Impact of Foreign Enterprises’ Capital Inflow on Urbanization Factors: Evidence from Northeastern Cities of China

Abstract

This study investigates the impact of foreign capital inflow and the number of contracted foreign direct investment projects on urbanization. The study focused on the less-explored provinces of Liaoning, Jilin, and Heilongjiang, covering the period from 2007 to 2021. The definition of urbanization was expanded to include three elements: the urbanized labor force, emission and pollution levels, and the productivity of services in the economy. Most importantly, an urbanization index was generated to estimate the total effect of foreign capital investment on sustainable green urbanization. The analysis employs both random and fixed effects regression models, complemented by robustness checks using the generalized least squares (GLS) method. The findings indicate that while foreign capital investments contribute positively to labor urbanization and service productivity, they have a notably adverse impact on environmental quality in the examined cities. Moreover, our findings confirm an overall inverse relationship between foreign capital investment and the urbanization index: the negative impact is primarily driven by inadequate procedures for emissions control in the northeastern Chinese cities. In conclusion, this research provides insights and strategic recommendations for promoting sustainable green urbanization.

1. Introduction

In recent decades, the Chinese economy has witnessed a large presence of foreign investments in various industries and sectors. These investments, which are often attracted by facilities and benefits, play an essential role in shaping urban development in the country [1,2,3]. One critical aspect that has garnered much attention in the literature is the impact of foreign direct investment on the economies of urbanization [4,5,6,7]. China has experienced relatively rapid urbanization compared to other countries [8,9,10,11,12]. According to the National Bureau of Statistics in China, Chinese urbanization has increased at a fast pace, from 17.92 percent since the opening-up policy for foreign direct investments and industrial reform in 1978, to 45.89 percent in 2007, exceeding half of the population, and reaching 64.72 percent in 2021.

The significance of this research stems from the imperative to formulate effective urban policies, particularly in the context of increasing capital investments. Understanding the impact of foreign investments on urbanization levels, environmental pollution, and the provision of essential services is of paramount importance, particularly in developing countries. In the case of China, there is a burgeoning interest in the trend of urbanization. Policymakers are placing greater emphasis on ensuring an adequate number of job opportunities, improving the economic environment, and improving the quality of people’s lives in response to the rapid urbanization trend, especially since some selective foreign investments have reshaped urbanization in Chinese cities. Chen and Wu [5] highlight FDI’s role in regional urbanization disparities, aligning with earlier research [13,14,15,16,17]. FDI significantly draws rural migrants to urban centers. The primary objective of this research is to examine the relationship between foreign investments and urbanization in northeastern Chinese cities. In this way, this study seeks to offer valuable insights to policymakers for strategic planning aimed at attracting foreign investment to the region.

Despite divergences in findings regarding the impact of urbanization on pollution emissions [18,19,20,21,22,23,24,25,26], urbanization consistently emerges as a factor correlated with emissions, gaining increased attention as the pace of urbanization intensifies. The environmental issues have become increasingly pronounced with the acceleration of urbanization. Notably, since the initiation of China’s economic liberalization and industrial reforms, the country’s urbanization has undergone marked advancements [18], the secondary objective of this research is to quantitatively assess the magnitude of the influence exerted by foreign investments on the urban environment within the northeastern cities of China. This aspect of the study aims to contribute to a better understanding of the environmental implications associated with foreign capital inflow. Furthermore, in alignment with the United Nations’ objectives aimed at enhancing the well-being of populations, this research examines how embracing foreign investments can lead to advancements in service quality within the economy and consequently improve the welfare of citizens. Our examination of the extant literature has revealed a notable gap regarding the impact of foreign investments on service productivity, a topic that has been relatively underexplored, with only a limited number of studies dedicated to its investigation. In light of this identified gap, the current study is deemed imperative to address this relationship. Our third objective is to empirically investigate and analyze the effects emanating from foreign capital investments on the development of services within urban cities. Thus, this research contributes substantially to the existing body of literature by examining the effects across the selected Chinese cities.

Since it is important to study the incentive factors that are closely related to the quality of urbanization [10], economists clarify how countries’ urbanization is influenced by foreign investment activities [4,5,6,7,27,28]. In addition, studies tend to analyze how foreign capital investments can affect different aspects of the economy, all of which refer to the deep meaning of urbanization and the modernization of cities. Foreign investments historically have been seen as a way to boost economies in the host countries because of the transfer of technologies, machinery, and expertise. Although most studies affirm the benefits of foreign investments, there might also be some challenges like environmental degradation, profit repatriation or even economic dependence on foreign investments [29,30]. Urbanization problems may emerge when foreign investment is not properly controlled.

This article analyzes the impact of foreign capital investment on green urbanization, emphasizing both the promotion of urbanization and reduction of urban pollution. In addition, from the perspective of sustainable urbanization and ensuring the necessary facilities and services for an urbanized population, therefore, apart from analyzing how the urban labor force is affected by foreign capital investment, two more urbanization components were added, namely: the level of air pollution and service productivity. The northeastern region of China is the study area of this paper; this region was the mainstay of the Chinese industrial economy [31] until the eastern and southeastern coast regions began to attract foreign investors’ capital. At that time, the Chinese economy began to transform. Since the beginning of the twenty-first century, high-tech industries have appeared, most of which are foreign-owned. This shift led to a decrease in heavy industries. Concurrently, the labor force began to move toward the south and east. The incentive from foreign investors to invest in the region has decreased, although northeastern Chinese cities are still seeking to return as an attraction for investments and experienced workforces. However, the current study is different from the existing research in the following points: Firstly, most studies used national province data or other specific regions; a few times the data covered the whole country; however, these data may not reflect the real effect in the northeastern Chinese cities, and overall, no study addressed this region of China in particular. Secondly, given that China is a huge country, drawing a common conclusion for the entire country may be biased, as there are different levels of urbanization and economic growth. For this reason, it was necessary to study this particular region and draw a unique conclusion. Thirdly, although many studies have been conducted in the Chinese context [4,7,32,33,34], this study is among the first to examine urbanization in an extended definition, beyond the traditional meaning of urbanization, and similarly to the study of Grekou and Owoundi [27], the two supplemental dimensions were studied in the northeastern cities of China. Fourthly, distinct from previous literature, the approach to measuring urbanization was different; for example, the focus of this study shifts from the percentage of all urban populations to the percentage of employed people in urban areas because using the entire urban population could yield distracting results, while the employment-based indicator is better-suited to representing the proportion of the workforce that may be influenced by foreign direct investments. Additionally, the utilization of SO₂ as an indicator provides valuable insights into environmental and health impacts. Calculating pollution emissions per capita further refines precision within the urbanization context. Moreover, the incorporation of the urbanization index enables the assessment of the comprehensive impact of foreign capital investment on the promotion of sustainable and environmentally friendly urbanization. These four aforementioned points highlight the uniqueness and significant contributions of the study. The remaining parts of the study are organized as follows: Section 2 reviews the previous literature and explains the related theories. Section 3 reviews data sources and methodologies. Section 4 discusses the results, Section 5 presents the discussion of the research findings, and Section 6 covers conclusions and policy recommendations.

2. Theory and Hypotheses

2.1. The Effects of Foreign Capital Investments on the Urbanized Labor Force

Urbanization, as described by Annez and Buckley [35], is essential for developing nations to maintain development, and no nation has previously attained a middle-income level without substantial individual movement toward urban areas. Moreover, urbanization is important for improving the level of the economy and the standards of living for the urban population. The main element of urbanization is people’s migration from rural to urban regions. This phenomenon has attracted the attention of scholars in many fields such as urban planning, economics, geography, and environmental science, given that it is a relatively recent phenomenon. Urbanization is expressed as the movement of people from the countryside to the city, or, in other words, it is the transition from primary industry to secondary and tertiary industry [36,37]. Specifically, promoting urbanization mainly occurs because of the availability of jobs in urban regions, higher wages than in rural areas, and better living conditions and services [38].

The selective distribution of foreign investment in certain regions of China has led to changes in the concentration of urbanization among Chinese cities, leading to the urbanization of the population in coastal areas. Chen and Wu [5] indicated that FDI does have an impact on China’s urbanization disparities among regions, in addition to other existing studies that accused foreign capital flows of being the reason for different levels of urbanization [13,14,15,16,17]. These studies support the effect that FDI has on urbanization, where it has drawn large numbers of migrant laborers from rural areas in China. The migration of people from rural to urban regions is primarily influenced by two forces: On the one hand, as the economy grows and industrialization and urbanization expand, the conventional agriculture sector will diminish as many farmers begin to lose their lands, resulting in a large number of additional rural employees. On the other hand, the growth of contemporary industrial and service industries in cities can provide plenty of jobs and higher wages, thereby attracting surplus rural labor to cities. These two forces are mainly a push from the agricultural land and a pull from the urban centers [39]. Using provincial data from China, Lu and Yan [40] demonstrated the need for adjusting FDI policies to regional new urbanization levels in order to maximize the beneficial impact of urbanization on China’s green inclusive growth. The study of Wu and Chen [4], which examined China up until 2012, suggests that foreign investment has clear advantages for urbanization in China’s coastal regions. Again, in a study in Vietnam [41], the FDI effect on employment was positive in the services and industry sectors. Another study in Mexico examined this link and found that foreign investments have led to a shift in production toward skill-intensive goods, resulting in a rising need for qualified labor [42]. However, the study of Axarloglou and Pournarakis [43], which focused on the local economies of the US states, found that while most US states see negligible impacts of FDI inflows on local employment and wages, some sectors do experience positive benefits, and negative impacts on other industries were also discovered. The study recommends that it is important to establish criteria for choosing industrial foreign investments in the US. Nonetheless, in developing countries, FDI is not only anticipated to have positive effects on the transfer of capital, technologies, and expertise spillovers [44], but will also influence developing countries’ urbanization processes. Wu and Chen [4] have extensively explained how this effect exists in several possibilities, first, by fostering systemic economic transformations and leading to the growth of both secondary and tertiary industries, in addition to providing employment opportunities in the host country. Moreover, the higher wages that the multinational enterprises offer can also increase the pulling effect on the rural population; rural laborers will opt to move to cities where they can find jobs offering better compensation for their labor, especially in developing countries. Similarly to other studies, it was found that this can also have a spillover effect, resulting in raising the level of wages in the local enterprises that are in the supply chain [45,46].

In this study, we have not focused on the percentage of all urban populations; instead, the indicator we used focused on the percentage of employed people in urban areas. That is for two reasons: First, this indicator can represent the percentage of the workforce that might be influenced by foreign direct investments. In other words, if this study had focused on the entire urban population, there would be distracting results due to the numbers of unemployed persons. Second, the aim of this study was to examine how foreign investment is providing work opportunities to individuals. Therefore, focusing on the employed population in urban areas can effectively yield a more accurate coefficient of this relationship. Overall, the hypothesis is formed as follows:

Hypothesis 1. 

A favorable relationship exists between foreign capital inflow and the level of labor force urbanization in northeastern Chinese cities.

2.2. The Foreign Capital Investment Inflow Effects on Environmental Pollution

In this study, as mentioned above, the concept of urbanization was extended to a broader concept of sustainable development and environmental preservation. Given that cities were held accountable for over 70 percent of the world’s carbon dioxide emissions according to the World Cities Report in 2016 (https://unhabitat.org/world-cities-report-2016, accessed on 5 May 2023) developed cities have recently tended to replace high-emission components with more environmentally friendly ones, where the amount of emissions indicates the city’s commitment toward sustainable development.

Earlier empirical research [18,19,20,21,22,23,24,25,26,47,48,49,50,51,52,53,54,55] has examined the association between urbanization and emission levels. Some studies have found a causal relationship between them. However, for various reasons, other studies could not detect an important impact of urbanization on pollution emissions. Despite differences in their results, urbanization is often linked to emissions, and environmental issues have received more attention as urbanization has accelerated.

In this context, we employed emissions per capita as a proxy for urbanization, aligning with the need for a deeper understanding of the environmental impact as an aspect of urbanization. This is particularly relevant in terms of investigating the stimulating factor of urbanization, namely foreign capital inflows, against the context of China’s significant urbanization progress since the opening up of the country to foreign investments and industrial reform [18].

There are two views of the process by which foreign investments affect pollution in urbanized regions. The first states that foreign investments are environmentally friendly. When they flow to host countries, foreign companies may comply with international standards in production lines, and by including high technologies that maintain a non-increasing level of emissions, they reduce existing pollution and thus have desirable effects on the environment. Several studies have proven the presence of this favorable relationship between foreign investment flows and pollution. For example, the study of Birdsall and Wheeler [56] is the first to refer to this theory and designate its name as the pollution halo hypothesis. Some studies attempted to investigate this theory in different regions, including the study by Rafindadi et al. [57] on the impact of investments on environmental pollution in the Gulf Cooperation Council countries during the period from 1990 to 2014. The study concluded that there is an inverse and significant relationship between environmental pollution and foreign investments. According to the study of Lan et al. [58], the influence of FDI on pollutant emissions is greatly reliant on the degree of human capital; in regions with a higher density of human capital, FDI is adversely related to environmental pollution. The long-run dynamic ARDL simulation results of Wang et al. [59] also show that foreign direct investment inflow and globalization have a negative impact on environmental deterioration. Again, Pao and Tsai [60] concluded that influxes of FDI would help reduce CO₂ emissions in developing countries. A different study used a quantile regression technique, and depending on the linear panel model, it was found that FDI helps host nations by reducing pollution levels [61]. Another study demonstrated that FDI can effectively promote local environmental protection supervision in addition to vigorously encouraging the deployment of environmental protection technologies [62]. The study of Kostakis et al. [63] aimed to investigate the impact of foreign investments in Brazil and Singapore on average per capita CO₂ emissions; their results also showed an inverse effect in Singapore. Another study, of 46 sub-Saharan African countries, showed that a 1% rise in foreign investment causes carbon emissions to go down by 0.004% [64]. Again, in a study on Indonesia, the ARDL results indicated an adverse interaction between the emissions of carbon and FDI, urbanization, and renewable energy sources. A different study showed that FDI influxes and CO₂ pollutants had a detrimental relationship [65]. The investigation of Mahadevan and Sun [66] found that the impact of China’s FDI on pollution in Belt and Road countries varies according to those countries’ development levels. Empirical research suggests that China’s FDI produces a pollution halo impact in poor BRICs. Given that China and the Belt and Road countries constitute over 50% of global carbon emissions [67], it is important to notice that China’s outward foreign direct investment does not contribute to exacerbating this detrimental impact. As for the study of Bokpin [68], who clarified that for 24 African countries, there is an inverse relationship between FDI flows and environmental sustainability, the relationship became positive when considering the institutions’ role in controlling the impact of foreign investment.

The findings of Apergis et al. [69] indicated that FDI originating from France, Germany, and Italy led to a decrease in carbon emissions within the BRICS countries, substantiating the observed pollution halo effect. The study of Demena and Afesorgbor [70] suggested in a meta-analysis that FDI had essentially no impact on emission levels. In a study of seven emerging Asian economies from 1991 to 2017, increased FDI inflows were found to be associated with reduced CO₂ emissions [71]. In addition, Pradhan et al. [72] concluded that foreign direct investment and gross domestic product in BRICS nations reduce CO₂ emissions. Again, based on a regional panel analysis from 1995 to 2010 in China, Zhang and Zhou [65] discovered that foreign direct investment helps reduce carbon dioxide emissions. Another study, by Liu et al. [73] using Chinese city-level data with a linear geographic panel regression, discovered that FDI inflow contributes to a decline in carbon dioxide levels. Again, a study carried out by Sung et al. [74] aimed to test the impact of foreign investments on environmental pollution by using data on 28 Chinese industries during the period 2002–2015; their results were consistent with the pollution halo hypothesis. Finally, the most recent research found that FDI subject to environmental regulations may promote China’s urbanization [6].

From another point of view, numerous studies have demonstrated a positive relationship between foreign investment flows and pollution. The pollution haven hypothesis is used in the literature to describe this interaction; the first ideas of this theory go back to two studies in the late 1970s [75,76], and according to this theory, some businesses with high levels of contaminated waste and emissions would be moved overseas via FDI to countries with lower standards of environmental protocols, yielding benefits from lower costs and higher profits but also leading to a significant rise in pollutant emissions. Researchers who examined this theory verified that FDI inflows exacerbate the degradation of the environment. Grimes and Kentor [77] have examined cross-country panel data for 66 developing nations and discovered that the growth in carbon emissions between 1980 and 1996 was significantly influenced by the introduction of foreign capital.

According to the study of Deng et al. [78], which covers 107 countries, they found that FDI decreases and increases air pollution before and after a threshold level in the overall panel as well as the upper-middle-income and low-income sub-panels. Additionally, FDI inflows generate increasing environmental contamination in lower-middle-income countries both before and after the threshold.

The study of Shahbaz et al. [79] found that increased energy consumption and FDI cause increased carbon emissions. Again, during the period from 1980 to 2013, the study of Gorus and Aslan [80] aimed to test the hypothesis and the environmental curve for nine countries of the Middle East and North Africa. The study proved that the pollution haven hypothesis held for the nine sample countries. Moreover, in research conducted in Pakistan, the researchers confirmed that with the increase in FDI, there was an increase in CO₂ emissions [81]. In a study on sub-Saharan African countries that produce oil, Gyamfi [82] found that foreign direct investment may raise consumption-based carbon emissions. Also, the study of Nasir et al. [83] used DOLS and FMOLS methodologies in the context of East Asian economies; their results showed that FDI has a highly significant long-term co-integration effect on pollution. The findings of Apergis et al. [69] demonstrate that FDI flows from Denmark and the United Kingdom boost carbon emissions in BRICS nations, supporting the pollution haven hypothesis. The study of Abdouli and Hammami [84] showed, by applying the VAR model, that there is a one-way causal relationship between environmental quality and FDI for 17 countries in the MENA region. When analyzing panel data for 100 countries from 1980 to 2019, Ponce et al. [85] found that FDI has a unidirectional effect on environmental deterioration in high-income countries. The research of Lan et al. [58] investigates the link between human capital, FDI, and environmental emissions. Their findings revealed that the pollution haven hypothesis was true only in regions with low levels of human capital, implying a positive connection between FDI and environmental emissions in these provinces. A study on China’s industrial sectors using data from 285 Chinese cities discovered that substantial FDI inflows raise and worsen CO₂ emissions there [86,87]. Another study confirmed the hypothesis for five Asian countries, namely Indonesia, Malaysia, the Philippines, Thailand, and Singapore, during the period from 1981–2010 [88]. This hypothesis was also verified in a study of 19 African nations [89]. The study of Hoffmann et al. [90] examined the causality between FDI flows and pollution over a period ranging between 15–28 years for a sample of 112 countries. The study confirmed the hypothesis only in low- and middle-income countries due to the lax environmental restrictions imposed on investments. Zhu et al. [91] also indicated that FDI has a considerable positive influence on SO₂ emissions in a study using data from 2000 to 2013 in the Beijing–Tianjin–Hebei area. Another study applied the REM random effects model to 14 Latin American countries during the period 1980–2010. The results were consistent with the pollution haven hypothesis [92]. Again, during the period from 1990 to 2013, another study proved the validity of the hypothesis using the ARDL model in 15 Asian countries [93]. Using the ARDL model for the country of Finland and data up to 2020, Georgescu and Kinnunen [94] showed that FDI does not mainly explain CO₂ emissions. Finally, Xie et al. [95] identified an inverse “S-shape” in emerging countries. Specifically, the influence of FDI on CO₂ was transformed from the pollution haven theory to the pollution halo hypothesis.

This study followed Grekou and Owoundi [27], who investigated emissions as one component of urbanization in Africa. It takes the relationship as a component of urbanization with the most recent data in the northeastern cities of China to settle the debate about the mixed results of foreign investment and pollution. Thus, this study assumes a negative relationship between foreign investments and emissions in the urbanized cities of northeastern China, taking into account China’s level of industrialization and its attempts to control environmental degradation under the emerging levels of urbanization.

Hypothesis 2. 

There is a negative relationship between foreign capital inflows and the volume of emissions in the cities of northeastern China.

2.3. The Effects of Foreign Capital Investments on Service Productivity

Several studies have examined the impact of foreign direct investment on productivity in various industries, including the service sector. One study by Aitken and Harrison [96] found what they termed the “market stealing effect” of foreign direct investment, which outweighs the positive effect of technology transfer on firm productivity in Venezuela [97]. Using firm-level data from Lithuania, Javorcik [98] found that foreign direct investment has a positive productivity effect on supplier industries but no significant effect on local competitors within the same industry. Foreign direct investment in the service sector is not only relevant because it provides foreign capital, but also because of its potential spillover effects on productivity in domestic sectors [99]. Foreign capital investment can benefit the service sector directly through investments that help modernize production and services by bringing in new capital and technologies [100,101,102,103,104]. Also, it can affect the service sector indirectly through the spillover effect of foreign investments existing in agglomeration economies. The inflow of foreign capital into a country may enhance urbanization quality particularly through the service sector, in areas such as finance, telecommunication, information technology, construction, transportation, businesses, and trade.

There is an extensive body of literature about the spillover effect of foreign investments on productivity [105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121] Most of these studies focused on the spillover in the manufacturing industries, be it horizontal or vertical. Nevertheless, there is a lack of literature on the effect of foreign investments on service productivity. Nguyen and Nguyen [122] were the first to estimate the spillover effects of foreign direct investment on the service sector. They discovered horizontal positive spillovers to domestic businesses, which is when local service providers can acquire knowledge from their competitive FDI enterprises. However, according to the same study, domestic service firms appear to be adversely affected by the vertical effect of FDI spillovers; the authors explained that in services, foreign-owned businesses are unlikely to have incentives to transfer their knowledge or technology to local businesses. When analyzing the impact of FDI spillover on productivity using firm-level data from Vietnam, the study of Nguyen et al. [123] found that intra-industrial and regional spillovers have a detrimental influence on domestic business productivity. Only backward intra-industrial spillovers have a beneficial impact on local production.

Another study examined the influence of FDI on the Chinese service industry [124]; according to their cointegration analysis, FDI in the service sector and the overall development of services have a long-term stable equilibrium. However, up until 2009, they found no Granger causality from the FDI in services to the added value of the service industry. Again, another study showed a positive link between foreign direct investment in services and the development of the service sectors in China [125], which was found using the OLS approach on data ranging from 1994 to 2003. Similarly, the relationship was investigated using VAR-based cointegration analysis and Granger causality, and the findings indicated that the growth of China’s services industry was being driven by FDI [126]. However, the novel idea of treating service productivity as an urbanization component was applied first in the study of Grekou and Owoundi [27] in the African countries. They utilized the added value of services as an expanded term of urbanization. We are trying to explore the effect of foreign capital investment on the level of service productivity as a proxy for the level of urbanization in the northeastern Chinese urban cities. Thus, we are assuming a positive relationship between foreign capital investment and the added value of services.

Hypothesis 3. 

Foreign capital inflow can enhance the levels of service productivity in the urbanized northeastern cities of China.

3. Data and Methodology

3.1. Data Collection Sources

The targeted cities in this study were chosen to examine the effects of foreign capital investments on urbanization characteristics. These data cover the period from 2007 to 2021, where cities were selected from three provinces: Liaoning, Jilin, and Heilongjiang. The main reasons for choosing the northeastern provinces were as follows. First, there are comparatively fewer studies focusing on these regions compared to other provinces in eastern and southern China. Second, most studies are conducted on the national level, i.e., by selecting provinces or cities. Even though China is one country, there many different characteristics among provinces. Third, the northeastern part of China received less foreign capital compared to other parts of China, and that is clearly seen by viewing the last 20 years of data on the economic regions of China (the national bureau of statistics in China).

The dataset primarily originates from the China City Statistical Yearbook, meticulously provided by the relevant departments of each city. This yearbook offers valuable insights into the economic and social development of cities across China. Numerous procedures were implemented to enhance the dataset’s utility. Initially, we compiled all city-level data spanning the study period from 2007 to 2021. Additionally, to address missing values, the Liaoning Statistical Yearbook, Heilongjiang Statistical Yearbook, and Jilin Statistical Yearbook were utilized. Furthermore, to reconcile variations in units of measurement across certain years, each indicator underwent standardization, ensuring a cohesive dataset. Price-related indicators were adjusted using the consumer price index (CPI). The dataset comprises 510 records encompassing 15 years and 34 cities at the city level. The analysis was then conducted using this effective sample dataset at the city level.

3.2. Definitions of Variables in the Study

The dependent variables in this research were the urbanization components as discussed in the literature, where urbanization is described by three elements. The first is the percentage of urbanized labor force in the city. The second is related to sustainable development and environmental preservation, which is the amount of emissions. The third element is the utilized added value of services. Thus, these three elements together represent sustainable green urbanization. This study provides an individual analysis of the three indicators. In addition, an index variable is produced in order to test the effect of foreign capital investment on urbanization as a whole.

Measuring the urban population by the traditional method involves taking the number of urbanized people in the cities as a proportion of the whole population; however, this traditional way is not adequate when focusing on the labor force [7,19,21,27,127]. This study instead focused on the urban labor force. In addition, regarding air pollutant emissions, sulfur dioxide is a highly reactive and harmful air pollutant. Several studies have adopted it as an indicator due to the major pollutants emitted by industrial plants and the adverse effects they can have on the environment and overall health [128,129,130]. SO₂ emissions can be used to estimated the relationship between urbanization and industrial growth, specifically foreign capital investment. The study calculates pollutant emissions per capita, which may have a better indication in this context of urbanization. Moreover, the services productivity indicator is calculated as the percentage of the service sector’s value added to the total gross domestic product of each city. It indicates the relative importance of the service sector in different cities in northeastern China over the period. This indicator is also widely used in the literature. Lastly, the principal component analysis (PCA) method was employed to examine the quality of urbanization. The weights for our urbanization index were determined by the coefficients of the eigenvector that captured the largest variance [131], ensuring an accurate reflection of the most significant patterns and trends in China’s urbanization process. This approach contrasts with the study of [132], which utilized the entropy method to evaluate urbanization quality across 13 towns in Zhuzhou, Chengdu, Beijing, and surrounding areas. However, we opted for PCA due to its advantage in capturing the largest variance in our dataset, further ensuring that the urbanization index accurately represents the most significant patterns inherent in the urbanization data. The values of the urbanization index are calculated based on the following equation:

$$\begin{gathered} {Urb\_indx}_{ i,t}=({Norm\_Urb\_Lb }_{i,t}\ast 0.1433)+({Norm\_Urb\_Poll }_{ i,t} \ast 0.8359) \\ +({Norm\_Urb\_SePr }_{i,t} \ast 0.5299) \end{gathered}$$

(1)

where the urbanized labor force ${Urb\_Lb }_{i,t}$ was given a weight of 0.1433, reflecting the significance of the urbanization process. Emission density ${Urb\_Poll }_{ i,t}$ received a weight of −0.8359, suggesting the negative impact of emissions on urban sustainability and quality of the environment. The added value of services ${Urb\_SePr }_{i,t}$ was weighted at 0.5299. This weight signifies its role in enhancing the quality of life through improved services, which in turn reflect the overall economic growth and prosperity of urban regions.

Table 1. Summery of the study variables and their measurements.

Variables	Definition	Taken as	Measurement
Dependent Variables
Urb_Lb	Urban labor force	Log form	Employed persons in urban unit, employed persons in urban private enterprises, and urban self-employed individuals divided by the total population collected in city i and year t.
Urb_Poll	Emission density	Log form	Total SO2 emissions (in tons) divided by the total population in city i and year t.
Urb_SePr	Services productivity	Log form	Value-added of the service sector divided by the gross domestic product in city i and year t.
Urb_Index	Urbanization index	Log form	A combination of the three previously mentioned elements that represent the overall representation of urbanization in city i and year t.
Independent Variables
FC	Foreign capital per GDP	Log form (first lagged)	Foreign capital in the Chinese currency divided by real GDP in the Chinese currency in city i and year t.
F_Cont	Projects of Contracted FDI	Log form	The number of projects for contracted FDI divided by real GDP in Chinese currency in city i and year t.
Control Variables
Edu_Ppl	Human capital	Log form (second lagged)	The number of highly educated persons divided by the total population in city i and year t.
Health	The health index	Log form (second lagged)	The index of healthcare resources represents hospitals, beds, and doctors in city i and year t.
Cult_Ppl	The culture indicator	Log form (second lagged)	The total_number_of_books multiplied by 10 and divided by the total population in city i and year t.
UnEmp	Unemployment Rate	Log form (first lagged)	Number of registered unemployed persons in urban areas divided by the total labor force in urban areas in city i and year t.
Wages	Average Wages	Log form (first lagged)	Total wages divided by the total employees in urban areas in city i and year t.
Ind_Sev	Industry to Services Ratio	Log form	Value-added of industry divided by value-added of services in city i and year t.
${\gamma }_1$			Panel models’ city-fixed effect
${\gamma }_2$			Panel models’ random effect

presents the definition and measurement of each variable.

3.3. Model Specifications and Methodology

In this study, the aim is to investigate the relationship between foreign capital investment and urbanization, air pollutant emissions, and value-added of services. According to the specific requirements of each model, this study utilizes both random and fixed-effect estimators for the hypotheses testing, in addition to the robustness checks that include an alternative proxy of the independent variable and the method of generalized least squares (GLS). Following is a description of the empirical models of this study, where the first model has the dependent variable of urbanized labor force, as follows:

$$\begin{gathered} {Urb\_Lb }_{i,t}=\beta 0+\beta 1\ast {Fc\_Gdp }_{i,t}+\beta 2\ast {Wages }_{i,t}+\beta 3\ast {UnEmp }_{i,t}+\beta 4 \\ \ast {Edu\_Ppl }_{i,t}+{\beta 5\ast Health }_{i,t}+\beta 6\ast {Ind\_Sev }_{i,t}+\beta 7 \\ \ast {Cult\_Ppl }_{i,t}+\alpha i+{\epsilon \_ }_{i,t} \end{gathered}$$

(2)

The second model has the level of pollution as the dependent variable in the urbanized cities, as follows:

$$\begin{gathered} {Urb\_Poll }_{ i,t}=\beta 0+\beta 1\ast {Fc\_Gdp }_{i,t}+\beta 2\ast {Wages }_{i,t}+\beta 3\ast {UnEmp }_{i,t}+\beta 4 \\ \ast {Edu\_Ppl }_{i,t}+{\beta 5\ast Health }_{i,t}+\beta 6\ast {Ind\_Sev }_{i,t}+\beta 7 \\ \ast {Cult\_Ppl }_{i,t}+\alpha i+{\epsilon \_ }_{i,t} \end{gathered}$$

(3)

The third model has the dependent variable of productivity of services in urbanized cities, as follows:

$$\begin{gathered} {Urb\_SePr }_{i,t}=\beta 0+\beta 1\ast {Fc\_Gdp }_{i,t}+\beta 2\ast {Wages }_{i,t}+\beta 3\ast {UnEmp }_{i,t}+\beta 4 \\ \ast {Edu\_Ppl }_{i,t}+{\beta 5\ast Health }_{i,t}+\beta 6\ast {Ind\_Sev }_{i,t}+\beta 7 \\ \ast {Cult\_Ppl }_{i,t}+\alpha i+{\epsilon \_ }_{i,t} \end{gathered}$$

(4)

The fourth model has the dependent variable urbanization index in the urbanized cities, as follows:

$$\begin{gathered} {Urb\_index }_{i,t}=\beta 0+\beta 1\ast {Fc\_Gdp }_{i,t}+\beta 2\ast {Wages }_{i,t}+\beta 3\ast {UnEmp }_{i,t}+\beta 4 \\ \ast {Edu\_Ppl }_{i,t}+{\beta 5\ast Health }_{i,t}+\beta 6\ast {Ind\_Sev }_{i,t}+\beta 7 \\ \ast {Cult\_Ppl }_{i,t}+\alpha i+{\epsilon \_ }_{i,t} \end{gathered}$$

(5)

where ${Urb\_Lb }_{i,t}$ is the level of urbanization of labor force in city i and year t. ${Urb\_Poll }_{ i,t}$ is the level of pollution in the urbanized cities in city i and year t. In addition, ${Urb\_SePr }_{i,t}$ is the productivity of services in the urbanized cities in city i and year t. ${Urb\_index }_{i,t}$ is the new indicator for urbanization index in city i and year t. ${Fc\_Gdp }_{i,t}$ is the foreign capital investment in city i and year t. ${Wages }_{i,t}$ is the level of average wages of employees in urban areas in city i and year t. ${UnEmp }_{i,t}$ is the unemployment rate of the labor force in the urban areas in city i and year t. ${Edu\_Ppl }_{i,t}$ is the percentage of highly educated persons in the urban areas in city i and year t. ${Health }_{i,t}$ is the level of healthcare facilities in the urban areas in city i and year t. ${Ind\_Sev }_{i,t}$ is the industry-to-services ratio indicator in the urban areas in city i and year t. ${Cult\_Ppl }_{i,t}$ is the level of people culture and awareness in the urban areas in city i and year t. $\alpha i$ represents the city-fixed effect of the panel data that captures the unobserved heterogeneity across cities. ${\epsilon \_ }_{i,t}$ is the error term of the model in city i and year t. The coefficients β1 to β7 indicate the effect of independent and control variables on urbanization factors, holding other factors constant.

4. Results

Firstly, the descriptive statistics for the variables’ distribution and variability are shown in

Table 2. Descriptive Statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
Dependent Variable
Urb_Lb	510	0.2412735	0.0916808	0.064617	0.62274
Urb_Poll	485	142.4949	116.7684	7.849036	876.5527
Urb_SePr	510	40.45137	9.797785	11.22	73.96
Independent variable
FC	455	0.0196153	0.0229724	0.0000178	0.1952124
Control variable
Wages	510	40,905.66	18,477.24	7378.07	10,0781
Edu_Ppl	510	1.414023	1.512175	0.0423142	6.812363
Health	510	0.1890584	0.1531137	0.0358504	0.8350626
Cult_Ppl	510	55.09969	53.61873	6.340423	482.7586
UnEmp	510	0.0384142	0.0145424	0.011021	0.114655
Ind_Sev	510	1.200944	0.8366133	0.1872634	7.632799
Prov	510	-	-	1	3
City_code	510	-	-	101	312
Year	510	-	-	2007	2021

. These statistics provide a brief description and show any potential outliers or data quality concerns.

Secondly,

Table 3. Correlation Analysis Matrix.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)
(1) FC	1.000
(2) Wages	−0.119	1.000
(3) UnEmp	−0.151	−0.149	1.000
(4) Edu_Ppl	0.270	0.380	−0.024	1.000
(5) Health	0.314	0.396	−0.109	0.674	1.000
(6) Ind_Sev	0.290	−0.353	0.003	−0.086	−0.221	1.000
(7) Cul_Ppl	0.188	0.454	0.064	0.538	0.514	0.055	1.000

reports the findings of correlations among the study variables of this empirical model. The table shows that the independent variable FC is not correlated with any of the control variables. Additionally, it is noted that the control variables—the level of average wages, unemployment rate, percentage of educated persons, health level, industry-to-service ratio, and level of cultural awareness are also independent and not correlated with each other.

The following figures present how the indicators have evolved in two different periods. First, Figure 1 displays the foreign capital per GDP. It is worth noting that the indicator’s values have declined during the recent period, especially when compared with the GDP of each city, yet some cities have taken the lead in attracting more capital compared to other cities.

Moreover, a significant expansion of urbanization can be seen in Figure 2. The recent map shows a higher density of labor force across the northeastern cities. Next, Figure 3 shows the emission density of industrial sulfur dioxide emissions. It is observed that the industrial pollution has decreased; however, there is a need to conduct further analysis to investigate this trend. Finally, Figure 4 represents the value-added percentage of services in each city. Clear improvements can be seen in the recent map, particularly in the major cities.

According to the variance inflation factor (VIF) and tolerance values presented in

Table 4. Multicollinearity check (variance inflation factor and tolerance).

Variables	VIF	Tolerance
Edu_Ppl	4.58	0.218152
Health	4.30	0.232451
Cult_Ppl	2.09	0.478499
Wages	1.52	0.659498
FC	1.32	0.760187
UnEmp	1.16	0.864890
Ind_Sev	1.12	0.894028
Mean Vif	2.30

, the VIF values range from 1.12 to 4.58 (less than 10), and the tolerance values range from 0.21 to 0.89 (greater than 0.02). Therefore, there is no multicollinearity issue among the studied variables within this range of values [133].

4.1. The Results of Multivariate Regression Models

To investigate the impact of foreign capital investments on urbanization components, multivariate regression tests were carried out. A Hausman test was conducted to decide between fixed-effect and random-effect models. It was confirmed that the fixed-effect model is appropriate for the first and third models, and the random-effect model is appropriate for the second and fourth models.

Table 5. Multivariate regression results using fixed effect and random effect.

Variables	Urb_Lb Fe	Urb_Poll Re	Urb_SePr Fe	Urb_Index Re
L.ln_FC	0.0127 *	0.130 ***	0.00914 **	−0.0251 ***
	(0.00763)	(0.0238)	(0.00370)	(0.00643)
L.ln_Wages	0.160 ***	−0.878 ***	0.0100	0.219 ***
	(0.0376)	(0.100)	(0.0183)	(0.0258)
L.ln_UnEmp	−0.191 ***	−0.0803	0.0274 *	0.0373
	(0.0313)	(0.0935)	(0.0152)	(0.0246)
L2.ln_Edu_Ppl	0.105 ***	0.0813	0.0274 *	0.0113
	(0.0323)	(0.0664)	(0.0157)	(0.0150)
L2.ln_Health	0.0160	−0.327 **	0.0721 **	0.0491 *
	(0.0574)	(0.131)	(0.0279)	(0.0297)
ln_Ind_Sev	0.0177	0.514 ***	−0.462 ***	−0.188 ***
	(0.0306)	(0.0860)	(0.0148)	(0.0217)
L2.ln_Cult_Ppl	0.0214	0.0615	−0.0412 **	0.00935
	(0.0337)	(0.0890)	(0.0164)	(0.0217)
Constant	−3.763 ***	13.23 ***	4.009 ***	−2.138 ***
	(0.435)	(1.064)	(0.211)	(0.265)
Observations	422	402	422	422
R-squared	0.319		0.861
Number of City_code	34	34	34	34

Notes: This table shows the results of fixed-effect and random-effect regressions of the four economic models. The dependent variables include urbanization Urb_Lb, air pollutant emissions density Urb_Poll, the value-added of services indicator Urb_SePr, and the urbanization index Urb_index. The independent variable is the foreign capital investment indicator FC. The control variables are the average wages Wages, the percentage of the population with education Edu_Ppl, health index indicator Health, culture per person Cult_Ppl, unemployment rate UnEmp, and the industry-to-services ratio Ind_Sev. Standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1.

presents the results of numerous regression analyses. The evidence suggests a favorable and significant effect of foreign capital investments FC on urbanization in three elements, i.e., Urb_Lb, Urb_Poll, and Urb_SePr, with (β = 0.013; p < 0.1) (β = 0.130; p < 0.01) (β = 0.009; p < 0.05), which indicates that more foreign capital investments are associated with an urbanized labor force and more emissions and pollution, in addition to higher levels of service productivity. However, the overall influence of the urbanization index is moving in a different direction. The findings indicate that there is a negative and significant coefficient (β = −0.025; p < 0.01) of the foreign capital investments FC on the urbanization index Urb_index, indicating that the overall effect is undesirable in the northeastern cities of China, where the effect of emissions and pollution overcomes the benefits of expanding labor force pooling and increasing the value-added of services.

Table 5 also details the findings related to the control variables used in the econometric models. The coefficient of average wages Wages has a significant positive effect on the urbanized labor force (β = 0.160; p < 0.01). The unemployment rate UnEmp has a negative and significant effect on the urbanized labor force (β = −0.191; p < 0.01) Also, the findings highlight a significant positive effect of educated people in the city Edu_Ppl on the urbanized labor force (β = 0.105; p < 0.1). Additionally, the effects of the health index Health have a positive but insignificant influence on the urbanized labor force (β = 0.016). The industry-to-services indicator Ind_Sev shows an insignificant positive effect on the urbanized labor force (β = 0.017). Moreover, the culture indicator Cult_Ppl also demonstrates a positive relationship (β = 0.021). The coefficients of the other three models can be interpreted in a similar manner from Table 5.

In this analysis, the nature of the dependent variable Urb_index differs from the first three dependent variables. Specifically, the first three models use individual indicators derived from log-transformed values. In contrast, the Urb_index indicator is constructed using the normalized values of these three indicators to account for their different scales. This approach ensures that the influence of each component aligns with its variability. This might explain the variations in coefficient magnitudes, especially when comparing across models.

4.2. Robustness Tests

4.2.1. Robustness Test Using the Generalized Least Squares

Due to the existence of an autocorrelation problem with the error term and the fact that the error term was not normally distributed, we used the feasible generalized least squares (FGLS) model in order to overcome these error term problems. This model is a versatile regression approach that can handle a wide range of error configurations, including non-normal errors and autocorrelation issues. The method was applied as a robustness check to address any autocorrelation in the error term or any heteroscedasticity in the data. It can simulate the error covariance pattern and change the standard errors of the regression coefficients correspondingly, making the findings more trustworthy and resilient.

Table 6. Robustness check for multivariate regression results using GLS.

Variables	Urb_Lb	Urb_Poll	Urb_SePr	Urb_Index
L.ln_FC	0.0272 **	0.121 ***	0.0193 ***	−0.0163 **
	(0.0110)	(0.0283)	(0.00545)	(0.00666)
L.ln_Wages	0.200 ***	−0.686 ***	0.0849 ***	0.199 ***
	(0.0389)	(0.0993)	(0.0193)	(0.0236)
L.ln_UnEmp	−0.300 ***	−0.0560	0.00844	0.00709
	(0.0354)	(0.0907)	(0.0176)	(0.0215)
L2.ln_Edu_Ppl	0.0681 ***	0.210 ***	0.0248 ***	4.06 × 10−5
	(0.0155)	(0.0393)	(0.00768)	(0.00939)
L2.ln_Health	−0.277 ***	−0.626 ***	−0.0162	0.0509 ***
	(0.0315)	(0.0808)	(0.0156)	(0.0191)
ln_Ind_Sev	0.0924 ***	0.665 ***	−0.375 ***	−0.207 ***
	(0.0278)	(0.0714)	(0.0138)	(0.0169)
L2.ln_Cul_Ppl	0.294 ***	0.161 **	0.0619 ***	0.0230
	(0.0252)	(0.0647)	(0.0125)	(0.0153)
Constant	−6.050 ***	10.35 ***	2.657 ***	−2.036 ***
	(0.383)	(0.965)	(0.190)	(0.233)
Observations	422	402	422	422
Number of City_code	34	34	34	34

Standard errors in parentheses. *** p < 0.01, ** p < 0.05.

shows the results of a multivariate regression study using the GLS model. This model produces findings that are equivalent to the main results. The relationship between foreign capital investment and the urbanization elements was significant and carried the same relationship directions. However, as for the control variables, there were some minor differences for the Health and Cult_Ppl indicators, but the rest of the coefficients held the same direction in their relationships.

4.2.2. Robustness Check Using an Alternative Measure of the Independent Variable

To confirm the validation of the main regression results, the logarithm of the Number of Projects for Contracted Foreign Direct Investment indicator ln_F_Cont was used as another proxy for the foreign investments, except for Urb_SePr.

Table 7. Multivariate Regression Results Using Alternative Measure of Independent Variable.

Variables	Urb_Lb	Urb_Poll	Urb_SePr	Urb_Index
ln_F_Cont	0.0285 **	0.179 ***	0.00704	−0.0265 ***
	(0.0136)	(0.0332)	(0.00647)	(0.00813)
L.ln_Wages	0.245 ***	−0.365 ***	0.0677 ***	0.146 ***
	(0.0484)	(0.118)	(0.0230)	(0.0289)
L.ln_UnEmp	−0.326 ***	−0.0475	0.0120	0.0116
	(0.0400)	(0.0973)	(0.0190)	(0.0239)
L2.ln_Edu_Ppl	0.0642 ***	0.189 ***	0.0175 **	0.00357
	(0.0176)	(0.0429)	(0.00838)	(0.0105)
L2.ln_Health	−0.273 ***	−0.709 ***	0.00361	0.0692 ***
	(0.0372)	(0.0922)	(0.0177)	(0.0222)
ln_Ind_Sev	0.127 ***	0.792 ***	−0.380 ***	−0.223 ***
	(0.0301)	(0.0748)	(0.0143)	(0.0180)
L2.ln_Cul_Ppl	0.267 ***	0.0386	0.0667 ***	0.0363 **
	(0.0298)	(0.0737)	(0.0142)	(0.0178)
Constant	−6.686 ***	6.339 ***	2.765 ***	−1.348 ***
	(0.493)	(1.194)	(0.235)	(0.295)
Observations	359	343	359	359
Number of City_code	34	34	34	34

Standard errors in parentheses. *** p < 0.01, ** p < 0.05.

shows significant relationships with the urbanization components, and they carry the same relationship directions. Moreover, the results of the control variables kept the same direction of the relationship with the main analysis table except for the Health coefficient in the first model and the Cult_Ppl coefficient in the third model, in addition to the Edu_Ppl coefficient in the fourth model. It is worth noticing that the number of observations declined due to the missing data in the independent variable; however, from all of the above results, the current study’s findings in terms of foreign capital investments and urbanization are considered robust.

5. Discussion of the Findings

The role of foreign capital as a driving force behind urbanization and its environmental implications have garnered substantial attention. With urban areas acting as centers for economic growth and development, it was important to understand the dynamics of foreign capital inflow. This is particularly relevant for developing cities that often face the challenges of promoting economic growth and maintaining a green environment.

Our findings align with the prevalent academic consensus. Foreign capital does foster urbanization and influences urban migration patterns by creating employment opportunities, either directly within foreign enterprises or indirectly within competitive local businesses. Specifically, an inflow of foreign capital can lead to increased labor mobility. Workers from rural areas or those surrounding urban centers might relocate in search of higher-quality job opportunities and better wages created by foreign investments. Moreover, foreign enterprises may employ both skilled and unskilled labor. However, specialized training programs and educational initiatives can increase the likelihood of successful job matches and enhance opportunities for workers from rural areas. Beyond the direct employment opportunities provided by foreign investments, their presence can also stimulate local entrepreneurship. Local companies may develop or expand in response to increased competition. Additionally, workers who gain experience from these foreign companies may eventually branch out, starting their own businesses and employing even more individuals [134]. Thus, similarly to the studies of [4,7,135], Hypothesis 1 is confirmed.

Secondly, the results reveal a positive and statistically significant coefficient for foreign capital investment in relation to urbanized emission density. In light of this result, the findings were consistent with the pollution haven hypothesis; this could be attributed to sectors that attract substantial foreign capital, particularly in heavy industries and other manufacturing sectors, which are major contributors to high emissions. Clearly, the economic–environmental trade-off is evident: economic advancement in urban cities, driven by foreign capital, might inadvertently lead to environmental degradation. This dynamic raises critical questions about its implications for urban development and the nature of these investments. It necessitates a comprehensive investigation into how foreign investors adhere to environmental regulations, the criteria that policymakers use for approving foreign investments, and the stringency of these regulations. Thus, these findings validate the acceptance of Hypothesis 2. As a consequence, this emphasizes the imperative of aligning environmental regulations with the incoming flow of foreign capital.

Thirdly, based on the analysis, the results align with the idea that foreign capital contributes to enhancing productivity levels in the service sector. Specifically, the results indicate a positive and statistically significant coefficient for foreign capital investment in relation to the value-added of services as an aspect of urbanization. The observed trend might be attributed to practices introduced alongside the inflow of foreign capital. Adopted foreign practices in operations, technology, and management can potentially elevate the proficiency of the local service sector and thereby enhance its productivity. Furthermore, the presence of foreign capital can spur the initiation of training programs. These programs, in turn, can foster the development of certain services, leading to increased productivity. New technologies introduced by foreign investments play an additional role in the advancement of numerous sectors, including the service sector. As foreign capital investments become more integral, there arises a corresponding demand for the evolution of specific services. This not only influences infrastructure development in urban areas but also promotes the quality of services. Beyond the direct impacts, the influx of foreign capital may indirectly influence local dynamics through cultural exchanges between foreign investors and local entrepreneurs. Thus, consistent with Grekou and Owoundi [27] who indicated a positive relationship for this indicator in Africa, and the study of [136] that demonstrated how foreign investment contributes to the growth and development of the service sector in Indonesia, this study supports the acceptance of Hypothesis 3.

Finally, according to the index urbanization results, since the resulting coefficient is negative, it is mainly due to the fact that foreign investment does greater harm than good to the environmental situation in these Chinese cities. However, that is not entirely attributable to foreign investment; in the sixth coefficient (the ratio of manufacturing industry to service industry), we found that the ratio of the whole industry of the city was more influential to some extent. Nevertheless, this does not deny that foreign capital investment accounts for a large proportion of emissions. Therefore, foreign capital investors should be monitored in order to ensure their compliance with environmental standards in the northeastern cities of China.

Foreign capital investments do have an influence on urbanization. While FDI may help cities expand and prosper economically, it can also have negative consequences for the environment. As for the control variables, the positive coefficient of wages in model 1 is explained by the fact that labor-intensive industries rely heavily on human labor, and when some industries employ more workers, they pay them higher wages, leading to less capital, fewer machines, and fewer emissions. Also, greener technologies and practices may lead to higher wages, which may explain the negative coefficient in model 2. Additionally, the positive sign in model 3 indicates that higher wages create incentives for workers to pay for more services. It will also motivate producers of services to innovate and increase their production. As for the unemployment indicator, the negative coefficients in models 1 and 2 are explained by the job opportunities that cities offer. In addition, more workers contribute to urban congestion by consuming more fuel and energy resources, resulting in higher levels of emissions compared to those who are unemployed. However, in model 4, the coefficient shows that cities are more urbanized when unemployment is low.

Next, as for the education indicator, the results show a positive relationship in model 1, which is due to the opportunities that educated people can obtain when moving to urban areas. The coefficient is also positive in models 2 and 3, due to the consumption of power resources, transportation, industrial goods, and services by people seeking education. Lastly, the overall effect on the urbanization index was positive but not significant; however, the robustness check in Table 6 (robust GLS) reveals an undesirable effect on the urbanization index, where the impact of emissions outweighs other effects. This can be overcome by enabling green technology in the educational process. Next, the results suggest that the overall impact of the health index indicator is supportive of the process of urbanization, where better health infrastructure can play a role in increasing urbanization levels. This finding is in line with the theories of healthcare. As for the industry-to-services ratio indicator, it shows a logical indication of the positive relationship between urban employed persons and emissions. implying a need for industries to adopt more environmentally friendly practices. Lastly, concerning the cultural index models, increased cultural awareness leads to higher urbanization levels as people seek better job opportunities and lifestyles. However, this also results in higher emissions. Another conflict in the results can be seen with the robustness checks; the positive coefficient in the two robustness analyses can be explained as follows: Increasing the level of people’s culture and awareness will lead people to demand more services or eco-friendly services and products. In the main analysis, the negative relationship can be explained by the fact that when urbanized people are more aware of services, it may create heavy competition among the suppliers, leading to a decrease in their profit margins, which will eventually lead to diminishing the added value of services, as seen in the case of network service providers.

6. Conclusions and Policy Recommendations

The study explored the effect of foreign capital on urbanization in northeastern Chinese cities from 2007 to 2021. In this paper, “Urbanization” was expanded beyond the traditional definition to encompass environmental sustainability and the development of services. By including these two additional components, we aim to ensure that urbanization not only provides job opportunities and welfare for individuals, but also aligns with the United Nations goals of sustainability. Since the industrial reforms as well as the opening up of the northeastern cities of China to foreign investments, the area has witnessed a significant level of urbanization. However, the rate of urbanization had received the most attention. This paper sought to determine the sustainability of urbanization in this region and how foreign capital investments might further the broader vision of green and sustainable urban growth. Throughout this study, it was found that attracting foreign capital investments does increase employment opportunities in northeastern cities, thereby improving living standards and driving cities to expand. Nevertheless, policymakers should be more prudent in directing capital toward service sectors or in attracting advanced technological industries that do not exacerbate industrial emissions.

The results indicate a significant negative impact on the urbanization index. While foreign capital investments have caused urbanization to accelerate, influenced service development, and enhanced productivity, a detrimental effect on the environment has been significantly proven in these cities.

In evaluating foreign capital investments and their environmental implications, this study presents several policy recommendations. By strengthening environmental guidelines and improving standards for foreign investments, policymakers can identify and address vulnerabilities that companies might exploit. Efforts can also be directed toward raising environmental consciousness. This can be achieved by introducing comprehensive educational programs and orientation sessions for foreign investors. Additionally, this study suggests the establishment of a pre-approval evaluation process for foreign industrial projects. Such a mechanism, complemented by regular assessments, ensures that factories consistently meet environmental standards.

Another recommended measure is the introduction of a sustainability index. This tool can gauge the extent to which foreign investors integrate environmental best practices into their production processes. Recognizing outstanding performance, the government can extend special benefits to companies surpassing a defined threshold on this index.

Moreover, the study advises the consideration of a special tax system, where companies that have higher emissions are subject to increased taxes. In contrast, companies that adopt eco-friendly technologies could be eligible for tax incentives. This strategy aims to encourage investors to adopt environmentally sustainable practices more readily. Lastly, the promotion of clean energy solutions, particularly for large-scale machinery, is also suggested. Furthermore, relocating factories, especially those within the heavy industry sectors, might be a strategic move to alleviate concentrated emissions in urban centers. In conclusion, urbanization plays a vital role in modern development; however, governments must continually assess urban expansion to ensure a balance between economic development and environmental sustainability. The limitations of this study may include insufficient data to discern the effect of foreign capital based on industry types, since different sectors might have different environmental impacts. Further investigation is required concerning other factors influencing urbanization in Chinese cities, with careful consideration of regional variations in foreign investment preferences.

Note: The dataset under examination spans a period of 15 years across 34 cities, theoretically resulting in 510 observations; however, the actual data left after data cleaning and accounting for a two-year lag for some indicators were 422 and 402. Additionally, the number was less in the multivariate regression results table with the alternative measure of the independent variable, and that is due to the missing value in the number of contracts in the foreign investments indicator.