# The Impact of Land Transportation Integration on Service Agglomeration in Yangtze River Delta Urban Agglomeration

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## Abstract

**:**

## 1. Introduction

## 2. Literature Review and Research Hypothesis

**Hypothesis**

**1.**

**Hypothesis**

**2.**

## 3. Background

#### 3.1. Transportation Development of Urban Agglomeration in the Yangtze River Delta

#### 3.2. Overview of Service Industry Development in Yangtze River Delta Urban Agglomeration

## 4. Methods

#### 4.1. Land Transportation Integration Evaluation Index System of Yangtze River Delta Urban Agglomeration

- In-degree centrality and out-degree centrality are measured by degree centrality. The formula is ${C}_{D}(i)={k}_{i}$, where ${k}_{i}$ is the degree of city $i$, which generally refers to the number of cities $i$ with direct rail connections in this study. Degree centrality refers to the number of points directly connected to a specific point, and measures the central position of a node in the network. The higher the degree centrality, the more nodes that are directly connected to the point, and the more central the point is. When the connection has a given direction, degree centrality can be divided into in-degree centrality and out-degree centrality [13].
- The formula of eigenvector centrality is ${C}_{{E}_{i}}={k}_{i}=c{\displaystyle \sum _{j=1}^{g}{a}_{ij}{k}_{j}}$, where ${k}_{i}$ is the degree of city $i$ [13].
- The formula of betweenness centrality is ${C}_{{B}_{i}}={\displaystyle \sum _{j=1}^{N}{\displaystyle \sum _{k=1}^{j-1}\frac{{\phi}_{jk}(i)}{{\phi}_{jk}}}};j\ne k\ne i,j<k$, where ${\phi}_{jk}$ is the number of shortest paths between cities, ${\phi}_{jk}(i)$ is the number of shortest paths through city $i$ between city $j$ and city $k$, and $\frac{{\phi}_{jk}(i)}{{\phi}_{jk}}$ refers to the mediations of city $i$ relative to city $j$ and city $k$ [13].
- The formula of closeness centrality is ${C}_{{C}_{i}}={\displaystyle \sum _{j=1}^{N}\frac{1}{{d}_{ij}}}$, where ${d}_{ij}$ is the shortest path length between city $i$ and city $j$ [13].
- Highway passenger capacity and highway freight volume can be obtained according to the statistical yearbook.
- The formula of effective average travel time [31] is ${A}_{i}={\displaystyle \sum _{j=1}^{n}\left({T}_{ij}\ast {M}_{j}\right)}/{\displaystyle \sum _{j=1}^{n}{M}_{j}}$, where ${T}_{ij}$ is the shortest travel time between two cities based on the land transport network, and ${M}_{j}$ is the economic quality of city $j$, which is measured by the square root of the product of the city’s GDP and population.
- The formula of economic potential is ${P}_{i}={\displaystyle \sum _{j=1}^{n}\frac{{M}_{j}}{{T}_{ij}}}$, where ${T}_{ij}$ is the shortest travel time between two cities based on the land transport network, and ${M}_{j}$ is the economic quality of city $j$, which is measured by the square root of the product of the city’s GDP and population.

- First, all indicators need to be de-dimensionalized;
- Then, we use the entropy weight method to assign weights to the traffic network structure index and the traffic network function index and calculate the traffic network structure index and the traffic network function index;
- The weighted equalization method is used to calculate the traffic integration index. The weight of both the traffic network structure index and the traffic network function index is 0.5.

#### 4.2. The Spatial Durbin Model for Hypothesis 1

- Explained variable. The main indicators to measure the level of industrial agglomeration include location entropy, Herfindahl index and Gini coefficient, etc. Considering the availability and computability of statistical data, service industry location entropy (SE) is adopted in this study to reflect the degree of urban industrial agglomeration. The SE calculation formula is $S{E}_{it}=\frac{{q}_{it}/{Q}_{it}}{{q}_{t}/{Q}_{t}}$, where ${q}_{it}$ is the service industry GDP of city $i$ in year $t$, ${Q}_{it}$ is the GDP of city $i$ in year $t$, ${q}_{t}$ is the national service industry GDP in year $t$, and ${Q}_{t}$ is the national GDP in year $t$. According to the requirements of the relevant state documents for service industry classification (http://www.gov.cn/zhengce/content/2012-12/12/content_3943.htm, accessed on 31 December 2020) and the development of the Yangtze River Delta urban agglomeration’s producer service industries, we choose the wholesale and retail services (WH), transportation, warehousing and postal services (TR), information transmission, software and information technology services (In), scientific research and technical services (SC), finance (FI), and the leasing and commercial services industry (LE) as the area of research of producer service industries. To explore the producer service industries’ locational entropy, we take the wholesale and retail services (WH) as an example: $W{H}_{it}=\frac{{s}_{it}/{S}_{it}}{{s}_{t}/{S}_{t}}$, where ${s}_{it}$ is the number of people employed in the wholesale and retail services of city $i$ in year $t$, ${S}_{it}$ is the number of people employed in the producer service industries of city $i$ in year $t$, ${s}_{t}$ is the national number of people employed in the wholesale and retail services in year $t$, and ${S}_{t}$ is the national number of people employed in producer service industries in year $t$.
- Core explanatory variable. The urban agglomeration transportation integration level (TI) is the core explanatory variable, and its related definition and calculation formula are detailed in Section 4.1.
- Control variables. The control variables include government investment intensity (Fin), level of market size (Ma), level of labor (Wo), and level of foreign trade (Ex). In this study, these control variables are measured by the ratio of general public financial expenditure to GDP of each city, the ratio of total retail sales of consumer goods to GDP in each city, the value of labor force in each city, and the ratio of total export to GDP in each city [32].
- Spatial weight matrix. In this study, the geographical adjacency spatial weight matrix is constructed as follows: ${\varpi}_{ij}^{1}=\{\begin{array}{c}{\varpi}_{ij}^{1}=1,i\text{}\mathrm{is}\text{}\mathrm{adjacent}\text{}\mathrm{to}\text{}j\\ {\varpi}_{ij}^{1}=0,i\text{}\mathrm{isn}\u2019\mathrm{t}\text{}\mathrm{adjacent}\text{}\mathrm{to}\text{}j\text{}\mathrm{or}\text{}i=j\end{array}$ [33].

#### 4.3. The Spatial Durbin Model for Hypothesis 2

#### 4.4. Data Set

- Railway time data. In terms of time scale, since the high-speed rail in the Yangtze River Delta urban agglomeration was rolled out year by year at the municipal level from 2011, and considering the impact of COVID-19, the time ranges of this study were selected as 2011–2019 and 2011–2020. The relevant data from the high-speed railway were partly derived from the National Railway Passenger Train Schedule from 2011 to 2016, and the railway time data from 2017 to 2020 were taken from the official website of the National Railway Corporation (www.12306.cn, accessed from 1 January 2017 to 31 December 2020).
- Road time data. The road time used came from the Baidu-related database.
- Highway passenger capacity and highway freight volume were obtained from the Statistical Yearbook of Chinese Cities (2012–2021) and the statistical yearbook of provinces and cities.
- Economic data. The economic data used were the economic data of 41 cities at the prefecture level or above from 2011 to 2020, all from the database of the National Bureau of Statistics, China Urban Statistical Yearbook (2012–2021) and the relevant provincial statistical yearbook from 2011 to 2020.

## 5. Results

#### 5.1. Result of the Land Transportation Integration Index

#### 5.2. Result of the Spatial Durbin Model for Hypothesis 1

#### 5.2.1. Spatial Autocorrelation Test

#### 5.2.2. LM Test

#### 5.2.3. The Empirical Analysis

#### 5.3. Result of the Spatial Durbin Model for Hypothesis 2

#### 5.3.1. Spatial Autocorrelation Test

#### 5.3.2. LM Test

#### 5.3.3. The Empirical Analysis

## 6. Discussion

## 7. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 1.**Spatial distribution map of highway mileage in the Yangtze River Delta urban agglomeration in 2020.

**Figure 2.**Spatial distribution map of expressway mileage in the Yangtze River Delta Urban agglomeration in 2020.

**Figure 3.**Spatial distribution map of land transportation integration value in the Yangtze River Delta urban agglomeration in 2020.

Year | Yangtze River Delta Urban Agglomeration (CNY Billion) | Percentage of the National GDP (%) |
---|---|---|

2011 | 11,593 | 23.76 |

2012 | 12,612 | 23.42 |

2013 | 13,737 | 23.17 |

2014 | 14,968 | 23.26 |

2015 | 16,013 | 23.25 |

2016 | 17,723 | 23.74 |

2017 | 19,529 | 23.47 |

2018 | 21,148 | 23.00 |

2019 | 23,725 | 24.05 |

2020 | 24,471 | 24.09 |

Indicator | Highway | Railway |
---|---|---|

Passenger volume (million passengers) | 1306 | 490 |

Passenger turnover (billion passenger-kilometers) | 83 | 159 |

Freight volume (million tons) | 6538 | 213 |

Freight turnover (billion ton-kilometers) | 983 | 131 |

Year | Shanghai (CNY Billion) | Jiangsu (CNY Billion) | Zhejiang (CNY Billion) | Anhui (CNY Billion) |
---|---|---|---|---|

2011 | 1114.29 | 2084.22 | 1418.02 | 497.60 |

2012 | 1219.92 | 2351.80 | 1568.11 | 562.85 |

2013 | 1344.51 | 2642.16 | 1733.72 | 628.68 |

2014 | 1527.57 | 3059.95 | 1922.08 | 737.87 |

2015 | 1702.26 | 3408.59 | 2134.19 | 860.21 |

2016 | 1966.29 | 3869.16 | 2409.16 | 1001.83 |

2017 | 2119.15 | 4316.97 | 2760.23 | 1159.75 |

2018 | 2284.30 | 4720.52 | 3072.43 | 1352.67 |

2019 | 2775.23 | 5106.47 | 3368.78 | 1886.04 |

2020 | 2830.75 | 5395.58 | 3603.12 | 1982.43 |

**Table 4.**Land transportation integration evaluation index system of Yangtze River Delta Urban agglomeration.

The Target Layer | Rule Layer | Index Layer |
---|---|---|

Urban agglomeration land transportation integration evaluation index | A1 Transportation network structure | A11 In-degree Centrality |

A12 Out-degree Centrality | ||

A13 Eigenvector centrality | ||

A14 Betweenness centrality | ||

A15 Closeness centrality | ||

A2 Transportation network function | A21 Highway passenger capacity | |

A22 Highway freight volume | ||

A23 Effective average travel time | ||

A24 Economic potential |

**Table 5.**Calculation results of the land transportation integration index in the Yangtze River Delta Urban agglomeration (2011–2020).

Year | Transportation Structure Index | Transportation Function Index | Transportation Integration Index |
---|---|---|---|

2011 | 0.5291 | 0.3780 | 0.5636 |

2012 | 0.5291 | 0.3871 | 0.5682 |

2013 | 0.5291 | 0.3946 | 0.5720 |

2014 | 0.5291 | 0.3895 | 0.5694 |

2015 | 0.5311 | 0.3801 | 0.5695 |

2016 | 0.5434 | 0.3762 | 0.5707 |

2017 | 0.5492 | 0.3993 | 0.5866 |

2018 | 0.5549 | 0.3900 | 0.5823 |

2019 | 0.5608 | 0.3958 | 0.5871 |

2020 | 0.5723 | 0.3742 | 0.5772 |

**Table 6.**Land transportation integration index of cities in Yangtze River Delta urban agglomeration in 2020.

Rank | City | Transportation Structure Index | Rank | City | Transportation Function Index | Rank | City | Transportation Integration Index |
---|---|---|---|---|---|---|---|---|

1 | Shanghai | 1.0000 | 1 | Shanghai | 0.8379 | 1 | Shanghai | 0.9189 |

2 | Nanjing | 1.0000 | 2 | Suzhou (Jiangsu) | 0.7383 | 2 | Hangzhou | 0.8613 |

3 | Hangzhou | 1.0000 | 3 | Hangzhou | 0.7227 | 3 | Hefei | 0.8351 |

4 | Xuzhou | 0.9298 | 4 | Hefei | 0.6980 | 4 | Suzhou (Jiangsu) | 0.8179 |

5 | Jiaxing | 0.9298 | 5 | Ningbo | 0.6417 | 5 | Nanjing | 0.8177 |

6 | Hefei | 0.8956 | 6 | Nanjing | 0.6355 | 6 | Xuzhou | 0.7801 |

7 | Wenzhou | 0.8517 | 7 | Xuzhou | 0.5858 | 7 | Ningbo | 0.7329 |

8 | Zhenjiang | 0.8318 | 8 | Wuxi | 0.5549 | 8 | Wuxi | 0.7262 |

9 | Wuhu | 0.8248 | 9 | Fuyang | 0.5089 | 9 | Jiaxing | 0.7036 |

10 | Wuxi | 0.7133 | 10 | Bengbu | 0.4767 | 10 | Fuyang | 0.6902 |

11 | Suzhouj | 0.7133 | 11 | Jiaxing | 0.4329 | 11 | Bengbu | 0.6854 |

12 | Changzhou | 0.7133 | 12 | Changzhou | 0.4303 | 12 | Changzhou | 0.6639 |

13 | Huainan | 0.6615 | 13 | Shaoxing | 0.4133 | 13 | Zhenjiang | 0.6367 |

14 | Bengbu | 0.6615 | 14 | Chuzhou | 0.4045 | 14 | Wuhu | 0.6257 |

15 | Yanzhou | 0.6457 | 15 | Huzhou | 0.3957 | 15 | Jinghua | 0.6142 |

16 | Fuyang | 0.6352 | 16 | Liuan | 0.3830 | 16 | Huainan | 0.6118 |

17 | Huaian | 0.6045 | 17 | Jinghua | 0.3809 | 17 | Wenzhou | 0.6117 |

18 | Jinghua | 0.5836 | 18 | Suzhoua | 0.3698 | 18 | Huzhou | 0.5998 |

19 | Chizhou | 0.5718 | 19 | Zhenjiang | 0.3473 | 19 | Chuzhou | 0.5808 |

20 | Huzhou | 0.5545 | 20 | Huainan | 0.3295 | 20 | Xuancheng | 0.5748 |

21 | Ningbo | 0.5449 | 21 | Xuancheng | 0.3266 | 21 | Suzhoua | 0.5629 |

22 | Xuancheng | 0.5279 | 22 | Nantong | 0.3230 | 22 | Yanzhou | 0.5614 |

23 | Liuan | 0.5114 | 23 | Taizhou | 0.3158 | 23 | Liuan | 0.5612 |

24 | Lianyungang | 0.4835 | 24 | Maanshan | 0.3067 | 24 | Shaoxing | 0.5506 |

25 | Anqing | 0.4746 | 25 | Wuhu | 0.3050 | 25 | Taizhou | 0.5136 |

26 | Chuzhou | 0.4628 | 26 | Yanzhou | 0.2923 | 26 | Chizhou | 0.5023 |

27 | Tongling | 0.4492 | 27 | Bozhou | 0.2908 | 27 | Huaian | 0.5019 |

28 | Suzhoua | 0.4491 | 28 | Quzhou | 0.2864 | 28 | Nantong | 0.4872 |

29 | Lishui | 0.4436 | 29 | Yancheng | 0.2864 | 29 | Anqing | 0.4840 |

30 | Huangshan | 0.4423 | 30 | Wenzhou | 0.2752 | 30 | Lianyungang | 0.4783 |

31 | Nantong | 0.4176 | 31 | Taizhou (Jiangsu) | 0.2467 | 31 | Quzhou | 0.4756 |

32 | Huaibei | 0.3982 | 32 | Lianyungang | 0.2396 | 32 | Bozhou | 0.4679 |

33 | Taizhouj | 0.3950 | 33 | Huaian | 0.2381 | 33 | Tongling | 0.4643 |

34 | Taizhou | 0.3902 | 34 | Suqian | 0.2215 | 34 | Maanshan | 0.4547 |

35 | Shaoxing | 0.3446 | 35 | Anqing | 0.2103 | 35 | Lishui | 0.4520 |

36 | Bozhou | 0.3115 | 36 | Chizhou | 0.1994 | 36 | Taizhou (Jiangsu) | 0.4382 |

37 | Quzhou | 0.3011 | 37 | Tongling | 0.1931 | 37 | Huangshan | 0.4348 |

38 | Maanshan | 0.2854 | 38 | Huangshan | 0.1754 | 38 | Huaibei | 0.4111 |

39 | Suqian | 0.2824 | 39 | Lishui | 0.1483 | 39 | Yancheng | 0.3818 |

40 | Yancheng | 0.2255 | 40 | Huaibei | 0.1308 | 40 | Suqian | 0.3714 |

41 | Zhoushan | 0.0000 | 41 | Zhoushan | 0.0426 | 41 | Zhoushan | 0.0213 |

**Table 7.**Spatial autocorrelation test of service industry location entropy under the geographical adjacency spatial weight matrix.

Year | Moran Index | Year | Moran Index |
---|---|---|---|

2011 | 0.2619 *** | 2016 | 0.1973 *** |

2012 | 0.3173 *** | 2017 | 0.1624 *** |

2013 | 0.3245 *** | 2018 | 0.2093 *** |

2014 | 0.3130 *** | 2019 | −0.0594 |

2015 | 0.2758 *** | 2020 | −0.0287 |

Test Parameters | Statistics (2011–2019) | Statistics (2011–2020) |
---|---|---|

LM-ERR | 0.54 | 125.02 *** |

LM-LAG | 3.16 ** | 64.18 *** |

RLM-ERR | 0.88 | 61.59 *** |

RLM-LAG | 3.50 * | 0.75 |

Variable | 2011–2019 | 2011–2020 |
---|---|---|

λ | 0.0359 *** | 0.0406 *** |

TI | −0.0553 | −0.0326 |

Fin | 0.0839 *** | 0.0911 *** |

Con | 0.3703 *** | 0.3252 *** |

Wo | 0.0148 | 0.0156 |

Ex | 0.0176 ** | 0.0213 *** |

W*Ti | −0.0297 ** | −0.0283 * |

W*Fin | −0.0522 *** | −0.0524 *** |

W*Con | 0.0231 *** | 0.0349 *** |

W*Wo | −0.0136 *** | −0.0157 *** |

W*Ex | −0.0009 | −0.0013 |

Variable | WH | TR | IN | SC | FI | LE |
---|---|---|---|---|---|---|

λ | 0.1192 *** | −0.0008 | 0.1290 *** | 0.0883 *** | 0.0335 * | 0.0338 * |

TI | 0.8078 *** | 0.3597 *** | −0.1180 | −0.2870 * | 0.0845 | −0.4962 * |

W*Ti | −0.3654 *** | 0.0172 | −0.3126 *** | −0.3270 *** | 0.1806 *** | 0.1403 |

Variable | WH | TR | IN | SC | FI | LE |
---|---|---|---|---|---|---|

λ | 0.1125 *** | −0.0068 | 0.1182 *** | 0.0587 *** | 0.0294 * | 0.0364 * |

Ti | 0.7945 *** | 0.4083 *** | −0.0957 | −0.2217 | 0.0919 | −0.4814 * |

Fdi | −0.0419 * | 0.0042 | −0.0327 | −0.0317 * | 0.0161 * | −0.0499 * |

Fin | −0.3321 * | −0.1077 | 0.1560 | −0.0896 | 0.1111 | −0.3664 |

Con | −0.4390 * | −0.0628 | −0.2532 | −0.3061* | 0.0608 | 0.1710 |

W*Ti | −0.2389 *** | 0.0716 | −0.2152 * | −0.1816 *** | 0.1364 *** | 0.0738 |

W*Fdi | 0.0085 * | 0.0007 | 0.0104 * | 0.0033 | −0.0022 | 0.0165 * |

W*Fin | −0.0516 | −0.0752 * | −0.1974 *** | −0.0516 | −0.0364 | −0.3077 *** |

W*Con | 0.0279 | −0.0240 | −0.0526 | −0.1146 *** | 0.0161 | 0.1163 |

**Table 12.**Spatial autocorrelation test of service industry location entropy under the economic spatial matrix.

Year | Moran Index | Year | Moran Index |
---|---|---|---|

2011 | 0.1984 * | 2016 | 0.1760 * |

2012 | 0.2233 * | 2017 | 0.1020 |

2013 | 0.2269 * | 2018 | 0.1400 |

2014 | 0.2199 * | 2019 | 0.0494 |

2015 | 0.2169 * | 2020 | 0.0515 |

**Table 13.**Test of spatial autocorrelation of service industry location entropy in Yangtze River Delta Urban agglomeration.

Test Parameters | Geographic Adjacency Spatial Matrix | Economic Distance Spatial Matrix |
---|---|---|

LM-ERR | 115.6529 *** | 89.2937 *** |

LM-LAG | 61.8625 *** | 31.4280 *** |

RLM-ERR | 53.9340 *** | 57.9919 *** |

RLM-LAG | 0.1435 | 0.1262 |

Variable | Geographic Adjacency Spatial Matrix | Economic Distance Spatial Matrix | ||
---|---|---|---|---|

Model (5) | Model (6) | Model (5) | Model (6) | |

λ | 0.0585 *** | 0.0327 *** | −0.8379 | −1.7582 *** |

TI | 0.2320 ** | 0.0079 | 0.3086 *** | 0.1334 * |

RD | 0.1128 *** | 0.0073 | 0.1304 *** | 0.0138 |

TI*RD | 0.0693 *** | 0.0137 | 0.0880 *** | 0.0409 ** |

Fin | 0.1016 *** | 0.0880 *** | ||

Fdi | −0.0037 | −0.0015 | ||

Con | 0.3299 *** | 0.3516 *** | ||

W*TI | 0.0114 | −0.1168 ** | 0.0384 * | −0.0304 ** |

W*RD | 0.8283 *** | −0.0241 * | 1.0618 *** | 0.4099 * |

W*TI*RD | −0.0709 | −0.017 7 * | −0.0683 | −0.0008 |

W*Fin | −0.0569 *** | −0.0530 *** | ||

W*Fdi | 0.0025 *** | 0.0019 ** | ||

W*Con | 0.0381 *** | 0.0341 *** |

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## Share and Cite

**MDPI and ACS Style**

Wei, G.; Li, X.; Yu, M.; Lu, G.; Chen, Z. The Impact of Land Transportation Integration on Service Agglomeration in Yangtze River Delta Urban Agglomeration. *Sustainability* **2022**, *14*, 12580.
https://doi.org/10.3390/su141912580

**AMA Style**

Wei G, Li X, Yu M, Lu G, Chen Z. The Impact of Land Transportation Integration on Service Agglomeration in Yangtze River Delta Urban Agglomeration. *Sustainability*. 2022; 14(19):12580.
https://doi.org/10.3390/su141912580

**Chicago/Turabian Style**

Wei, Gongding, Xueyan Li, Mingyuan Yu, Guangquan Lu, and Zhiyu Chen. 2022. "The Impact of Land Transportation Integration on Service Agglomeration in Yangtze River Delta Urban Agglomeration" *Sustainability* 14, no. 19: 12580.
https://doi.org/10.3390/su141912580