.004)
LnDIST 0.029 * 0.039 ** 0.073 *** 0.095 ***
(0.016) (0.019) (0.010) (0.011)
CONTIG 0.137 *** 0.143 *** 0.041 ** 0.031 *
(0.025) (0.025) (0.017) (0.016)
COMLANG −0.059 −0.061 −0.024 −0.020
(0.039) (0.038) (0.025) (0.025)
MARKET 1.433 *** 1.400 *** 0.338 *** 0.295 **
(0.229) (0.221) (0.121) (0.119)
FTA 0.243 *** 0.240 *** 0.126 *** 0.140 ***
(0.029) (0.027) (0.017) (0.017)
Constant 1.399 *** 9.022 *** 2.480 *** 1.758 *** 1.870 *** 0.181
(0.153) (1.719) (0.370) (0.070) (0.578) (0.157)
R-squared 0.297 0.761 0.862 0.236 0.826 0.873
Log pseudo-
likelihood −492.8 −477.9
Wald chi2 436.5 1 479.0
Prob > chi2 0.000 0.000
Note: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively.
Sustainability 2025,17, 26 11 of 24
4.5. Heterogeneity Analysis
A number of studies have shown that a country’s OFDI may either create (increase
trade volume) or transfer (decrease trade volume) [
34
] import and export trade. According
to the trade effects of OFDI flows at different levels, it can further provide enterprises
with flexible overseas strategic decisions [
35
]. Therefore, by constructing a panel quantile
regression model, this paper examines the impact of China’s agricultural OFDI on the
heterogeneity of agricultural trade of RCEP member countries at different loci levels.
The quantile regression results of export trade are shown in Table 4. First of all, most
OFDI for agriculture is significantly positive at the 1% level, and its magnitude is around
0.20. At the level of 10% of export trade, agricultural OFDI has the largest positive impact
on China’s agricultural exports to RCEP countries, which indicates that keeping other
factors unchanged, every one percentage point increase in OFDI will increase China’s
agricultural exports to RCEP countries by 0.35%. Secondly, from columns (7) to (9), it can be
seen that agricultural OFDI no longer exerts an impact at a significance level of 1%, which
indicates that at a higher level of export trade, the export creation effect of China’s OFDI on
RCEP countries becomes smaller. Finally, the trade effect at all sub-points is positive, which
means that China has an export creation effect on agricultural products of RCEP countries’
agricultural OFDI. However, this effect decreases with the increase in agricultural exports.
This indicates that when China’s export trade with RCEP member countries is at a low
level, agricultural OFDI can further open China’s trade market with RCEP countries.
Table 4. Quantile regression results (export trade).
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Variables 10% 20% 30% 40% 50% 60% 70% 80% 90%
LnOFDI 0.356 *** 0.285 *** 0.202 *** 0.181 *** 0.200 *** 0.265 *** 0.122 0.137 * 0.236 **
(0.088) (0.081) (0.075) (0.067) (0.075) (0.093) (0.086) (0.071) (0.104)
LnGDP 0.792 *** 0.804 *** 1.037 *** 1.128 *** 1.159 *** 1.134 *** 1.144 *** 1.162 *** 1.156 ***
(0.056) (0.094) (0.117) (0.088) (0.082) (0.079) (0.073) (0.064) (0.057)
LnDIST 0.931 *** 0.910 *** 0.794 *** 0.782 *** 0.832 *** 0.990 *** 0.806 *** 0.795 *** 0.703 ***
(0.070) (0.178) (0.172) (0.137) (0.132) (0.127) (0.108) (0.111) (0.196)
CONTIG 0.708 * −0.361 0.307 0.349 0.445 0.322 0.476 *** 0.502 *** 0.399 ***
(0.360) (0.382) (0.333) (0.301) (0.313) (0.242) (0.181) (0.133) (0.113)
COMLANG
0.221 0.347 0.065 0.008 −0.215 −0.395 −0.301 0.815 0.742
(0.237) (0.287) (0.303) (0.266) (0.298) (0.432) (0.675) (0.745) (0.540)
MARKET 0.003 −0.038 0.713 * 1.041 *** 1.313 *** 1.524 *** 1.484 *** 1.670 *** 1.850 ***
(0.467) (0.459) (0.394) (0.284) (0.287) (0.340) (0.328) (0.248) (0.266)
FTA 9.293 10.14 3.850 −3.921 −3.730 4.631 −8.635 −7.148 2.728
(15.38) (14.52) (12.71) (10.19) (9.662) (10.84) (10.48) (9.215) (10.59)
Observations
224
Note: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively.
Quantile regression results for import trade are shown in Table 5. For the first 30% of
the sub-points, the regression coefficients of OFDI reach the maximum, all of which are
significantly positive at 1% level, and their sizes are all above 0.70. This shows that when
the import volume reaches the top 30% level or above, every one percentage point increase
in agricultural OFDI will increase the import volume of agricultural products by 0.70% for
China to RCEP countries. In other words, when the level of import trade between China
and RCEP countries is low, agricultural OFDI can well promote the import of agricultural
products whose “self-sufficiency rate” is insufficient in China, so as to keep meeting the
domestic demand for agricultural products. And at the sub-points of 40–80%, agricultural
OFDI has a positive effect on agricultural imports at a significance level of at least 10%.
However, there is no significant effect at 90% sub-points, indicating that when the import
Sustainability 2025,17, 26 12 of 24
trade between China and RCEP member countries is at a high level, the promotion effect of
agricultural OFDI on imports is no longer significant.
Table 5. Quantile regression results (import trade).
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Variables 10% 20% 30% 40% 50% 60% 70% 80% 90%
LnOFDI 1.151 *** 1.002 *** 0.703 *** 0.5270
*** 0.492 ** 0.431 ** 0.366 ** 0.450 *** 0.137
(0.171) (0.228) (0.218) (0.1890) (0.200) (0.175) (0.154) (0.161) (0.211)
LnGDP 1.204 *** 1.181 *** 1.283 *** 1.3080
*** 1.299 *** 1.241 *** 1.143 *** 1.040 *** 1.086 ***
(0.105) (0.085) (0.072) (0.0820) (0.102) (0.108) (0.116) (0.091) (0.058)
LnDIST 0.283 0.556 ** 0.905 *** 0.9520
*** 0.834 *** 0.742 *** 0.721 *** 0.806 *** 0.406
(0.190) (0.237) (0.257) (0.2160) (0.171) (0.144) (0.154) (0.229) (0.272)
CONTIG 1.535 *** 1.449 *** 1.596 *** 1.5040
*** 1.198 *** 0.780 ** 0.584 ** 0.325 * 0.115
(0.328) (0.265) (0.265) (0.3300) (0.369) (0.353) (0.266) (0.195) (0.174)
COMLANG
2.082 *** 2.159 *** 2.151 *** 1.956 *** 1.784 ** 2.218 *** 1.813 * −0.812 1.121 *
(0.372) (0.474) (0.469) (0.6010) (0.755) (0.848) (1.011) (0.861) (0.623)
MARKET 2.841 *** 2.879 *** 2.326 *** 2.1910
*** 2.275 *** 2.297 *** 2.099 *** 1.973 *** 1.385 **
(0.500) (0.472) (0.565) (0.5840) (0.661) (0.581) (0.497) (0.623) (0.581)
FTA 77.23 *** 66.16 *** 32.53 * 16.86 10.66 −3.441 −3.215 3.235 −14.88
(16.11) (20.24) (19.39) (15.61) (18.55) (15.79) (12.23) (13.19) (16.55)
Observations
224
Note: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively.
According to the results of heterogeneity regression, although the influence of OFDI
on imports and exports is limited when they reach a certain level, in general, OFDI has a
positive relationship with RCEP countries and most of the sub-points have a significant
impact, indicating that China’s agricultural OFDI has a creative effect on the import
and export trade of agricultural products in RCEP countries and can expand bilateral
agricultural trade.
5. Analysis Based on TVP-VAR Model
The previous empirical results have found that agricultural OFDI has trade effects on
agricultural products between China and RCEP countries. But what is the dynamic impact
of China’s agricultural OFDI on bilateral agricultural trade? Are there different effects of
OFDI in each period? In order to further study the relationship, this paper further uses
the TVP-VAR model and the monthly data of 15 RCEP countries from February 2005 to
December 2020 as samples to further study the time-varying impact of China’s agricultural
OFDI on agricultural trade of RCEP countries.
5.1. Model Setting
The vector autoregressive (VAR) model is a kind of multi-variable prediction algorithm,
which is often used to analyze the possible interaction mechanism of variables in a period
of time. Based on the VAR model, the TVP-VAR model assumes that the coefficient matrix
and covariance matrix are time-varying, and uses variable parameters to calculate the
results of the impulse response of each variable to different lag periods at all time points, so
that the change characteristics of the relationship between variables can be analyzed over
time, and the relationship between variables in various states can be studied regarding
Sustainability 2025,17, 26 13 of 24
whether there are structural mutations. With reference to Nakajima et al.’s research [
36
],
the TVP-VAR model of this paper is set as follows:
Ayt=F1yt−1+· · · +FsTt−s+µt,t=s+1, · · · n, (3)
yt
is the
k×
1 dimensional observation vector,
A
is the
k×k
dimensional observation
vector,
F1
,...
Fs
is a
k×
1 maintenance matrix, and the disturbance term is a
k×k
dimen-
sional structural shock. Assuming
µt
~N(0,
Σ2
),
Σ
=
σ10· · · 0
0.......
.
.
.
.
.......0
0· · · 0σk
, the structural
shock of the simultaneous relation is judged by recursion, and the simultaneous parameter
matrix A is a lower triangular matrix:
A=
1 0 · · · 0
a21
.......
.
.
.
.
.......0
am1 · · · am1, am−11
,
The structural VAR model can be simplified as
yt=B1yt−1+· · · +Bsyt−s+A−1Σεt,εt∼N(0, Ik
where
Bi=A−1Fi
,
i=
1,
· · ·
,s, the row elements in
Bi
are stacked to form a
k2s×
1
dimensional vector. Let
Xt=Ik⊗(y′
t−1
,
. . .
,
yt−s
), where
⊗
represents the Kronecker score;
then, the model can be simplified as
yt=Xtβt+At−1∑εt(t=s+1, · · · , n)(4)
At this time, the original formula becomes an SVAR model whose parameters do not
change with time. Assuming that all parameters are time-varying, stack the non-0 and
non-1 elements in
At
as
at
= (
a21,t
,
a31,t
,
a41,t
,
am1,m−1)
,
ht=(h1t, h2t ,· · · , hkt
), and have
hjt =log (σjt
2
). Let
βt
,
at
, and
ht
obey the first-order random walk process and have
no correlation. In this paper, the dynamic changes in model coefficients are expressed
as follows:
βt+1=βt+µβt(5)
αt+1=αt+µαt(6)
ht+1=ht+µht (7)
εt
µβt
µat
µht
∼N
0,
I 0 · · · 0
0∑β....
.
.
.
.
....∑α0
0· · · 0∑h
(8)
where I represents the identity matrix;
∑βt
,
∑
a, and
∑
h are positive definite diagonal
matrices. Based on the above assumptions, the TVP-VAR model can effectively capture
potential structural changes. In order to obtain more accurate and robust empirical results,
the Markov Monte Carlo (MCMC) method is used to estimate the parameters of the
TVP-VAR
model, which can overcome the problem of too many parameters to be estimated
in nonlinear estimation.
Sustainability 2025,17, 26 14 of 24
In order to determine the stationarity of the time series data and avoid the pseudo-
regression problem that may occur in the regression analysis, the unit root test is carried out
on the logarithmic raw variable series, and the ADF method is used to test the stationarity
of the data. It can be seen from Table 6that the ADF statistics of LNOFDI, LNIMPORT, and
LNEXPORT are greater than the critical value at the significance level of 1%, 5%, and 10%,
indicating that the original sequence of the three variables is not stable. In order to meet the
stationary data required by the self-vector regression model, the unit root test values of the
ADF statistics of the three variables after first-order difference are all less than the critical
values at the significance levels of 1%, 5%, and 10%, indicating that these three variables
are stable after first-order difference and can be used in subsequent models.
Table 6. Results of ADF test.
Variable ADF
Statistics
1% Critical
Value
5% Critical
Value
10%
Threshold pValue Smoothness
LNOFDI −1.813 −4.992 −3.875 −3.388 0.633 Non-smooth
DLNOFDI −5.767 −4.004 −3.098 −2.690 0.000 Smooth
LNIMPORT −1.236 −3.461 −2.874 −2.574 0.658 Non-smooth
DLNIMPORT −15.65 −3.461 −2.874 −2.575 0.000 Smooth
LNEXPORT −1.859 −3.464 −2.876 −2.574 0.351
nonstationarity
DLNEXPORT −8.761 −2.577 −1.942 −1.615 0.000 Smooth
5.2. Parameter Simulation Test
The MCMC sampling method was used to simulate 10,000 times to obtain valid
samples, and the estimated results are shown in Figure 3. The sample autocorrelation
coefficient showed a steady development trend, and the sample data were stable. The
simulation path of the parameters showed significant fluctuation aggregation, and the
different parameters all converged to the simulated tail. The estimation results of the
sample mean once again demonstrated the robustness of the model estimation.
Sustainability 2025, 17, x FOR PEER REVIEW 14 of 24
arity of the data. It can be seen from Table 6 that the ADF statistics of LNOFDI, LNIM-
PORT, and LNEXPORT are greater than the critical value at the significance level of 1%,
5%, and 10%, indicating that the original sequence of the three variables is not stable. In
order to meet the stationary data required by the self-vector regression model, the unit
root test values of the ADF statistics of the three variables after first-order difference are
all less than the critical values at the significance levels of 1%, 5%, and 10%, indicating that
these three variables are stable after first-order difference and can be used in subsequent
models.
Table 6. Results of ADF test.
Variable ADF Statistics 1% Critical Value 5% Critical Value 10% Threshold p Value Smoothness
LNOFDI −1.813 −4.992 −3.875 −3.388 0.633 Non-smooth
DLNOFDI −5.767 −4.004 −3.098 −2.690 0.000 Smooth
LNIMPORT −1.236 −3.461 −2.874 −2.574 0.658 Non-smooth
DLNIMPORT −15.65 −3.461 −2.874 −2.575 0.000 Smooth
LNEXPORT −1.859 −3.464 −2.876 −2.574 0.351 nonstationarity
DLNEXPORT −8.761 −2.577 −1.942 −1.615 0.000 Smooth
5.2. Parameter Simulation Test
The MCMC sampling method was used to simulate 10,000 times to obtain valid sam-
ples, and the estimated results are shown in Figure 3. The sample autocorrelation coeffi-
cient showed a steady development trend, and the sample data were stable. The simula-
tion path of the parameters showed significant fluctuation aggregation, and the different
parameters all converged to the simulated tail. The estimation results of the sample mean
once again demonstrated the robustness of the model estimation.
Figure 3. Parameter estimation results.
Table 7 shows the index values of the posterior distribution of parameters of the TVP-
VAR model. It can be seen that the convergence diagnosis values of Geweke are all less
than the critical value at a 95% significance level. At the 95% confidence level, the maxi-
mum value of the non-valid factor is 183.64, and it can be calculated that at least 54
(10,000/183.64) uncorrelated samples can be obtained under the condition of 10,000 con-
tinuous sampling, which means that the Markov chain can tend to converge in a more
Figure 3. Parameter estimation results.
Table 7shows the index values of the posterior distribution of parameters of the
TVP-VAR
model. It can be seen that the convergence diagnosis values of Geweke are
all less than the critical value at a 95% significance level. At the 95% confidence level,
Sustainability 2025,17, 26 15 of 24
the maximum value of the non-valid factor is 183.64, and it can be calculated that at
least 54
(10,000/183.64) uncorrelated samples can be obtained under the condition of
10,000 continuous
sampling, which means that the Markov chain can tend to converge in a
more concentrated manner through 10,000 pre-sampling. It is proved that the estimation of
this model is valid, and the dynamic relationship can be further explored.
Table 7. Parameter estimation results of TVP-VAR model.
Parameter
Mean Stdev 95%L 95%U Geweke Inef.
sb1 0.021 0.002 0.018 0.027 0.135 10.20
sb2 0.018 0.001 0.015 0.021 0.654 8.990
sa1 0.087 0.062 0.042 0.202 0.215 122.7
sa2 0.085 0.045 0.040 0.214 0.771 110.5
sh1 0.736 0.139 0.510 1.032 0.576 183.6
Sh2 0.655 0.117 0.446 0.905 0.492 47.96
5.3. Analysis of Time-Varying Characteristics of Lower Equal Interval Pulse Response in Different
Lag Periods
In this paper, lag 4, 8, and 12 periods are selected to represent the short, medium,
and long term, respectively, the horizontal axis represents the time node, and the vertical
axis represents the impulse response intensity. As can be seen from Figure 4, the impulse
response curves of China’s agricultural OFDI to RCEP countries’ agricultural trade in
different lag periods all have obvious trends and fluctuations, which indicates that China’s
agricultural OFDI has different effects at different time points and has obvious time-varying
characteristics. In addition, the response curves of 4 months, 8 months, and 12 months of
lag are obviously separated and have similar fluctuation characteristics.
First, when the positive impact of China’s agricultural OFDI is 1 percentage, if the
lag period is 4 months, the impact of RCEP countries’ agricultural import trade from
2004 to 2021
will be positive. If the lag period is 8 and 12 months, there is a weak negative
impact from 2004 to 2008, and the impact turns positive from 2008 to 2021. The possible
reason is that in the early stage of outbound investment, many companies carry out
investment activities when they lack sufficient estimation of overseas investment risks and
a complex environment in order to seek higher return on capital, resulting in a rise in sunk
costs. The return cannot reach the expected effect. After investing a certain cost, many
companies pay more attention to the early market assessment, prudently make overseas
investment decisions, attach importance to the localization of overseas project construction
and risk control, etc., so that the foreign investment has a positive effect and stimulates
foreign trade. It can be further concluded from Figure 4that the short-term effect is greater
than the medium-term effect, and the medium-term effect is greater than the long-term
effect. This indicates that increasing the level of China’s agricultural OFDI will increase the
RCEP countries’ imports of agricultural products from China, and this effect will gradually
weaken with the extension of time.
Second, regardless of the short-, medium-, and long-term impulse response, the export
trade effect produced by China’s agricultural OFDI shows a basically consistent trend,
which means that RCEP countries’ agricultural product export trade to China will show
a positive effect over a long period of time, with the short-term effect being the most
significant, the medium-term effect being more significant, and the long-term effect being
the least significant. The peak of the negative effect appeared between 2018 and 2020. A
possible reason is that China’s agricultural products, due to the lack of “self-sufficiency rate”
and the emphasis on “food security”, have an inhibitory effect on the agricultural product
export trade of RCEP countries, and the outbreak of the novel coronavirus pneumonia in
2020 has encouraged trade protectionism in some countries. Countries have successively
Sustainability 2025,17, 26 16 of 24
introduced restrictive foreign investment and trade policies, resulting in a decline in
foreign direct investment in 2020, and a significant decline in the positive impact of their
export trade.
Third, when RCEP countries’ agricultural trade is given a positive impact of 1 percent-
age, the impulse response of China’s OFDI shows a large jagged-like change, indicating that
the impact of RCEP countries’ agricultural trade on China’s agricultural OFDI lags behind,
and the impulse response with a lag of 4 months is the strongest and the most sensitive to
the impact of China’s agricultural OFDI. The cumulative total effect of China’s agricultural
OFDI showed positive fluctuations in the short, medium, and long term, reaching a peak
in 2010 and 2016. It is further verified that China’s agricultural OFDI and RCEP countries
have a two-way promotion effect on agricultural trade, but the import promotion effect is
greater than the export promotion effect.
Sustainability 2025, 17, x FOR PEER REVIEW 16 of 24
Third, when RCEP countries’ agricultural trade is given a positive impact of 1 per-
centage, the impulse response of China’s OFDI shows a large jagged-like change, indicat-
ing that the impact of RCEP countries’ agricultural trade on China’s agricultural OFDI
lags behind, and the impulse response with a lag of 4 months is the strongest and the most
sensitive to the impact of China’s agricultural OFDI. The cumulative total effect of China’s
agricultural OFDI showed positive fluctuations in the short, medium, and long term,
reaching a peak in 2010 and 2016. It is further verified that China’s agricultural OFDI and
RCEP countries have a two-way promotion effect on agricultural trade, but the import
promotion effect is greater than the export promotion effect.
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 4. Impulse response function of different lag periods. (a) OFDI–OFDI. (b) OFDI–IMPORT.
(c) OFDI–EXPORT. (d) IMPORT–OFDI. (e) IMPORT–IMPORT. (f) IMPORT–EXPORT. (g) EXPORT–
OFDI. (h) EXPORT–IMPORT. (i) EXPORT–EXPORT. Note: Short and green, long and purple, and
solid and red lines indicate short (4 months), medium (8 months), and long (12 months), respec-
tively.
5.4. Impulse Response at Different Time Points
In order to determine the impact of major events, this paper selects three time points
in January 2012, September 2013, and June 2018 for simulation, corresponding to the first
proposal of the RCEP agreement, the first proposal of the “Belt and Road” development
strategy, and the implementation of a series of trade protection policies against China by
former US President Donald Trump. Figure 5 shows that the impulse response of China’s
agricultural OFDI to RCEP countries’ agricultural trade at the three time points is gener-
ally characterized by a rapid response at first and then gradually stabilizing; that is, the
impact of a 1 percentage positive impact of China’s agricultural OFDI at the three time
points tends to be consistent. This indicates that the impact of China’s agricultural OFDI
Figure 4. Impulse response function of different lag periods. (a) OFDI–OFDI. (b) OFDI–IMPORT.
(c) OFDI–EXPORT
. (d) IMPORT–OFDI. (e) IMPORT–IMPORT. (f) IMPORT–EXPORT. (g) EXPORT–
OFDI. (h) EXPORT–IMPORT. (i) EXPORT–EXPORT. Note: Short and green, long and purple, and
solid and red lines indicate short (4 months), medium (8 months), and long (12 months), respectively.
5.4. Impulse Response at Different Time Points
In order to determine the impact of major events, this paper selects three time points
in January 2012, September 2013, and June 2018 for simulation, corresponding to the first
proposal of the RCEP agreement, the first proposal of the “Belt and Road” development
strategy, and the implementation of a series of trade protection policies against China by
former US President Donald Trump. Figure 5shows that the impulse response of China’s
agricultural OFDI to RCEP countries’ agricultural trade at the three time points is generally
Sustainability 2025,17, 26 17 of 24
characterized by a rapid response at first and then gradually stabilizing; that is, the impact
of a 1 percentage positive impact of China’s agricultural OFDI at the three time points tends
to be consistent. This indicates that the impact of China’s agricultural OFDI on agricultural
trade of RCEP countries is relatively stable at different economic levels, and there will be
no significant difference due to different time points.
Sustainability 2025, 17, x FOR PEER REVIEW 17 of 24
on agricultural trade of RCEP countries is relatively stable at different economic levels,
and there will be no significant difference due to different time points.
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
Figure 5. Impulse response of China’s agricultural OFDI to RCEP countries’ agricultural trade at
different time points. (a) OFDI–OFDI. (b) OFDI–IMPORT. (c) OFDI–EXPORT. (d) IMPORT–OFDI.
(e) IMPORT–IMPORT. (f) IMPORT–EXPORT. (g) EXPORT–OFDI. (h) EXPORT–IMPORT. (i) EX-
PORT–EXPORT. Note: The plus sign (+), cross (×), and triangle (▲) indicate January 2012, Septem-
ber 2013, and June 2018, respectively.
The results of impulse response at different time points were further compared. First,
the impact on agricultural trade was greater in January 2012, indicating that the proposal
of the RCEP agreement is conducive to the bilateral agricultural trade between China and
RCEP countries, and the proposal of the “Belt and Road” strategy has a certain promoting
effect, but the difference is not obvious at different time points. China’s agricultural OFDI
has a negative effect on agricultural trade in the current period. The OFDI began to show
a positive effect after a lag of one period. Second, for the import impact of agricultural
products, the positive impact effect reaches the maximum in the first phase, but immedi-
ately presents a trend of rapid decline, while the negative effect reaches the maximum in
the third phase, and then maintains a certain weak positive effect. Thirdly, for the export
effect of agricultural products, the impulse response of China’s agricultural OFDI at the
three time points is obviously volatile, experiencing a negative effect in the early stage,
turning to a positive effect in the later stage, and tending to a weak positive effect after the
sixth stage.
Figure 5. Impulse response of China’s agricultural OFDI to RCEP countries’ agricultural trade
at different time points. (a) OFDI–OFDI. (b) OFDI–IMPORT. (c) OFDI–EXPORT. (d) IMPORT–
OFDI.
(e) IMPORT–IMPORT
. (f) IMPORT–EXPORT. (g) EXPORT–OFDI. (h) EXPORT–IMPORT.
(i) EXPORT–EXPORT. Note: The plus sign (+), cross (
×
), and triangle (
▲
) indicate January 2012,
September 2013, and June 2018, respectively.
The results of impulse response at different time points were further compared. First,
the impact on agricultural trade was greater in January 2012, indicating that the proposal
of the RCEP agreement is conducive to the bilateral agricultural trade between China and
RCEP countries, and the proposal of the “Belt and Road” strategy has a certain promoting
effect, but the difference is not obvious at different time points. China’s agricultural OFDI
has a negative effect on agricultural trade in the current period. The OFDI began to show
a positive effect after a lag of one period. Second, for the import impact of agricultural
products, the positive impact effect reaches the maximum in the first phase, but immediately
presents a trend of rapid decline, while the negative effect reaches the maximum in the
third phase, and then maintains a certain weak positive effect. Thirdly, for the export effect
of agricultural products, the impulse response of China’s agricultural OFDI at the three
time points is obviously volatile, experiencing a negative effect in the early stage, turning to
a positive effect in the later stage, and tending to a weak positive effect after the sixth stage.
Sustainability 2025,17, 26 18 of 24
5.5. Heterogeneity Analysis of Impulse Response
5.5.1. Impulse Response to RCEP Countries’ Agricultural Imports
Research on China’s OFDI in RCEP member countries reveals significant heterogeneity
in the influencing factors across these states. Specifically, market size and labor resources
exert a positive influence on OFDI [
37
], whereas elements such as tax burden and natural
resource endowment exhibit certain inhibitory effects. In nations that have successfully
attracted substantial Chinese investments, political stability plays a crucial role in shaping
OFDI patterns [
38
]. Geographical and institutional distances generally facilitate OFDI;
however, economic distance demonstrates varying impacts among RCEP countries [
39
].
The increase in GDP growth rates and per capita GDP of host nations has also stimulated
Chinese investment, while fluctuations in per capita net national income, per capita GDP
growth rate, and trade in goods and services have had a constraining effect on such invest-
ments [
40
]. These findings underscore the intricate interplay of factors affecting China’s
OFDI within RCEP countries and emphasize the necessity for developing
region-specific
investment strategies.
As can be seen from Figure 6, China’s agricultural OFDI has the most significant
positive impact on Laos, as well as on Malaysia, Thailand, and Vietnam. In other words,
agricultural OFDI further improves trade openness and stabilizes China’s import market of
key commodities and commodities. Laos and Vietnam share borders with China, making it
more convenient for foreign investment and agricultural trade [
41
]. In particular, Vietnam
is a major importer of fish meal used as feed for key commodities, Thailand is a major
importer of rice, chicken, and its by-products, and Malaysia is a major importer of natural
rubber. The impact effect of China’s agricultural OFDI on other RCEP countries is obviously
volatile. Malaysia, Vietnam, Thailand, Singapore, and Laos show a continuous positive
effect, South Korea and Myanmar show a continuous negative effect, and the impact on
Cambodia, Japan, the Philippines, and Brunei fluctuates around a value of 0. This indicates
that, compared with other countries, China’s agricultural OFDI can significantly promote
import trade in Malaysia, Vietnam, Thailand, Singapore, and Laos. In addition, the impact
effect of China’s agricultural OFDI on Vietnam, Thailand, Singapore, Laos, and Australia is
significant in the short term, while the impact on Malaysia, South Korea, Myanmar, and
Japan is significant in the long term.
Sustainability 2025, 17, x FOR PEER REVIEW 18 of 24
5.5. Heterogeneity Analysis of Impulse Response
5.5.1. Impulse Response to RCEP Countries’ Agricultural Imports
Research on China’s OFDI in RCEP member countries reveals significant heteroge-
neity in the influencing factors across these states. Specifically, market size and labor re-
sources exert a positive influence on OFDI [37], whereas elements such as tax burden and
natural resource endowment exhibit certain inhibitory effects. In nations that have suc-
cessfully aracted substantial Chinese investments, political stability plays a crucial role
in shaping OFDI paerns [38]. Geographical and institutional distances generally facilitate
OFDI; however, economic distance demonstrates varying impacts among RCEP countries
[39]. The increase in GDP growth rates and per capita GDP of host nations has also stim-
ulated Chinese investment, while fluctuations in per capita net national income, per capita
GDP growth rate, and trade in goods and services have had a constraining effect on such
investments [40]. These findings underscore the intricate interplay of factors affecting
China’s OFDI within RCEP countries and emphasize the necessity for developing region-
specific investment strategies.
As can be seen from Figure 6, China’s agricultural OFDI has the most significant pos-
itive impact on Laos, as well as on Malaysia, Thailand, and Vietnam. In other words, ag-
ricultural OFDI further improves trade openness and stabilizes China’s import market of
key commodities and commodities. Laos and Vietnam share borders with China, making
it more convenient for foreign investment and agricultural trade [41]. In particular, Vi-
etnam is a major importer of fish meal used as feed for key commodities, Thailand is a
major importer of rice, chicken, and its by-products, and Malaysia is a major importer of
natural rubber. The impact effect of China’s agricultural OFDI on other RCEP countries is
obviously volatile. Malaysia, Vietnam, Thailand, Singapore, and Laos show a continuous
positive effect, South Korea and Myanmar show a continuous negative effect, and the im-
pact on Cambodia, Japan, the Philippines, and Brunei fluctuates around a value of 0. This
indicates that, compared with other countries, China’s agricultural OFDI can significantly
promote import trade in Malaysia, Vietnam, Thailand, Singapore, and Laos. In addition,
the impact effect of China’s agricultural OFDI on Vietnam, Thailand, Singapore, Laos, and
Australia is significant in the short term, while the impact on Malaysia, South Korea, My-
anmar, and Japan is significant in the long term.
(a) (b) (c)
(d) (e) (f)
Figure 6. Cont.
Sustainability 2025,17, 26 19 of 24
Sustainability 2025, 17, x FOR PEER REVIEW 19 of 24
(g) (h) (i)
(j) (k) (l)
(m) (n)
Figure 6. Impulse response to agricultural imports of RCEP countries. (a) Malaysia. (b) Vietnam. (c)
Thailand. (d) Cambodia. (e) Burma. (f) Korea. (g) Japan. (h) The Philippines. (i) Indonesia. (j) Aus-
tralia. (k) New Zealand. (l) Singapore. (m) Laos. (n) Brunei. Note: Short and green, long and purple,
and solid and red lines indicate short (4 months), medium (8 months), and long (12 months), respec-
tively.
5.5.2. Impulse Response of Agricultural Exports to RCEP Countries
Similar to the impulse response effect of imports, it can be seen from Figure 7 that
China’s agricultural OFDI can easily promote the agricultural product export of RCEP
countries, help to improve trade openness, and stabilize China’s export market in the field
of important commodities. Among them, the positive impact effect on Thailand and Aus-
tralia is significant, and it also has a significant positive impact on Vietnam and Indonesia.
Thailand is one of the top three markets for China’s export of peanut kernels, culefish
and squid, and other commodities, while Indonesia is the main market for China’s export
of tobacco, pulp, and palm oil. In terms of the volatility of the impact effect, Vietnam,
Thailand, Indonesia, and Japan have a continuous positive effect, South Korea has a con-
tinuous negative effect, and the impact of Malaysia, Cambodia, and Brunei shows a sig-
nificant zigzag effect. At the same time, there are obvious differences in the impact effects
of different countries in different periods. China’s agricultural OFDI significantly in-
creases the export volume of agricultural products to Vietnam, Thailand, Indonesia, and
Australia in the short term, while the long-term impact effect on the Philippines and Bru-
nei is more significant.
Figure 6. Impulse response to agricultural imports of RCEP countries. (a) Malaysia. (b) Vietnam.
(c) Thailand. (d) Cambodia. (e) Burma. (f) Korea. (g) Japan. (h) The Philippines. (i) Indonesia.
(j) Australia
. (k) New Zealand. (l) Singapore. (m) Laos. (n) Brunei. Note: Short and green,
long and purple, and solid and red lines indicate short (4 months), medium (8 months), and long
(12 months), respectively.
5.5.2. Impulse Response of Agricultural Exports to RCEP Countries
Similar to the impulse response effect of imports, it can be seen from Figure 7that
China’s agricultural OFDI can easily promote the agricultural product export of RCEP
countries, help to improve trade openness, and stabilize China’s export market in the
field of important commodities. Among them, the positive impact effect on Thailand
and Australia is significant, and it also has a significant positive impact on Vietnam and
Indonesia. Thailand is one of the top three markets for China’s export of peanut kernels,
cuttlefish and squid, and other commodities, while Indonesia is the main market for
China’s export of tobacco, pulp, and palm oil. In terms of the volatility of the impact effect,
Vietnam, Thailand, Indonesia, and Japan have a continuous positive effect, South Korea
has a continuous negative effect, and the impact of Malaysia, Cambodia, and Brunei shows
a significant zigzag effect. At the same time, there are obvious differences in the impact
effects of different countries in different periods. China’s agricultural OFDI significantly
increases the export volume of agricultural products to Vietnam, Thailand, Indonesia, and
Australia in the short term, while the long-term impact effect on the Philippines and Brunei
is more significant.
Sustainability 2025,17, 26 20 of 24
Sustainability 2025, 17, x FOR PEER REVIEW 20 of 24
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
(j) (k) (l)
(m) (n)
Figure 7. Impulse response to RCEP countries’ exports. (a) Malaysia. (b) Vietnam. (c) Thailand. (d)
Cambodia. (e) Burma. (f) Korea. (g) Japan. (h) The Philippines. (i) Indonesia. (j) Australia. (k) New
Zealand. (l) Singapore. (m) Laos. (n) Brunei. Note: Short and green, long and purple, and solid and
red lines indicate short (4 months), medium (8 months), and long (12 months), respectively.
6. Conclusions and Implications
6.1. Conclusions
Based on the trade gravity model, this paper analyzes the sustainability trade effect
of China’s agricultural OFDI on the agricultural products of RCEP member countries, and
Figure 7. Impulse response to RCEP countries’ exports. (a) Malaysia. (b) Vietnam. (c) Thailand.
(d) Cambodia
. (e) Burma. (f) Korea. (g) Japan. (h) The Philippines. (i) Indonesia. (j) Australia.
(k) New Zealand
. (l) Singapore. (m) Laos. (n) Brunei. Note: Short and green, long and purple, and
solid and red lines indicate short (4 months), medium (8 months), and long (12 months), respectively.
Sustainability 2025,17, 26 21 of 24
6. Conclusions and Implications
6.1. Conclusions
Based on the trade gravity model, this paper analyzes the sustainability trade effect of
China’s agricultural OFDI on the agricultural products of RCEP member countries, and
further analyzes the dynamic impact results between China’s agricultural OFDI and RCEP
member countries’ agricultural products trade by using the TVP-VAR model. Important
conclusions can be drawn as follows:
First, based on the analysis of sample global regression results, China’s agricultural
OFDI has significant positive spillover effects on RCEP member countries. A 1% increase
in China’s agricultural OFDI has a significant positive impact on the import and export of
agricultural products of RCEP countries in the short, medium, and long term, and there is
a continuous positive impact effect, which reflects that “China’s agricultural development
is good, and the agricultural development of RECP member countries is good” to a certain
extent. The reason for the existence of this trade creation effect may lie in that, on the
one hand, in order to find low-cost agricultural production means, Chinese agricultural
enterprises realize the transfer of agricultural production through international trade and
technology export, which brings a trade creation effect to the agricultural trade of RECP
countries and generates export demand. On the other hand, China participates in the
international agricultural industry chain through the export of capital and technology.
The agricultural products of RCEP countries are sensitive to the price of the international
market, while China’s huge and stable consumer market produces an import transfer effect.
As RCEP countries are mainly developing countries, they still face food security problems,
and the problems of poverty and hunger are far from being solved. Bilateral trade and
investment in agricultural products is of great significance to ensuring food security and
livelihood security for all participants.
Second, according to the quantile regression results, although agricultural OFDI has a
limited impact on agricultural trade between China and RCEP countries when it reaches a
certain level, in general, agricultural OFDI maintains a positive and significant impact, and
agricultural OFDI will still have a trade-creating effect in the future. The reason is that both
in the short and long term, the tariff rates and non-tariff barriers between China and RCEP
countries are already low, and China’s agricultural OFDI has little impact on the bilateral
trade partnership. Therefore, encouraging China’s competitive agricultural enterprises to
“go global” through OFDI strategy can reduce trade frictions and explore foreign markets,
and deepen cooperation and innovation with RCEP countries in the agricultural industrial
chain, supply chain, and value chain.
Thirdly, from the analysis of impulse response results, although China’s agricultural
OFDI will show the alternating phenomenon of an export creation effect and import
transfer effect in different periods, the total effect of one standard deviation impact from
China’s agricultural OFDI causes the import promotion effect to be greater than the export
promotion effect; that is, the “spillover effect” has an increasing trend. This means that
China’s agricultural OFDI not only improves the ability of Chinese agricultural enterprises
to allocate global agricultural resources, but also improves the ability of RCEP countries to
shape and expand the agricultural industry, providing new opportunities for it to explore
the Chinese consumer market, and forming a “mutually beneficial and win-win” situation.
Through the heterogeneity results of impulse response, it is further found that China’s
agricultural OFDI has a more significant trade creation effect on low-income developing
countries and neighboring Southeast Asian countries. China should pay attention to
sharing agricultural development experience and practical agricultural technology with
RCEP countries, so as to promote the development of modern agriculture and increase
farmers’ income in RCEP countries through OFDI.
Sustainability 2025,17, 26 22 of 24
The following points can be further discussed in this study: Referring to existing
literature, this paper mainly studies the selection variables affected by foreign investment
and trade. The impact of policy changes has been taken into account, and non-economic
factors such as FTA accession, territorial border, and language have been introduced
into the model as control variables. However, there are many influencing factors among
international trade, and the influence of policy changes and non-economic factors (such
as natural disasters and political conflicts) between countries can be classified for further
discussion. In this paper, soybean, corn, wheat, oat, soybean meal, and rice are selected
as representatives to analyze the impact of foreign investment on its trade volume and
discuss the impact from 2004 to 2019. The data cover a long period of time and have
certain reference opinions. However, due to the availability of data, this paper does not
adequately discuss the impact in recent years following the outbreak, which could be
further supplemented.
6.2. Implication
According to the above research conclusions, this paper has the following policy
implications: First, actively adjust the international trade strategy, expand the scope of
international trade, take advantage of China’s OFDI advantages in RCEP countries, greatly
improve the OFDI level of Chinese enterprises, and make breakthroughs in related in-
dustries such as agricultural machinery, commercialization of agricultural science and
technology, and cross-border marketing of agricultural products. Second, attach impor-
tance to the export creation effect and import transfer effect of China’s agricultural OFDI,
and combine with the upgrading and adjustment of agricultural industrial structure of
RCEP member countries to form misplaced competition and achieve mutual benefit and a
win–win situation. Third, take into account the actual characteristics of RCEP countries to
achieve functional complementarity between the two countries, so as to comprehensively
stimulate investment in different industrial types. It will have a driving effect on RCEP
countries and serve as a positive example, further raising the sustainability of economic
growth of China and RCEP member countries.
Author Contributions: Supervision, project administration, funding acquisition, Q.M.; conceptu-
alization, methodology, software, formal analysis, resources, data curation, writing—original draft
preparation, X.W. All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by National Social Science Fund of China, grant number.
72363031, and was funded by National Social Science Fund of China, grant number 71863033,
and was funded by Humanities and Social Sciences Program or Educational Cooperation among
Provincial Institutes and Universitiesin Yunnan Province, grant number SYSX202209.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data are contained within the article.
Conflicts of Interest: The authors declare no conflicts of interest.
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