# The impact of shipping activities on air quality and residents' health in China's port cities

Ling Sun, Jingyi Zhang, César Ducruet, Hidekazu Itoh, Xiangqi Liu

<figure><img src="image_1.png" /></figure>

## To cite this version:

Ling Sun, Jingyi Zhang, César Ducruet, Hidekazu Itoh, Xiangqi Liu. The impact of shipping activities on air quality and residents' health in China's port cities. Journal of Transport Geography, 2024, 123, pp.104099. 10.1016/j.jtrangeo.2024.104099. halshs-04838170

HAL Id: halshs-04838170

https://shs.hal.science/halshs-04838170v1

Submitted on 14 Dec 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>
# The impact of shipping activities on air quality and residents' health
in China's port cities

Pre-final version of the article published in Journal of Transport Geography (2024), 123: 104099

Ling SUN <sup>a,d</sup>, Jingyi ZHANG <sup>a</sup>, César DUCRUET <sup>b,*</sup>, Hidekazu ITOH <sup>c</sup>, Xiangqi LIU <sup>a</sup>

<sup>a</sup> Shanghai Maritime University, China

<sup>b</sup> French National Centre for Scientific Research (CNRS), UMR 7235 Economix, Nanterre, France

<sup>c</sup> Kwansei Gakuin University, Nishinomiya, Japan

<sup>d</sup> Fudan University, China

* Corresponding author

E-mail addresses: cesar.ducruet@economix.fr (C. Ducruet), sunl@shmtu.edu.cn (L. Sun), 202230610029@stu.shmtu.edu.cn (J. Zhang), hito@kwansei.ac.jp (H. Itoh), 202230610084@stu.shmtu.edu.cn (X. Liu)

**Abstract.** Shipping activities emit pollutants such as NOX, SOX, and PM, which are detrimental to air quality and to the health of local residents. As one of the major shipping nations, China's population and port-related industries concentrate in coastal areas. However, an interesting phenomenon is that the average life expectancy of coastal port cities in China has not significantly decreased in the past few decades, but has instead shown an upward trend, and often higher than that of non-port cities. To explain this, this paper uses panel data from 36 port cities in China from 2001 to 2019. The relationship between shipping activities, air quality, and average life expectancy is investigated through a nonlinear mediation effect model, which is used to study the impact of shipping on health, as well as the mediating role of air quality. Main results underline that except for average turnaround time, other indicators representing shipping intensity are positively correlated with air quality index and average life expectancy, indicating that the economic benefits of ports are closely related to their operational efficiency. Shipping activities have a significant negative impact on the health level of port cities. This impact is influenced by the mediating variable of air quality and has a partial mediating effect. However, the impact of air quality on health presents a complex nonlinear relationship, with an inverted N-shaped curve, and the two inflection points are about 68 and 120 respectively. Our research provides guidance for the sustainable development of port cities, in order to achieve the balance between shipping activities and environment and health.

**Keywords:** Shipping activity; Air pollution; Public health; Port city; China

## Acknowledgements

The research leading to these results has received funding from the French National Research Agency (ANR) project No. [ANR-22-CE22-0002] "MAGNETICS". This work was also supported by the Soft Science Research Program of Shanghai (No.24692107200).
# 1 Introduction

Maritime transportation is an efficient and low-cost way to transport bulk commodities and raw materials (Liu et al., 2019), and more than 80% of global trade volumes are carried by sea (UNCTAD, 2022). Owing to globalization and trade growth, the total volume of shipping transportation has witnessed a steady increase, from 3.7 billion tons in 1980 to 12 billion tons in 2021 (UNCTAD, 2022). Shipping activities include maritime navigation, port handling operations, and ship turnover (Jahangiri et al., 2018), linked with other segments of the value chain (Robinson, 2002). However, while such activities significantly contribute to social and economic development worldwide (Verschuur et al., 2022), they also cause significant releases of air pollutants. Pollutants generated by the combustion of fuel in shipping activities contain harmful substances such as NOx, SOx, and PM, which seriously harm air quality near ports and other transport infrastructures (Colvile et al., 2001; Kopela, 2017). According to the literature, nearly 70% of the ships' exhaust emissions occur within 400 km from the coastline (Corbett et al., 2007). Multiple case studies have already shown that ship and port emissions have among the principal pollution sources in local areas (Garbatov & Georgiev, 2022; Zhao et al., 2013; Zhao et al., 2022). Long-term severe air pollution not only poses a threat to the natural environment, it also has a negative impact on the physical and mental health of the local population – residents and workers (Chen et al., 2019; Sofiev et al., 2018). In addition, due to the dense population in most coastal areas, the impact of port activity emissions on public health is much higher than that of emissions from sea transportation processes (Mueller et al., 2023).

As the world's largest product exporter and importer, China is one of the shipping countries with the largest cargo throughput and the largest port scale globally (Chen et al., 2019). In 2022, total port throughput reached 15.68 billion tons, and port container throughput reached 296 million Twenty-Foot Equivalent Units (TEUs), ranking first in the world in both indicators (MOT, 2023). Among the top ten world ports in terms of total throughput and container throughput, the ports in China occupy 8 and 7 positions respectively. At the same time, China has a large and unevenly distributed population, with densely populated coastal regions (Guo & Zhang, 2019). In 2021, over 430 million residents were living in coastal port cities in China, including megacities such as Shanghai, Shenzhen, Tianjin, and Guangzhou, each with a population exceeding tens of millions of inhabitants (NBS, 2021). This exposes China to more severe air pollution and health risks associated with maritime activities than other maritime countries.

However, while air pollution caused by shipping activities has been widely discussed, Chinese coastal port cities exhibit a higher-than-average life expectancy (Wu et al., 2020). This has sparked extensive attention and a series of debates emerged in academia and society (Crippa et al., 2019; Lindgren, 2021). Scholars underlined possible causes of such a phenomenon, such as economic development, resource allocation, climate, lifestyle, diet, and social factors. Therefore, it is necessary to conduct in-depth research on the impact of port city development and shipping industry on the environment and public health. What is the correlation between different types of shipping activities and air quality and health in port cities? Does the development of shipping in port cities have a positive or negative impact on residents' health? What mechanism is it achieved through? These are important questions that not only involve issues of environmental protection and sustainable development, but also directly relate to the living environment and life quality of residents in port cities.

Therefore, this study conducts an empirical analysis based on panel data on 36 port cities in
China from 2001 to 2019. Following the relevant theories of panel models, we used nonlinear
mediation effect tests to verify the impact of shipping activities on residents' health levels as well as
the mediating role of air quality, and determine the two inflection points of the impact of air quality
on residents' health. This study addresses the impacts of different shipping activities on the economy,
environment and health, and how port cities should develop the shipping industry at different stages.
It provides strong evidence and scientific suggestions for decision-makers and city planners,
promoting the balance between shipping activities, ecological environment and public health,
thereby contributing to the sustainable development of port cities.

The remaining sections are organized as follows: Section 2 conducts a review of previous literature research, Section 3 presents the data and methods, Section 4 reports and discusses the results, and Section 5 provides the main conclusions and policy implications.

## 2 Literature Review:

### 2.1 The impact of shipping activities on environment

#### 2.1.1 Pollution caused by shipping activities

Shipping activities are known to affect the environment in several ways. Shipping pollutes marine waters through the discharge of pollutants from domestic sewage (Chen et al., 2022; Ytreberg et al., 2020), bilge water (Tiselius & Magnusson, 2017), and scrubber water (Koski et al., 2017), resulting in the eutrophication and acidification of water bodies. In addition, the toxicization of water bodies caused by oil/chemical leakage and the spread of invasive species from hulls or ballast water are also harmful to marine ecosystems (Aydin et al., 2021; Havel et al., 2015). Another major impact of shipping on the environment is noise. During loading and unloading operations, as well as ship repair or dismantling, high airborne noise levels may be radiated (Fredianelli et al., 2022), to such an extent that Badino et al. (2016) considered shipping as a significant contributor to the "soundscape" of port cities. However, compared to the types of pollution mentioned above, air pollution caused by shipping activities is currently the most mature monitoring and research field, and this article will also mainly focus on it.

Air emissions of shipping may be categorized amongst emissions causing air pollution and emissions contributing to climate change. The first category includes emissions of SOₓ, NOₓ, PM, CO and VOC, and the second includes CO₂, HCFC, and CH₄ (Chatzinikolaou et al., 2015). The focus of this study is on the former. According to Ramacher et al. (2019), the annual NOₓ emitted by ships can reach 5-10% of total global emissions, and some regions like Denmark and Norway can even reach 30-40%. Shipping emissions of SOₓ account for about 4-9% of global total emissions (Jonson et al., 2019). Some studies also estimated the contributions of ship emissions to particulate matter (PM) concentration through the application of source apportionment methods, with an average of 5-15%, and in some specific harbor areas in Asia, ships can contribute up to 26% (Chen et al., 2019; Y. Zhang et al., 2022). Chen et al. (2019) showed that air pollution levels from shipping sources are expected to increase in the future due to global trade growth, maritime transport, and ship traffic demand.

#### 2.1.2 Impact of shipping activities on coastal air quality

Both global- and regional-scale calculations show that for PM<sub>2.5</sub> and depositions of oxidized nitrogen and sulfur, the effects of ship emissions are much larger when emissions occur close to the shore rather than at open seas (Jonson et al., 2020). In port areas, ships sailing offshore and ships
parked at berth will cause a large amount of air pollution (Jahangiri et al., 2018). Fameli et al. (2020) showed that most of the ship emissions in the ports occur during berthing, while maneuvering itself only lasts 15-20 minutes. Most studies on the contribution of port emissions to air pollution have been based on measurements or simulations. Some studies consisted in on-site measurements near ports to determine the contribution of ship traffic emissions (Mamoudou et al., 2018; Shen et al., 2022), while other studies have simulated the diffusion of air pollutants based on ship emission inventories (Gan et al., 2023; Toscano et al., 2021; Yang et al., 2021). However, existing research mostly focuses on ports rather than cities, and the impact of shipping activities on air quality at the city level is fiercely lacking, as less than ten peer-reviewed articles dealt with residents' health in port cities in the last two decades. Instead, a recent study across the OECD area showed that port regions emit more greenhouse gases (GHG) than non-port regions (Ducruet et al., 2024). This study also underlined the existence of two main types of port regions. Metropolitan port regions concentrate GHG emissions, PM<sub>2.5</sub>, population, GDP per capita, and container traffic, while industrial port regions carry more solid and liquid bulk traffic and have higher-than average mortality rates.

It is also worth noting that the emissions from non-ship factors, such as port operating machinery on terminals and transportation vehicles to/from the hinterland, are considered important, but seem to have been relatively ignored (Mueller et al., 2023; Rowangould et al., 2018). Only a few studies have evaluated the pollution caused by other factors such as cargo loading machinery, heavy vehicles, and trains at ports (Merico et al., 2019; Sorte et al., 2021; Kara et al., 2021). In addition, various port industries also have an impact on air quality, such as shipbuilding and maintenance, heavy industry, energy production and so on (Lecue & Darbra, 2019; Yuan et al., 2022).

## 2.2 The impact of shipping activities on health

Although many studies have focused on the impact of shipping activities on the environment in recent years, few have studied the resulting health impacts. Lloret et al. (2021) pointed out in their study that from the health perspective, affected people include crew, residents living near ports, those in the pathways of long-range air and marine global pollution, and workers in shipyards. Discharge of wastewater from ships may contribute to the introduction of pathogens to non-endemic areas, which consequently increases the number of waterborne diseases, compromising human health (Chen et al., 2022; Endres et al., 2018). The studies of Murphy and King (2014) and Fredianelli et al. (2022) shows that environmental noise have the potential to be significant public health concerns, triggering mental stress and mental illness. Ship collisions and explosions of dangerous cargoes can also pose a serious threat to the safety and health of port workers (Hua et al., 2021; Khan et al. 2023). Finally, the threat of air pollution from shipping to the health of residents in port cities remains the most widely studied area. The transportation, loading and unloading of goods, navigation and berthing of ships, as well as ship maintenance and dismantling activities, which emit a large amount of air pollutants and toxic substances, posing a significant threat to the respiratory and cardiovascular systems of coastal residents (Zhang et al., 2021; Barregard et al., 2019; Chen et al., 2019; Lloret et al., 2021). Ducruet et al. (2024) reviewed the handful of papers dealing explicitly with the health impacts of ports, such as the higher mortality from all cancers and neurological diseases near the port in Civitavecchia (Bauleo et al., 2019), the increasing unplanned hospital admissions for people living under winds carrying PM<sub>10</sub> and NO<sub>2</sub> from the port and industrial areas in Brindisi (Gianicolo et al., 2013), and the higher number of hospital, heart-related,
and psychiatric visits for Black people than for white people when living near ports in U.S. counties (Gillingham and Huang, 2021).

International shipping emissions were responsible for 16%, or about 60,000, of global transportation-related premature deaths from 2010 to 2015 (Anenberg et al., 2019). Lin et al. (2018) used a time series Poisson model to quantify the association of cardiovascular mortality with shipping pollution emission indices in Guangzhou, China, and identified it as an important risk factor. Tang et al. (2020) studied the impact of shipping emissions on human health in the Gothenburg area, and estimated a mean loss of the life expectancy of 0.015 years/person associated with shipping related PM<sub>2.5</sub> exposure and 2.6 premature deaths/year associated with shipping-related NO<sub>2</sub> exposure. More recently, Mueller et al. (2023) conducted a review of relevant literature on the Health Impact Assessment (HIA) of global shipping/port air pollution, which provided us with an understanding of how to assess the associated health burden so far, as well as the possible implications for policy and research. These studies suggests that shipping emission is an important detrimental factor of illness and mortality, and should be emphasized in air pollution control and management in order to protect public health. Without effective emission control measures, as shipping demand increases, ship emissions will continue to increase, leading to more health problems (Chen et al., 2019).

## 2.3 Summary

Ship emissions have a significant impact on the environment and health, and will continue to do so in the future. Although scholars have explored the impact of shipping activities on the environment, especially on air quality, from multiple perspectives, there are few studies on the health effects, which often remain only implicit. The principal focus of current research is on ports rather than cities, although it would better involve sustainable development. Ports have gradually shifted away from cities due to port growth, urban growth, and environmental issues (Hoyle, 1989), but many of them still remain “urban”, so that air pollution in their vicinity tends to cause greater health losses. Moreover, many studies have focused on the impact of shipping activities on local air quality, typically based on different methods, including experimental observations, numerical modeling, and emission inventory analysis, and systematic analyses from econometric or statistical perspective remain relatively scarce. The current analysis of 36 port cities in China, from a statistical perspective, is thus relatively new, and can potentially contribute to the literature on ports, shipping, environment, and health.

# 3 Data and methodology

## 3.1 Study area

China has over 50 coastal port cities, which corresponds to five main port clusters, from north to south, namely the Bohai Rim, the Yangtze River Delta, the Southeast, the Pearl River, and the Southwest. Due to the availability of data and the representativeness of samples, we selected 36 port cities from different port clusters in China as the research sample (Figure 1). In 2019, the total throughput of these port cities accounted for over 95% of the total throughput of Chinese ports. The specific criteria for selecting samples include: 1. Data availability. Ensure the openness and completeness of environmental, economic, and policy data in these cities. 2. Regional representativeness. The samples cover five major port clusters, with a balanced number of port cities belonging to each cluster to reflect regional differences. 3. Functional diversity. The samples cover
different port types such as commercial ports, industrial ports, transit ports and so on. 4. The scale of shipping activities. Selected cities have different levels of shipping development and economic scale to make the results more representative.

As a port city may contain multiple port areas, we matched the geographical location of port areas with the jurisdiction scope of the port cities, in order to merge the ports at the city level. For example, in addition to Shenzhen port area, Shekou, Yantian and Chiwan port areas also belong to the port city of Shenzhen (see Wang, 2016 for a recent overview of port-city dynamics in China).

It should be noted that as a special administrative region of China, Hong Kong enjoys a higher degree of autonomy, which sets it apart from other China's port cities in terms of health and environment. As an international financial, trade, and shipping hub, Hong Kong is one of the most densely populated cities in the world, and high population density and international exchanges lead to some special challenges, such as limited land resources, public health management, and disease transmission risks. Despite all this, the government allocated significant resources to environmental protection, resulting in lower levels of environmental pollution and better air quality. Secondly, Hong Kong boasts a well-developed healthcare system that covers the entire population, making its average life expectancy among the highest in the world, far higher than the average of other Chinese port cities.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Geographic distribution of selected Chinese port cities</figcaption></figure>
## 3.2 Variable selection and data processing

### 3.2.1 Independent variables: Shipping activities

Appropriate variables need to be selected to represent the intensity of shipping activities onshore and offshore at port cities, in order to gain a more comprehensive understanding and evaluation of their potential impacts on air quality and residents' health. In this paper, taking into account the actual needs of port operations as well as the availability of existing research and data, we selected the following five indicators to represent different aspects of shipping activities in the port:

(1) Number of vessel calls: It refers to the number of times a vessel stops at a berth and loads and unloads goods. A higher number of calls means that more ships choose this port as the loading and unloading point for goods. So it indicates the level of attractiveness and busyness of the port, which to some extent represents the importance of the port's geographical location in the maritime transportation network (Ducruet et al., 2020; Nguyen et al., 2022).

(2) Weighted average turnaround time: It refers to the turnaround time a vessel spends at a port divided by its tonnage, which is crucial for evaluating the operational efficiency and congestion situation of ports. For ships of the same size, a shorter turnaround time usually means higher efficiency and better resource utilization, while a longer turnaround time may indicates potential problems such as slow loading and unloading of goods, port equipment failures, or port processes that are not smooth enough (Ducruet & Itoh, 2022).

(3) Maximum deadweight tonnage: It refers to the maximum ship load capacity that a port can accept, which can reflect the port's facilities and carrying capacity. A larger maximum deadweight tonnage means that the port can accommodate larger ships, which is of great significance for handling large amounts of cargo, but it may lead to higher pollution emissions and traffic congestion (Chang, 2016).

(4) Ship traffic volume: It corresponds to the product between ship capacity (gross registered tons) and number of calls on a yearly basis of each port city. In various shipping activities, the pollution at offshore section mainly comes from the discharge of ships, so it represents the overall scale and attractiveness of port city shipping activities at sea (Ducruet et al., 2024).

(5) Cargo throughput: It refers to the total amount of cargo handled by a port within a specific time frame. This variable reflects the port's handling capacity, the momentum of port cargo logistics, and the level of trade activity (Wang et al., 2020), including transshipment as well as import-export traffic. It is not only related to the operational strength of port machinery, but also closely related to the land transportation activities.

By selecting these five indicators, we can comprehensively evaluate the intensity of port shipping activities from multiple aspects such as frequency, efficiency, capacity, scale, and logistics. These dimensions complement each other, providing a comprehensive perspective that helps to scientifically analyze the impact of shipping activities on air quality and residents' health.

### 3.2.2 Dependent variable: Average life expectancy

This indicator represents the expected average lifespan of people born in a specific region, country, or population. It is calculated based on the age distribution of survival and death for a specific population, usually measured in years. Higher average life expectancy is usually associated with better health conditions. Lower incidence rate, fewer chronic diseases, and lower rates of premature death often lead to longer life expectancy. Average life expectancy is an internationally
recognized indicator for evaluating the overall health level of residents, and has been widely applied in previous studies on residents' health (Zheng et al. 2022; Kim et al. 2021).

### 3.2.3 Mediating variable: Air Quality Index

Air Quality Index (AQI) is a composite indicator for detecting the state of total air pollution, which is applied to represent actual air pollution people experience (coming from the Ministry of Ecology and Environment of the People's Republic of China (https://www.mee.gov.cn/)). It simplifies the concentration of monitored air pollutants into a single conceptual index based on the environmental air quality standards and the impact of six major atmospheric pollutants (PM₁₀, PM₂.₅, SO₂, NO₂, O₃, CO) on human health, ecology, and the environment. In general, the higher the air quality index, the more severe the air pollution.

AQI is a widely used index across countries, but each country uses different equations. Due to the fact that the research units of this article are all Chinese port cities, the calculation standards for AQI are uniformly referred to the "Technical Regulation on Ambient Air Quality Index" (MEE, 2012). The specific calculation process is as follows: compare the graded concentration limits of each pollutant, and calculate the Individual Air Quality Index (IAQI) based on the measured concentration values of each pollutant.

$$IAQI_{P} = \frac{IAQI_{Hi} - IAQI_{Lo}}{BP_{Hi} - BP_{Lo}} (C_{P} - BP_{Lo}) + IAQI_{Lo}$$

where: IAQI<sub>P</sub> — the air quality sub-index of pollutant item P;

C<sub>P</sub> — the mass concentration value of pollutant item P;

BP<sub>Hi</sub> — The high value of pollutant concentration limit values similar to C<sub>P</sub>;

BP<sub>Lo</sub> — The low value of pollutant concentration limit values similar to C<sub>P</sub>;

IAQI<sub>Hi</sub> — The air quality sub index corresponding to BP<sub>Hi</sub>;

IAQI<sub>Lo</sub> — The air quality sub index corresponding to BP<sub>Lo</sub>;

Then select the maximum value from the IAQI of each pollutant to determine as AQI, and determine the pollutant with the highest IAQI as the primary pollutant:

$$AQI = \max\{IAQI_{1}, IAQI_{2}, IAQI_{3}, \dots, IAQI_{n}\}$$

Where, AQI — Air Quality Index;

n — Pollutant projects.

### 3.2.4 Control variables

Due to differences in operating modes, transportation methods, and pollutant emissions among various ship/cargo types and port types, their impact on air quality and public health in port cities may also vary. To ensure the accuracy of this research, we added these influencing factors as control variables to the model. Specifically, for ship or cargo types, we use the proportion of traffic volume for different ship types (liquid bulk, solid bulk, general cargo, containers) to reflect the cargo structure of each port city. For port types, we define three binary variables: IMV indicates whether the port is an intermodal port (1 for yes, 0 for no), TGV indicates whether the port is a transshipment or gateway port (1 for transshipment port, 0 for gateway port), and ICV indicates whether the port
is an industrial or commercial port (1 for industrial port, 0 for commercial port).

Moreover, the control variables also include six socioeconomic factors: GDP per capita, Railway freight volume, Highway freight volume, Population density, Industrial output, Doctor density; and two natural factors: Elevation and Wind speed. GDP per capita and Industrial output are important indicators of a city's level of economic development, which will bring pollution but also improve people's living standards, so they have multifaceted impacts on health (Ye, 2022). Compared to shipping activities far from the city, railway and highway transportation emissions have a more significant impact on air quality and residents' health in urban areas (Liu et al., 2021). As observed by Gibbs (2014) in the case of Felixstowe container port in UK, hinterland trucking can represent twice the amount of emissions of the port. Population density refers to the concentration of people within a certain geographic area. A denser population means more pollutant emissions, but also means more complete public facilities (Xing, 2020). Doctor density is usually used to measure the supply of medical resources in a country or region, which is the ratio between the number of doctors and the total population (Jin et al., 2015). Elevation and wind speed may be related to the deposition, transformation, and dissipation of pollutants; thus they can have an impact on air quality and health (Yan et al., 2021; Yang et al., 2020; Linares et al., 2021).

### 3.2.5 Data processing

It should be noted that the sample scope of this study is made of relevant data for various ship types for 36 coastal cities in China from 2001 to 2019, including container ships, bulk carriers, general cargo ships, oil tankers, and so on. To show the overall intensity of shipping activities in different port cities, we should assign weights to each indicator and calculate a comprehensive score. The common methods include subjective and objective weighting methods. Subjective weighting methods mainly determine weights based on experts' assessments of each indicator, which rely heavily on subjective awareness (Guo et al., 2022). And they have little transparency and reappearance (Yu, 2021). Objective weighting methods used in previous studies mainly include Principal component analysis (PCA) and entropy weighting method. But the former is mainly used for dimensionality reduction, and the extracted main components may be difficult to explain directly, making it unsuitable for weight allocation (Yu, 2021). Therefore, we ultimately decided to only use the entropy weight method to calculate the weight of each indicator. The entropy weight method assigns weight values to each indicator based on the amount of information contained in the data, which is particularly useful in dealing with multi-indicator decision-making problems. According to Zhou et al. (2015) and Tomal (2021), the calculation steps are as follows:

Step 1. To eliminate the influence of different measurement units and magnitudes on each indicator for calculating the comprehensive score of shipping activities, the initial indicator data needs to be normalized. For this, the min-max normalization method is used to transform the initial data to fall within the range of [0,1]:

$$ u_{ijt} = \begin{cases} \frac{(x_{ijt} - \min_{it}(x_{ijt}))}{(\max_{it}(x_{ijt}) - \min_{it}(x_{ijt}))}, & \text{if } u_{ijt} \text{ is a positive indicator} \\ \frac{(\max_{it}(x_{ijt}) - x_{ijt})}{(\max_{it}(x_{ijt}) - \min_{it}(x_{ijt}))}, & \text{if } u_{ijt} \text{ is a negative indicator} \end{cases} \quad (1) $$

where $u_{ijt}$ is the normalized value of indicator j for city i in year t ($x_{ijt}$). For a positive
indicator, the higher the indicator value, the higher the level of the just transition.

$$e_j = -\frac{1}{\ln(km)} \sum_{t=1}^{k} \sum_{i=1}^{m} p_{ijt} * \ln(p_{ijt}) \quad (2)$$

Step 2. Calculate entropy value ($e_j$) for indicator j:

$$p_{ijt} = u_{ijt} / \sum_{t=1}^{k} \sum_{i=1}^{m} u_{ijt} \quad (3)$$

where m and k are the total number of cities and years, respectively; $p_{ijt}$ denotes the proportion of indicator j for city i in year t, which can be expressed by the following equation:

Step 3. Calculate the weight of indicator j ($w_j$):

$$w_j = (1 - e_j) / \sum_{j=1}^{n} (1 - e_j) \quad (4)$$

where n is the number of indicators.

Step 4. Calculate the weighted index of city i in year t ($S_{ij}$):

$$S_{ij} = \sum_{j=1}^{n} u_{ijt} * w_j \quad (5)$$

After calculation by formula (4), among the five variables that represent the intensity of shipping activities, ship traffic volume has the highest weight of 33.31%, number of calls has a weight of 25.26%, weighted average turnaround time has a weight of 22.52%, cargo throughput has a weight of 13.61%, and maximum deadweight tonnage has the lowest weight of 5.58%. On this basis, we can calculate the comprehensive score of annual shipping intensity for each port city through formula (5).

For the remaining variables, in order to avoid the loss of their actual economic meaning and original scaling relationships, and for more intuitive data analysis and interpretation, we only changed the magnitude of their units. **Table 1** reports the symbols and data sources for each variable.

<table><caption>**Table 1**
Variables introduction.</caption><thead><tr><th>Type</th><th>Variables</th><th>Symbols</th><th>Data sources</th></tr></thead><tbody><tr><td rowspan="3">Independent variable</td><td>Number of calls</td><td>CALL</td><td>Lloyd's List</td></tr><tr><td>Weighted average turnaround time</td><td>WAT</td><td>Intelligence</td></tr><tr><td>Maximum deadweight tonnage</td><td>DWT</td><td></td></tr></tbody></table>
<table><thead><tr><th></th><th>Ship traffic in total</th><th>TRAFFIC</th><th></th></tr></thead><tbody><tr><td></td><td>Cargo throughput</td><td>CARGO</td><td>China Port Yearbook</td></tr><tr><td></td><td>Comprehensive score of shipping activity intensity</td><td>SCORE</td><td>Calculated</td></tr><tr><td>Mediating variable</td><td>Air quality index</td><td>AQI</td><td>China Meteorological Administration</td></tr><tr><td>Dependent variable</td><td>Average life expectancy</td><td>LIFE</td><td>Local health committees</td></tr><tr><td rowspan="12">Control variables</td><td>Traffic volume share of bulk ship</td><td>BULK</td><td></td></tr><tr><td>Traffic volume share of general cargo ship</td><td>GEN</td><td>Lloyd's List</td></tr><tr><td>Traffic volume share of liquid ship</td><td>LIQ</td><td>Intelligence</td></tr><tr><td>Traffic volume share of container ship</td><td>CONT</td><td></td></tr><tr><td>Transhipment/Gateway port variable</td><td>TGV</td><td></td></tr><tr><td>Industrial/Commercial port variable</td><td>ICV</td><td>China Port Yearbook</td></tr><tr><td>Intermodal port variable</td><td>IMV</td><td></td></tr><tr><td>GDP per capita</td><td>PCGDP</td><td></td></tr><tr><td>Population density</td><td>POP</td><td></td></tr><tr><td>Railway freight volume</td><td>RF</td><td>China City Statistical Yearbook</td></tr><tr><td>Highway freight volume</td><td>HF</td><td></td></tr><tr><td>Industrial output</td><td>INDU</td><td></td></tr><tr><td>Doctor density</td><td>DOCTOR</td><td></td></tr><tr><td>Elevation</td><td>ELEV</td><td>China Meteorological Administration</td></tr><tr><td>Wind speed</td><td>WIND</td><td></td></tr></tbody></table>

### 3.3 Nonlinear mediating effect model setting

Mediating effect models are used to examine and study the role of intermediate variables in the causal relationship between explanatory variables and explained variables, thus exploring the underlying mechanism of this relationship (He et al., 2022). Fig. 2 shows that the independent variable (X) has a comprehensive effect of *d* on the dependent variable (Y) and the direct influence of *a* from X to Y. Meanwhile, it indicates that X has indirect effects on Y by affecting the intermediate variable (M). This study used the stepwise regression method proposed by Baron and Kenny (1986). First, the independent and dependent variables were used for a regression analysis as well as to judge the sign and significance of the coefficient. Next, the independent variable and
the mediating variable were used for the regression analysis and to judge the sign and significance of the coefficients. Finally, all three kinds of variables were entered into one regression model and then used to judge the sign and significance of the coefficients. If the estimated coefficients of *b*, *c* and *d* are all significant, the existence of the mediating effect can be proved. And if *a* is significant, then the mediating effect is a partial mediating effect, otherwise it is a full mediating effect.

<figure><img src="image_4.png" /><figcaption>Fig. 2. The verification path of the mediating effect model.</figcaption></figure>

This paper uses panel data methodology, following mediating effect model to explore the impact mechanism between shipping activities, air quality, and residents' health. Air pollution can harm residents' health and lead to a decrease in average life expectancy, which has been widely proven in previous studies (Wu et al., 2020; Hill et al., 2019). However, it is interesting to observe that the air pollution of large port cities (such as Tianjin and Shanghai) generally are more serious than that of smaller port cities (such as Beihai and Fangchenggang), but the average life expectancy of developed regions is often higher than that of underdeveloped regions, which is not entirely consistent with previous research conclusions. But there is no doubt that the health level of residents cannot be permanently improved with the continuous aggravation of pollution. It can be seen that there may be a complex nonlinear relationship between air quality and human health and lifespan, influenced by factors such as residents' living standards and medical and health conditions. Therefore, based on the traditional mediation effect model structure, we use a nonlinear mediation effect model and add the quadratic and cubic terms of the air quality index to the regression equation.

Compared to other econometric models, a panel data model considers both the cross-section and time-series of data and reflects the characteristics and changes from the two dimensions (Li et al., 2019). Before implementing this model, the multicollinearity test and the Hausman test for variables are required. Multicollinearity may jeopardize the credit of estimating results (Kovács et al., 2005), And the Variance Inflation Factor (VIF) is usually used to estimate the multicollinearity between explanatory variables. If the VIF value of the variables is >7.5, it suggests that the issue of multicollinearity exists in the current regression model, and thus that variable should be removed. Panel data modeling generally employs two typical estimators, i.e., random-effects estimator and fixed-effects estimator, and the Hausman test is employed to identify which one is superior in our case (Hausman, 1978).

After testing, the VIF values of each regression equation variable are all less than 7.5, indicating that there is no multicollinearity problem. The results of the Hausman test all indicate rejection of the original hypothesis, indicating that the fixed effects model should be chosen. To explore whether shipping activities have a significant effect on residents' health through the mediating factors of air quality, and the direction and magnitude of their influencing effects, several
theoretical models were constructed as follows:

$$ LIFE_{it} = \alpha + \beta_1 SCORE_{it} + \gamma_1 BULK_{it} + \gamma_2 GEN_{it} + \gamma_3 LIQ_{it} + \gamma_4 CONT_{it} + \gamma_5 TGV_{it} \\ + \gamma_6 ICV_{it} + \gamma_7 IMV_{it} + \gamma_8 PCGDP_{it} + \gamma_9 POP_{it} + \gamma_{10} RF_{it} + \gamma_{11} HF_{it} \\ + \gamma_{12} INDU_{it} + \gamma_{13} DOCTOR_{it} + \gamma_{14} ELEV_{it} + \gamma_{15} WIND_{it} + \mu_t + \vartheta_i + \varepsilon_{it} \quad (6) $$

$$ AQI_{it} = \alpha + \beta_1 SCORE_{it} + \gamma_1 BULK_{it} + \gamma_2 GEN_{it} + \gamma_3 LIQ_{it} + \gamma_4 CONT_{it} + \gamma_5 TGV_{it} \\ + \gamma_6 ICV_{it} + \gamma_7 IMV_{it} + \gamma_8 PCGDP_{it} + \gamma_9 POP_{it} + \gamma_{10} RF_{it} + \gamma_{11} HF_{it} \\ + \gamma_{12} INDU_{it} + \gamma_{13} DOCTOR_{it} + \gamma_{14} ELEV_{it} + \gamma_{15} WIND_{it} + \mu_t + \vartheta_i + \varepsilon_{it} \quad (7) $$

$$ LIFE_{it} = \alpha + \beta_1 SCORE_{it} + \beta_2 AQI_{it}^3 + \beta_3 AQI_{it}^2 + \beta_4 AQI_{it} + \gamma_1 BULK_{it} + \gamma_2 GEN_{it} + \gamma_3 LIQ_{it} \\ + \gamma_4 CONT_{it} + \gamma_5 TGV_{it} + \gamma_6 ICV_{it} + \gamma_7 IMV_{it} + \gamma_8 PCGDP_{it} + \gamma_9 POP_{it} + \gamma_{10} RF_{it} \\ + \gamma_{11} HF_{it} + \gamma_{12} INDU_{it} + \gamma_{13} DOCTOR_{it} + \gamma_{14} ELEV_{it} + \gamma_{15} WIND_{it} + \mu_t + \vartheta_i + \varepsilon_{it} \quad (8) $$

In equations (6)-(8), i represents the year; j represents the city; α denotes the constant term; β₁~β₄ are the estimated coefficients of the core independent variables, γ₁~γ₁₅ are the estimated coefficients of the control variable, μₜ is the time fixed effect, ϑᵢ is the individual fixed effect, and εᵢₜ is the random perturbation term.

# 4 Results and Discussions

## 4.1 Background statistics

Fig. 3 shows the spatiotemporal characteristics of air pollution, death rate and cargo throughput in China's port cities during the study period. In terms of temporal trends, the air pollution, average life expectancy, and intensity of shipping activities in China's port cities are all showing an upward trend. From the spatial pattern, Northern China is the area with the most severe air pollution, especially in Tianjin, Tangshan, and Jinzhou. This is because they have developed heavy industries and are major transit areas for coal trade (Wang and Ducruet, 2014). The southern coastal areas maintain a relatively good level of environment and health. The most developed areas for shipping activities are concentrated in the central coastal areas, especially Shanghai, Ningbo, and Zhoushan. The descriptive statistics of relevant variables are presented in Table 2. For better understanding, the appendix provides more detailed descriptive statistical data of all variables in all 36 port cities.
<figure><img src="image_5.png" /><figcaption>Fig. 3 The spatiotemporal changes of main variables in China's port cities from 2001 to 2019</figcaption></figure>

<caption>**Table 2**</caption>

The descriptive statistics of variables.

<table><thead><tr><th>Variable</th><th>Observations</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th></tr></thead><tbody><tr><td>CALL</td><td>684</td><td>1381.39</td><td>1</td><td>15683</td><td>2022.821</td></tr><tr><td>WAT</td><td>684</td><td>0.801</td><td>0.042</td><td>12.969</td><td>1.122</td></tr><tr><td>DWT</td><td>684</td><td>113.36</td><td>6.895</td><td>237.373</td><td>59.082</td></tr><tr><td>TRAFFIC</td><td>684</td><td>36.784</td><td>0.02</td><td>366.397</td><td>62.49</td></tr><tr><td>CARGO</td><td>684</td><td>17.129</td><td>0.252</td><td>70.542</td><td>15.848</td></tr><tr><td>SCORE</td><td>684</td><td>0.125</td><td>0.019</td><td>0.668</td><td>0.116</td></tr><tr><td>AQI</td><td>684</td><td>68.061</td><td>30.233</td><td>146.214</td><td>17.332</td></tr><tr><td>LIFE</td><td>684</td><td>78.052</td><td>70.102</td><td>85.501</td><td>2.783</td></tr><tr><td>BULK</td><td>684</td><td>0.397</td><td>0.005</td><td>0.932</td><td>0.25</td></tr><tr><td>GEN</td><td>684</td><td>0.1</td><td>0.003</td><td>0.667</td><td>0.922</td></tr><tr><td>LIQ</td><td>684</td><td>0.161</td><td>0.002</td><td>0.954</td><td>0.165</td></tr><tr><td>CONT</td><td>684</td><td>0.23</td><td>0.002</td><td>0.964</td><td>0.269</td></tr><tr><td>TGV</td><td>684</td><td>0.5</td><td>0</td><td>1</td><td>0.5</td></tr></tbody></table>
<table><tr><td>ICV</td><td>684</td><td>0.361</td><td>0</td><td>1</td><td>0.481</td></tr><tr><td>IMV</td><td>684</td><td>0.306</td><td>0</td><td>1</td><td>0.461</td></tr><tr><td>PCGDP</td><td>684</td><td>6.523</td><td>0.69</td><td>46.775</td><td>5.295</td></tr><tr><td>POP</td><td>684</td><td>0.917</td><td>0.126</td><td>7.15</td><td>1.098</td></tr><tr><td>RF</td><td>684</td><td>14.146</td><td>0</td><td>121.61</td><td>20.243</td></tr><tr><td>HF</td><td>684</td><td>12.516</td><td>0.175</td><td>88.352</td><td>11.857</td></tr><tr><td>INDU</td><td>684</td><td>6.388</td><td>0.017</td><td>42.688</td><td>7.86</td></tr><tr><td>DOCTOR</td><td>684</td><td>25.713</td><td>6.8</td><td>88.451</td><td>13.853</td></tr><tr><td>ELEV</td><td>684</td><td>101.258</td><td>7.337</td><td>438.084</td><td>106.699</td></tr><tr><td>WIND</td><td>684</td><td>5.738</td><td>3.336</td><td>9.958</td><td>1.092</td></tr></table>

## 4.2 Result explanation of the nonlinear mediating effect model

Table 3 shows the regression results of nonlinear mediating effect model. According to the principle of mediating effect testing, the coefficients of SCORE in model (6) (7) and AQI3 in model (8) are both significant, indicating a mediating effect. This result indicates that the hypothesis that shipping activities affect residents' health by affecting air quality is valid. In addition, the coefficient of SCORE in model (8) is also significant at the 1% level, indicating that this influence mechanism is partially mediated.

Such results indicate that shipping activities have a significant negative impact on the health level of port cities. On the one hand, shipping activities emit a large amount of pollutants (including sulfur dioxide, nitrogen oxides, particulate matter, etc.), leading to deterioration of air quality, thereby endangering the respiratory and cardiovascular systems of coastal residents, increasing their risk of illness and death (Wu et al., 2020). On the other hand, maritime activities also influence the health levels of port cities through alternative means. This conclusion is consistent with the research of other scholars. For example, in addition to air, shipping activities can also cause water pollution and noise pollution, which have a negative impact on the health of port city residents. Water pollution can damage the water ecosystem of port cities, affect fishery resources and water source security, and threaten the dietary health and quality of life of port city residents (Endres et al., 2018; Aydin et al., 2021). Noise pollution can interfere with the sleep and rest of port city residents, trigger mental stress and mental illness, and reduce the happiness and satisfaction of port city residents (Duarte et al., 2021; Jalkanen et al., 2022). In addition, accidental deaths caused by port production activities will also reduce average life expectancy to a certain extent, directly affecting the health level of residents in port cities (Hua et al., 2021; Khan et al. 2023). Accidents such as ship collisions, oil tanker explosions, and cargo collapses can cause casualties and property damage to port city residents.

From the analysis, we have found that shipping activities have a negative impact on air quality and health, which is linear and consistent. However, the impact of air quality on health presents a complex nonlinear relationship, with a negative coefficient of the cubic term of air quality, indicating an inverted N-shaped curve. This is relatively simple to explain: initially, the deterioration of air quality can harm the respiratory and cardiovascular systems of port city residents, increasing the risk of illness and death. However, the deterioration of air quality is often accompanied by economic development and urbanization, which in turn improves residents' living conditions and medical and health standards (Wu et al., 2018; Wang et al., 2022). Therefore, the curve enters the second stage, where the decrease in air quality leads to an increase in average life expectancy. But
there is no doubt that we cannot let air quality deteriorate without restraint. When air pollutants exceed the urban load level, it will cause serious harm to the health of local residents again (Wang et al., 2022).

For different types of ships/cargo (bulk, general cargo, liquid, container), These control variables are not significant in statistical results. This is because our research aims to provide a comprehensive analysis to evaluate the overall impact of shipping activity intensity on air quality and health. The intermediate and dependent variables we selected are also comprehensive air quality index and average life expectancy, without being subdivided into specific pollutant types, disease types, or causes of death. This proves that the shipping activity intensity index we selected matches the research level, thus maintaining consistency in variable selection.

The empirical results show that different types of ports have varying impacts on air quality and residents' health. Specifically, transshipment ports have better air quality and higher average life expectancy compared to gateway ports. This may be due to the fact that transshipment ports involve less operational activities (cargo loading/unloading, storage, and logistics distribution), and the efficient circulation of cargoes reduces the accumulation of pollutants (Yu et al., 2022). At the same time, the economic benefits brought by the transshipment business also improve the living standards of residents and improve public health (Peng et al., 2023). Transshipment terminals also locate relatively far from urban areas, as seen with Yangshan, Shanghai's new port located on an island connected to mainland via a 30 kilometers bridge (Wang and Ducruet, 2012). In contrast, industrial ports significantly lower life expectancy despite having little impact on overall air quality, possibly because the handling of bulky goods (such as coal, ore, and chemicals) leads to higher localized exposure and health risks, even if these risks are not reflected in the overall air quality indicators (Vos et al., 2024). Intermodal port refers to a port that integrates multiple modes of transportation (such as highway, railway, barge, sea...) to improve transport efficiency and reduce environmental impact. However, due to the intensive logistics and operational activities, such ports may have complex impacts on the localized environment and residents' health (Hricko et al., 2014). The regression results show that intermodal ports have no significant difference in air quality compared to non-intermodal ports, but are associated with lower average life expectancy. High-intensity operation of intermodal ports may increase the cumulative effects of non-traditional pollution such as noise and traffic congestion, thereby having adverse effects on residents' health.

Among other control variables, both GDP per capita and industrial output tend to have a negative impact on air quality. This means that with economic development and the acceleration of industrialization, the emissions of exhaust gases and pollutants will also increase, resulting in a negative impact on air quality. But at the same time, economic development and urbanization will also improve the quality of life and medical level of residents, thereby increasing the average life expectancy (Li et al., 2020). The increase in population density will positively impact the environment, because the development of social progress and the increase of population density are synchronous (Xing, 2020). Higher population density often accompanies higher possibility of establishing urban infrastructure to control air pollution, such as public transportation and urban greening. Lastly, wind speed and altitude, as natural factors, also have a significant impact on air quality. When the wind speed is low, the airflow is slow, and pollutants are not easily diluted and diffused, leading to a deterioration of air quality as pollutants linger in the air for longer periods (Yan et al., 2021). Higher altitude leads to thinner air, and it is more difficult for pollutants to disperse, so the air quality is relatively worse (Yang et al., 2020). Higher doctor density indicates
more abundant local medical resources, enabling timely disease prevention, diagnosis, treatment, and rehabilitation, thus promoting improvements in health levels (Jin et al., 2015).

<caption>**Table 3**</caption>

The results of nonlinear mediating effect model

<table><thead><tr><th></th><th>Model (6)</th><th>Model (7)</th><th>Model (8)</th></tr></thead><tbody><tr><td>SCORE</td><td>-6.364***<br>(-10.70)</td><td>20.630***<br>(3.00)</td><td>-6.4048***<br>(-10.75)</td></tr><tr><td>AQI<sup>3</sup></td><td>—</td><td>—</td><td>-5.49e-06***<br>(-2.66)</td></tr><tr><td>AQI<sup>2</sup></td><td>—</td><td>—</td><td>0.0015***<br>(2.93)</td></tr><tr><td>AQI</td><td>—</td><td>—</td><td>-0.1336***<br>(-3.12)</td></tr><tr><td>BULK</td><td>0.8483***<br>(3.14)</td><td>-0.5776<br>(-0.18)</td><td>0.8066***<br>(2.99)</td></tr><tr><td>GEN</td><td>0.0106<br>(0.02)</td><td>6.2199<br>(1.24)</td><td>0.1559<br>(0.36)</td></tr><tr><td>LIQ</td><td>-0.1096<br>(-0.33)</td><td>-8.6769**<br>(-2.27)</td><td>-0.1751<br>(-0.53)</td></tr><tr><td>CONT</td><td>-0.1116<br>(-0.34)</td><td>2.0932<br>(0.55)</td><td>-0.0851<br>(-0.26)</td></tr><tr><td>TGV</td><td>3.8968***<br>(10.21)</td><td>-9.2919**<br>(-2.10)</td><td>3.7955***<br>(9.93)</td></tr><tr><td>ICV</td><td>-3.7893***<br>(-9.01)</td><td>1.7751<br>(0.36)</td><td>-3.7682***<br>(-9.01)</td></tr><tr><td>IMV</td><td>-1.2741***<br>(-3.14)</td><td>4.8039<br>(1.02)</td><td>-1.2649***<br>(-3.13)</td></tr><tr><td>PCGDP</td><td>0.0493***<br>(3.50)</td><td>0.4581***<br>(2.81)</td><td>0.0544***<br>(3.84)</td></tr><tr><td>POP</td><td>-0.8632***<br>(-5.23)</td><td>-6.4714***<br>(-3.39)</td><td>-0.9167***<br>(-5.51)</td></tr><tr><td>RF</td><td>0.0096***<br>(3.33)</td><td>0.0451<br>(1.34)</td><td>0.0105***<br>(3.61)</td></tr><tr><td>HF</td><td>0.0146***<br>(2.81)</td><td>-0.1216**<br>(-2.02)</td><td>0.0153***<br>(2.94)</td></tr><tr><td>INDU</td><td>0.0688***<br>(7.17)</td><td>-0.3236***<br>(-2.91)</td><td>0.0611***<br>(6.16)</td></tr><tr><td>DOCTOR</td><td>0.0138**<br>(2.36)</td><td>-0.0055<br>(-0.08)</td><td>0.0151***<br>(2.60)</td></tr><tr><td>ELEV</td><td>0.1224***<br>(14.78)</td><td>0.3237***<br>(3.38)</td><td>0.1255***<br>(14.99)</td></tr></tbody></table>
<table><tr><td>WIND</td><td>0.2272***<br>(3.61)</td><td>-1.0795*<br>(-1.48)</td><td>0.2042***<br>(3.23)</td></tr><tr><td>Constant term</td><td>67.4055***<br>(140.47)</td><td>52.056***<br>(9.37)</td><td>70.9836***<br>(58.28)</td></tr><tr><td>R<sup>2</sup></td><td>0.9582</td><td>0.8557</td><td>0.9590</td></tr><tr><td>Root MSE</td><td>0.598</td><td>6.9211</td><td>0.5941</td></tr><tr><td>Var VIF &lt; 7.5</td><td>YES</td><td>YES</td><td>YES</td></tr><tr><td>F test (Prob&gt;F)</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr><tr><td>Obs</td><td>684</td><td>684</td><td>684</td></tr></table>

Note: The t statistic of the corresponding explanatory variable is shown in parentheses. ***, ** and * indicate significant at the significance levels of 1%, 5%, and 10% respectively.

## 4.3 Discussion

It can be seen that although shipping activities are an important economic source for port cities, they also pose threats and challenges to the environment and residents' health of port cities. Effective measures need to be taken to reduce the environmental and health costs of shipping activities and improve the sustainable development level of port cities. According to Sun et al. (2023), the inflection point of the cubic curve between the air quality index and average life expectancy can be calculated using the following equation:

$$ \mu_{1,2}^* = - \frac{\beta_2 \pm \sqrt{(\beta_2^2 - 3\beta_1\beta_3)}}{3\beta_3} \quad (9) $$

where $\beta_3$ represents the regression coefficient of the cubic power of the core explanatory variable, $\beta_2$ represents the regression coefficient of the square power of the core explanatory variable, and $\beta_1$ represents the regression coefficient of the core explanatory variable. $\mu_{1,2}^*$ denote the two inflection points estimated by eq. (9).

According to the calculation results, there is an inverted U-shaped curve between air quality and average life expectancy, with two inflection points of approximately 69 and 118, respectively. Among the selected port cities in China, cities with an air quality index below 69 include Fuzhou, Quanzhou, Shantou, Zhanjiang, Zhuhai, Beihai, Fangchenggang, Haikou, Zhoushan, etc. For these cities, the current development focus should be on vigorously developing the economy and shipping industry to enhance their comprehensive strength. Specific measures such as strengthening port infrastructure construction, improving port operation efficiency, developing transshipment business, and promoting the prosperity of the shipping industry.

Most cities have an air quality index between 69 and 118, including Guangzhou, Qinhuangdao, Lianyungang, Nantong, Suzhou, Wuxi, Yangzhou, Dalian, Qingdao, Rizhao, Shanghai, Dandong, Wenzhou etc. These cities are currently in a win-win stage of economic development and health improvement. Therefore, while maintaining the development of the shipping industry and economic growth, clean energy and environmental protection technologies should be adopted to improve energy utilization efficiency, in order to minimize the impact on health. Such efforts contribute to achieving sustainable development, enabling cities to maintain economic prosperity while also
ensuring the health of their residents (Nogué-Algueró, 2020).

There are fewer cities with an air quality index close or higher than 118, mainly including Qinhuangdao, Tangshan, and Tianjin. For these cities, the growth rate of the shipping industry should be moderately slowed down to avoid the negative impact of excessive development on the environment. It is recommended to establish stricter industry environmental standards, increase efforts to rectify high pollution emissions, prevent further environmental degradation, and avoid serious harm to the health of local residents in the future, while increasing investment in air pollution control facilities, introducing advanced governance technologies, and ensuring compliance with atmospheric emissions (Wu et al., 2022). Overall, formulating corresponding development strategies for the shipping industry based on the air quality status of different cities is crucial to ensuring sustainable economic development and residents' health.

Based on our research results, we propose the following policy suggestions: For port cities that have not yet reached the first turning point of air quality index, the development of the shipping industry should be accelerated to promote economic efficiency and improve residents' living standards. For port cities located between two inflection points, efforts should be made to promote clean production technologies, improve energy efficiency, and foster smooth cargo transfers in order to minimize the negative impacts of shipping activities. This can extend the duration of this stage and enhance its positive effects. For port cities that have already exceeded the second turning point of air quality index, it is crucial to expedite the green transformation of shipping industry, so it is preferable not to excessively develop highly polluting shipping activities. Instead, stricter environmental regulation should be implemented, and pollution governance should be strengthened, in order to ensure the sustainable development of port cities. This is seen in China and elsewhere, with new ports created at distance from inner cities, such as Yangshan (Shanghai), New Priok (Jakarta), Port Qasim (Karachi), Nava Sheva (Mumbai), and Tuas (Singapore), to name but a few examples in Asia.

Regarding the current research, several limitations should be noted. Firstly, due to data availability limitations, we uniformly used air quality index to represent air pollution levels without separately considering the main pollutants emitted by maritime activities, such as PM, SOx, and NOx. Secondly, as there were no available data sources related to incidence rate and cause of death, we used only the average life expectancy as a proxy for public health, while the impact of shipping activities on health could be measured more accurately by indicators such as the incidence rate of related diseases. That is to say, the current study only considered the overall level, and has not been disaggregated. Future research could strive for larger-scale data support, select more abundant and scientifically sound variables, in order to analyze the impact mechanisms of shipping activities on air quality and public health in China and other cities of the world.

# 5 Conclusions and policy implications

To study the impact of shipping activities on urban air quality and public health, this paper conducts correlation and regression analysis on panel data of 36 port cities in China from 2001 to 2019. We analyzed the relationship between different shipping activities and air quality and average life expectancy, and examined the impact of shipping activities on the health level of port city residents as well as the mediating effect of air quality. The main conclusions of this study are as follows.

Shipping activities have a significant impact on the health level of port cities. This impact is
influenced by the mediating variable of air quality and has a partial mediating effect. Shipping activities not only emit a large amount of exhaust gas, leading to deterioration of air quality, increasing the risk of illness and death for port city residents, but also affecting the average life expectancy of port cities through other means, such as water pollution, noise pollution, accidents, and port terminal operations as well as hinterland connectivity.

Shipping activities have a negative impact on air quality and health, which is linear and consistent. However, the impact of air quality on health presents a complex nonlinear relationship, with a negative coefficient of the cubic power of air quality, indicating an inverted N-shaped curve. The two inflection points of the curve between air quality and average expected life are about 69 and 118 respectively.

Our conclusions indicate that shipping activities have complex impacts on the air quality and public health in China's port cities, depending on the development mode and stage of shipping. Although the initial development of shipping may have negative impacts on the environment and health, it also improved the economic development level of port cities and the living and medical conditions of residents. Through appropriate policy measures and technological innovation, the negative impact of shipping activities can be alleviated or even transformed into positive impacts. However, in order to achieve sustainable development, port cities need to recognize the limitations of shipping development and take corresponding planning and management measures to ensure a balance between shipping, environment, and health.

**Declaration of Competing Interest**

None.

**References**

Anenberg, S., Miller, J., Henze, D., et al. (2019). A global snapshot of the air pollution-related health impacts of transportation sector emissions in 2010 and 2015.

Aydin, M., Camliyurt, G., Akyuz, E., et al. (2021). Analyzing human error contributions to maritime environmental risk in oil/chemical tanker ship. Human and Ecological Risk Assessment: an International Journal, 27(7), 1838–1859.
https://doi.org/10.1080/10807039.2021.1910011

Badino, A., Borelli, D., Gaggero, T., et al. (2016). Airborne noise emissions from ships: Experimental characterization of the source and propagation over land. Applied Acoustics, 104, 158-171. https://doi.org/10.1016/j.apacoust.2015.11.005

Baron, R.M., Kenny, D.A., 1986. The moderator mediator variable distinction in social psychological-research - conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51 (6), 1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173.

Barregard, L., Molnar, P., Jonson, J. E., et al. (2019). Impact on Population Health of Baltic Shipping Emissions. International Journal of Environmental Research and Public Health, 16(11), 1954. https://doi.org/10.3390/ijerph16111954
Bauleo L., Bucci S., Antonucci C., Sozzi R., Davoli M., Forastiere F. et al. (2019) Long-term exposure to air pollutants from multiple sources and mortality in an industrial area: a cohort study. Journal of Occupational and Environmental Medicine, 76(1): 48–57.

Chang, C.-C. (2016). Causal analysis of carbon emissions, deadweight tonnage of global shipping fleet, fuel oil consumption, and economic activities in marine transportation. Energy Sources, Part B: Economics, Planning, and Policy, 11(4), 303-308.
https://doi.org/10.1080/15567249.2011.607884

Chatzinikolaou, S. D., Oikonomou, S. D., & Ventikos, N. P. (2015). Health externalities of ship air pollution at port – Piraeus port case study. Transportation Research Part D: Transport and Environment, 40, 155-165. https://doi.org/10.1016/j.trd.2015.08.010

Chen, C., Saikawa, E., Comer, B., et al. (2019). Ship Emission Impacts on Air Quality and Human Health in the Pearl River Delta (PRD) Region, China, in 2015, With Projections to 2030. Geohealth, 3(9), 284-306. https://doi.org/10.1029/2019GH000183

Chen, Q., Wu, W., Guo, Y., et al. (2022). Environmental impact, treatment technology and monitoring system of ship domestic sewage: A review. Science of the Total Environment, 811, 151410. https://doi.org/10.1016/j.scitotenv.2021.151410

Colvile, R. N., Hutchinson, E. J., Mindell, J. S., et al. (2001). The transport sector as a source of air pollution. Atmospheric Environment, 35, 1537–1565. https://doi.org/10.1016/s1352-2310(00)00551-3

Corbett, J. J., Winebrake, J. J., Green, E. H., et al. (2007). Mortality from Ship Emissions: A Global Assessment. Environmental science & technology, 41(24), 8512–8518.
https://doi.org/10.1021/es071686z

Crippa, M., Janssens-Maenhout, G., Guizzardi, D., et al. (2019). Contribution and uncertainty of sectorial and regional emissions to regional and global PM2.5 health impacts. Atmospheric Chemistry and Physics, 19(7), 5165-5186. https://doi.org/10.5194/acp-19-5165-2019

Duarte, C. M., Chapuis, L., Collin, S. P., et al. (2021). The soundscape of the Anthropocene ocean. Science, 371(6529), eaba4658. https://doi.org/10.1126/science.aba4658

Ducruet, C. (2020). Port specialization and connectivity in the global maritime network. Maritime Policy & Management, 49(1), 1-17. https://doi.org/10.1080/03088839.2020.1840640

Ducruet, C., & Itoh, H. (2022). Spatial Network Analysis of Container Port Operations: The Case of Ship Turnaround Times. Networks and Spatial Economics, 22(4), 883-902.
https://doi.org/10.1007/s11067-022-09570-z

Ducruet, C., Polo Martin, B., Sene, M.A., et al. (2024). Ports and their influence on local air pollution and public health: a global analysis. Science of The Total Environment, 915, 170099. https://doi.org/10.1016/j.scitotenv.2024.170099

Endres, S., Maes, F., Hopkins, F., et al. (2018). A New Perspective at the Ship-Air-Sea-Interface: The Environmental Impacts of Exhaust Gas Scrubber Discharge. Frontiers in Marine Science, 5, 139. https://doi.org/10.3389/fmars.2018.00139
Fameli, K. M., Kotrikla, A. M., Psanis, C., et al. (2020). Estimation of the emissions by transport in two port cities of the northeastern Mediterranean, Greece. Environmental Pollution, 257, 113598. https://doi.org/10.1016/j.envpol.2019.113598

Fredianelli, L., Gaggero, T., Bolognese, M., et al. (2022). Source characterization guidelines for noise mapping of port areas. Heliyon, 8(3), e09021.
https://doi.org/10.1016/j.heliyon.2022.e09021

Gan, L., Lu, T., & Shu, Y. (2023). Diffusion and Superposition of Ship Exhaust Gas in Port Area Based on Gaussian Puff Model: A Case Study on Shenzhen Port. Journal of Marine Science and Engineering, 11(2), 330. https://doi.org/10.3390/jmse11020330

Garbatov, Y., & Georgiev, P. (2022). Air Pollution and Economic Impact from Ships Operating in the Port of Varna. Atmosphere, 13(9), 1526. https://doi.org/10.3390/atmos13091526

Gianicolo E.A.L., Bruni A., Mangia C., Cervino M., Vigotti M.A. (2013) Acute effects of urban and industrial pollution in a government-designated “Environmental risk area”: the case of Brindisi, Italy. International Journal of Environmental Health Research, 23(5): 446-460.

Gibbs, D., Rigot-Muller, P., Mangan, J., et al. (2014). The role of sea ports in end-to-end maritime transport chain emissions. Energy Policy, 64, 337-348.
https://doi.org/10.1016/j.enpol.2013.09.024

Gillingham K., Huang P. (2021) Racial disparities in the health effects from air pollution: evidence from ports. National Bureau of Economic Research Working Paper No. 29108, http://www.nber.org/papers/w29108

Guo, T., Chen, J., & Liu, P. (2022). Impact of Emerging Transport Technologies on Freight Economic and Environmental Performance: A System Dynamics View. International journal of environmental research and public health, 19(22), 15077.
https://doi.org/10.3390/ijerph192215077

Guo, Z., & Zhang, M. (2019). The conservation efficacy of coastal wetlands in China based on landscape development and stress. Ocean & Coastal Management, 175, 70-78.
https://doi.org/10.1016/j.ocecoaman.2019.03.018

Hausman, J.A. (1978). Specification tests in econometrics. Econome. J. Econ. Soc. 1251-1271.

Havel, J. E., Kovalenko, K. E., Thomaz, S. M., et al. (2015). Aquatic invasive species: challenges for the future. Hydrobiologia, 750(1), 147-170. https://doi.org/10.1007/s10750-014-2166-0

He, X., & Shi, J. (2022). The effect of air pollution on Chinese green bond market: The mediation role of public concern. In Journal of Environmental Management.
https://doi.org/10.1016/J.JENVMAN.2022.116522

Hill, T. D., Jorgenson, A. K., Ore, P., Balistreri, K. S., Clark, B. (2018). Air quality and life expectancy in the United States: An analysis of the moderating effect of income
inequality. SSM - population health, 7, 100346.
https://doi.org/10.1016/j.ssmph.2018.100346

Hoyle B.S. (1989) The port-City interface: Trends, problems and examples. Geoforum, 20(4): 429-435. https://doi.org/10.1016/0016-7185(89)90026-2

Hricko, A., Rowland, G., Eckel, S., et al. (2014). Global Trade, Local Impacts: Lessons from California on Health Impacts and Environmental Justice Concerns for Residents Living near Freight Rail Yards. International Journal of Environmental Research and Public Health, 11(2), 1914-1941. https://doi.org/10.3390/ijerph110201914

Hua, W., Chen, J., Qin, Q., Wan, Z., Song, L. (2021). Causation analysis and governance strategy for hazardous cargo accidents at ports: Case study of Tianjin Port's hazardous cargo explosion accident. Marine pollution bulletin, 173(Pt B), 113053.
https://doi.org/10.1016/j.marpolbul.2021.113053

Jahangiri, S., Nikolova, N., & Tenekedjiev, K. (2018). An improved emission inventory method for estimating engine exhaust emissions from ships. Sustainable Environment Research, 28(6), 374-381. https://doi.org/10.1016/j.serj.2018.08.005

Jalkanen, J. P., Johansson, L., Andersson, M. H., et al. (2022). Underwater noise emissions from ships during 2014-2020. Environmental Pollution, 311, 119766.
https://doi.org/10.1016/j.envpol.2022.119766

Jin, C., Cheng, J., Lu, Y., et al. (2015). Spatial inequity in access to healthcare facilities at a county level in a developing country: a case study of Deqing County, Zhejiang, China. International Journal for Equity in Health, 14, 67. https://doi.org/10.1186/s12939-015-0195-6

Jonson, J., E. Gauss, M., Jalkanen, J., P, et al. (2019). Effects of strengthening the Baltic Sea ECA regulations. Atmospheric Chemistry and Physics, 19(21), 13469-13487.
https://doi.org/10.5194/acp-19-13469-2019

Jonson, J. E., Gauss, M., Schulz, M., et al. (2020). Effects of global ship emissions on European air pollution levels. Atmospheric Chemistry and Physics, 20(19), 11399-11422.
https://doi.org/10.5194/acp-20-11399-2020

Kara, G., Emecen Kara, E. G., & Okşaş, O. (2021). Estimation of land-based emissions during container terminal operations in the Ambarlı Port, Turkey. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 236, 779-788. https://doi.org/10.1177/14750902211052223

Khan, R., Yin, J, Mustafa, F., Shi, W. (2023). Factor assessment of hazardous cargo ship berthing accidents using an ordered logit regression model. Ocean Engineering. 284, 115211.
https://doi.org/10.1016/j.oceaneng.2023.115211

Kim, SY., Pope, A.C., Marshall, J.D. et al. Reanalysis of the association between reduction in long-term PM2.5 concentrations and improved life expectancy. Environ Health 20, 102 (2021). https://doi.org/10.1186/s12940-021-00785-0
Kopela, S. (2017). Making ships cleaner: Reducing air pollution from international shipping. Review of European. Comparative & International Environmental Law, 26(3), 231-242.
https://doi.org/10.1111/reel.12220

Koski, M., Stedmon, C., Trapp, S. (2017). Ecological effects of scrubber water discharge on coastal plankton: Potential synergistic effects of contaminants reduce survival and feeding of the copepod Acartia tonsa. Marine Environmental Research, 129, 374-385.
https://doi.org/10.1016/j.marenvres.2017.06.006.

Kovács, P., Petres, T., Tóth, L. (2005). A new measure of multicollinearity in linear regression models. Int. Stat. Rev. 73 (3), 405-412.

Lecue, M., & Darbra, R. M. (2019). Accidents in European ports involving chemical substances: Characteristics and trends. Safety Science, 115, 278-284.
https://doi.org/10.1016/j.ssci.2019.02.015

Li, S., Zhou, C., & Wang, S. (2019). Does modernization affect carbon dioxide emissions? A panel data analysis. Science of the Total Environment, 663, 426-435.
https://doi.org/10.1016/j.scitotenv.2019.01.373

Li, Z., He, S., Su, S., et al. (2020). Public Services Equalization in Urbanizing China: Indicators, Spatiotemporal Dynamics and Implications on Regional Economic Disparities. Social Indicators Research, 152(1), 1-65. https://doi.org/10.1007/s11205-020-02405-9

Lin, H., Tao, J., Qian, Z. M., et al. (2018). Shipping pollution emission associated with increased cardiovascular mortality: A time series study in Guangzhou, China. Environmental Pollution, 241, 862-868. https://doi.org/10.1016/j.envpol.2018.06.027

Linares, C., Belda, F., Lopez-Bueno, J. A., et al. (2021). Short-term associations of air pollution and meteorological variables on the incidence and severity of COVID-19 in Madrid (Spain): a time series study. Environmental Science Europe, 33(1), 107.
https://doi.org/10.1186/s12302-021-00548-1

Lindgren, S. (2021). The coast is clear: Shipping emission standards, air quality and infant health. Transportation Research Part D: Transport and Environment, 100, 103067.
https://doi.org/10.1016/j.trd.2021.103067

Liu, H., Huang, F., Deng, F., et al. (2021). Road freight emission in China: From supply chain perspective. Environmental Pollution, 285, 117511.
https://doi.org/10.1016/j.envpol.2021.117511

Liu, H., Meng, Z.-H., Lv, Z.-F., et al. (2019). Emissions and health impacts from global shipping embodied in US-China bilateral trade. Nature Sustainability, 2(11), 1027-1033.
https://doi.org/10.1038/s41893-019-0414-z

Lloret, J., Carreño, A., Carić, H., et al., (2021). Environmental and human health impacts of cruise tourism: A review. Marine Pollution Bulletin, 173, Part A, 112979.
https://doi.org/10.1016/j.marpolbul.2021.112979.
Mamoudou, I., Zhang, F., Chen, Q., et al. (2018). Characteristics of PM₂.₅ from ship emissions and their impacts on the ambient air: A case study in Yangshan Harbor, Shanghai. Science of the Total Environment, 640-641, 207-216. https://doi.org/10.1016/j.scitotenv.2018.05.261

MEE. (2012). Technical Regulation on Ambient Air Quality Index (HJ 633—2012).

Merico, E., Dinoi, A., & Contini, D. (2019). Development of an integrated modelling-measurement system for near-real-time estimates of harbour activity impact to atmospheric pollution in coastal cities. Transportation Research Part D: Transport and Environment, 73, 108-119. https://doi.org/10.1016/j.trd.2019.06.009

MOT. (2023). Statistical Bulletin on the Development of the Transportation Industry in 2022

Mueller, N., Westerby, M., & Nieuwenhuijsen, M. (2023). Health impact assessments of shipping and port-sourced air pollution on a global scale: A scoping literature review. Environmental Research, 216, 114460. https://doi.org/10.1016/j.envres.2022.114460

Murphy, E., King, E.A. (2014). An assessment of residential exposure to environmental noise at a shipping port. Environment International, 63, 207-215.
https://doi.org/10.1016/j.envint.2013.11.001.

NBS. (2021). Seventh National Population Census Bulletin.

Nguyen, P.-N., Woo, S.-H., & Kim, H. (2022). Ship emissions in hotelling phase and loading/unloading in Southeast Asia ports. Transportation Research Part D: Transport and Environment, 105, 103223. https://doi.org/10.1016/j.trd.2022.103223

Nogué-Algueró, B. (2020). Growth in the docks: ports, metabolic flows and socio-environmental impacts. Sustainability Science, 15(1), 11-30. https://doi.org/10.1007/s11625-019-00764-y

Peng, P., Poon, J. P. H., Cheng, S., et al. (2023). Container port influence: a spatial diffusion analysis of global transshipments. Maritime Policy & Management, 1-18.
https://doi.org/10.1080/03088839.2023.2289493

Ramacher, M. O. P., Karl, M., Bieser, J., et al. (2019). Urban population exposure to NOₓ emissions from local shipping in three Baltic Sea harbour cities – a generic approach. Atmospheric Chemistry and Physics, 19(14), 9153-9179. https://doi.org/10.5194/acp-19-9153-2019

Robinson R. (2002) Ports as elements in value-driven chain systems: the new paradigm. Maritime Policy and Management, 29(3): 241-255. https://doi.org/10.1080/03088830210132623

Rowangould, D., Rowangould, G., & Niemeier, D. (2018). Evaluation of the Health Impacts of Rolling Back a Port Clean Trucks Program. Transportation Research Record: Journal of the Transportation Research Board, 2672(11), 53-64.
https://doi.org/10.1177/0361198118793328

Shen, L., Hou, Y., Yang, Q., et al. (2022). Synergistic path planning for ship-deployed multiple UAVs to monitor vessel pollution in ports. Transportation Research Part D: Transport and Environment, 110, 103415. https://doi.org/10.1016/j.trd.2022.103415
Sofiev, M., Winebrake, J. J., Johansson, L., et al. (2018). Cleaner fuels for ships provide public health benefits with climate tradeoffs. Nature Communications, 9(1), 406.
https://doi.org/10.1038/s41467-017-02774-9

Sorte, S., Rodrigues, V., Lourenço, R., et al. (2021). Emission inventory for harbour-related activities: comparison of two distinct bottom-up methodologies. Air Quality, Atmosphere & Health, 14(6), 831-842. https://doi.org/10.1007/s11869-021-00982-3

Sun, B., Fang, C., Liao, X., et al. (2023). The relationship between urbanization and air pollution affected by intercity factor mobility: A case of the Yangtze River Delta region. Environmental Impact Assessment Review, 100, 107092.
https://doi.org/10.1016/j.eiar.2023.107092

Tang, L., Ramacher, M. O. P., Moldanová, J., et al. (2020). The impact of ship emissions on air quality and human health in the Gothenburg area – Part 1: 2012 emissions. Atmospheric Chemistry and Physics, 20(12), 7509-7530. https://doi.org/10.5194/acp-20-7509-2020

Tiselius, P., & Magnusson, K. (2017). Toxicity of treated bilge water: The need for revised regulatory control. Marine Pollution Bulletin, 114(2), 860-866.
https://doi.org/10.1016/j.marpolbul.2016.11.010

Tomal, M. (2021). Evaluation of coupling coordination degree and convergence behaviour of local development: A spatiotemporal analysis of all Polish municipalities over the period 2003 - 2019. Sustainable Cities and Society, 71, 102992.
DOI:10.1016/J.SCS.2021.102992

Toscano, D., Murena, F., Quaranta, F., et al. (2021). Assessment of the impact of ship emissions on air quality based on a complete annual emission inventory using AIS data for the port of Naples. Ocean Engineering, 232, 109166.
https://doi.org/10.1016/j.oceaneng.2021.109166

UNCTAD. (2022). Review of Maritime Transport, 2021.

Verschuur, J., Koks, E. E., & Hall, J. W. (2022). Ports' criticality in international trade and global supply-chains. Nature Communications, 13(1), 4351. https://doi.org/10.1038/s41467-022-32070-0

Vos, H.C., Kangueehi, K.I., Toesie, R. et al. (2024) Spatial variability of dust concentration and deposition around an industrial port in South Africa emphasises the complexity of sources and transport. Air Qual Atmos Health. https://doi.org/10.1007/s11869-024-01581-8

Wang, C., & Ducruet, C. (2014). Transport corridors and regional balance in China: the case of coal trade and logistics. Journal of Transport Geography, 40, 3-16.
https://doi.org/10.1016/j.jtrangeo.2014.08.009

Wang, J. (2016). Port-City Interplays in China. https://doi.org/10.4324/9781315601533

Wang, L., Peng, C., Shi, W., et al. (2020). Carbon dioxide emissions from port container distribution: Spatial characteristics and driving factors. Transportation Research Part D: Transport and Environment, 82, 102318. https://doi.org/10.1016/j.trd.2020.102318
Wang, Y., Sun, K., Li, L., et al. (2022). The impacts of economic level and air pollution on public health at the micro and macro level. Journal of Cleaner Production, 366, 132932.
https://doi.org/10.1016/j.jclepro.2022.132932

Wu, J., Zheng, H., Zhe, F., et al. (2018). Study on the relationship between urbanization and fine particulate matter (PM2.5) concentration and its implication in China. Journal of Cleaner Production, 182, 872-882.
https://doi.org/10.1016/j.jclepro.2018.02.060

Wu, Y., Wang, W., Liu, C., et al. (2020). The association between long-term fine particulate air pollution and life expectancy in China, 2013 to 2017. Science of the Total Environment, 712, 136507.
https://doi.org/10.1016/j.scitotenv.2020.136507

Wu, Z., Zhang, C., Li, Y., et al. (2022). Environmental governance investment and Air Quality: Based on China's provincial panel data. Frontiers in Environmental Science, 10, 977036.
https://doi.org/10.3389/fenvs.2022.977036

Xing, W. (2020). Spatial Layout Planning of High Density Urban Green Space along the Coast and Its Empirical Analysis. Journal of Coastal Research, 104, 925-929.
https://doi.org/10.2112/JCR-SI104-160.1

Yan, L., Hu, W., & Yin, M.-Q. (2021). An Investigation of the Correlation between Pollutant Dispersion and Wind Environment: Evaluation of Static Wind Speed. Polish Journal of Environmental Studies, 30(5), 4311-4323.
https://doi.org/10.15244/pjoes/130040

Yang, J., Kang, S., Ji, Z., et al. (2020). Investigating air pollutant concentrations, impact factors, and emission control strategies in western China by using a regional climate-chemistry model. Chemosphere, 246, 125767.
https://doi.org/10.1016/j.chemosphere.2019.125767

Yang, L., Zhang, Q., Zhang, Y., et al. (2021). An AIS-based emission inventory and the impact on air quality in Tianjin port based on localized emission factors. Science of the Total Environment, 783, 146869.
https://doi.org/10.1016/j.scitotenv.2021.146869

Ye, P. (2022). Policy Effects of Ecological Red Lines on Industrial Upgrading and Health Promotion: Evidence From China Based on DID Model. Front Public Health, 10, 844593.
https://doi.org/10.3389/fpubh.2022.844593

Ytreberg, E., Eriksson, M., Maljutenko, I., et al. (2020). Environmental impacts of grey water discharge from ships in the Baltic Sea. Marine Pollution Bulletin, 152, 110891.
https://doi.org/10.1016/j.marpolbul.2020.110891

Yu, B. (2021). Ecological effects of new-type urbanization in China. Renewable & Sustainable Energy Reviews, 135, 110239. DOI:10.1016/j.rser.2020.110239

Yu, Y., Sun, R., Sun, Y., et al. (2022). China's Port Carbon Emission Reduction: A Study of Emission-Driven Factors. Atmosphere, 13 (4), 550.
https://doi.org/10.3390/atmos13040550

Yuan, C.-S., Cheng, W.-H., & Huang, H.-Y. (2022). Spatiotemporal distribution characteristics and potential sources of VOCs at an industrial harbor city in southern Taiwan: Three-year
VOCs monitoring data analysis. Journal of Environmental Management, 303, 114259.
https://doi.org/10.1016/j.jenvman.2021.114259

Zhang, Y., Eastham, S. D., Lau, A. K. H., et al. (2021). Global air quality and health impacts of domestic and international shipping. Environmental Research Letters, 16(8), 084055.
https://doi.org/10.1088/1748-9326/ac146b

Zhang, Y., Zhou, R., Hu, D., et al. (2022). Modelling driving factors of PM<sub>2.5</sub> concentrations in port cities of the Yangtze River Delta. Marine Pollution Bulletin, 184, 114131.
https://doi.org/10.1016/j.marpolbul.2022.114131

Zhao, M., Zhang, Y., Ma, W., et al. (2013). Characteristics and ship traffic source identification of air pollutants in China's largest port. Atmospheric Environment, 64, 277-286.
https://doi.org/10.1016/j.atmosenv.2012.10.007

Zhao, T., Chen, M., & Lee, H. (2022). A Study on the Framework for Estimating Ship Air Pollutant Emissions—Focusing on Ports of South Korea. Atmosphere, 13(7), 1141.
https://doi.org/10.3390/atmos13071141

Zheng, Y., Xue, T., Zhao, H., & Lei, Y. (2022). Increasing life expectancy in China by achieving its 2025 air quality target. Environmental science and ecotechnology, 12, 100203.
https://doi.org/10.1016/j.ese.2022.100203

Zhou, D., Xu, J., Wang, L.P., & Lin, Z. (2015). Assessing urbanization quality using structure and function analyses: A case study of the urban agglomeration around Hangzhou Bay (UAHB), China. Habitat International, 49, 165-176.
DOI:10.1016/J.HABITATINT.2015.05.020
<table><caption>Appendix: Detailed Descriptive Statistics</caption><thead><tr><th>City</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th></tr></thead><tbody><tr><td>Dalian</td><td></td><td>19</td><td>2231.42</td><td>638</td><td>6115</td><td>1549.69</td><td></td><td>19</td><td>0.59</td><td>0.10</td><td>1.95</td><td>0.51</td></tr><tr><td>Dandong</td><td></td><td>19</td><td>161.74</td><td>49</td><td>306</td><td>66.51</td><td></td><td>19</td><td>1.77</td><td>0.14</td><td>3.88</td><td>1.06</td></tr><tr><td>Jinzhou</td><td></td><td>19</td><td>213.26</td><td>17</td><td>597</td><td>203.70</td><td></td><td>19</td><td>0.92</td><td>0.10</td><td>3.83</td><td>0.96</td></tr><tr><td>Qinhuangdao</td><td></td><td>19</td><td>1124.90</td><td>179</td><td>2661</td><td>937.58</td><td></td><td>19</td><td>0.42</td><td>0.12</td><td>0.96</td><td>0.30</td></tr><tr><td>Qingdao</td><td></td><td>19</td><td>3140.53</td><td>903</td><td>5391</td><td>1489.91</td><td></td><td>19</td><td>0.33</td><td>0.05</td><td>0.95</td><td>0.28</td></tr><tr><td>Rizhao</td><td></td><td>19</td><td>1226.79</td><td>301</td><td>2561</td><td>895.60</td><td></td><td>19</td><td>0.35</td><td>0.08</td><td>0.72</td><td>0.24</td></tr><tr><td>Tangshan</td><td></td><td>19</td><td>1251.58</td><td>15</td><td>3904</td><td>1459.37</td><td></td><td>19</td><td>0.57</td><td>0.07</td><td>1.85</td><td>0.54</td></tr><tr><td>Tianjin</td><td></td><td>19</td><td>3145.26</td><td>787</td><td>6453</td><td>2160.08</td><td></td><td>19</td><td>0.51</td><td>0.09</td><td>1.39</td><td>0.42</td></tr><tr><td>Weihai</td><td></td><td>19</td><td>508.05</td><td>45</td><td>1025</td><td>284.47</td><td></td><td>19</td><td>0.63</td><td>0.21</td><td>3.52</td><td>0.75</td></tr><tr><td>Yantai</td><td></td><td>19</td><td>1757.68</td><td>257</td><td>4083</td><td>1207.75</td><td></td><td>19</td><td>0.50</td><td>0.08</td><td>1.37</td><td>0.45</td></tr><tr><td>Yingkou</td><td></td><td>19</td><td>724.68</td><td>148</td><td>1823</td><td>486.50</td><td></td><td>19</td><td>0.76</td><td>0.12</td><td>2.30</td><td>0.69</td></tr><tr><td>Jiaxing</td><td></td><td>19</td><td>345.89</td><td>11</td><td>1225</td><td>385.64</td><td></td><td>19</td><td>0.88</td><td>0.20</td><td>3.20</td><td>0.78</td></tr><tr><td>Lianyungang</td><td></td><td>19</td><td>1152.90</td><td>222</td><td>2206</td><td>722.67</td><td></td><td>19</td><td>0.57</td><td>0.10</td><td>1.94</td><td>0.51</td></tr><tr><td>Nanjing</td><td></td><td>19</td><td>366.16</td><td>88</td><td>1108</td><td>246.95</td><td></td><td>19</td><td>1.02</td><td>0.21</td><td>2.79</td><td>0.73</td></tr><tr><td>Nantong</td><td></td><td>19</td><td>624.84</td><td>206</td><td>1334</td><td>354.51</td><td></td><td>19</td><td>1.12</td><td>0.17</td><td>2.60</td><td>0.93</td></tr><tr><td>Ningbo</td><td></td><td>19</td><td>3272.26</td><td>663</td><td>8512</td><td>2193.37</td><td></td><td>19</td><td>0.18</td><td>0.04</td><td>0.53</td><td>0.15</td></tr><tr><td>Shanghai</td><td></td><td>19</td><td>5286.16</td><td>1848</td><td>8427</td><td>1883.32</td><td></td><td>19</td><td>0.30</td><td>0.09</td><td>0.98</td><td>0.29</td></tr><tr><td>Suzhou</td><td></td><td>19</td><td>1736.79</td><td>316</td><td>4303</td><td>1317.12</td><td></td><td>19</td><td>0.94</td><td>0.17</td><td>2.21</td><td>0.77</td></tr><tr><td>Wenzhou</td><td></td><td>19</td><td>150.21</td><td>12</td><td>400</td><td>131.98</td><td></td><td>19</td><td>1.23</td><td>0.24</td><td>4.20</td><td>1.09</td></tr><tr><td>Wuxi</td><td></td><td>19</td><td>485.16</td><td>166</td><td>1446</td><td>355.77</td><td></td><td>19</td><td>0.93</td><td>0.16</td><td>3.04</td><td>0.77</td></tr><tr><td>Yangzhou</td><td></td><td>19</td><td>273.63</td><td>16</td><td>973</td><td>305.95</td><td></td><td>19</td><td>1.17</td><td>0.21</td><td>4.45</td><td>1.11</td></tr><tr><td>Zhenjiang</td><td></td><td>19</td><td>197.26</td><td>1</td><td>981</td><td>262.93</td><td></td><td>19</td><td>0.74</td><td>0.05</td><td>2.17</td><td>0.66</td></tr><tr><td>Zhoushan</td><td></td><td>19</td><td>1185.74</td><td>28</td><td>4755</td><td>1339.12</td><td></td><td>19</td><td>0.73</td><td>0.17</td><td>2.70</td><td>0.72</td></tr><tr><td>Beihai</td><td></td><td>19</td><td>145.95</td><td>29</td><td>444</td><td>123.76</td><td></td><td>19</td><td>1.50</td><td>0.20</td><td>6.08</td><td>1.42</td></tr><tr><td>Fangchenggang</td><td></td><td>19</td><td>373.79</td><td>57</td><td>876</td><td>266.97</td><td></td><td>19</td><td>0.75</td><td>0.12</td><td>2.20</td><td>0.59</td></tr><tr><td>Fuzhou</td><td></td><td>19</td><td>762.42</td><td>195</td><td>1823</td><td>499.23</td><td></td><td>19</td><td>1.18</td><td>0.12</td><td>3.61</td><td>1.19</td></tr><tr><td>Guangzhou</td><td></td><td>19</td><td>2095.74</td><td>471</td><td>5203</td><td>1609.58</td><td></td><td>19</td><td>0.57</td><td>0.13</td><td>1.66</td><td>0.48</td></tr><tr><td>Haikou</td><td></td><td>19</td><td>327.37</td><td>25</td><td>1082</td><td>297.30</td><td></td><td>19</td><td>1.43</td><td>0.16</td><td>8.36</td><td>2.28</td></tr><tr><td>Jiangmen</td><td></td><td>19</td><td>99.26</td><td>7</td><td>329</td><td>95.22</td><td></td><td>19</td><td>2.51</td><td>0.28</td><td>12.97</td><td>3.51</td></tr><tr><td>Quanzhou</td><td></td><td>19</td><td>204.32</td><td>42</td><td>519</td><td>160.53</td><td></td><td>19</td><td>1.06</td><td>0.11</td><td>5.15</td><td>1.61</td></tr><tr><td>Xiamen</td><td></td><td>19</td><td>1662.74</td><td>701</td><td>2813</td><td>660.59</td><td></td><td>19</td><td>0.17</td><td>0.06</td><td>0.35</td><td>0.11</td></tr><tr><td>Shantou</td><td></td><td>19</td><td>181.74</td><td>31</td><td>456</td><td>119.83</td><td></td><td>19</td><td>1.08</td><td>0.12</td><td>7.39</td><td>1.66</td></tr><tr><td>Shenzhen</td><td></td><td>19</td><td>3350.47</td><td>730</td><td>7850</td><td>2008.23</td><td></td><td>19</td><td>0.14</td><td>0.04</td><td>0.41</td><td>0.12</td></tr><tr><td>Hong Kong</td><td></td><td>19</td><td>8866.16</td><td>4953</td><td>15683</td><td>2199.90</td><td></td><td>19</td><td>0.11</td><td>0.07</td><td>0.21</td><td>0.03</td></tr><tr><td>Zhanjiang</td><td></td><td>19</td><td>358.84</td><td>54</td><td>1112</td><td>274.75</td><td></td><td>19</td><td>0.43</td><td>0.09</td><td>1.13</td><td>0.33</td></tr><tr><td>Zhuhai</td><td></td><td>19</td><td>738.16</td><td>99</td><td>2302</td><td>696.88</td><td></td><td>19</td><td>0.76</td><td>0.20</td><td>2.39</td><td>0.64</td></tr></tbody></table>
<table><thead><tr><th>City</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th></tr></thead><tbody><tr><td>Dalian</td><td></td><td>19</td><td>177.72</td><td>160.58</td><td>234.01</td><td>22.69</td><td></td><td>19</td><td>45.43</td><td>6.56</td><td>135.00</td><td>39.24</td></tr><tr><td>Dandong</td><td></td><td>19</td><td>75.28</td><td>19.87</td><td>164.37</td><td>39.92</td><td></td><td>19</td><td>1.72</td><td>0.18</td><td>5.55</td><td>1.50</td></tr><tr><td>Jinzhou</td><td></td><td>19</td><td>68.46</td><td>33.19</td><td>163.66</td><td>35.33</td><td></td><td>19</td><td>4.49</td><td>0.29</td><td>14.40</td><td>5.15</td></tr><tr><td>Qinhuangdao</td><td></td><td>19</td><td>151.37</td><td>91.56</td><td>200.68</td><td>28.07</td><td></td><td>19</td><td>28.75</td><td>4.42</td><td>72.88</td><td>25.66</td></tr><tr><td>Qingdao</td><td></td><td>19</td><td>180.57</td><td>161.31</td><td>232.62</td><td>22.10</td><td></td><td>19</td><td>109.67</td><td>15.60</td><td>236.15</td><td>75.80</td></tr><tr><td>Rizhao</td><td></td><td>19</td><td>138.82</td><td>81.59</td><td>201.38</td><td>37.96</td><td></td><td>19</td><td>39.78</td><td>4.28</td><td>113.89</td><td>37.58</td></tr><tr><td>Tangshan</td><td></td><td>19</td><td>127.37</td><td>17.06</td><td>210.69</td><td>66.16</td><td></td><td>19</td><td>46.88</td><td>0.09</td><td>146.35</td><td>56.70</td></tr><tr><td>Tianjin</td><td></td><td>19</td><td>151.03</td><td>75.68</td><td>232.62</td><td>50.29</td><td></td><td>19</td><td>85.55</td><td>10.45</td><td>194.45</td><td>72.50</td></tr><tr><td>Weihai</td><td></td><td>19</td><td>52.96</td><td>12.72</td><td>141.51</td><td>32.27</td><td></td><td>19</td><td>7.10</td><td>0.21</td><td>17.33</td><td>4.63</td></tr><tr><td>Yantai</td><td></td><td>19</td><td>124.05</td><td>79.76</td><td>233.03</td><td>40.59</td><td></td><td>19</td><td>34.61</td><td>3.36</td><td>100.65</td><td>30.15</td></tr><tr><td>Yingkou</td><td>DWT</td><td>19</td><td>132.16</td><td>39.87</td><td>170.62</td><td>40.93</td><td></td><td>19</td><td>16.57</td><td>1.31</td><td>53.75</td><td>15.45</td></tr><tr><td>Jiaxing</td><td></td><td>19</td><td>43.74</td><td>7.97</td><td>57.59</td><td>11.71</td><td></td><td>19</td><td>3.24</td><td>0.05</td><td>12.04</td><td>3.96</td></tr><tr><td>Lianyungang</td><td></td><td>19</td><td>116.77</td><td>40.67</td><td>172.50</td><td>45.93</td><td></td><td>19</td><td>25.41</td><td>2.53</td><td>62.60</td><td>20.02</td></tr><tr><td>Nanjing</td><td></td><td>19</td><td>47.01</td><td>30.05</td><td>88.91</td><td>12.91</td><td></td><td>19</td><td>4.21</td><td>1.04</td><td>12.72</td><td>3.35</td></tr><tr><td>Nantong</td><td></td><td>19</td><td>145.45</td><td>65.53</td><td>201.99</td><td>46.94</td><td></td><td>19</td><td>10.86</td><td>1.58</td><td>27.02</td><td>9.56</td></tr><tr><td>Ningbo</td><td></td><td>19</td><td>186.61</td><td>164.37</td><td>234.01</td><td>23.66</td><td></td><td>19</td><td>138.33</td><td>11.76</td><td>366.17</td><td>110.82</td></tr><tr><td>Shanghai</td><td></td><td>19</td><td>170.83</td><td>108.08</td><td>236.58</td><td>37.54</td><td></td><td>19</td><td>159.45</td><td>25.57</td><td>272.20</td><td>90.00</td></tr><tr><td>Suzhou</td><td></td><td>19</td><td>92.51</td><td>38.68</td><td>134.84</td><td>28.89</td><td></td><td>19</td><td>23.10</td><td>1.79</td><td>57.25</td><td>20.93</td></tr><tr><td>Wenzhou</td><td></td><td>19</td><td>67.10</td><td>7.32</td><td>94.92</td><td>25.32</td><td></td><td>19</td><td>2.01</td><td>0.09</td><td>5.69</td><td>2.04</td></tr><tr><td>Wuxi</td><td></td><td>19</td><td>84.92</td><td>43.19</td><td>237.77</td><td>43.57</td><td></td><td>19</td><td>8.08</td><td>1.71</td><td>30.94</td><td>8.35</td></tr><tr><td>Yangzhou</td><td></td><td>19</td><td>54.03</td><td>20.91</td><td>203.48</td><td>40.27</td><td></td><td>19</td><td>3.22</td><td>0.13</td><td>11.15</td><td>3.50</td></tr><tr><td>Zhenjiang</td><td></td><td>19</td><td>50.99</td><td>23.27</td><td>152.23</td><td>29.33</td><td></td><td>19</td><td>2.75</td><td>0.02</td><td>15.34</td><td>4.14</td></tr></tbody></table>
<table><tr><td>Zhoushan</td><td>19</td><td>177.8<br>1</td><td>152.0<br>2</td><td>217.6<br>7</td><td>20.19</td><td>19</td><td>37.79</td><td>0.65</td><td>131.2<br>2</td><td>40.33</td></tr><tr><td>Beihai</td><td>19</td><td>75.27</td><td>15.93</td><td>136.8<br>9</td><td>40.31</td><td>19</td><td>1.77</td><td>0.09</td><td>6.55</td><td>1.92</td></tr><tr><td>Fangchenggan<br>g</td><td>19</td><td>102.2<br>1</td><td>77.14</td><td>147.7<br>0</td><td>17.19</td><td>19</td><td>9.64</td><td>0.83</td><td>27.15</td><td>8.51</td></tr><tr><td>Fuzhou</td><td>19</td><td>107.4<br>7</td><td>22.34</td><td>168.4<br>2</td><td>56.22</td><td>19</td><td>12.79</td><td>1.46</td><td>31.71</td><td>11.53</td></tr><tr><td>Guangzhou</td><td>19</td><td>155.0<br>6</td><td>68.72</td><td>217.6<br>1</td><td>43.32</td><td>19</td><td>45.27</td><td>5.25</td><td>100.8<br>3</td><td>36.46</td></tr><tr><td>Haikou</td><td>19</td><td>56.75</td><td>22.60</td><td>157.8<br>8</td><td>38.97</td><td>19</td><td>2.78</td><td>0.17</td><td>10.11</td><td>2.78</td></tr><tr><td>Jiangmen</td><td>19</td><td>66.04</td><td>6.90</td><td>150.6<br>7</td><td>37.35</td><td>19</td><td>0.86</td><td>0.02</td><td>3.02</td><td>0.78</td></tr><tr><td>Quanzhou</td><td>19</td><td>127.0<br>8</td><td>26.52</td><td>167.0<br>5</td><td>46.46</td><td>19</td><td>4.25</td><td>0.17</td><td>12.22</td><td>3.99</td></tr><tr><td>Xiamen</td><td>19</td><td>154.3<br>4</td><td>81.49</td><td>217.6<br>7</td><td>48.56</td><td>19</td><td>47.68</td><td>11.84</td><td>86.98</td><td>27.63</td></tr><tr><td>Shantou</td><td>19</td><td>45.93</td><td>27.99</td><td>60.14</td><td>8.60</td><td>19</td><td>1.96</td><td>0.34</td><td>4.16</td><td>1.27</td></tr><tr><td>Shenzhen</td><td>19</td><td>164.6<br>8</td><td>92.20</td><td>234.0<br>1</td><td>47.11</td><td>19</td><td>132.7<br>3</td><td>24.92</td><td>211.9<br>6</td><td>66.24</td></tr><tr><td>Hong Kong</td><td>19</td><td>175.0<br>7</td><td>93.50</td><td>217.6<br>7</td><td>29.23</td><td>19</td><td>207.8<br>9</td><td>104.3<br>1</td><td>366.4<br>0</td><td>47.44</td></tr><tr><td>Zhanjiang</td><td>19</td><td>154.1<br>1</td><td>85.91</td><td>172.5<br>0</td><td>25.10</td><td>19</td><td>11.44</td><td>2.36</td><td>38.53</td><td>10.28</td></tr><tr><td>Zhuhai</td><td>19</td><td>79.49</td><td>47.06</td><td>168.1<br>9</td><td>37.78</td><td>19</td><td>6.19</td><td>0.89</td><td>17.56</td><td>5.65</td></tr></table>
<table><thead><tr><th>City</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th></tr></thead><tbody><tr><td>Dalian</td><td></td><td>19</td><td>29.42</td><td>10.05</td><td>46.78</td><td>12.66</td><td></td><td>19</td><td>0.184</td><td>0.105</td><td>0.343</td><td>0.078</td></tr><tr><td>Dandong</td><td></td><td>19</td><td>6.61</td><td>0.54</td><td>15.79</td><td>5.46</td><td></td><td>19</td><td>0.063</td><td>0.037</td><td>0.097</td><td>0.017</td></tr><tr><td>Jinzhou</td><td></td><td>19</td><td>6.71</td><td>1.11</td><td>15.81</td><td>4.10</td><td></td><td>19</td><td>0.050</td><td>0.031</td><td>0.082</td><td>0.015</td></tr><tr><td>Qinhuangdao</td><td></td><td>19</td><td>21.74</td><td>11.17</td><td>28.77</td><td>5.76</td><td></td><td>19</td><td>0.127</td><td>0.065</td><td>0.195</td><td>0.045</td></tr><tr><td>Qingdao</td><td></td><td>19</td><td>34.13</td><td>10.40</td><td>57.74</td><td>15.38</td><td></td><td>19</td><td>0.263</td><td>0.098</td><td>0.454</td><td>0.121</td></tr><tr><td>Rizhao</td><td></td><td>19</td><td>21.94</td><td>2.93</td><td>46.38</td><td>14.13</td><td></td><td>19</td><td>0.135</td><td>0.039</td><td>0.268</td><td>0.079</td></tr><tr><td>Tangshan</td><td></td><td>19</td><td>26.97</td><td>1.10</td><td>65.67</td><td>23.26</td><td></td><td>19</td><td>0.153</td><td>0.019</td><td>0.372</td><td>0.126</td></tr><tr><td>Tianjin</td><td></td><td>19</td><td>37.53</td><td>11.37</td><td>55.06</td><td>15.01</td><td></td><td>19</td><td>0.243</td><td>0.084</td><td>0.433</td><td>0.131</td></tr><tr><td>Weihai</td><td></td><td>19</td><td>2.57</td><td>0.67</td><td>4.47</td><td>1.41</td><td></td><td>19</td><td>0.040</td><td>0.021</td><td>0.069</td><td>0.015</td></tr><tr><td>Yantai</td><td></td><td>19</td><td>16.75</td><td>2.19</td><td>44.31</td><td>12.43</td><td></td><td>19</td><td>0.128</td><td>0.051</td><td>0.287</td><td>0.073</td></tr><tr><td>Yingkou</td><td></td><td>19</td><td>20.40</td><td>2.52</td><td>37.00</td><td>12.58</td><td></td><td>19</td><td>0.108</td><td>0.054</td><td>0.171</td><td>0.042</td></tr><tr><td>Jiaxing</td><td></td><td>19</td><td>4.70</td><td>1.02</td><td>10.91</td><td>3.09</td><td></td><td>19</td><td>0.040</td><td>0.020</td><td>0.064</td><td>0.012</td></tr><tr><td>Lianyungang</td><td></td><td>19</td><td>12.99</td><td>3.06</td><td>23.46</td><td>7.09</td><td></td><td>19</td><td>0.102</td><td>0.038</td><td>0.172</td><td>0.046</td></tr><tr><td>Nanjing</td><td></td><td>19</td><td>15.44</td><td>5.79</td><td>25.69</td><td>6.72</td><td></td><td>19</td><td>0.066</td><td>0.043</td><td>0.092</td><td>0.012</td></tr><tr><td>Nantong</td><td></td><td>19</td><td>15.73</td><td>3.51</td><td>33.62</td><td>8.30</td><td></td><td>19</td><td>0.102</td><td>0.069</td><td>0.160</td><td>0.025</td></tr><tr><td>Ningbo</td><td></td><td>19</td><td>38.91</td><td>12.85</td><td>58.41</td><td>14.42</td><td></td><td>19</td><td>0.299</td><td>0.093</td><td>0.637</td><td>0.163</td></tr><tr><td>Shanghai</td><td></td><td>19</td><td>53.22</td><td>22.10</td><td>70.54</td><td>15.15</td><td></td><td>19</td><td>0.377</td><td>0.137</td><td>0.568</td><td>0.143</td></tr><tr><td>Suzhou</td><td></td><td>19</td><td>31.52</td><td>3.53</td><td>60.46</td><td>20.01</td><td></td><td>19</td><td>0.146</td><td>0.054</td><td>0.254</td><td>0.071</td></tr><tr><td>Wenzhou</td><td></td><td>19</td><td>5.75</td><td>1.65</td><td>8.93</td><td>2.40</td><td></td><td>19</td><td>0.050</td><td>0.030</td><td>0.089</td><td>0.012</td></tr><tr><td>Wuxi</td><td></td><td>19</td><td>17.13</td><td>7.82</td><td>29.50</td><td>5.65</td><td></td><td>19</td><td>0.082</td><td>0.050</td><td>0.135</td><td>0.023</td></tr><tr><td>Yangzhou</td><td></td><td>19</td><td>7.85</td><td>1.25</td><td>14.13</td><td>4.16</td><td></td><td>19</td><td>0.053</td><td>0.027</td><td>0.101</td><td>0.021</td></tr><tr><td>Zhenjiang</td><td></td><td>19</td><td>10.68</td><td>2.22</td><td>32.92</td><td>6.91</td><td></td><td>19</td><td>0.048</td><td>0.029</td><td>0.116</td><td>0.022</td></tr><tr><td>Zhoushan</td><td></td><td>19</td><td>24.30</td><td>3.28</td><td>53.60</td><td>16.30</td><td></td><td>19</td><td>0.153</td><td>0.064</td><td>0.353</td><td>0.085</td></tr><tr><td>Beihai</td><td></td><td>19</td><td>1.45</td><td>0.25</td><td>3.50</td><td>1.05</td><td></td><td>19</td><td>0.048</td><td>0.024</td><td>0.107</td><td>0.020</td></tr><tr><td>Fangchenggang</td><td></td><td>19</td><td>6.55</td><td>1.00</td><td>11.50</td><td>4.17</td><td></td><td>19</td><td>0.062</td><td>0.041</td><td>0.084</td><td>0.014</td></tr><tr><td>Fuzhou</td><td></td><td>19</td><td>10.08</td><td>2.96</td><td>21.30</td><td>5.03</td><td></td><td>19</td><td>0.087</td><td>0.054</td><td>0.136</td><td>0.025</td></tr><tr><td>Guangzhou</td><td></td><td>19</td><td>38.34</td><td>12.82</td><td>60.62</td><td>14.93</td><td></td><td>19</td><td>0.193</td><td>0.079</td><td>0.336</td><td>0.087</td></tr><tr><td>Haikou</td><td></td><td>19</td><td>5.80</td><td>0.89</td><td>12.45</td><td>4.04</td><td></td><td>19</td><td>0.054</td><td>0.020</td><td>0.153</td><td>0.035</td></tr><tr><td>Jiangmen</td><td></td><td>19</td><td>2.42</td><td>0.39</td><td>4.48</td><td>1.60</td><td></td><td>19</td><td>0.063</td><td>0.022</td><td>0.247</td><td>0.061</td></tr><tr><td>Quanzhou</td><td></td><td>19</td><td>8.09</td><td>2.10</td><td>12.99</td><td>3.99</td><td></td><td>19</td><td>0.069</td><td>0.031</td><td>0.116</td><td>0.021</td></tr><tr><td>Xiamen</td><td></td><td>19</td><td>12.91</td><td>2.10</td><td>21.70</td><td>7.39</td><td></td><td>19</td><td>0.132</td><td>0.054</td><td>0.216</td><td>0.058</td></tr><tr><td>Shantou</td><td></td><td>19</td><td>3.27</td><td>1.31</td><td>5.18</td><td>1.44</td><td></td><td>19</td><td>0.038</td><td>0.022</td><td>0.137</td><td>0.025</td></tr><tr><td>Shenzhen</td><td></td><td>19</td><td>19.19</td><td>6.64</td><td>25.80</td><td>5.56</td><td></td><td>19</td><td>0.251</td><td>0.090</td><td>0.407</td><td>0.107</td></tr><tr><td>Hong Kong</td><td></td><td>19</td><td>24.84</td><td>17.83</td><td>29.77</td><td>3.12</td><td></td><td>19</td><td>0.421</td><td>0.259</td><td>0.668</td><td>0.077</td></tr><tr><td>Zhanjiang</td><td></td><td>19</td><td>13.61</td><td>2.21</td><td>30.19</td><td>9.31</td><td></td><td>19</td><td>0.084</td><td>0.039</td><td>0.141</td><td>0.029</td></tr><tr><td>Zhuhai</td><td></td><td>19</td><td>7.12</td><td>1.98</td><td>13.84</td><td>4.30</td><td></td><td>19</td><td>0.061</td><td>0.030</td><td>0.110</td><td>0.026</td></tr></tbody></table>
<table><thead><tr><th>City</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th></tr></thead><tbody><tr><td>Dalian</td><td></td><td>19</td><td>70.41</td><td>55.79</td><td>97.36</td><td>10.35</td><td></td><td>19</td><td>79.92</td><td>76.66</td><td>81.49</td><td>1.70</td></tr><tr><td>Dandong</td><td></td><td>19</td><td>68.08</td><td>56.66</td><td>76.66</td><td>5.60</td><td></td><td>19</td><td>76.49</td><td>73.34</td><td>79.16</td><td>1.97</td></tr><tr><td>Jinzhou</td><td></td><td>19</td><td>90.15</td><td>82.10</td><td>98.49</td><td>4.95</td><td></td><td>19</td><td>75.65</td><td>72.79</td><td>77.85</td><td>1.59</td></tr><tr><td>Qinhuangdao</td><td></td><td>19</td><td>73.12</td><td>54.25</td><td>129.60</td><td>17.98</td><td></td><td>19</td><td>76.62</td><td>74.38</td><td>79.50</td><td>1.47</td></tr><tr><td>Qingdao</td><td></td><td>19</td><td>79.06</td><td>62.18</td><td>112.40</td><td>10.31</td><td></td><td>19</td><td>79.51</td><td>76.74</td><td>80.98</td><td>1.54</td></tr><tr><td>Rizhao</td><td></td><td>19</td><td>67.20</td><td>43.48</td><td>96.70</td><td>19.30</td><td></td><td>19</td><td>75.63</td><td>72.74</td><td>78.94</td><td>1.92</td></tr><tr><td>Tangshan</td><td></td><td>19</td><td>105.90</td><td>92.44</td><td>126.60</td><td>9.06</td><td></td><td>19</td><td>74.69</td><td>72.50</td><td>77.28</td><td>1.49</td></tr><tr><td>Tianjin</td><td></td><td>19</td><td>94.42</td><td>72.51</td><td>146.20</td><td>19.34</td><td></td><td>19</td><td>79.89</td><td>76.71</td><td>81.84</td><td>1.87</td></tr><tr><td>Weihai</td><td></td><td>19</td><td>61.29</td><td>49.90</td><td>82.34</td><td>8.89</td><td></td><td>19</td><td>78.81</td><td>75.93</td><td>81.49</td><td>1.60</td></tr><tr><td>Yantai</td><td></td><td>19</td><td>66.49</td><td>53.61</td><td>83.93</td><td>9.13</td><td></td><td>19</td><td>77.29</td><td>74.38</td><td>80.47</td><td>1.82</td></tr><tr><td>Yingkou</td><td></td><td>19</td><td>81.99</td><td>71.30</td><td>96.93</td><td>6.47</td><td></td><td>19</td><td>76.54</td><td>73.48</td><td>78.52</td><td>1.62</td></tr><tr><td>Jiaxing</td><td></td><td>19</td><td>77.90</td><td>67.53</td><td>91.99</td><td>6.69</td><td></td><td>19</td><td>79.18</td><td>75.43</td><td>82.49</td><td>2.36</td></tr><tr><td>Lianyungang</td><td></td><td>19</td><td>78.18</td><td>67.81</td><td>97.00</td><td>9.40</td><td></td><td>19</td><td>75.83</td><td>73.74</td><td>78.00</td><td>1.25</td></tr><tr><td>Nanjing</td><td></td><td>19</td><td>87.20</td><td>73.18</td><td>122.90</td><td>12.51</td><td></td><td>19</td><td>79.51</td><td>75.38</td><td>83.59</td><td>2.68</td></tr><tr><td>Nantong</td><td></td><td>19</td><td>74.70</td><td>61.01</td><td>91.73</td><td>8.66</td><td></td><td>19</td><td>78.39</td><td>74.50</td><td>82.82</td><td>2.74</td></tr><tr><td>Ningbo</td><td></td><td>19</td><td>68.17</td><td>48.01</td><td>81.86</td><td>7.97</td><td></td><td>19</td><td>80.54</td><td>79.36</td><td>81.66</td><td>0.74</td></tr><tr><td>Shanghai</td><td></td><td>19</td><td>72.49</td><td>58.27</td><td>89.68</td><td>8.16</td><td></td><td>19</td><td>81.73</td><td>79.52</td><td>83.66</td><td>1.37</td></tr><tr><td>Suzhou</td><td></td><td>19</td><td>78.78</td><td>63.32</td><td>96.22</td><td>9.21</td><td></td><td>19</td><td>80.73</td><td>77.25</td><td>83.82</td><td>2.27</td></tr><tr><td>Wenzhou</td><td></td><td>19</td><td>65.68</td><td>58.52</td><td>77.12</td><td>5.15</td><td></td><td>19</td><td>78.46</td><td>75.54</td><td>81.71</td><td>1.84</td></tr><tr><td>Wuxi</td><td></td><td>19</td><td>86.60</td><td>78.03</td><td>101.60</td><td>5.76</td><td></td><td>19</td><td>79.41</td><td>75.64</td><td>83.00</td><td>2.76</td></tr><tr><td>Yangzhou</td><td></td><td>19</td><td>77.57</td><td>65.70</td><td>94.86</td><td>9.66</td><td></td><td>19</td><td>76.53</td><td>74.20</td><td>78.56</td><td>1.30</td></tr><tr><td>Zhenjiang</td><td></td><td>19</td><td>78.55</td><td>65.09</td><td>97.90</td><td>11.25</td><td></td><td>19</td><td>77.92</td><td>73.85</td><td>81.98</td><td>2.54</td></tr><tr><td>Zhoushan</td><td></td><td>19</td><td>50.64</td><td>38.66</td><td>66.16</td><td>8.01</td><td></td><td>19</td><td>78.90</td><td>77.71</td><td>80.10</td><td>0.75</td></tr><tr><td>Beihai</td><td></td><td>19</td><td>48.23</td><td>36.14</td><td>62.36</td><td>7.26</td><td></td><td>19</td><td>78.16</td><td>75.20</td><td>79.92</td><td>1.51</td></tr><tr><td>Fangchenggang</td><td></td><td>19</td><td>47.26</td><td>41.76</td><td>52.64</td><td>3.31</td><td></td><td>19</td><td>75.49</td><td>72.76</td><td>76.95</td><td>1.38</td></tr><tr><td>Fuzhou</td><td></td><td>19</td><td>58.32</td><td>51.51</td><td>66.60</td><td>3.94</td><td></td><td>19</td><td>77.17</td><td>74.27</td><td>80.31</td><td>1.82</td></tr><tr><td>Guangzhou</td><td></td><td>19</td><td>67.46</td><td>57.36</td><td>81.79</td><td>7.84</td><td></td><td>19</td><td>79.25</td><td>75.46</td><td>82.52</td><td>2.29</td></tr><tr><td>Haikou</td><td></td><td>19</td><td>40.11</td><td>30.23</td><td>62.50</td><td>7.23</td><td></td><td>19</td><td>76.15</td><td>73.26</td><td>78.78</td><td>1.73</td></tr><tr><td>Jiangmen</td><td></td><td>19</td><td>60.97</td><td>52.98</td><td>75.60</td><td>6.66</td><td></td><td>19</td><td>77.44</td><td>73.30</td><td>80.12</td><td>2.25</td></tr><tr><td>Quanzhou</td><td></td><td>19</td><td>57.17</td><td>49.10</td><td>63.50</td><td>3.93</td><td></td><td>19</td><td>74.22</td><td>70.10</td><td>78.10</td><td>2.60</td></tr><tr><td>Xiamen</td><td></td><td>19</td><td>54.57</td><td>49.88</td><td>63.10</td><td>3.43</td><td></td><td>19</td><td>78.00</td><td>73.39</td><td>80.89</td><td>2.51</td></tr><tr><td>Shantou</td><td></td><td>19</td><td>53.55</td><td>42.06</td><td>63.98</td><td>5.27</td><td></td><td>19</td><td>76.10</td><td>74.12</td><td>78.08</td><td>1.24</td></tr><tr><td>Shenzhen</td><td></td><td>19</td><td>55.33</td><td>46.74</td><td>75.57</td><td>6.18</td><td></td><td>19</td><td>81.56</td><td>80.26</td><td>82.90</td><td>0.79</td></tr><tr><td>Hong Kong</td><td></td><td>19</td><td>53.27</td><td>44.74</td><td>73.57</td><td>6.14</td><td></td><td>19</td><td>83.46</td><td>81.57</td><td>85.50</td><td>1.20</td></tr><tr><td>Zhanjiang</td><td></td><td>19</td><td>49.08</td><td>40.87</td><td>55.58</td><td>4.72</td><td></td><td>19</td><td>75.53</td><td>74.12</td><td>77.28</td><td>0.87</td></tr><tr><td>Zhuhai</td><td></td><td>19</td><td>50.26</td><td>38.45</td><td>70.86</td><td>9.36</td><td></td><td>19</td><td>79.16</td><td>76.50</td><td>83.00</td><td>2.30</td></tr></tbody></table>
<table><thead><tr><th>City</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th></tr></thead><tbody><tr><td>Dalian</td><td></td><td>19</td><td>0.172</td><td>0.101</td><td>0.299</td><td>0.060</td><td></td><td>19</td><td>0.086</td><td>0.025</td><td>0.178</td><td>0.057</td></tr><tr><td>Dandong</td><td></td><td>19</td><td>0.437</td><td>0.176</td><td>0.849</td><td>0.224</td><td></td><td>19</td><td>0.250</td><td>0.051</td><td>0.667</td><td>0.149</td></tr><tr><td>Jinzhou</td><td></td><td>19</td><td>0.432</td><td>0.227</td><td>0.587</td><td>0.098</td><td></td><td>19</td><td>0.093</td><td>0.028</td><td>0.256</td><td>0.061</td></tr><tr><td>Qinhuangdao</td><td></td><td>19</td><td>0.851</td><td>0.610</td><td>0.932</td><td>0.076</td><td></td><td>19</td><td>0.040</td><td>0.016</td><td>0.090</td><td>0.024</td></tr><tr><td>Qingdao</td><td></td><td>19</td><td>0.250</td><td>0.192</td><td>0.319</td><td>0.030</td><td></td><td>19</td><td>0.057</td><td>0.025</td><td>0.142</td><td>0.032</td></tr><tr><td>Rizhao</td><td></td><td>19</td><td>0.741</td><td>0.652</td><td>0.921</td><td>0.068</td><td></td><td>19</td><td>0.087</td><td>0.038</td><td>0.119</td><td>0.025</td></tr><tr><td>Tangshan</td><td></td><td>19</td><td>0.514</td><td>0.014</td><td>0.888</td><td>0.342</td><td></td><td>19</td><td>0.029</td><td>0.007</td><td>0.067</td><td>0.016</td></tr><tr><td>Tianjin</td><td></td><td>19</td><td>0.429</td><td>0.292</td><td>0.523</td><td>0.067</td><td></td><td>19</td><td>0.087</td><td>0.035</td><td>0.169</td><td>0.046</td></tr><tr><td>Weihai</td><td></td><td>19</td><td>0.089</td><td>0.005</td><td>0.258</td><td>0.076</td><td></td><td>19</td><td>0.084</td><td>0.038</td><td>0.409</td><td>0.082</td></tr><tr><td>Yantai</td><td></td><td>19</td><td>0.343</td><td>0.242</td><td>0.606</td><td>0.095</td><td></td><td>19</td><td>0.081</td><td>0.019</td><td>0.203</td><td>0.058</td></tr><tr><td>Yingkou</td><td></td><td>19</td><td>0.535</td><td>0.424</td><td>0.673</td><td>0.079</td><td></td><td>19</td><td>0.116</td><td>0.051</td><td>0.286</td><td>0.071</td></tr><tr><td>Jiaxing</td><td></td><td>19</td><td>0.332</td><td>0.009</td><td>0.652</td><td>0.230</td><td></td><td>19</td><td>0.158</td><td>0.029</td><td>0.384</td><td>0.103</td></tr><tr><td>Lianyungang</td><td></td><td>19</td><td>0.624</td><td>0.504</td><td>0.733</td><td>0.061</td><td></td><td>19</td><td>0.129</td><td>0.053</td><td>0.248</td><td>0.061</td></tr><tr><td>Nanjing</td><td></td><td>19</td><td>0.519</td><td>0.277</td><td>0.684</td><td>0.125</td><td></td><td>19</td><td>0.132</td><td>0.062</td><td>0.265</td><td>0.063</td></tr><tr><td>Nantong</td><td></td><td>19</td><td>0.634</td><td>0.352</td><td>0.813</td><td>0.148</td><td></td><td>19</td><td>0.122</td><td>0.054</td><td>0.277</td><td>0.064</td></tr><tr><td>Ningbo</td><td></td><td>19</td><td>0.183</td><td>0.067</td><td>0.338</td><td>0.071</td><td></td><td>19</td><td>0.018</td><td>0.005</td><td>0.069</td><td>0.016</td></tr><tr><td>Shanghai</td><td></td><td>19</td><td>0.127</td><td>0.091</td><td>0.237</td><td>0.032</td><td></td><td>19</td><td>0.063</td><td>0.030</td><td>0.125</td><td>0.033</td></tr><tr><td>Suzhou</td><td></td><td>19</td><td>0.495</td><td>0.284</td><td>0.675</td><td>0.144</td><td></td><td>19</td><td>0.218</td><td>0.103</td><td>0.435</td><td>0.114</td></tr><tr><td>Wenzhou</td><td></td><td>19</td><td>0.413</td><td>0.033</td><td>0.782</td><td>0.269</td><td></td><td>19</td><td>0.100</td><td>0.021</td><td>0.221</td><td>0.057</td></tr><tr><td>Wuxi</td><td></td><td>19</td><td>0.556</td><td>0.320</td><td>0.818</td><td>0.165</td><td></td><td>19</td><td>0.114</td><td>0.034</td><td>0.199</td><td>0.053</td></tr><tr><td>Yangzhou</td><td></td><td>19</td><td>0.585</td><td>0.415</td><td>0.897</td><td>0.132</td><td></td><td>19</td><td>0.183</td><td>0.030</td><td>0.459</td><td>0.134</td></tr><tr><td>Zhenjiang</td><td></td><td>19</td><td>0.535</td><td>0.042</td><td>0.882</td><td>0.276</td><td></td><td>19</td><td>0.224</td><td>0.026</td><td>0.479</td><td>0.138</td></tr><tr><td>Zhoushan</td><td></td><td>19</td><td>0.488</td><td>0.292</td><td>0.743</td><td>0.103</td><td></td><td>19</td><td>0.031</td><td>0.017</td><td>0.066</td><td>0.013</td></tr><tr><td>Beihai</td><td></td><td>19</td><td>0.610</td><td>0.279</td><td>0.740</td><td>0.118</td><td></td><td>19</td><td>0.199</td><td>0.042</td><td>0.519</td><td>0.116</td></tr><tr><td>Fangchenggang</td><td></td><td>19</td><td>0.800</td><td>0.581</td><td>0.879</td><td>0.076</td><td></td><td>19</td><td>0.096</td><td>0.042</td><td>0.228</td><td>0.051</td></tr><tr><td>Fuzhou</td><td></td><td>19</td><td>0.323</td><td>0.132</td><td>0.594</td><td>0.135</td><td></td><td>19</td><td>0.135</td><td>0.036</td><td>0.366</td><td>0.103</td></tr><tr><td>Guangzhou</td><td></td><td>19</td><td>0.337</td><td>0.139</td><td>0.529</td><td>0.108</td><td></td><td>19</td><td>0.077</td><td>0.024</td><td>0.195</td><td>0.054</td></tr><tr><td>Haikou</td><td></td><td>19</td><td>0.061</td><td>0.008</td><td>0.227</td><td>0.067</td><td></td><td>19</td><td>0.131</td><td>0.017</td><td>0.468</td><td>0.126</td></tr><tr><td>Jiangmen</td><td></td><td>19</td><td>0.210</td><td>0.040</td><td>0.417</td><td>0.122</td><td></td><td>19</td><td>0.099</td><td>0.005</td><td>0.205</td><td>0.063</td></tr><tr><td>Quanzhou</td><td></td><td>19</td><td>0.249</td><td>0.091</td><td>0.438</td><td>0.095</td><td></td><td>19</td><td>0.098</td><td>0.040</td><td>0.299</td><td>0.054</td></tr><tr><td>Xiamen</td><td></td><td>19</td><td>0.093</td><td>0.042</td><td>0.157</td><td>0.040</td><td></td><td>19</td><td>0.038</td><td>0.016</td><td>0.112</td><td>0.023</td></tr><tr><td>Shantou</td><td></td><td>19</td><td>0.322</td><td>0.110</td><td>0.616</td><td>0.121</td><td></td><td>19</td><td>0.074</td><td>0.012</td><td>0.355</td><td>0.076</td></tr><tr><td>Shenzhen</td><td></td><td>19</td><td>0.024</td><td>0.010</td><td>0.083</td><td>0.015</td><td></td><td>19</td><td>0.009</td><td>0.003</td><td>0.026</td><td>0.006</td></tr><tr><td>Hong Kong</td><td></td><td>19</td><td>0.103</td><td>0.037</td><td>0.190</td><td>0.046</td><td></td><td>19</td><td>0.033</td><td>0.021</td><td>0.063</td><td>0.011</td></tr><tr><td>Zhanjiang</td><td></td><td>19</td><td>0.558</td><td>0.260</td><td>0.692</td><td>0.115</td><td></td><td>19</td><td>0.073</td><td>0.011</td><td>0.194</td><td>0.058</td></tr><tr><td>Zhuhai</td><td></td><td>19</td><td>0.314</td><td>0.127</td><td>0.467</td><td>0.126</td><td></td><td>19</td><td>0.047</td><td>0.005</td><td>0.211</td><td>0.045</td></tr></tbody></table>
<table><thead><tr><th>City</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th></tr></thead><tbody><tr><td>Dalian</td><td></td><td>19</td><td>0.236</td><td>0.133</td><td>0.530</td><td>0.098</td><td></td><td>19</td><td>0.273</td><td>0.017</td><td>0.393</td><td>0.086</td></tr><tr><td>Dandong</td><td></td><td>19</td><td>0.017</td><td>0.006</td><td>0.055</td><td>0.013</td><td></td><td>19</td><td>0.029</td><td>0.002</td><td>0.082</td><td>0.020</td></tr><tr><td>Jinzhou</td><td></td><td>19</td><td>0.295</td><td>0.121</td><td>0.540</td><td>0.126</td><td></td><td>19</td><td>0.157</td><td>0.028</td><td>0.347</td><td>0.096</td></tr><tr><td>Qinhuangdao</td><td></td><td>19</td><td>0.063</td><td>0.020</td><td>0.230</td><td>0.049</td><td></td><td>19</td><td>0.018</td><td>0.003</td><td>0.045</td><td>0.014</td></tr><tr><td>Qingdao</td><td></td><td>19</td><td>0.117</td><td>0.075</td><td>0.178</td><td>0.024</td><td></td><td>19</td><td>0.535</td><td>0.411</td><td>0.627</td><td>0.069</td></tr><tr><td>Rizhao</td><td></td><td>19</td><td>0.105</td><td>0.029</td><td>0.185</td><td>0.051</td><td></td><td>19</td><td>0.031</td><td>0.004</td><td>0.088</td><td>0.023</td></tr><tr><td>Tangshan</td><td></td><td>19</td><td>0.421</td><td>0.045</td><td>0.954</td><td>0.349</td><td></td><td>19</td><td>0.031</td><td>0.004</td><td>0.061</td><td>0.016</td></tr><tr><td>Tianjin</td><td></td><td>19</td><td>0.077</td><td>0.043</td><td>0.130</td><td>0.022</td><td></td><td>19</td><td>0.305</td><td>0.137</td><td>0.436</td><td>0.073</td></tr><tr><td>Weihai</td><td></td><td>19</td><td>0.036</td><td>0.005</td><td>0.300</td><td>0.067</td><td></td><td>19</td><td>0.115</td><td>0.059</td><td>0.245</td><td>0.046</td></tr><tr><td>Yantai</td><td></td><td>19</td><td>0.073</td><td>0.007</td><td>0.161</td><td>0.038</td><td></td><td>19</td><td>0.150</td><td>0.039</td><td>0.300</td><td>0.089</td></tr><tr><td>Yingkou</td><td></td><td>19</td><td>0.095</td><td>0.022</td><td>0.240</td><td>0.065</td><td></td><td>19</td><td>0.159</td><td>0.003</td><td>0.346</td><td>0.135</td></tr><tr><td>Jiaxing</td><td></td><td>19</td><td>0.382</td><td>0.168</td><td>0.747</td><td>0.194</td><td></td><td>19</td><td>0.073</td><td>0.018</td><td>0.159</td><td>0.041</td></tr><tr><td>Lianyungang</td><td></td><td>19</td><td>0.028</td><td>0.012</td><td>0.068</td><td>0.015</td><td></td><td>19</td><td>0.155</td><td>0.023</td><td>0.275</td><td>0.085</td></tr><tr><td>Nanjing</td><td></td><td>19</td><td>0.305</td><td>0.151</td><td>0.628</td><td>0.121</td><td></td><td>19</td><td>0.016</td><td>0.002</td><td>0.045</td><td>0.010</td></tr><tr><td>Nantong</td><td></td><td>19</td><td>0.138</td><td>0.022</td><td>0.250</td><td>0.084</td><td></td><td>19</td><td>0.040</td><td>0.006</td><td>0.154</td><td>0.036</td></tr><tr><td>Ningbo</td><td></td><td>19</td><td>0.155</td><td>0.118</td><td>0.225</td><td>0.029</td><td></td><td>19</td><td>0.629</td><td>0.329</td><td>0.777</td><td>0.109</td></tr><tr><td>Shanghai</td><td></td><td>19</td><td>0.037</td><td>0.028</td><td>0.048</td><td>0.005</td><td></td><td>19</td><td>0.690</td><td>0.601</td><td>0.759</td><td>0.044</td></tr><tr><td>Suzhou</td><td></td><td>19</td><td>0.203</td><td>0.079</td><td>0.312</td><td>0.067</td><td></td><td>19</td><td>0.034</td><td>0.004</td><td>0.086</td><td>0.025</td></tr><tr><td>Wenzhou</td><td></td><td>19</td><td>0.124</td><td>0.016</td><td>0.378</td><td>0.104</td><td></td><td>19</td><td>0.176</td><td>0.018</td><td>0.516</td><td>0.169</td></tr><tr><td>Wuxi</td><td></td><td>19</td><td>0.237</td><td>0.104</td><td>0.392</td><td>0.072</td><td></td><td>19</td><td>0.038</td><td>0.002</td><td>0.135</td><td>0.036</td></tr><tr><td>Yangzhou</td><td></td><td>19</td><td>0.168</td><td>0.019</td><td>0.294</td><td>0.085</td><td></td><td>19</td><td>0.033</td><td>0.009</td><td>0.092</td><td>0.020</td></tr><tr><td>Zhenjiang</td><td></td><td>19</td><td>0.213</td><td>0.043</td><td>0.511</td><td>0.141</td><td></td><td>19</td><td>0.026</td><td>0.005</td><td>0.078</td><td>0.024</td></tr><tr><td>Zhoushan</td><td></td><td>19</td><td>0.349</td><td>0.177</td><td>0.644</td><td>0.131</td><td></td><td>19</td><td>0.108</td><td>0.008</td><td>0.354</td><td>0.092</td></tr><tr><td>Beihai</td><td></td><td>19</td><td>0.092</td><td>0.017</td><td>0.145</td><td>0.034</td><td></td><td>19</td><td>0.025</td><td>0.006</td><td>0.050</td><td>0.012</td></tr><tr><td>Fangchenggang</td><td></td><td>19</td><td>0.023</td><td>0.011</td><td>0.063</td><td>0.012</td><td></td><td>19</td><td>0.048</td><td>0.019</td><td>0.088</td><td>0.020</td></tr><tr><td>Fuzhou</td><td></td><td>19</td><td>0.021</td><td>0.008</td><td>0.069</td><td>0.015</td><td></td><td>19</td><td>0.504</td><td>0.279</td><td>0.766</td><td>0.183</td></tr><tr><td>Guangzhou</td><td></td><td>19</td><td>0.149</td><td>0.069</td><td>0.266</td><td>0.068</td><td></td><td>19</td><td>0.339</td><td>0.062</td><td>0.656</td><td>0.197</td></tr><tr><td>Haikou</td><td></td><td>19</td><td>0.054</td><td>0.012</td><td>0.192</td><td>0.055</td><td></td><td>19</td><td>0.121</td><td>0.013</td><td>0.256</td><td>0.070</td></tr><tr><td>Jiangmen</td><td></td><td>19</td><td>0.355</td><td>0.106</td><td>0.673</td><td>0.165</td><td></td><td>19</td><td>0.221</td><td>0.031</td><td>0.684</td><td>0.191</td></tr><tr><td>Quanzhou</td><td></td><td>19</td><td>0.416</td><td>0.256</td><td>0.565</td><td>0.099</td><td></td><td>19</td><td>0.164</td><td>0.014</td><td>0.400</td><td>0.135</td></tr><tr><td>Xiamen</td><td></td><td>19</td><td>0.016</td><td>0.007</td><td>0.030</td><td>0.006</td><td></td><td>19</td><td>0.824</td><td>0.742</td><td>0.909</td><td>0.049</td></tr><tr><td>Shantou</td><td></td><td>19</td><td>0.052</td><td>0.002</td><td>0.182</td><td>0.057</td><td></td><td>19</td><td>0.418</td><td>0.088</td><td>0.612</td><td>0.174</td></tr><tr><td>Shenzhen</td><td></td><td>19</td><td>0.011</td><td>0.003</td><td>0.027</td><td>0.006</td><td></td><td>19</td><td>0.947</td><td>0.874</td><td>0.964</td><td>0.020</td></tr><tr><td>Hong Kong</td><td></td><td>19</td><td>0.046</td><td>0.026</td><td>0.062</td><td>0.009</td><td></td><td>19</td><td>0.721</td><td>0.578</td><td>0.771</td><td>0.055</td></tr><tr><td>Zhanjiang</td><td></td><td>19</td><td>0.301</td><td>0.150</td><td>0.654</td><td>0.134</td><td></td><td>19</td><td>0.053</td><td>0.002</td><td>0.143</td><td>0.050</td></tr><tr><td>Zhuhai</td><td></td><td>19</td><td>0.390</td><td>0.108</td><td>0.621</td><td>0.181</td><td></td><td>19</td><td>0.086</td><td>0.022</td><td>0.198</td><td>0.065</td></tr></tbody></table>
<table><thead><tr><th>City</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th></tr></thead><tbody><tr><td>Dalian</td><td></td><td>19</td><td>7.41</td><td>2.23</td><td>12.95</td><td>3.46</td><td></td><td>19</td><td>0.459</td><td>0.436</td><td>0.474</td><td>0.013</td></tr><tr><td>Dandong</td><td></td><td>19</td><td>2.65</td><td>0.77</td><td>4.61</td><td>1.27</td><td></td><td>19</td><td>0.158</td><td>0.152</td><td>0.162</td><td>0.003</td></tr><tr><td>Jinzhou</td><td></td><td>19</td><td>2.66</td><td>0.71</td><td>4.43</td><td>1.34</td><td></td><td>19</td><td>0.303</td><td>0.292</td><td>0.314</td><td>0.007</td></tr><tr><td>Qinhuangdao</td><td></td><td>19</td><td>3.10</td><td>1.15</td><td>5.24</td><td>1.34</td><td></td><td>19</td><td>0.373</td><td>0.343</td><td>0.386</td><td>0.012</td></tr><tr><td>Qingdao</td><td></td><td>19</td><td>6.98</td><td>1.86</td><td>12.85</td><td>3.79</td><td></td><td>19</td><td>0.689</td><td>0.651</td><td>0.736</td><td>0.022</td></tr><tr><td>Rizhao</td><td></td><td>19</td><td>3.83</td><td>0.85</td><td>7.53</td><td>2.24</td><td></td><td>19</td><td>0.542</td><td>0.521</td><td>0.573</td><td>0.016</td></tr><tr><td>Tangshan</td><td></td><td>19</td><td>5.56</td><td>1.44</td><td>8.79</td><td>2.73</td><td></td><td>19</td><td>0.539</td><td>0.520</td><td>0.564</td><td>0.013</td></tr><tr><td>Tianjin</td><td></td><td>19</td><td>7.34</td><td>2.02</td><td>14.31</td><td>3.87</td><td></td><td>19</td><td>0.834</td><td>0.767</td><td>0.926</td><td>0.047</td></tr><tr><td>Weihai</td><td></td><td>19</td><td>7.60</td><td>2.54</td><td>12.88</td><td>3.28</td><td></td><td>19</td><td>0.444</td><td>0.436</td><td>0.458</td><td>0.008</td></tr><tr><td>Yantai</td><td></td><td>19</td><td>6.21</td><td>1.52</td><td>11.02</td><td>3.28</td><td></td><td>19</td><td>0.472</td><td>0.470</td><td>0.474</td><td>0.002</td></tr><tr><td>Yingkou</td><td></td><td>19</td><td>3.77</td><td>0.85</td><td>6.49</td><td>2.02</td><td></td><td>19</td><td>0.434</td><td>0.421</td><td>0.449</td><td>0.010</td></tr><tr><td>Jiaxing</td><td></td><td>19</td><td>5.89</td><td>1.82</td><td>11.28</td><td>2.88</td><td></td><td>19</td><td>0.863</td><td>0.834</td><td>0.893</td><td>0.016</td></tr><tr><td>Lianyungang</td><td></td><td>19</td><td>2.97</td><td>0.69</td><td>6.95</td><td>2.09</td><td></td><td>19</td><td>0.662</td><td>0.617</td><td>0.701</td><td>0.031</td></tr><tr><td>Nanjing</td><td></td><td>19</td><td>8.10</td><td>2.10</td><td>16.57</td><td>4.64</td><td></td><td>19</td><td>0.955</td><td>0.838</td><td>1.078</td><td>0.066</td></tr><tr><td>Nantong</td><td></td><td>19</td><td>5.40</td><td>1.03</td><td>12.83</td><td>3.77</td><td></td><td>19</td><td>0.887</td><td>0.720</td><td>0.978</td><td>0.113</td></tr><tr><td>Ningbo</td><td></td><td>19</td><td>7.66</td><td>2.42</td><td>14.32</td><td>3.58</td><td></td><td>19</td><td>0.592</td><td>0.575</td><td>0.619</td><td>0.012</td></tr><tr><td>Shanghai</td><td></td><td>19</td><td>8.30</td><td>3.74</td><td>15.73</td><td>3.32</td><td></td><td>19</td><td>2.210</td><td>2.079</td><td>2.317</td><td>0.077</td></tr><tr><td>Suzhou</td><td></td><td>19</td><td>10.85</td><td>3.04</td><td>19.90</td><td>5.02</td><td></td><td>19</td><td>0.750</td><td>0.684</td><td>0.835</td><td>0.042</td></tr><tr><td>Wenzhou</td><td></td><td>19</td><td>3.79</td><td>1.26</td><td>7.12</td><td>1.77</td><td></td><td>19</td><td>0.660</td><td>0.627</td><td>0.687</td><td>0.022</td></tr><tr><td>Wuxi</td><td></td><td>19</td><td>10.46</td><td>3.13</td><td>18.00</td><td>4.76</td><td></td><td>19</td><td>0.996</td><td>0.916</td><td>1.087</td><td>0.054</td></tr><tr><td>Yangzhou</td><td></td><td>19</td><td>5.69</td><td>1.12</td><td>12.89</td><td>3.92</td><td></td><td>19</td><td>0.693</td><td>0.680</td><td>0.701</td><td>0.006</td></tr><tr><td>Zhenjiang</td><td></td><td>19</td><td>7.19</td><td>1.89</td><td>12.90</td><td>4.05</td><td></td><td>19</td><td>0.702</td><td>0.694</td><td>0.709</td><td>0.005</td></tr><tr><td>Zhoushan</td><td></td><td>19</td><td>6.50</td><td>1.29</td><td>11.68</td><td>3.64</td><td></td><td>19</td><td>0.671</td><td>0.665</td><td>0.681</td><td>0.004</td></tr><tr><td>Beihai</td><td></td><td>19</td><td>3.45</td><td>0.86</td><td>7.70</td><td>2.45</td><td></td><td>19</td><td>0.472</td><td>0.434</td><td>0.521</td><td>0.031</td></tr><tr><td>Fangchenggang</td><td></td><td>19</td><td>4.01</td><td>0.81</td><td>7.94</td><td>2.66</td><td></td><td>19</td><td>0.143</td><td>0.126</td><td>0.160</td><td>0.012</td></tr><tr><td>Fuzhou</td><td></td><td>19</td><td>5.27</td><td>1.85</td><td>12.09</td><td>3.15</td><td></td><td>19</td><td>0.518</td><td>0.487</td><td>0.579</td><td>0.028</td></tr><tr><td>Guangzhou</td><td></td><td>19</td><td>10.11</td><td>3.80</td><td>18.53</td><td>4.43</td><td></td><td>19</td><td>1.089</td><td>0.959</td><td>1.283</td><td>0.094</td></tr><tr><td>Haikou</td><td></td><td>19</td><td>3.76</td><td>1.67</td><td>7.22</td><td>1.84</td><td></td><td>19</td><td>0.906</td><td>0.604</td><td>2.707</td><td>0.610</td></tr><tr><td>Jiangmen</td><td></td><td>19</td><td>3.78</td><td>1.62</td><td>6.82</td><td>1.65</td><td></td><td>19</td><td>0.410</td><td>0.399</td><td>0.421</td><td>0.006</td></tr><tr><td>Quanzhou</td><td></td><td>19</td><td>5.07</td><td>1.67</td><td>11.41</td><td>3.00</td><td></td><td>19</td><td>0.634</td><td>0.605</td><td>0.691</td><td>0.028</td></tr><tr><td>Xiamen</td><td></td><td>19</td><td>7.64</td><td>3.50</td><td>15.32</td><td>3.47</td><td></td><td>19</td><td>1.148</td><td>0.859</td><td>1.534</td><td>0.191</td></tr><tr><td>Shantou</td><td></td><td>19</td><td>2.46</td><td>1.01</td><td>4.77</td><td>1.21</td><td></td><td>19</td><td>2.482</td><td>2.236</td><td>2.648</td><td>0.116</td></tr><tr><td>Shenzhen</td><td></td><td>19</td><td>13.50</td><td>4.64</td><td>46.77</td><td>9.46</td><td></td><td>19</td><td>1.404</td><td>0.677</td><td>2.759</td><td>0.577</td></tr><tr><td>Hong Kong</td><td></td><td>19</td><td>24.25</td><td>19.85</td><td>33.36</td><td>4.18</td><td></td><td>19</td><td>6.723</td><td>6.394</td><td>7.150</td><td>0.245</td></tr><tr><td>Zhanjiang</td><td></td><td>19</td><td>2.22</td><td>0.75</td><td>4.17</td><td>1.17</td><td></td><td>19</td><td>0.598</td><td>0.562</td><td>0.644</td><td>0.027</td></tr><tr><td>Zhuhai</td><td></td><td>19</td><td>9.41</td><td>3.27</td><td>17.55</td><td>4.46</td><td></td><td>19</td><td>0.600</td><td>0.465</td><td>0.766</td><td>0.085</td></tr></tbody></table>
<table><thead><tr><th>City</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th></tr></thead><tbody><tr><td>Dalian</td><td></td><td>19</td><td>22.04</td><td>15.05</td><td>29.83</td><td>4.27</td><td></td><td>19</td><td>22.37</td><td>15.54</td><td>30.01</td><td>5.21</td></tr><tr><td>Dandong</td><td></td><td>19</td><td>4.15</td><td>3.08</td><td>5.58</td><td>0.80</td><td></td><td>19</td><td>5.02</td><td>2.01</td><td>7.74</td><td>2.05</td></tr><tr><td>Jinzhou</td><td></td><td>19</td><td>5.86</td><td>5.43</td><td>6.74</td><td>0.39</td><td></td><td>19</td><td>10.58</td><td>4.46</td><td>17.40</td><td>5.57</td></tr><tr><td>Qinhuangdao</td><td></td><td>19</td><td>12.07</td><td>4.39</td><td>16.80</td><td>3.79</td><td></td><td>19</td><td>4.96</td><td>3.46</td><td>6.93</td><td>1.02</td></tr><tr><td>Qingdao</td><td></td><td>19</td><td>42.33</td><td>5.02</td><td>62.80</td><td>19.67</td><td></td><td>19</td><td>24.80</td><td>16.30</td><td>34.17</td><td>5.68</td></tr><tr><td>Rizhao</td><td></td><td>19</td><td>45.01</td><td>2.31</td><td>82.32</td><td>28.49</td><td></td><td>19</td><td>6.55</td><td>1.36</td><td>12.97</td><td>3.96</td></tr><tr><td>Tangshan</td><td></td><td>19</td><td>32.72</td><td>23.06</td><td>44.14</td><td>6.75</td><td></td><td>19</td><td>25.77</td><td>9.55</td><td>44.15</td><td>13.78</td></tr><tr><td>Tianjin</td><td></td><td>19</td><td>81.53</td><td>37.27</td><td>121.61</td><td>21.87</td><td></td><td>19</td><td>26.18</td><td>19.38</td><td>36.71</td><td>6.46</td></tr><tr><td>Weihai</td><td></td><td>19</td><td>0.95</td><td>0.45</td><td>2.62</td><td>0.58</td><td></td><td>19</td><td>5.53</td><td>2.82</td><td>8.81</td><td>1.89</td></tr><tr><td>Yantai</td><td></td><td>19</td><td>13.31</td><td>5.63</td><td>23.62</td><td>5.99</td><td></td><td>19</td><td>14.96</td><td>6.98</td><td>21.39</td><td>4.14</td></tr><tr><td>Yingkou</td><td></td><td>19</td><td>16.08</td><td>6.77</td><td>25.81</td><td>5.62</td><td></td><td>19</td><td>10.00</td><td>2.60</td><td>16.49</td><td>5.43</td></tr><tr><td>Jiaxing</td><td></td><td>19</td><td>0.46</td><td>0.22</td><td>0.96</td><td>0.21</td><td></td><td>19</td><td>6.86</td><td>1.91</td><td>14.53</td><td>4.40</td></tr><tr><td>Lianyungang</td><td></td><td>19</td><td>27.84</td><td>9.23</td><td>45.47</td><td>13.03</td><td></td><td>19</td><td>7.30</td><td>2.91</td><td>12.30</td><td>3.16</td></tr><tr><td>Nanjing</td><td></td><td>19</td><td>12.93</td><td>8.17</td><td>18.20</td><td>3.28</td><td></td><td>19</td><td>13.61</td><td>9.16</td><td>22.12</td><td>4.11</td></tr><tr><td>Nantong</td><td></td><td>19</td><td>0.75</td><td>0.12</td><td>1.42</td><td>0.41</td><td></td><td>19</td><td>10.80</td><td>4.39</td><td>20.20</td><td>4.15</td></tr><tr><td>Ningbo</td><td></td><td>19</td><td>19.59</td><td>7.25</td><td>29.60</td><td>7.07</td><td></td><td>19</td><td>17.63</td><td>8.30</td><td>37.04</td><td>8.41</td></tr><tr><td>Shanghai</td><td></td><td>19</td><td>18.25</td><td>4.82</td><td>68.14</td><td>20.14</td><td></td><td>19</td><td>37.60</td><td>28.87</td><td>43.81</td><td>4.96</td></tr><tr><td>Suzhou</td><td></td><td>19</td><td>1.76</td><td>0.91</td><td>4.05</td><td>0.71</td><td></td><td>19</td><td>11.23</td><td>4.74</td><td>23.83</td><td>4.56</td></tr><tr><td>Wenzhou</td><td></td><td>19</td><td>3.39</td><td>0.33</td><td>7.15</td><td>2.38</td><td></td><td>19</td><td>9.59</td><td>5.08</td><td>15.18</td><td>3.02</td></tr><tr><td>Wuxi</td><td></td><td>19</td><td>1.48</td><td>0.72</td><td>2.16</td><td>0.54</td><td></td><td>19</td><td>10.80</td><td>5.89</td><td>17.39</td><td>3.54</td></tr><tr><td>Yangzhou</td><td></td><td>19</td><td>0.28</td><td>0.00</td><td>0.48</td><td>0.16</td><td></td><td>19</td><td>5.65</td><td>3.69</td><td>7.65</td><td>1.28</td></tr><tr><td>Zhenjiang</td><td></td><td>19</td><td>3.84</td><td>1.34</td><td>8.05</td><td>2.32</td><td></td><td>19</td><td>6.69</td><td>3.46</td><td>12.42</td><td>2.49</td></tr><tr><td>Zhoushan</td><td></td><td>19</td><td>0.09</td><td>0.01</td><td>0.21</td><td>0.08</td><td></td><td>19</td><td>4.44</td><td>1.27</td><td>10.37</td><td>3.01</td></tr><tr><td>Beihai</td><td></td><td>19</td><td>2.92</td><td>1.17</td><td>4.39</td><td>0.89</td><td></td><td>19</td><td>4.01</td><td>1.77</td><td>7.18</td><td>1.85</td></tr><tr><td>Fangchenggang</td><td></td><td>19</td><td>27.19</td><td>8.90</td><td>42.62</td><td>11.14</td><td></td><td>19</td><td>3.48</td><td>0.18</td><td>9.17</td><td>2.41</td></tr><tr><td>Fuzhou</td><td></td><td>19</td><td>3.54</td><td>2.57</td><td>4.35</td><td>0.53</td><td></td><td>19</td><td>10.23</td><td>4.85</td><td>18.02</td><td>4.95</td></tr><tr><td>Guangzhou</td><td></td><td>19</td><td>57.49</td><td>16.58</td><td>70.13</td><td>15.52</td><td></td><td>19</td><td>45.40</td><td>13.29</td><td>88.35</td><td>25.57</td></tr><tr><td>Haikou</td><td></td><td>19</td><td>6.35</td><td>0.00</td><td>11.85</td><td>3.54</td><td></td><td>19</td><td>2.62</td><td>0.32</td><td>5.33</td><td>1.37</td></tr><tr><td>Jiangmen</td><td></td><td>19</td><td>0.03</td><td>0.00</td><td>0.07</td><td>0.02</td><td></td><td>19</td><td>6.00</td><td>2.04</td><td>13.23</td><td>3.67</td></tr><tr><td>Quanzhou</td><td></td><td>19</td><td>8.61</td><td>2.43</td><td>12.10</td><td>2.91</td><td></td><td>19</td><td>9.23</td><td>4.72</td><td>20.01</td><td>4.90</td></tr><tr><td>Xiamen</td><td></td><td>19</td><td>6.63</td><td>2.37</td><td>10.99</td><td>2.46</td><td></td><td>19</td><td>8.90</td><td>1.33</td><td>23.16</td><td>8.03</td></tr><tr><td>Shantou</td><td></td><td>19</td><td>1.00</td><td>0.60</td><td>1.36</td><td>0.22</td><td></td><td>19</td><td>3.10</td><td>1.03</td><td>6.64</td><td>1.89</td></tr><tr><td>Shenzhen</td><td></td><td>19</td><td>2.77</td><td>0.67</td><td>4.80</td><td>1.42</td><td></td><td>19</td><td>16.03</td><td>4.27</td><td>25.40</td><td>8.22</td></tr><tr><td>Hong Kong</td><td></td><td>19</td><td>0.22</td><td>0.10</td><td>0.48</td><td>0.13</td><td></td><td>19</td><td>29.52</td><td>17.44</td><td>40.47</td><td>7.81</td></tr><tr><td>Zhanjiang</td><td></td><td>19</td><td>23.91</td><td>8.12</td><td>32.48</td><td>7.61</td><td></td><td>19</td><td>7.76</td><td>3.18</td><td>16.92</td><td>4.42</td></tr><tr><td>Zhuhai</td><td></td><td>19</td><td>1.91</td><td>0.00</td><td>8.82</td><td>3.09</td><td></td><td>19</td><td>5.41</td><td>1.51</td><td>9.97</td><td>3.42</td></tr></tbody></table>
<table><thead><tr><th>City</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th><th>Variable</th><th>Obs</th><th>Mean</th><th>Min</th><th>Max</th><th>Std. Dev</th></tr></thead><tbody><tr><td>Dalian</td><td></td><td>19</td><td>6.52</td><td>1.10</td><td>12.19</td><td>3.90</td><td></td><td>19</td><td>28.35</td><td>20.89</td><td>37.86</td><td>4.49</td></tr><tr><td>Dandong</td><td></td><td>19</td><td>0.69</td><td>0.11</td><td>1.36</td><td>0.47</td><td></td><td>19</td><td>19.37</td><td>14.24</td><td>26.69</td><td>3.57</td></tr><tr><td>Jinzhou</td><td></td><td>19</td><td>1.73</td><td>0.19</td><td>3.65</td><td>1.23</td><td></td><td>19</td><td>20.62</td><td>12.14</td><td>31.99</td><td>5.83</td></tr><tr><td>Qinhuangdao</td><td></td><td>19</td><td>1.08</td><td>0.17</td><td>2.02</td><td>0.60</td><td></td><td>19</td><td>22.59</td><td>14.89</td><td>32.72</td><td>5.79</td></tr><tr><td>Qingdao</td><td></td><td>19</td><td>10.90</td><td>1.67</td><td>21.48</td><td>6.67</td><td></td><td>19</td><td>27.14</td><td>18.67</td><td>45.50</td><td>8.50</td></tr><tr><td>Rizhao</td><td></td><td>19</td><td>1.77</td><td>0.11</td><td>3.60</td><td>1.17</td><td></td><td>19</td><td>15.53</td><td>8.73</td><td>25.54</td><td>4.64</td></tr><tr><td>Tangshan</td><td></td><td>19</td><td>6.97</td><td>0.68</td><td>13.87</td><td>4.40</td><td></td><td>19</td><td>21.29</td><td>15.48</td><td>32.11</td><td>4.87</td></tr><tr><td>Tianjin</td><td></td><td>19</td><td>17.79</td><td>2.94</td><td>35.54</td><td>11.23</td><td></td><td>19</td><td>30.98</td><td>23.97</td><td>41.90</td><td>5.30</td></tr><tr><td>Weihai</td><td></td><td>19</td><td>4.78</td><td>1.06</td><td>8.52</td><td>2.35</td><td></td><td>19</td><td>24.94</td><td>14.46</td><td>38.77</td><td>7.03</td></tr><tr><td>Yantai</td><td></td><td>19</td><td>10.01</td><td>1.13</td><td>19.96</td><td>6.23</td><td></td><td>19</td><td>22.65</td><td>14.78</td><td>31.29</td><td>5.64</td></tr><tr><td>Yingkou</td><td></td><td>19</td><td>1.87</td><td>0.19</td><td>3.84</td><td>1.26</td><td></td><td>19</td><td>20.99</td><td>13.10</td><td>28.26</td><td>4.35</td></tr><tr><td>Jiaxing</td><td></td><td>19</td><td>4.96</td><td>0.64</td><td>9.78</td><td>3.02</td><td></td><td>19</td><td>23.40</td><td>12.81</td><td>36.87</td><td>6.74</td></tr><tr><td>Lianyungang</td><td></td><td>19</td><td>2.75</td><td>0.21</td><td>6.86</td><td>2.48</td><td></td><td>19</td><td>14.16</td><td>8.61</td><td>24.10</td><td>4.80</td></tr><tr><td>Nanjing</td><td></td><td>19</td><td>8.78</td><td>1.77</td><td>16.72</td><td>5.00</td><td></td><td>19</td><td>29.71</td><td>17.91</td><td>50.33</td><td>8.92</td></tr><tr><td>Nantong</td><td></td><td>19</td><td>7.90</td><td>0.78</td><td>16.85</td><td>5.65</td><td></td><td>19</td><td>18.19</td><td>11.05</td><td>27.44</td><td>5.00</td></tr><tr><td>Ningbo</td><td></td><td>19</td><td>9.91</td><td>1.63</td><td>18.46</td><td>5.38</td><td></td><td>19</td><td>30.35</td><td>13.47</td><td>48.63</td><td>9.20</td></tr><tr><td>Shanghai</td><td></td><td>19</td><td>25.65</td><td>7.00</td><td>42.69</td><td>10.99</td><td></td><td>19</td><td>34.84</td><td>26.82</td><td>52.91</td><td>8.05</td></tr><tr><td>Suzhou</td><td></td><td>19</td><td>21.94</td><td>2.79</td><td>41.64</td><td>12.47</td><td></td><td>19</td><td>30.30</td><td>14.50</td><td>49.16</td><td>10.94</td></tr><tr><td>Wenzhou</td><td></td><td>19</td><td>3.77</td><td>0.83</td><td>6.50</td><td>1.73</td><td></td><td>19</td><td>22.83</td><td>8.59</td><td>36.22</td><td>7.82</td></tr><tr><td>Wuxi</td><td></td><td>19</td><td>11.19</td><td>2.01</td><td>20.04</td><td>5.67</td><td></td><td>19</td><td>28.10</td><td>15.29</td><td>46.06</td><td>8.67</td></tr><tr><td>Yangzhou</td><td></td><td>19</td><td>5.60</td><td>0.61</td><td>11.88</td><td>3.97</td><td></td><td>19</td><td>18.16</td><td>11.06</td><td>27.48</td><td>4.45</td></tr><tr><td>Zhenjiang</td><td></td><td>19</td><td>4.81</td><td>0.61</td><td>10.28</td><td>3.48</td><td></td><td>19</td><td>22.72</td><td>14.31</td><td>31.37</td><td>5.59</td></tr><tr><td>Zhoushan</td><td></td><td>19</td><td>0.98</td><td>0.09</td><td>2.08</td><td>0.69</td><td></td><td>19</td><td>29.34</td><td>15.94</td><td>65.23</td><td>11.14</td></tr><tr><td>Beihai</td><td></td><td>19</td><td>0.95</td><td>0.05</td><td>2.98</td><td>1.02</td><td></td><td>19</td><td>16.87</td><td>10.13</td><td>23.45</td><td>3.31</td></tr><tr><td>Fangchenggang</td><td></td><td>19</td><td>0.70</td><td>0.02</td><td>1.98</td><td>0.67</td><td></td><td>19</td><td>13.94</td><td>9.33</td><td>21.85</td><td>4.87</td></tr><tr><td>Fuzhou</td><td></td><td>19</td><td>4.88</td><td>0.79</td><td>9.75</td><td>3.07</td><td></td><td>19</td><td>22.78</td><td>14.15</td><td>34.31</td><td>5.73</td></tr><tr><td>Guangzhou</td><td></td><td>19</td><td>12.95</td><td>2.83</td><td>23.26</td><td>6.83</td><td></td><td>19</td><td>43.34</td><td>30.76</td><td>61.50</td><td>9.36</td></tr><tr><td>Haikou</td><td></td><td>19</td><td>0.39</td><td>0.10</td><td>0.65</td><td>0.17</td><td></td><td>19</td><td>40.20</td><td>17.21</td><td>72.61</td><td>14.90</td></tr><tr><td>Jiangmen</td><td></td><td>19</td><td>2.94</td><td>0.94</td><td>5.05</td><td>1.41</td><td></td><td>19</td><td>18.59</td><td>12.77</td><td>27.34</td><td>4.40</td></tr><tr><td>Quanzhou</td><td></td><td>19</td><td>6.74</td><td>0.57</td><td>14.28</td><td>4.75</td><td></td><td>19</td><td>14.05</td><td>7.22</td><td>23.37</td><td>5.72</td></tr><tr><td>Xiamen</td><td></td><td>19</td><td>3.61</td><td>0.80</td><td>6.44</td><td>1.78</td><td></td><td>19</td><td>41.40</td><td>26.99</td><td>58.26</td><td>9.79</td></tr><tr><td>Shantou</td><td></td><td>19</td><td>1.85</td><td>0.33</td><td>3.68</td><td>1.15</td><td></td><td>19</td><td>12.80</td><td>8.60</td><td>19.20</td><td>3.50</td></tr><tr><td>Shenzhen</td><td></td><td>19</td><td>17.92</td><td>3.08</td><td>33.15</td><td>9.55</td><td></td><td>19</td><td>76.38</td><td>56.31</td><td>88.45</td><td>10.78</td></tr><tr><td>Hong Kong</td><td></td><td>19</td><td>0.32</td><td>0.21</td><td>0.43</td><td>0.07</td><td></td><td>19</td><td>17.77</td><td>15.07</td><td>19.95</td><td>1.29</td></tr><tr><td>Zhanjiang</td><td></td><td>19</td><td>1.49</td><td>0.28</td><td>2.91</td><td>0.89</td><td></td><td>19</td><td>11.03</td><td>6.80</td><td>16.10</td><td>2.79</td></tr><tr><td>Zhuhai</td><td></td><td>19</td><td>2.83</td><td>0.66</td><td>5.03</td><td>1.38</td><td></td><td>19</td><td>39.94</td><td>19.65</td><td>58.20</td><td>11.95</td></tr></tbody></table>
<table><thead><tr><th>City</th><th>Variabl e</th><th>Ob s</th><th>Mea n</th><th>Min</th><th>Max</th><th>Std. Dev</th><th>Variabl e</th><th>Ob s</th><th>Mea n</th><th>Min</th><th>Ma x</th><th>Std. Dev</th></tr></thead><tbody><tr><td>Dalian</td><td></td><td>19</td><td>77.5</td><td>77.5</td><td>77.5</td><td>0</td><td></td><td>19</td><td>6.39</td><td>5.90</td><td>6.75</td><td>0.22</td></tr><tr><td>Dandong</td><td></td><td>19</td><td>252.3</td><td>252.3</td><td>252.3</td><td>0</td><td></td><td>19</td><td>4.16</td><td>3.68</td><td>4.48</td><td>0.24</td></tr><tr><td>Jinzhou</td><td></td><td>19</td><td>76.2</td><td>76.2</td><td>76.2</td><td>0</td><td></td><td>19</td><td>5.66</td><td>5.24</td><td>6.05</td><td>0.23</td></tr><tr><td>Qinhuangdao</td><td></td><td>19</td><td>233.4</td><td>233.4</td><td>233.4</td><td>0</td><td></td><td>19</td><td>4.33</td><td>3.34</td><td>5.04</td><td>0.56</td></tr><tr><td>Qingdao</td><td></td><td>19</td><td>51.1</td><td>51.1</td><td>51.1</td><td>0</td><td></td><td>19</td><td>6.09</td><td>5.55</td><td>7.22</td><td>0.51</td></tr><tr><td>Rizhao</td><td></td><td>19</td><td>130.3</td><td>130.3</td><td>130.3</td><td>0</td><td></td><td>19</td><td>4.98</td><td>4.14</td><td>6.16</td><td>0.64</td></tr><tr><td>Tangshan</td><td></td><td>19</td><td>60.5</td><td>60.5</td><td>60.5</td><td>0</td><td></td><td>19</td><td>4.64</td><td>3.75</td><td>5.21</td><td>0.42</td></tr><tr><td>Tianjin</td><td></td><td>19</td><td>20.0</td><td>20.0</td><td>20.0</td><td>0</td><td></td><td>19</td><td>5.40</td><td>4.63</td><td>6.11</td><td>0.40</td></tr><tr><td>Weihai</td><td></td><td>19</td><td>59.3</td><td>59.3</td><td>59.3</td><td>0</td><td></td><td>19</td><td>8.55</td><td>7.56</td><td>9.96</td><td>0.74</td></tr><tr><td>Yantai</td><td></td><td>19</td><td>100.5</td><td>100.5</td><td>100.5</td><td>0</td><td></td><td>19</td><td>6.08</td><td>5.32</td><td>7.42</td><td>0.61</td></tr><tr><td>Yingkou</td><td>ELEV</td><td>19</td><td>165.3</td><td>165.3</td><td>165.3</td><td>0</td><td></td><td>19</td><td>6.36</td><td>5.66</td><td>6.85</td><td>0.30</td></tr><tr><td>Jiaxing</td><td></td><td>19</td><td>7.3</td><td>7.3</td><td>7.3</td><td>0</td><td></td><td>19</td><td>5.69</td><td>5.31</td><td>6.03</td><td>0.23</td></tr><tr><td>Lianyungang</td><td></td><td>19</td><td>18.5</td><td>18.5</td><td>18.5</td><td>0</td><td></td><td>19</td><td>4.78</td><td>4.37</td><td>5.17</td><td>0.20</td></tr><tr><td>Nanjing</td><td></td><td>19</td><td>25.8</td><td>25.8</td><td>25.8</td><td>0</td><td></td><td>19</td><td>5.02</td><td>4.61</td><td>5.25</td><td>0.21</td></tr><tr><td>Nantong</td><td></td><td>19</td><td>8.4</td><td>8.4</td><td>8.4</td><td>0</td><td></td><td>19</td><td>6.19</td><td>5.70</td><td>6.80</td><td>0.30</td></tr><tr><td>Ningbo</td><td></td><td>19</td><td>121.5</td><td>121.5</td><td>121.5</td><td>0</td><td></td><td>19</td><td>6.45</td><td>6.14</td><td>7.09</td><td>0.26</td></tr><tr><td>Shanghai</td><td></td><td>19</td><td>7.7</td><td>7.7</td><td>7.7</td><td>0</td><td></td><td>19</td><td>7.12</td><td>6.38</td><td>7.64</td><td>0.31</td></tr><tr><td>Suzhou</td><td></td><td>19</td><td>7.7</td><td>7.7</td><td>7.7</td><td>0</td><td></td><td>19</td><td>6.06</td><td>5.68</td><td>6.44</td><td>0.20</td></tr><tr><td>Wenzhou</td><td></td><td>19</td><td>348.3</td><td>348.3</td><td>348.3</td><td>0</td><td></td><td>19</td><td>4.26</td><td>3.69</td><td>4.64</td><td>0.23</td></tr><tr><td>Wuxi</td><td></td><td>19</td><td>21.8</td><td>21.8</td><td>21.8</td><td>0</td><td></td><td>19</td><td>4.99</td><td>4.75</td><td>5.36</td><td>0.13</td></tr><tr><td>Yangzhou</td><td></td><td>19</td><td>11.2</td><td>11.2</td><td>11.2</td><td>0</td><td></td><td>19</td><td>5.16</td><td>4.71</td><td>5.58</td><td>0.23</td></tr></tbody></table>
<table><tr><td>Zhenjiang</td><td>19</td><td>25.9</td><td>25.9</td><td>25.9</td><td>0</td><td>19</td><td>5.01</td><td>4.7<br>4</td><td>5.25</td><td>0.16</td></tr><tr><td>Zhoushan</td><td>19</td><td>51.1</td><td>51.1</td><td>51.1</td><td>0</td><td>19</td><td>5.47</td><td>4.7<br>4</td><td>6.16</td><td>0.48</td></tr><tr><td>Beihai</td><td>19</td><td>27.6</td><td>27.6</td><td>27.6</td><td>0</td><td>19</td><td>5.47</td><td>4.8<br>3</td><td>6.29</td><td>0.46</td></tr><tr><td>Fangchenggan<br>g</td><td>19</td><td>251.<br>9</td><td>251.<br>9</td><td>251.<br>9</td><td>0</td><td>19</td><td>4.31</td><td>4.0<br>2</td><td>4.99</td><td>0.35</td></tr><tr><td>Fuzhou</td><td>19</td><td>344.<br>2</td><td>344.<br>2</td><td>344.<br>2</td><td>0</td><td>19</td><td>6.57</td><td>5.6<br>3</td><td>7.48</td><td>0.56</td></tr><tr><td>Guangzhou</td><td>19</td><td>108.<br>4</td><td>108.<br>4</td><td>108.<br>4</td><td>0</td><td>19</td><td>4.99</td><td>4.5<br>9</td><td>5.41</td><td>0.27</td></tr><tr><td>Haikou</td><td>19</td><td>41.8</td><td>41.8</td><td>41.8</td><td>0</td><td>19</td><td>5.59</td><td>4.0<br>5</td><td>6.73</td><td>0.91</td></tr><tr><td>Jiangmen</td><td>19</td><td>79.5</td><td>79.5</td><td>79.5</td><td>0</td><td>19</td><td>7.11</td><td>6.7<br>2</td><td>7.36</td><td>0.21</td></tr><tr><td>Quanzhou</td><td>19</td><td>438.<br>1</td><td>438.<br>1</td><td>438.<br>1</td><td>0</td><td>19</td><td>7.27</td><td>6.6<br>8</td><td>8.16</td><td>0.35</td></tr><tr><td>Xiamen</td><td>19</td><td>146.<br>1</td><td>146.<br>1</td><td>146.<br>1</td><td>0</td><td>19</td><td>5.97</td><td>5.3<br>3</td><td>6.57</td><td>0.30</td></tr><tr><td>Shantou</td><td>19</td><td>54.8</td><td>54.8</td><td>54.8</td><td>0</td><td>19</td><td>4.81</td><td>3.9<br>8</td><td>5.59</td><td>0.60</td></tr><tr><td>Shenzhen</td><td>19</td><td>81.9</td><td>81.9</td><td>81.9</td><td>0</td><td>19</td><td>5.56</td><td>5.3<br>2</td><td>6.26</td><td>0.21</td></tr><tr><td>Hong Kong</td><td>19</td><td>122.<br>0</td><td>122.<br>0</td><td>122.<br>0</td><td>0</td><td>19</td><td>6.24</td><td>5.7<br>3</td><td>7.38</td><td>0.41</td></tr><tr><td>Zhanjiang</td><td>19</td><td>33.8</td><td>33.8</td><td>33.8</td><td>0</td><td>19</td><td>5.78</td><td>5.3<br>3</td><td>6.37</td><td>0.30</td></tr><tr><td>Zhuhai</td><td>19</td><td>33.9</td><td>33.9</td><td>33.9</td><td>0</td><td>19</td><td>8.06</td><td>7.6<br>6</td><td>8.38</td><td>0.18</td></tr></table>