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Citation: Shi, Y.; Jiang, Y.; Xie, C.; Li,
C. Regional Innovation and
Sustainable Development Interplay:
Analyzing the Spatial Externalities of
Domestic Demand in the New
Development Paradigm. Sustainability
2024,16, 2365. https://doi.org/
10.3390/su16062365
Academic Editors: Fabio Carlucci and
Chengpeng Lu
Received: 24 January 2024
Revised: 29 February 2024
Accepted: 11 March 2024
Published: 13 March 2024
Copyright: © 2024 by the authors.
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4.0/).
sustainability
Article
Regional Innovation and Sustainable Development Interplay:
Analyzing the Spatial Externalities of Domestic Demand in the
New Development Paradigm
Yufang Shi, Yufeng Jiang * , Can Xie and Cong Li
School of Management, Xi’an University of Science and Technology, Xi’an 710054, China;
shiyufangguoguo@163.com (Y.S.); 22202001002@stu.xust.edu.cn (C.X.); lionlee0826@126.com (C.L.)
*Correspondence: jiangyf0219@163.com
Abstract: Building a great modern socialist country in all respects requires enhancing innovation
capacity and establishing a new development pattern, especially in the context of sustainable de-
velopment. This paper begins by analyzing the theoretical relationship between innovation and
the spatial externality of domestic demand, constructing a theoretical model, and then empirically
testing this model using provincial panel data from 2012 to 2020 through the Spatial Durbin model.
The study underscores the importance of innovation in promoting sustainable economic growth,
highlighting how it expands domestic demand through both supply and demand sides and positively
affects the domestic demand in surrounding areas through spatial spillover effects. The empirical
results reveal that innovation significantly boosts the level of domestic demand in the region and its
environs, with the spatial spillover effect of domestic demand constituting 66.92% of the total effect.
This underscores the relevance of spatial externality in sustainable economic planning. Innovation
mainly stimulates domestic demand through consumption, aligning with sustainable consumption
patterns, while exerting a moderate inhibitory effect on investment demand. The spatial externality
of investment demand appears less significant. Overall, innovation drives the spatial externality of
China’s domestic demand and significantly contributes to establishing a new development pattern
of “dual circulation”, primarily focusing on the domestic cycle, within a framework of sustainable
development. The paper concludes with policy recommendations that align innovation strategies
with sustainable development goals.
Keywords: regional innovation; domestic demand; new development paradigm; spatial spillover
effects; spatial Durbin model; new economic geography
1. Introduction
As China’s economy enters a new development stage, the report of the 20th National
Congress of the Communist Party of China sets out the overall goal of “achieving high-
level technological self-reliance, entering the forefront of innovative countries; building a
modern economic system and forming a new development pattern”. This goal not only
emphasizes the importance of independent innovation but also proposes the strategic
direction of constructing a new development pattern with domestic circulation as the main
focus and the mutual promotion of domestic and international circulations. In light of this
context, investigating how innovation can stimulate domestic demand and generate spatial
externalities within the framework of innovation-driven development holds significant
importance for China in realizing the new dual circulation development pattern.
The early discussions on the concept of “dual circulation” can be traced back to 2008,
when perspectives suggested that China should reconsider its “catch-up” industrializa-
tion strategy. The proposal emphasized reshaping the industrial landscape, enhancing
innovation capabilities, and utilizing the domestic market as a driving force for economic
growth. It strategically advocated forming a mutually beneficial division of labor with
Sustainability 2024,16, 2365. https://doi.org/10.3390/su16062365 https://www.mdpi.com/journal/sustainability
Sustainability 2024,16, 2365 2 of 15
resource-supplying countries. These viewpoints laid the foundation for the later develop-
ment paradigm of “placing domestic circulation as the mainstay”. This strategic emphasis
on high-level technological self-reliance and the development of a new dual circulation de-
velopment pattern not only signifies a shift towards enhancing China’s innovation-driven
economic growth but also aligns with the global imperative for sustainable development.
By fostering an innovation-led economy, China aims to reduce its environmental footprint,
promote green technologies, and ensure economic resilience, thus contributing to the envi-
ronmental, cultural, and economic dimensions of sustainability. The pursuit of self-reliance
in technology and innovation, coupled with the emphasis on domestic circulation, presents
a unique opportunity to harmonize economic growth with environmental conservation
and social well-being. This approach underscores the necessity of integrating sustain-
able practices into the heart of China’s development strategy, aiming to create a balanced
and sustainable future that addresses the dual challenges of economic development and
sustainability.
Against this development background, our research revolves around the following
core question: how does innovation affect domestic demand within the large domestic
cycle, and can this effect be transmitted to surrounding regions through spatial spillover
effects, thereby promoting the overall uplift of regional economies? Based on this, we
propose the following hypothesis: innovation activities can not only directly enhance the
consumption and investment demand within a region but can also generate positive spatial
spillover effects by improving economic links and interactions between regions, further
promoting the expansion of domestic demand and comprehensive economic development.
This study aims to verify the direct role of innovation in promoting domestic demand and
its indirect role in inter-regional dissemination, which is of great theoretical and practical
significance for understanding and optimizing the large domestic cycle and promoting the
mutual reinforcement of domestic and international dual circulations.
2. Literature Review
(1)
Innovation’s Role in Economic and Social Advancement
Recently, with the increasing emphasis of the Chinese government on the role of
innovation in economic and social development, innovation has become a hot topic in the
academic community. It is well-known that innovation in knowledge and technology is a
key driving force for economic development and is considered one of the core elements of
national and corporate competitiveness [
1
,
2
]. At the national level, there are also diverse
factors influencing innovation performance. Ding, using the NCA analysis method, reveals
that research and development investment, globalization, and national wealth are neces-
sary conditions for innovation performance [
3
]. Shi’s research focuses on seven fiscally
decentralized countries, such as the United Kingdom and the United States, and finds
that targeted fiscal policies, such as research and development expenditures and public
debt management, have greatly changed the technological innovation landscape of these
countries [
4
]. Mao’s research finds that industrial innovation affects carbon prices, which
are directly related to socioeconomic development [
5
]. Appiah, using 29 emerging countries
as samples, finds a negative correlation between a country’s innovation input and carbon
emissions, highlighting the importance of innovation in environmental governance [
6
].
Xie’s research on coal mining enterprises shows that innovation can reduce the cost of
safety rectification, which is of great significance for maintaining social stability [
7
]. Litina
et al. investigates the impact of product market policies on innovation and concludes that
regulations that clarify negative behaviors have a positive impact on innovation, while
those that clarify negative behaviors have a negative impact on innovation [
8
]. Shi be-
lieves that the digital economy has enhanced the resilience of cities through technological
innovation and has generated positive spatial spillover effects, bringing many benefits to
society [
9
]. Through the analysis above, it can be seen that whether at the enterprise level or
the national level, a deep understanding and effective management of innovation are cru-
cial for promoting economic development and social progress. However, there is currently
Sustainability 2024,16, 2365 3 of 15
limited scholarly exploration into the relationship between innovation and the construction
of a new development pattern. Exploring this field is essential for understanding and
promoting the role of innovation in future socioeconomic development.
(2)
How Innovation Influences China’s Construction of a New Development Pattern
In exploring how innovation influences the topic of China constructing a new de-
velopment pattern, Chinese scholars have conducted extensive and in-depth research.
The prevailing view is that enhancing regional innovation capacity and achieving innova-
tive development are the key paths to building the “dual-cycle” new development pattern.
This perspective is based on the notion that the improvement in innovation capacity will
establish a foundation for high-quality mutual promotion and a dynamic balance between
domestic demand and supply, thereby enhancing the dynamism of China’s economic
cycles. Jiang and Meng emphasize that accelerating independent innovation is a necessary
path to drive high-quality domestic cycles [
10
]. They point out that this involves not only
the enhancement of the demand side but also the strengthening of the real economy on
the supply side through the cultivation of emerging industrial chains, thereby providing
impetus for the sustained and healthy development of the socioeconomic system [11,12].
From the perspective of political economics, economic cycles are defined as the process
of the movement of a country’s material and service products in the stages of production,
distribution, circulation, and consumption [
13
] to achieve value increment and enhance
the well-being of national consumption. In this framework, technological innovation is
regarded as the foundation for achieving these goals [
14
,
15
]. Therefore, by enhancing
regional innovation capacity and improving people’s well-being, it ultimately promotes
the realization of the “dual-cycle” new development pattern. On this basis, another group
of scholars believes that innovation plays a promoting role in domestic demand through
enterprises providing new products to the market and raising the national income level.
They explore the impact of innovation on the domestic cycle from the perspective of do-
mestic demand. The overall idea is that the improvement in innovation capacity can lead
to the expansion of domestic demand by improving the circulation effect of the internal
cycle [
16
,
17
]. Qian and Xiang argue that innovation can stimulate the potential of domestic
demand by creating high-quality products and services, thus expanding the domestic
cycle [
18
].
Ren and Gong
emphasize that core technological innovation is crucial for con-
structing a secure and independent domestic cycle [
19
]. Domestic demand, including
consumption demand and investment demand, is a core element of this process [
20
]. From
the perspective of consumption demand, Wang and Meng argue, from the perspective
of supply-side reform, that technological innovation promotes cultural consumption in
China [
21
], while Xu and Song find that corporate innovation behavior positively drives
consumer behavior [
22
]. Wang also points out, from the perspective of rural service indus-
tries, that innovative service formats have a significant positive impact on consumption
upgrading [
23
]. From the perspective of investment demand, Ye, starting from the mi-
cro perspective of manufacturing enterprises, finds that enterprises with higher levels of
technological innovation bring about greater investment demand [24].
(3)
Innovation’s Spatial Externality on Domestic Demand
Although the mechanism by which innovation promotes domestic demand is widely
recognized, there has not been in-depth research on the spatial externality of innovation
in promoting domestic demand. This indicates that, despite the valuable insights pro-
vided by the existing literature on the impact of innovation on China’s new development
pattern, there is still room for further exploration in this field. The existence of spatial
externality is mainly due to imperfect competition, increasing returns, financial externality
under market interaction, and market imperfections and coordination failures in spatial
economics. The spatial externality of domestic demand refers to the influence of domestic
demand in a local area on the domestic demand of surrounding areas during the expansion
process. In the research field of the spatial externality of consumption demand, scholars
have achieved a series of significant results covering various scenarios, demonstrating
Sustainability 2024,16, 2365 4 of 15
the impact and importance of the spatial externality of consumption demand in different
contexts. Firstly, Burlig’s research focuses on the spatial externality of groundwater ex-
traction in California agriculture, highlighting the critical role of externality in resource
management. This study not only reveals the potential impact of groundwater extraction
on surrounding regions but also provides empirical evidence for understanding spatial
externality in natural resource utilization [
25
]. Next, Ten Kate proposes an innovative
framework for understanding the dynamics of consumer demand in bilateral markets. By
modeling the externality between customer groups and its impact on the demand and
price sensitivity of both sides of the market, this framework provides a new theoretical
perspective for analyzing consumer behavior in complex market structures [
26
]. Vitali’s
research focuses on how consumer information friction drives businesses to choose loca-
tions within a city. This study combines various data from Uganda’s clothing industry,
including transaction, customer, and mystery shopper data, revealing the significant role of
information friction in influencing market positioning and consumer decision-making [
27
].
Additionally, Cohen provides a theoretical and empirical framework focusing on measuring
and evaluating the impact of agglomeration economies and industry location decisions.
In particular, this research focuses on spillover effects related to knowledge or other types
of horizontal and vertical externality, providing profound insights into understanding
industry agglomeration and its mechanisms’ impact on the market [
28
]. Zhang and Liu
use the spatial Durbin model to study the impact of specialization and diversification
agglomeration in the service industry on consumption structure. They find that both forms
of agglomeration show a significant positive correlation with consumption structure in
space, providing important empirical evidence for understanding the impact of service
industry development on consumer behavior [
29
]. Finally, Qi and Zhang’s research focuses
on the impact of the common prosperity goal on China, specifically the spatial spillover
effects of consumption growth in the initially prosperous regions on the consumption
growth rate in the later prosperous regions. This finding emphasizes the impact of regional
development imbalances on consumption demand, providing an important perspective for
understanding and addressing regional economic disparities [
30
]. Overall, these studies
deepen our understanding of the spatial externality of consumption demand from multiple
perspectives and offer valuable insights for policy-making and business strategies.
Regarding the research on the spatial externality of investment demand, another
group of scholars has achieved certain research results. Bertinelli’s study tests the positive
spatial autocorrelation of R&D investment and how the local environment influences firms’
decisions in R&D investment [
31
]. Häckner’s research extends the Hotelling spatial duopoly
competition model to include R&D investment, analyzing how cost reduction through
individual investment and spillover effects from competitors affect firms’ location choices
and agglomeration [
32
]. Bode employs the System Generalized Method of Moments (GMM)
estimation method to consider the potential endogeneity and spatial lags of foreign direct
investment. Using data from U.S. states from 1977 to 2003, the results indicate positive
external effects from foreign direct investment, while domestic firms’ external effects are
negative [
33
]. In summary, the existing research results indicate the existence of spatial
externality in China’s consumption and investment demands. However, the mechanisms
through which innovation influences the spatial externality of domestic demand still need
further clarification, and specific mechanisms need to be constructed. Additionally, the
above-mentioned studies have not examined the spatial externality of China’s domestic
demand from the perspective of the new development pattern. Based on this, building
on existing research outcomes, this paper may contribute by: (1) examining the spatial
externality of China’s domestic demand under innovation from a spatial econometrics
perspective. Compared to existing research, this paper (2) enriches theoretical achievements
and empirically verifies the promoting role of innovation in constructing a domestic large
cycle when examining the spatial externality of domestic demand, further refining the
concept of domestic demand, systematically explaining the promoting effect of innovation
on consumption and investment demands, and constructing a theoretical model that
Sustainability 2024,16, 2365 5 of 15
expands the research perspectives and models of both, thus broadening the understanding
of the interaction between innovation and domestic demand expansion.
3. Theoretical Modeling
The new double-cycle development pattern is an economic modernization strategy,
characterized by innovation-driven unimpeded economic circulation, with scientific and
technological self-reliance and independent innovation as its essential features. In August
2020, General Secretary Xi Jinping emphasized the urgent need to strengthen our capacity
for independent innovation and achieve breakthroughs in key core technologies as soon as
possible. This is a major issue related to the overall situation of our country’s development
and it is also the key to the formation of a domestic macrocycle as the main body. The above
discussion provides a theoretical basis for this paper to construct an evolutionary model of
regional innovation and domestic general circulation. Regional innovation capacity serves
as a basis for building a new development pattern. The specific theoretical model is shown
in Figure 1.
Sustainability 2024, 16, x FOR PEER REVIEW 5 of 15
constructing a domestic large cycle when examining the spatial externality of domestic
demand, further refining the concept of domestic demand, systematically explaining the
promoting effect of innovation on consumption and investment demands, and construct-
ing a theoretical model that expands the research perspectives and models of both, thus
broadening the understanding of the interaction between innovation and domestic de-
mand expansion.
3. Theoretical Modeling
The new double-cycle development paern is an economic modernization strategy,
characterized by innovation-driven unimpeded economic circulation, with scientific and
technological self-reliance and independent innovation as its essential features. In August
2020, General Secretary Xi Jinping emphasized the urgent need to strengthen our capacity
for independent innovation and achieve breakthroughs in key core technologies as soon
as possible. This is a major issue related to the overall situation of our country’s develop-
ment and it is also the key to the formation of a domestic macrocycle as the main body.
The above discussion provides a theoretical basis for this paper to construct an evolution-
ary model of regional innovation and domestic general circulation. Regional innovation
capacity serves as a basis for building a new development paern. The specific theoretical
model is shown in Figure 1.
Figure 1. Theoretical model of regional innovation impact on domestic demand.
The new economic geography believes that current regional economic activities have
surpassed the traditional assumption in theories where interregional trade transportation
costs are zero. It considers the existence of transportation costs and spatial spillovers in
interregional economic activities. That is, the development of transportation infrastruc-
ture will not only affect the local economy but also have an impact on surrounding areas
along the transportation infrastructure network. Therefore, only by comprehensively con-
sidering the effects of economic activities on both the local area and surrounding areas
can the operational mode of economic activities be more fully revealed. As shown in Fig-
ure 1, within the theoretical framework of the impact of regional innovation on domestic
demand, the operational mode of regional innovation includes the impact on both domes-
tic demand in the local area and domestic demand in surrounding areas through spatial
externalities. This impact is based on the high-level development of China’s transporta-
tion infrastructure. The impact of the improvement in regional innovation capacity on do-
mestic demand in the local area can be examined from both the supply side and the de-
mand side. On the supply side, innovation activities expand the market size by providing
new products to the market, nurturing the industrial chain, and strengthening the real
economy, laying the foundation for building a unified domestic market and providing
impetus for promoting internal circulation. On the demand side, innovation increases res-
idents’ income levels and utilizes China’s advantage of having the world’s largest middle-
income group, unleashing the huge potential of China’s domestic demand market. Under
Figure 1. Theoretical model of regional innovation impact on domestic demand.
The new economic geography believes that current regional economic activities have
surpassed the traditional assumption in theories where interregional trade transportation
costs are zero. It considers the existence of transportation costs and spatial spillovers in
interregional economic activities. That is, the development of transportation infrastructure
will not only affect the local economy but also have an impact on surrounding areas along
the transportation infrastructure network. Therefore, only by comprehensively considering
the effects of economic activities on both the local area and surrounding areas can the
operational mode of economic activities be more fully revealed. As shown in Figure 1,
within the theoretical framework of the impact of regional innovation on domestic demand,
the operational mode of regional innovation includes the impact on both domestic demand
in the local area and domestic demand in surrounding areas through spatial externalities.
This impact is based on the high-level development of China’s transportation infrastructure.
The impact of the improvement in regional innovation capacity on domestic demand in
the local area can be examined from both the supply side and the demand side. On the
supply side, innovation activities expand the market size by providing new products to
the market, nurturing the industrial chain, and strengthening the real economy, laying the
foundation for building a unified domestic market and providing impetus for promoting
internal circulation. On the demand side, innovation increases residents’ income levels and
utilizes China’s advantage of having the world’s largest middle-income group, unleashing
the huge potential of China’s domestic demand market. Under the dual effects of the
supply side and the demand side, consumption demand and investment demand in China
are driven to expand, ultimately leading to the expansion of domestic demand in the local
Sustainability 2024,16, 2365 6 of 15
area. Meanwhile, due to the rapid development of China’s transportation infrastructure,
the costs of the cross-regional flow of various production and innovation factors have
been significantly reduced. Therefore, when domestic demand in surrounding areas
cannot be met by the local market, it will spread to surrounding areas to seek release
channels. The products provided by local innovation activities and the increased income
not only meet the domestic demand of the local area but also provide direction for the
release of domestic demand in surrounding areas. That is, the improvement in local
innovation capacity spreads along interregional transportation networks to surrounding
areas, generating externalities for domestic demand in surrounding areas. In summary,
within the framework of new economic geography, considering the combined impact of
regional innovation on domestic demand in both the local area and surrounding areas
helps to more comprehensively reveal the spatial externalities of innovation demand.
4. Research Design
4.1. Selection of Variables
Explained variable: Domestic demand (DD) serves as the foundation of the domestic
macrocycle, aiming to unleash the potential of China’s domestic demand and achieve
high-quality economic development. During benchmark regression analysis, the level of
regional GDP is utilized to represent the level of domestic demand. Furthermore, in the
mechanism test, consumption demand and investment demand are examined separately
to assess the mechanism of promoting domestic demand through innovation capacity.
Consumption demand (cons) is quantified by per capita consumption expenditure, while
investment demand (invest) is quantified by social investment in fixed assets.
Explanatory variable: This study focuses on regional innovation (inno) and its rela-
tionship with internal circulation. Within the framework of the new development pattern,
innovation stimulates internal circulation by introducing new products to the market.
As such, the sales of new products are used to assess regional innovation capacity, with
technology market turnover serving as the measurement.
Control variables: Various factors influence the construction of the domestic macro-
cycle, including the impact of the foreign cycle. Import and export levels (inandout) are
measured by the total import and export volumes of each province, while foreign direct
investment (FDI) levels are determined by the amount of FDI in each province. Urbaniza-
tion levels (urban) are quantified by the proportion of the urban population to the total
population at the end of each province’s year, and government support levels (gov) are
assessed by each province’s government’s financial expenditures.
4.2. Data Sources and Processing
A sample of 31 provinces (autonomous regions and municipalities directly under the
central government) in China from 2011 to 2020 was selected, excluding Hong Kong, Macao,
and Taiwan. Meanwhile, to ensure the authenticity of the data, the study adopts all the data
from the China Statistical Yearbook of each year, and part of the missing data is made up
by looking up the Provincial Statistical Yearbook of each province in that year, as well as by
using the linear interpolation method. Given the varied sources of data acquisition in this
paper, both the scale and significance differ. Therefore, to calculate the levels of common
prosperity and high-quality economic development, normalization is necessary to eliminate
differences in the quantitative representation of each indicator. The data normalization
formula is presented in the following equation:
a′
xy =
axy −min
xaxy
max
xaxy −min
xaxy
×99 +1 (1)
4.3. Description of Spatial Matrix
The first theorem of geography posits a correlation among all phenomena, with
proximity generally indicating stronger correlations. Therefore, in spatial measurement
Sustainability 2024,16, 2365 7 of 15
research, it is common practice to establish a spatial weight matrix to quantify the distances
between phenomena. This paper adopts the adjacency matrix, geographic distance matrix,
and economic distance matrix as the spatial weighting matrices for empirical research,
respectively. The adjacency matrix considers that there is a connection between subjects
with common boundaries or vertices, and there is no connection between subjects without
common boundaries or vertices.
The specific elements of the matrix are set, as shown in Table 1below:
Table 1. Spatial weight matrix.
Matrix Name Element Setting Explanation
Adjacency Matri (w1)If there is a common border between province iand
province j, the element is set to 1; otherwise, it is set to 0.
When i=j,wij = 0
Geographic Distance Matrix (w2)The reciprocal of the geographic distance between
province iand province j.
Economic Distance Matrix (w3)The reciprocal of the absolute difference in GDP
between province iand province jin the current year.
4.4. Spatial Correlation Test
To test the spatial autocorrelation of China’s domestic demand, this paper selects the
domestic cycles of 31 provinces (cities and districts) in China from 2011 to 2020, which are
discriminated by Moran’s Iindex with the following formula:
Iict=
n×∑n
i=1∑n
j=1wij icit−icicjt−ic
∑n
i=1∑n
j=1wij ∑n
i=1icit−ic2(2)
Formula,
Iict
represents the Moran’s Iindex of domestic demand in 31 provinces (cities
and districts) of China in year t;t= 2011, 2012, . . ., 2020.
i, j are defined as above.
wij
represents the elements of the spatial matrix for province i
and province j. The element settings are shown in Table 1, and
ic
represents the average of
the domestic demand in each province in year t. The results of the Moran’s Iindex test for
domestic demand for each year are shown in Table 2below:
Table 2. Moran’s Iindex of domestic demand in China for each year.
Year
Adjacency Matrix
w1
Geographic Distance Matrix
w2
Economic Distance Matrix
w3
IZp I Zp I Zp
2011 0.279 2.816 0.002 0.037 2.108 0.018 0.227 3.219 0.001
2012 0.275 2.776 0.003 0.036 2.075 0.019 0.227 3.210 0.001
2013 0.275 2.776 0.003 0.035 2.061 0.020 0.224 3.186 0.001
2014 0.275 2.783 0.003 0.035 2.060 0.020 0.215 3.078 0.001
2015 0.282 2.846 0.002 0.036 2.102 0.018 0.204 2.944 0.002
2016 0.296 2.985 0.001 0.041 2.241 0.013 0.146 2.232 0.013
2017 0.296 2.987 0.001 0.041 2.265 0.012 0.135 2.102 0.018
2018 0.295 2.976 0.001 0.043 2.306 0.011 0.312 2.062 0.020
2019 0.309 3.101 0.001 0.056 2.697 0.003 0.112 1.805 0.036
2020 0.312 3.135 0.001 0.055 2.685 0.004 0.110 1.791 0.037
As shown in Table 2, Moran’s Iindex, calculated with three kinds of spatial weight
matrices, is significantly positive, indicating that there is a positive spatial correlation in
China’s domestic demand, i.e., the domestic demand of each region shows a “high-high”
agglomeration or “low-low” agglomeration, and the gap in the domestic demand between
the neighboring regions is relatively small. In this case, the Moran’s Iindex calculated by
Sustainability 2024,16, 2365 8 of 15
constructing a spatial matrix based on geographic location criteria increased over time,
and the Moran’s Iindex calculated by constructing a spatial weighting matrix based on
the level of economic development decreased slightly over time. This shows that in recent
years, economic exchanges among regions in China have predominantly been influenced
by geographic proximity. Closer geographic distances between regions correlate with
more frequent exchanges, leading to a decreased significance of inter-regional economic
disparities in these exchanges.
4.5. Model Setup
This paper sets up the spatial measurement model as follows:
ic =α0+β0×innoit +β1×w×innoit +δ0×Cit +δ1×w×Cit +µi+θt+λit (3)
In Equation (3), ic is the explanatory variable; inno is the explanatory variable,
β0
is the
coefficient of the explanatory variable, and
β1
is the spatial coefficient of the explanatory
variable; wis the spatial weight matrix; Cis the control variable,
δ0
is the control variable
coefficient, and
δ1
is the control variable spatial coefficient;
µi
stands for individual fixed
effects;
θt
stands for time fixed effects; and
λit
stands for double fixed effects. In practical
tests, specific spatial econometric models also need to be identified through Wald tests and
LM tests.
5. Model Regression and Analysis of Results
5.1. Model Selection
The presence of spatial effects was first determined by the LM test and Robust LM test,
and the use of the spatial Durbin model was determined by the Wald test. The results of
the tests are shown in Table 3.
Table 3. Spatial econometric model selection results.
Test Statistic W1 W2 W3
Test Value p-Value Test Value p-Value Test Value p-Value
LM Error 0.186 0.666 0.221 0.638 0.204 0.651
Robust LM Error 0.280 0.597 0.725 0.394 7.266 0.007
LM Lag 6.844 0.009 3.349 0.067 5.340 0.021
Robust LM Lag 6.937 0.008 3.853 0.050 12.402 0.000
Wald Spatial Lag 23.37 0.0015 47.52 0.0000 58.21 0.0000
Wald Spatial Error
124.78 0.0000 39.28 0.0000 37.66 0.0000
From Table 2above, the comprehensive three spatial weight matrix results, the LM
test, and the Robust LM test indicate that spatial econometric analysis should be carried
out, and the Wald test results indicate that the test results of the spatial lag model and
spatial error model satisfy the requirement of 1% significance, i.e., spatial Durbin model
will not degenerate into the spatial lag model and spatial error model; therefore, this paper
chooses the spatial Durbin model to test the data.
5.2. Baseline Regression Results
The results of benchmark regression are shown in Table 4below.
Sustainability 2024,16, 2365 9 of 15
Table 4. Spatial Durbin model benchmark regression results.
Variables
W1 W2 W3
SDM SDM SDM
DD DD DD
Inno 0.1811 ***
(0.0321)
0.1813 ***
(0.0328)
0.1081***
(0.0333)
Inandout 0.1765 ***
(0.0619)
0.1706 ***
(0.0594)
0.2295 ***
(0.0617)
Fdi −0.0451 ***
(0.0103)
−0.0442 ***
(0.0104)
−0.0190 *
(0.0109)
Urban 0.3334 ***
(0.0680)
0.2409 ***
(0.6677)
0.3604 ***
(0.0709)
Gov 0.6064 ***
(0.0313)
0.6519 ***
(0.0298)
0.6585 ***
(0.0322)
W×Inno −0.1161 *
(0.0631)
−0.4085 **
(0.2007)
0.2565 **
(0.1052)
W×Inandout 0.2389 **
(0.1138)
0.5501 *
(0.3222)
0.2284
(0.1647)
W×Fdi 0.0678 ***
(0.0207)
0.0660
(0.0669)
0.0602 **
(0.0248)
W×Urban −0.2698 ***
(0.0932)
0.0328
(0.3789)
−0.9321 ***
(0.2109)
W×gov 0.2462 ***
(0.0857)
1.5310 ***
(0.2757)
0.0298
(0.1531)
ϱ0.1156
(0.0777)
−0.2808
(0.2232)
−0.2284 **
(0.1095)
σ23.4777 ***
(0.2783)
3.3262 ***
(0.2651)
3.5222 ***
(0.2838)
Fixed/Random Fixed Fixed Fixed
OBS 310 310 310
R20.7222 0.6299 0.6300
Note: *, **, and *** indicate that the variables are significant at the 10%, 5%, and 1% levels, respectively, with
standard errors in parentheses. W1 is the adjacency matrix, W2 is the geographic distance matrix, and W3 is the
economic distance matrix, as follows.
As shown in Table 4, the regression results of the model using the adjacency matrix
and the geographic distance matrix as the spatial weight matrix show that the coefficient
of the impact of innovation on the promotion effect of domestic demand in the region is
significantly positive at the 1% level. In contrast, the coefficient of the spatial spillover
effect of innovation on domestic demand is negative.
The results show that innovation significantly contributes to the level of domestic
demand in the region under the conditions of the adjacency matrix and the geographic
distance matrix as a spatial weighting matrix, while innovative activities in the neighboring
regions have a certain inhibiting effect on the domestic demand in the region. For the
economic distance matrix, it can be seen that the promotion effect of innovation on domestic
demand is significantly positive at the 1% level, and innovation significantly enhances the
level of domestic demand in the region, while the coefficient of the spatial spillover effect
of innovation on domestic demand meets the requirement of significance and is positive,
which indicates that the innovation behavior of the surrounding areas also has a significant
effect on the promotion of the level of domestic demand in the region.
The above results show that when the spatial externality of innovation on domestic
demand is measured by geographic location, the spatial externality of innovation on
domestic demand is negative under the adjacency matrix and the geographic distance
matrix, because when the innovation results of the region have a positive effect on the
domestic demand of the region, they will generate spatial spillover effects along the
transportation network to the surrounding regions, attracting the domestic demand of the
surrounding regions to be transferred to the region, thereby inhibiting the domestic demand
Sustainability 2024,16, 2365 10 of 15
of the surrounding regions, which mainly unfolds along the transportation infrastructure
network.
When economic development levels serve as the spatial weighting matrix, inter-
regional exchanges primarily depend on economic development levels. Closer exchanges
occur between regions with similar economic development levels. Innovation drives
domestic demand expansion within regions, attracting the introduction of innovative
technologies from similarly developed regions, thus further stimulating domestic demand.
5.3. Decomposition of Spatial Effects
The analysis results indicate that innovation plays a significant role in promoting
domestic demand within a region, and there is considerable spatial externality present.
Here, partial differential equations are used to further calculate the direct, indirect, and total
effects of innovation on domestic demand. Due to space constraints, only the decomposed
effects under the economic distance matrix are listed, as shown in Table 5.
Table 5. Spatial effects decomposition.
Variables Direct Effect Indirect Effect Total Effect
W3 W3 W3
Inno 0.1020 ***
(0.0341)
0.2045 **
(0.0928)
0.3065 ***
(0.1004)
Inandout 0.2212 ***
(0.0596)
0.1399
(0.1398)
0.3611 **
(0.1516)
Fdi −0.0198 *
(0.0106)
0.0546 ***
(0.0209)
0.0349
(0.0231)
Urban 0.3909 ***
(0.0702)
−0.8455 ***
(0.1819)
−0.4545 ***
(0.1703)
Gov 0.6623 ***
(0.0298)
−0.1033
(0.0951)
0.5590 ***
(0.1060)
Note: *, **, and *** signify that the variables are significant at the 10%, 5%, and 1% levels, respectively, with
standard errors in parentheses. W1 represents the adjacency matrix, W2 is the geographic distance matrix, and
W3 is the economic distance matrix, as stated below.
From the perspective of the core explanatory variables, the direct effect coefficient of
regional innovation is 0.1020, which is significant at the 1% confidence level, indicating
that innovation significantly promotes domestic demand in the region. The indirect effect
coefficient of regional innovation is 0.2045, which is significant at the 5% confidence level,
suggesting that innovation also has significant spatial externality on the domestic demand
of surrounding areas. Further analysis reveals that the total effect coefficient of regional
innovation is 0.3065, with the spillover effect of innovation on domestic demand accounting
for 66.92% of the total effect, indicating that the indirect effects are predominant. The spatial
spillover effect of innovation on domestic demand should be taken into account.
In terms of control variables, the direct effects of the levels of import/export, urban-
ization, and government policy are 0.2212, 0.3909, and 0.6623, respectively, which are all
significant at the 1% confidence level, indicating that all three also significantly promote
domestic demand in the region. However, the direct effect coefficient of the level of exter-
nal investment is
−
0.0198, which is significant at the 10% level, suggesting a significant
inhibitory effect on domestic demand. The indirect effect coefficient for the level of external
investment is 0.0546, which is significant at the 1% level, indicating significant spatial exter-
nality on the domestic demand of neighboring areas. The indirect effect coefficients for the
levels of import/export and government policy are 0.1399 and
−
0.1033, respectively, with
no significant indirect effects. The total effect coefficients for the levels of import/export
and government policy are 0.3611 and 0.5590, respectively, with both predominantly con-
tributed by direct effects, where the level of import/export accounts for 61.26% of the total
effect, indicating that the direct impact of import/export levels and government policy on
domestic demand should be considered. The total effect coefficient for urbanization level is
−
0.4545, with the indirect effects being predominant, suggesting that the inhibitory effect
Sustainability 2024,16, 2365 11 of 15
of urbanization level on the spatial spillover of domestic demand should be considered.
The total effect coefficient for the level of external investment is 0.0349, but it not significant,
indicating that the direct effects are predominant (Figure 2).
Sustainability 2024, 16, x FOR PEER REVIEW 11 of 15
level is −0.4545, with the indirect effects being predominant, suggesting that the inhibitory
effect of urbanization level on the spatial spillover of domestic demand should be consid-
ered. The total effect coefficient for the level of external investment is 0.0349, but it not
significant, indicating that the direct effects are predominant (Figure 2).
Figure 2. Spatial effects decomposition.
5.4. Mechanism Testing
To further illustrate the promotion mechanism of innovation on domestic demand,
this paper refines the concept of domestic demand into consumption demand and invest-
ment demand and examines the promotion effect of innovation on consumption demand
and investment demand, respectively.
The results of the mechanism test are presented in Table 6.
Table 6. Spatial Durbin model mechanism test results.
Variables
W1 W2 W3 W1 W2 W3
SDM SDM SDM SDM SDM SDM
Cons Cons Cons Invest Invest Invest
Inno 0.1441 ***
(0.0281)
0.1653 ***
(0.0309)
0.0674 **
(0.0315)
−0.0670
(0.0610)
0.0150
(0.0629)
−0.1265 *
(0.0651)
Inandout 0.13905 ***
(0.0523)
0.1480 ***
(0.0544)
0.2257 ***
(0.0586)
−0.1583
(0.1123)
−0.1881 *
(0.1077)
−0.1778
(0.1223)
Fdi 0.0128
(0.0089)
0.0228 **
(0.0097)
0.0161
(0.0102)
0.1338 ***
(0.0191)
0.1407 ***
(0.0190)
0.0916 ***
(0.0211)
Urban −0.6636 ***
(0.0591)
−0.7631 ***
(0.0613)
−0.6577 ***
(0.0678)
1.2157 ***
(0.1268)
1.2986 ***
(0.1211)
0.6458 ***
(0.1427)
Gov 0.0154
(0.0388)
−0.0017
(0.0444)
−0.0295
(0.0113)
0.5302 ***
(0.0829)
0.5106 ***
(0.0874)
0.3056 ***
(0.0954)
W×nno 0.0009
(0.0568)
−0.1905
(0.1824)
0.3865 ***
(0.0973)
0.7334 ***
(0.1130)
2.4150 ***
(0.3598)
−0.0376
(0.2033)
W×Inandout −0.0577
(0.0984)
0.1970
(0.2990)
0.0680
(0.1550)
−0.6354 ***
(0.2112)
−1.3379 **
(0.5913)
−0.2310
(0.3223)
W×Fdi 0.0345 **
(0.0177)
0.2591 ***
(0.0616)
0.0368
(0.0232)
−0.0371
(0.0384)
−0.0517
(0.1208)
0.1186 **
(0.0512)
W×Urban 0.1641 ** 0.3549 −0.4432 ** −0.7182 *** −0.4316 −0.3242
Figure 2. Spatial effects decomposition.
5.4. Mechanism Testing
To further illustrate the promotion mechanism of innovation on domestic demand, this
paper refines the concept of domestic demand into consumption demand and investment
demand and examines the promotion effect of innovation on consumption demand and
investment demand, respectively.
The results of the mechanism test are presented in Table 6.
The results of the economic distance matrix regression are analyzed here, since the
level of model fit is better under the economic distance matrix. The regression results with
consumption demand as the explanatory variable show that the enhancement of innovation
ability significantly promotes the level of consumption demand in the region, and from the
perspective of spatial spillover effect, the spatial lag coefficient
ϱ
passes the test of the 1%
significance level and is positive, which indicates that there is significant positive spatial
externality in inter-regional consumption demand, and there is a significant spatial spillover
effect of innovation on consumption demand. The reason for this is that when innovative
behavior in the region produces innovative results, on the one hand, it will provide high-
quality products to the local market and stimulate the consumption demand of the region’s
residents. On the other hand, the benefits brought by the output of innovation results
will raise the income level of the residents and increase the consumption enthusiasm
of the residents in the region. This leads to the expansion of consumer demand in the
region, both in terms of product supply and market demand. The spatial externalities of
innovation on consumer demand come mainly from the cross-regional sales of innovations;
when innovations are sold in markets outside the region through transportation networks,
they push to unleash the potential of domestic demand in neighboring regions, which,
in turn, expands domestic demand in neighboring regions. With investment demand
as the explanatory variable, innovation has a dampening effect on regional investment
demand, and the spatial coefficient of innovation turns out to be insignificant. This may
be because the innovation process requires a large amount of capital and manpower,
Sustainability 2024,16, 2365 12 of 15
among other innovative factors. Therefore, when the innovation capacity of the local area
improves, it will obtain high-quality innovation resources from various aspects through
China’s transportation infrastructure, rather than just obtaining single innovation resources
from the local area. Therefore, innovation will, to some extent, suppress local investment
demand while also generating negative spatial externalities for investment behavior in
surrounding areas.
Table 6. Spatial Durbin model mechanism test results.
Variables
W1 W2 W3 W1 W2 W3
SDM SDM SDM SDM SDM SDM
Cons Cons Cons Invest Invest Invest
Inno 0.1441 ***
(0.0281)
0.1653 ***
(0.0309)
0.0674 **
(0.0315)
−0.0670
(0.0610)
0.0150
(0.0629)
−0.1265 *
(0.0651)
Inandout 0.13905 ***
(0.0523)
0.1480 ***
(0.0544)
0.2257 ***
(0.0586)
−0.1583
(0.1123)
−0.1881 *
(0.1077)
−0.1778
(0.1223)
Fdi 0.0128
(0.0089)
0.0228 **
(0.0097)
0.0161
(0.0102)
0.1338 ***
(0.0191)
0.1407 ***
(0.0190)
0.0916 ***
(0.0211)
Urban −0.6636 ***
(0.0591)
−0.7631 ***
(0.0613)
−0.6577 ***
(0.0678)
1.2157 ***
(0.1268)
1.2986 ***
(0.1211)
0.6458 ***
(0.1427)
Gov 0.0154
(0.0388)
−0.0017
(0.0444)
−0.0295
(0.0113)
0.5302 ***
(0.0829)
0.5106 ***
(0.0874)
0.3056 ***
(0.0954)
W×nno 0.0009
(0.0568)
−0.1905
(0.1824)
0.3865 ***
(0.0973)
0.7334 ***
(0.1130)
2.4150 ***
(0.3598)
−0.0376
(0.2033)
W
×
Inandout
−0.0577
(0.0984)
0.1970
(0.2990)
0.0680
(0.1550)
−0.6354 ***
(0.2112)
−1.3379 **
(0.5913)
−0.2310
(0.3223)
W×Fdi 0.0345 **
(0.0177)
0.2591 ***
(0.0616)
0.0368
(0.0232)
−0.0371
(0.0384)
−0.0517
(0.1208)
0.1186 **
(0.0512)
W×Urban 0.1641 **
(0.0765)
0.3549
(0.3575)
−0.4432 **
(0.2134)
−0.7182 ***
(0.1644)
−0.4316
(0.6860)
−0.3242
(0.4619)
W×gov −0.3964 ***
(0.0872)
−0.8910 **
(0.3526)
−0.3665 **
(0.1712)
0.4044 **
(0.1864)
1.0570
(0.7304)
−0.5452
(0.3628)
ϱ0.4519 ***
(0.0800)
0.3334 **
(0.1663)
0.2833 ***
(0.0997)
−0.0320
(0.0817)
−0.6104 **
(0.2616)
−0.5151 ***
(0.1197)
σ22.4034 ***
(0.1982)
2.6842 ***
(0.2175)
2.9521 ***
(0.2416)
10.9567 ***
0.8813
10.4831 ***
(0.8451)
12.8695 ***
(1.0553)
Fixed/Random
Fixed Fixed Fixed Fixed Fixed Fixed
OBS 310 310 310 310 310 310
R20.5468 0.4299 0.6920 0.7398 0.6621 0.7615
Note: *, **, and *** indicate that the variables are significant at the 10%, 5%, and 1% levels, respectively, with
standard errors in parentheses, W1 is the adjacency matrix, W2 is the geographic distance matrix, and W3 is the
economic distance matrix.
In summary, innovation not only significantly promotes local consumption demand
but also generates significant spatial externalities for consumption demand in surrounding
areas. As for investment demand, innovation suppresses local investment demand, failing
to meet significant requirements for investment demand in surrounding areas.
6. Conclusions and Recommendations
6.1. Conclusions of the Study
The construction of a new development pattern of a double cycle, with “the domestic
macrocycle as the main focus and the domestic and international double cycle promoting
each other”, provides a suitable narrative background for the study of the influence mecha-
nism and spatial externality between regional innovation and domestic demand. Based on
absorbing the existing theoretical results on innovation and domestic demand, this paper
elaborates on the interactive mechanism of innovation to promote the spatial externality
of domestic demand from a macro perspective and constructs a theoretical model. At the
same time, using the spatial panel data of 31 provinces (cities and districts) in China from
2011 to 2020, the spatial Durbin model is used to carry out empirical tests and decompose
Sustainability 2024,16, 2365 13 of 15
its spatial spillover effects. Finally, the mechanism of innovation to promote the expansion
of domestic demand is tested.
The main conclusions are as follows: (1) theoretical research in new economic geog-
raphy indicates that innovation plays a dual role in boosting China’s domestic demand.
On the supply side, innovation drives internal circulation by introducing new products to
the market. On the demand side, it stimulates domestic demand by increasing population
income. Furthermore, innovation and domestic demand generate spatial externalities
in neighboring regions through spatial spillovers; (2) the empirical results demonstrate
that innovative behavior significantly stimulates domestic demand expansion within the
region under all three spatial weight matrices. While the adjacency matrix and geographic
distance matrix exhibit a certain inhibitory effect of innovation on neighboring regions’
domestic demand, the economic distance matrix shows a significant promotional effect of
innovation on neighboring regions’ domestic demand; (3) analyzing the spatial spillover
effect of innovation on domestic demand, the results reveal that the indirect effect, termed
spatial externality, contributes 66.92% to the total effect. This suggests that spatial spillover
plays a significant role in promoting domestic demand and warrants further emphasis in
research; (4) through the refinement of domestic demand into consumption demand and
investment demand and research on the mechanism of innovation to promote domestic
demand, the results show that the promotion effect of innovation on domestic demand is
mainly realized through consumption demand, and innovation has a significant promotion
effect on the consumption demand of the region and the surrounding areas, while innova-
tion has a certain inhibitory effect on the investment demand of the region, and the effect
on the investment demand of the surrounding areas is not significant. Overall, innovation
promotes the spatial externality of our domestic demand, which in turn has a positive effect
on our domestic demand.
6.2. Policy Recommendations
1.
Adhering to the innovation-driven development strategy: Continuously shaping new
momentum for development by adhering to the central position of innovation in
the overall situation of China’s modernization, we will enhance people’s well-being
through innovation, upgrade the industrial chain, ensure the stability of China’s
supply chain, provide the sustained impetus for China’s economic development, pro-
mote the expansion of domestic demand through the enhancement of the capacity for
innovation, and provide favorable support for the construction of a new development
pattern of the “double cycle”.
2.
Continuing to actively build a new development pattern that “focuses on the domestic
cycle”: Grasping the dominant position of the domestic cycle in the double cycle
and insisting on the expansion of domestic demand as the basis for development.
Governments at all levels and in all parts of the world, in the process of realizing the
expansion of domestic demand, will not only have a positive impact on the region but
also have a positive effect on neighboring regions. Specifically, governments should
face up to the differences in development between regions and implement innovative
strategies to promote the expansion of domestic demand in the region according to
local conditions, such as for the eastern region, because the level of innovation and
domestic demand have a great advantage over inland regions, so it is necessary to
pay attention to the spatial externality that arises from the process of expanding the
region’s domestic demand to promote the realization of the expansion of the inland
region’s domestic demand through spatial spillover effects.
3.
Emphasize the centrality of consumer demand to domestic demand: Although do-
mestic demand consists of consumption demand and investment demand, in practice,
the expansion of domestic demand is mainly realized through consumption demand,
so in the process of building the domestic cycle in the future, we should actively grasp
the role of consumption demand for the domestic cycle, taking advantage of the fact
that our country has the world’s largest middle-income group, and focus on unleash-
Sustainability 2024,16, 2365 14 of 15
ing China’s huge potential for domestic demand and realizing a two-way drive of the
demand side and the supply side through consumption demand to ultimately help
China’s domestic demand expansion.
7. Limitations of the Study and Future Research Directions
This study investigates the impact of regional innovation on China’s domestic demand,
facing limitations due to data availability only up to 2020. This restricts our ability to ana-
lyze recent global economic shifts and their influences comprehensively. While employing
provincial panel data and the spatial Durbin model offers a substantial overview, it may
not entirely encapsulate the intricate dynamics and externalities associated with regional
innovation. Future research should aim to broaden the temporal and spatial scope of
analysis, integrate more varied datasets, and delve into the implications of digitalization
and technological progress on innovation ecosystems. Furthermore, conducting compara-
tive analyses across diverse economic and policy landscapes would greatly enhance our
understanding of the universal relevance and consequences of our findings, facilitating
a deeper and more detailed perspective on the role of regional innovation in economic
development globally.
Author Contributions: Writing—original draft, Y.S.; Writing—review & editing, Y.J., C.X. and C.L.
All authors have read and agreed to the published version of the manuscript.
Funding: This research was supported by the National Natural Science Foundation of China (Youth
Fund) Project (grant number 70901065).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on request from the
corresponding author.
Conflicts of Interest: The authors declare no conflict of interest.
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