Artificial intelligence and population growth: evidence from China

Artificial intelligence and population growth: evidence from China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Artificial intelligence and population growth: evidence from China Xiaohui Luo, Yihua Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6702628/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Artificial intelligence (AI) significantly impacts all aspects of human life; promoting population growth is a global issue. How will AI affect population growth? This paper investigates the impact of AI on population growth using a city and year two-way fixed effects model, analyzing data from Chinese cities from 2008 to 2020. The results demonstrate that AI facilitates population growth in China. Specifically, a one-fold increase in AI development is associated with a 0.2357 percentage point increase in the population growth rate. Furthermore, AI is found to reduce the negative impact of education on population growth and enhance employment's positive impact on population growth. The influence of AI on population growth also shows temporal variation, with its impact increasing over time. This study offers novel insights into the socio-economic effects of AI, providing valuable references for policymakers seeking to harness AI to promote population growth. artificial intelligence population growth education employment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Over the past half-century, many countries have witnessed a steep decline in their populations (Aitken, 2022 ). Decreasing population growth will not only lead to rapid population decline but also cause a high degree of population aging, leading to a series of social problems (Wu, 2019 ). Artificial intelligence (AI) automates tasks through algorithm development (Frey & Osborne, 2017 ), impacts every aspect of people's lives (Haseeb et al., 2019 ), promotes capital to replace labor (Acemoglu & Restrepo, 2019 ), and raises concerns about people becoming increasingly replaceable (Acemoglu et al., 2018; Acemoglu et al., 2020 ). This implies that AI may decrease people's desire to reproduce, thereby reducing population growth. “Time poverty” inhibits people's desire to reproduce. AI can promote population growth by improving social production efficiency (Haseeb et al., 2019 ) and alleviating time poverty. Scholars globally have developed two generations of models to study birth rates and population growth (Doepke et al., 2022 ). While China has emerged as a key player in global AI (Kshetri, 2021 ), the impact of AI on China's population growth remains unexplored to date. For Chinese scholars, studying the country’s problems at the city level is often the preferred approach. China is a vast country with a large gap in city development. First of all, this paper takes "AI" as the keyword, extracts the number of AI patent applications and the number of grants in each city from the State Intellectual Property Office, adds one to the number of AI patent applications and the number of grants in each city, and then takes the natural logarithm to get AI and rAI , which are proxy variables of AI development level. Taking the population growth rate of each city as the proxy variable for the PGRW , the regression analysis of the natural logarithm of the variables can represent the variable growth rate. This study uses the natural logarithm of the total population of each city to obtain the rPGRW as another proxy variable for population growth rate. To empirically test the impact of AI on the population growth rate, this study uses the city and year two-way fixed effects model, with the city population growth rate as the dependent variable and the city AI development level as the independent variable. Then, the interaction terms of city education level, employment level, and city AI development level were used to empirically study the weakening effect of AI on education, reducing the population growth rate, and exploring the strengthening effect of AI on employment, thereby increasing the population growth rate. Finally, we analyze the regional heterogeneity of financially developed and underdeveloped, high-risk, and low-risk areas. Based on the above research design, we find that: First, AI can promote the population growth of Chinese cities. When AI development level doubles, the population growth rate increases by 0.2357 percentage points. AI shortens the working hours of Chinese people, improves the environment, and weakens the transfer of the rural labor force, which is conducive to promoting the growth of city populations in China. Second, education reduces the birth rate and is not conducive to population growth. AI can improve quality of life and reduce the opportunity costs of women's fertility, thus weakening the adverse impact of education on population growth. Furthermore, employment promotes population growth. China's unique social system weakens the substitution effect of AI on the labor force, which can increase the remuneration of workers, thus strengthening the positive effect of employment on population growth. Third, the role of AI in promoting population growth exhibits temporal heterogeneity, which increases over time. Our research contributes in several ways. First, it enriches the literature on population growth. Existing literature examines population growth from cultural, economic, and social perspectives. In terms of culture, late marriage (Greenhaus & Beutell, 1985 ), childless culture (Stegen et al., 2021 ), and the pursuit of freedom and equality (Poston Jr. & Trent, 1982 ) reduce the population growth rate. Economically, factors like development (Lappegård et al., 2022 ), industrial structure (Skakkebæk et al., 2022; Ma & Ding, 2023 ), resident income (Lee, 1990 ; Keller & Utar, 2022 ), and house prices (Ge & Zhang, 2019 ) influence population growth. For example, most industrialized regions have low fertility rates and slow population growth (Skakkebæk et al., 2022). In general, these include working hours (Greenhaus & Beutell, 1985 ; Ma & Ding, 2023 ), environment (Aitken, 2022 ), education (Cygan-Rehm & Maeder, 2013 ; Liu & Raftery, 2020 ; Doepke et al., 2022 ), and employment (Alderotti et al., 2019 ; Van Wijk et al., 2021 ). For example, Doepke et al. ( 2022 ) found that higher levels of education across society mean higher costs for having children and that parents choose to have fewer children overall instead of higher-quality children. Scholars have conducted extensive research on the factors influencing population growth; however, to the best of our knowledge, the literature does not cover the impact of AI on China's population growth. Thus, this study expands the literature by examining AI's effects on population growth. Second, this study further contributes to the literature on AI's social impact. Existing literature has conducted extensive research from both economic and individual aspects, drawing a series of useful conclusions. On the economic side, AI promotes investment growth (Chen et al., 2016), thereby promoting economic growth (Chen et al., 2016; Agrawal et al., 2019 ; Kshetri, 2021 ). AI also provides new methods that are conducive to technological innovation (Cockburn et al., 2018 ). Based on the necessary supervision, AI can promote the sustainable development of the economy (Vinuesa et al., 2020). The impact of AI on financial services may be transformative, but it remains complex and uncertain (Bholat & Susskind, 2021 ). On the personal side, AI automates routine tasks based on clear rules (Frey & Osborne, 2017 ), eliminates existing jobs, adds new jobs, prefers highly skilled individuals (Yang, 2022 ), eliminates low-end skilled jobs, and generates new highly skilled jobs (Acemoglu & Restrepo, 2019 ), with both job elimination and job creation effects (Acemoglu et al., 2018). This profoundly affects individual employment (Haseeb et al., 2019 ). Although scholars have conducted extensive research on the social impact of AI, its impact on population growth has not yet been studied. The remainder of this paper is organized as follows: Section 2 clarifies our testable hypothesis. Section 3 describes the econometric models, variables, and data. Section 4 discusses the direct effects of AI on population growth. Section 5 tests the moderating effects of education and employment levels. Section 6 tests the time heterogeneity. Section 7 provides a discussion. Finally, this study is summarised. 2. Testable Hypotheses 2.1 Direct influence Given the existing findings, we posit that AI can reduce working hours, enhance environmental quality, and mitigate the migration of the rural labor force, thereby fostering population growth. First, AI can mitigate time poverty, thereby promoting population growth. Firstly, it extends fertility opportunities, further promoting population growth. The work-family conflict theory holds that people's energy and time are limited, and the distribution of work and family will inevitably be biased (Greenhaus & Beutell, 1985 ). Therefore, ‘time poverty’ restricts the reproductive intention of both men and women, and individuals with greater time autonomy are more likely to have a strong intention to reproduce (Chen, 2017 ). This means that reducing people's working hours can increase their desire to reproduce and thus promote population growth. Since 2008, AI's integration into the economic and financial sectors has deepened (Chen et al., 2022 ). AI has automated tasks previously performed by workers (Acemoglu et al., 2018) and has improved social production efficiency (Haseeb et al., 2019 ). This can reduce people's working hours, thereby increasing their desire for fertility and promoting population growth (Pan, 2018 ). Secondly, AI reduces rates of singleness, thereby promoting population growth. Regular overtime work leaves little leisure time for social interactions, potentially reducing individuals' attractiveness to potential partners (Zhang & Shi, 2020 ). In other words, long working hours have been a cause of increased social singleness. Business work limits people's social lives and their desire for love. An increase in working hours reduces the time and energy devoted to love (Ma & Ding, 2023 ), delaying or even reducing the time for young people to marry and have children. AI has improved social production efficiency (Haseeb et al., 2019 ), compressed people's working hours, reduced the rate of singles, increased fertility, and promoted population growth. Thirdly, it improves the efficiency and quality of continuing education and promotes population growth. China's urban and rural labor supply exceeds demand, and working workers, such as job seekers, face huge market competition pressure (Zhang & Shi, 2020 ). To avoid the “survival of the fittest” scenario, workers often dedicate significant time to education for skill improvement. AI promotes the transformation of educational services from mobile terminals to smart terminals (Luo & Song, 2023 ), enabling tailored education programs for employed individuals, which improves the quality and efficiency of education, reduces education time, and increases workers’ leisure time and fertility willingness, thereby promoting population growth. Second, AI can improve the public environment and fertility conditions, thereby promoting population growth. Firstly, by enhancing the public environment, AI contributes to population growth. Public environments inhibit fertility (Aitken, 2022 ). This association stems from increased exposure to chemicals, directly or indirectly derived from fossil fuels (Skakkebæk et al., 2022). In other words, the deterioration of the public environment reduces fertility and is not conducive to population growth. In addition, concerns about the adverse effects of the population on the environment make it easier to accept the concept of childlessness in modern society (Stegen et al., 2021 ; Aitken, 2022 ). This is not conducive to population growth. By analyzing big data, AI can gain a more accurate understanding of the market's environmental needs, public environmental conditions, prominent areas of public environmental problems, concentrated distribution areas of pollution sources, etc. Through the optimal allocation of resources such as capital, technology, and equipment, energy conservation and emission reduction can be achieved, and the public environment can be improved (Zhang & Li, 2021 ). As a result, these improvements promote population growth. Secondly, by improving the working environment, AI also fosters population growth. AI can automate simple repetitive tasks (Frey & Osborne, 2017 ). China is a developing country. China performs several dangerous, heavy, and harsh tasks. Most of these tasks are simple and repetitive. AI automation of these tasks improves the working environment. For instance, the use of bionic aerial robots to replace human aerial workers can significantly improve their working conditions. AI can liberate people from dangerous, heavy, and harsh work tasks and improve their working environments (Pan, 2018 ). Improving the work environment can increase people's happiness, thereby increasing their desire to reproduce and promoting population growth (Yang & Xie, 2022 ). Thirdly, AI enhances fertility conditions further, thereby promoting population growth. AI is widely used in healthcare (Yu et al., 2018 ). Utilizing diverse healthcare data, both structured and unstructured, AI assists in diagnosing a range of major diseases (Jiang et al., 2017). Additionally, by leveraging healthcare data, AI facilitates pregnancy testing, enhances maternal and fetal health, reduces pregnancy-related risks, and thereby promotes population growth. This indicates that AI can improve fertility conditions and increase the willingness to reproduce, thereby promoting population growth. Third, AI can improve farmers’ production and living conditions and promote population growth. Firstly, it promotes the return of migrant workers and population growth. Some three decades ago, the acceleration of China's industrialization led to large-scale population movements in China (Chen et al., 2019 ); a large number of farmers began moving to cities, leading to large-scale migration (Yin et al., 2020 ). To support the elderly, one partner in a rural couple often works in urban areas, while the other remains in the countryside. This separation reduced the cohabitation time of young rural couples. In China, farmers have historically been crucial to national fertility rates. During the One Child Policy era, rural couples were often dubbed as the “super army” for fertility. AI has changed the labor demand structure of enterprises (Yang, 2022 ) and eliminated low-end skilled workers (Acemoglu & Restrepo, 2019 ; Yang, 2022 ). Due to limited education, among other factors, migrant workers, often in low-skilled jobs, have been displaced by AI, leading to their return to rural areas. This increases the time young farming couples spend together, enhancing their relationship, increasing the probability of rural women's pregnancy, and promoting population growth. Secondly, it enhances farmers' income, thereby further promoting population growth. China is predominantly an agricultural country, where farmers primarily earn from agricultural production. AI has penetrated all areas of the economy, including agricultural production (Chen et al., 2022 ). Through the application of AI, agricultural production has become more efficient, leading to an increase in farmers’ income. Income is an important variable that affects fertility decision-making (Giuntella et al., 2022 ). An increase in farmers' income creates a positive scenario that increases their fertility willingness (Lappegård et al., 2022 ) and promotes population growth. Thirdly, AI's role in increasing farmers' leisure time further supports population growth. It takes a lot of time for people to give birth and raise their children (Ma & Ding, 2023 ). Engaged in agriculture, farmers spend much of their time working, and with China's aging population issue intensifying, the burden of agricultural farming falls predominantly on young couples. AI can automate simple repetitive tasks (Frey & Osborne, 2017 ). Given that much of agricultural work involves simple, repetitive tasks, AI can automate these processes, significantly reducing the need for manual labor. Therefore, AI can provide farmers with more time to give birth to and raise their children. This can increase the farmers’ desire for fertility and promote population growth. In summary, AI can promote population growth by alleviating time poverty, enhancing the public environment and fertility conditions, and boosting farmers’ production and living conditions (see Fig. 1 ). Therefore, this study proposes the following hypothesis: H1: AI can promote population growth. 2.2 Regulation mechanism 2.2.1 The regulating mechanism of education On the one hand, education reduces fertility and inhibits population growth. Firstly, education increases the explicit costs of childbearing and child-rearing. As the largest developing country, China has faced enormous employment pressure in its process of rapid economic development. To improve their children's employment competitiveness and avoid being eliminated from a fiercely competitive environment, Chinese people are increasingly hoping to improve their children's level of education. For example, as undergraduate degrees have become more common, Chinese parents increasingly support their children in pursuing master's and doctoral studies. The societal improvement in education levels means that parents must invest more to elevate their children's education, enhancing their employment competitiveness. A societal increase in education levels leads to higher child-rearing costs, prompting parents to opt for fewer children to focus on enhancing their children's quality of life (Doepke et al., 2022 ). Secondly, education increases the opportunity cost of having children. Education improves people's ability to work, particularly for women. Women with more education face higher opportunity costs of leaving the labor market and raising children than those with less education (Tavares, 2010 ). An increase in people's education level, especially for women, will weaken fertility desire through the value of time (Cygan-Rehm & Maeder, 2013 ), thus inhibiting population growth. Thirdly, education compresses the timeframe for childbirth. Improvement in the education level of society as a whole means that people have to spend more time on education. Fertility and parenting require considerable time (Ma & Ding, 2023 ). To receive more education, people must delay childbirth and marriage. Delaying childbearing and marriage reduces the number of children within the same period, resulting in a slowdown in population growth. Late marriage is also a direct cause of low fertility (Jones, 2007 ). This also inhibited population growth. On the other hand, AI can weaken the negative effect of education on population growth. Firstly, it can lower the explicit cost of education. AI generates new positions, such as data administrators and analysts (Acemoglu et al., 2018). Furthermore, it has prompted enterprises to implement digital transformation, and the demand for technical, service-oriented, and highly skilled employees has increased significantly (Yang et al., 2023 ). Both data administrators and analysts, as well as technical, service-oriented, highly skilled employees—all considered applied talents—benefit from on-the-job training, or “learning by doing” rather than traditional school education. This may decrease the inclination of Chinese parents to unilaterally pursue higher education levels for their children, thereby lowering the direct costs associated with education. Secondly, AI can reduce the opportunity cost of education-induced fertility. The deep integration of digital technologies, such as AI, and the economy has produced a digital economy (Chen et al., 2022 ) that has infused flexible employment with new vitality. The digital economy has transformed flexible employment from a passive choice to a career that many young people, women, college students, and other groups voluntarily or actively choose. Some flexible self-employment models can bring inner satisfaction and autonomy to practitioners and attract more workers to choose this form of employment (Qi et al., 2021 ). This enables women, particularly during their childbearing years, to opt for flexible employment and participate in the labor market. This approach not only offers inner satisfaction, mitigating loneliness and irritability during childbirth but also reduces the opportunity costs of childbirth. Thirdly, AI makes up for the crowding out of childbearing time by education. Intelligent information technologies, like AI and big data, have enhanced data mining, analysis, utilization, and various smart educational services (Yu, 2023 ). It has transformed teaching from mobile terminals to intelligent terminals (Luo & Song, 2023 ), revolutionizing educational access and enabling learning during fragmented times from anywhere, at any time. Therefore, AI can alleviate the time conflict between education and fertility and compensate for the crowding out of fertility time through education. In summary, while advancements in social education levels may inhibit population growth, AI can mitigate this effect (Fig. 2 ). Therefore, this study proposes the following hypothesis: H2: AI can weaken the inhibitory effect of education on population growth. 2.2.2 The regulating mechanism of education employment On the one hand, employment can promote population growth. Firstly, employment fosters professional satisfaction and increases the desire for fertility. According to Maslow's hierarchy of needs, self-actualization holds the highest level among people's needs. Employment generates income not only to meet lower-level needs. In employment, people can get a high sense of achievement by completing more difficult work tasks. The greater the task difficulty, the greater the sense of achievement after completion. The acquisition of a sense of achievement can meet employees’ esteemed needs. Therefore, employment can promote occupational well-being (Ma & Ding, 2023 ), which can increase the desire to reproduce (Yang & Xie, 2022 ) and promote population growth. Secondly, employment creates positive economic conditions, thereby increasing reproductive willingness. People's judgments of their futures also affect their desire to reproduce. Economic uncertainty regarding the future is a significant factor influencing reproductive desires (Lappegård et al., 2022 ). Negative economic conditions result in a marked decline in the intention to have children, whereas positive conditions foster an increase in such intentions (Lappegård et al., 2022 ). Rising employment rates and better employment prospects can create a positive economic environment, thereby enhancing the willingness to have children and promoting population growth. Thirdly, employment increases family income and provides child-rearing protection. "Wangzi Chenglong" is a traditional deep-rooted concept of the Chinese people. The vast majority of Chinese people would opt not to have children if their wealth was insufficient to support their children's healthy development. Over time in China, an increasing number of women are choosing to remain employed after marriage, leading many urban families to hire nannies for child care. Consequently, this trend raises the domestic service costs for Chinese couples (Ma & Ding, 2023 ). Good economic conditions can increase the desire for improved fertility and fertility rates (Myrskylä et al., 2009 ). Therefore, employment contributes to an increase in household income, boosts fertility desires, and promotes population growth. On the other hand, AI can strengthen the positive effect of employment on the population growth rate. Firstly, it enhances professional satisfaction. AI automates simple and repetitive work tasks (Frey & Osborne, 2017 ), and while eliminating these simple and repetitive jobs, it will also create relatively challenging high-skilled jobs (Acemoglu & Restrepo, 2019 ; Yang, 2022 ) and improve the technical content of positions that are not eliminated. Employees need to continually enhance their skills to meet AI-driven job requirements. After improving their business skills and completing job tasks, people gain a sense of achievement and experience greater professional happiness. Secondly, AI can strengthen positive economic conditions. The deep integration of digital technologies such as AI and the economy has created a digital economy (Chen et al., 2022 ), which has spawned emerging flexible employment such as “Sunday engineers”, "Didi", and live commerce. As of 2021, the number of individuals in flexible employment in China was expected to have reached approximately 200 million. These emerging flexible divisions of labor, large demand, and broad development space have provided people with a large number of employment opportunities (Qi et al., 2021 ). Upon facing unemployment, traditional employees could opt for flexible employment options. This means that emerging flexible employment can provide a buffer for traditional employment, thus enhancing the positive economic scenario for traditional employees due to employment. Thirdly, AI increases leisure time while ensuring family income. AI can improve social production efficiency (Haseeb et al., 2019 ). For employees, AI can improve work efficiency and reduce working hours. Thus, AI alleviates employees' time poverty while safeguarding their family income, boosting fertility desires, and promoting population growth. In summary, an improvement in the social employment level promotes population growth, and AI has a strengthening effect on this promotion (Fig. 3 ). Therefore, this study proposes the following hypothesis: H3: AI can strengthen the role of employment in promoting population growth. 3. Models, Variables, and Data 3.1 Model 3.1.1 Model of direct impact In the study of issues specific to China, Chinese scholars prioritize empirical analysis at the city level. This study examines the effects of AI on population growth at the city level. To this end, this study draws on existing literature (Alderotti et al., 2019 ; Giuntella et al., 2022 ; Lappegård et al., 2022 ; Ma & Ding, 2023 ) and designs the year and city two-way fixed effect model: $$\:{PGRW}_{it}={\alpha\:}_{0}+{\beta\:}_{1}\ast\:{AI}_{it}+\eta\:\ast\:X+{\alpha\:}_{i}+{\lambda\:}_{t}+{\epsilon\:}_{it}\:\:\:\:\:\:\:\:\:\:\:\:$$ 1 where i and t are the subscripts of city and year respectively; \(\:{\alpha\:}_{I}\:\) is to capture the city fixed effect; \(\:\:{\lambda\:}_{t}\) is the capture year fixed effect, and \(\:\:{\epsilon\:}_{it}\:\) is the random error term. \(\:{PGRW}_{it}\:\) is the dependent variable (the population growth rate of the city i in year t). \(\:{AI}_{it}\:\) represents an independent variable, namely, the AI development level in city i in year t, and \(\:{\beta\:}_{1}\) is its coefficient; if it is significantly positive, then AI can promote population growth. X is the control variable, as detailed below. 3.1.2 Model of regulation mechanism To test the regulation mechanism, we designed the following model: $$\:{PGRW}_{it}={\alpha\:}_{0}+{\beta\:}_{1}\ast\:{AI}_{it}+{\beta\:}_{2}\ast\:{AI}_{it}\ast\:{ADJ}_{it}+{\beta\:}_{3}\ast\:{ADJ}_{it}+\eta\:\ast\:X+{\alpha\:}_{i}+{\lambda\:}_{t}+{\epsilon\:}_{it}\:\:$$ 2 \(\:{ADJ}_{it}\) is the moderating variable, namely, the education level ( EDUC ) or employment level ( EMPL ) of the city i in year t. \(\:{AI}_{it}\ast\:{ADJ}_{it}\) is the cross-term of AI development level and moderating variables. If \(\:{\beta\:}_{2}\) is significantly positive, AI can weaken the inhibitory effect of education on population growth or strengthen the promoting effect of employment on population growth. X is a control variable, the same as in Eq. ( 1 ). 3.2 Variables Based on the literature, the independent, dependent, regulatory, and control variables used in this study are presented in Table 1 . Table 1 Variable description Type Name Symbol Variable Definition References Dependent variable Population rate of increase PGRW Population increments in year t / total population at the beginning of year t * 100 Ma & Ding ( 2023 ) rPGRW Ln (population at the end of year t / population at the beginning of year t) Zhao & Zhang ( 2018 ) Independent variable AI development level AI Ln (1 + number of patent applications for AI) Huang et al. ( 2023 ); Xu et al. ( 2023 ) rAI Ln (1 + number of AI patents granted) Control variable Economic development Level PGDP Ln (real per capita gross domestic product [GDP]), with 2008 as the base period. Giuntella et al. ( 2022 ); Lappegård et al. ( 2022 ) Financial development level FSIZ Loan balance/GDP Ge & Zhang ( 2019 ) Industrial structure level INDS The added value of tertiary industry/secondary industry Ma & Ding ( 2023 ) educational level EDUC Number of college students/total population Liu & Raftery ( 2020 ); Doepke et al. ( 2022 ) Employment level EMPL Ln (urban employment) Alderotti et al. ( 2019 ) Unemployment rate UNEM Unemployment/urban employment Ma & Ding ( 2023 ) Urbanization rate CITY Urban population/total population Ma & Ding ( 2023 ) Savings level SAVE Savings deposits/balance of various deposits 3.2.1 Dependent variables The dependent variable in this study is the population growth rate ( PGRW ). PGRW is obtained by calculating “the total population increment divided by the population at the beginning of the year, multiplied by 100”, serving as the proxy variable for the population growth rate. In addition, the natural logarithm can be used to represent the growth rate (Zhao & Zhang, 2018 ). This study takes the natural logarithm of the ratio of the total population at the end of the year to the total population at the beginning of the year to get rPGRW , which is another proxy variable for the population growth rate. 3.2.2 Independent variables The independent variable in this paper is the AI development level ( AI ). Referring to the existing literature (Huang et al., 2023 ; Xu et al., 2023 ), this study takes the natural logarithm of the number of patents, adds one to the number of AI patent applications in each city, and then takes the natural logarithm to obtain AI, which is used as a proxy variable for AI development level. rAI is obtained by adding the number of AI patents granted in each city and then taking the natural logarithm, which is another proxy variable of the AI development level. 3.2.3 Control Variables Referring to the existing literature (Alderotti et al., 2019 ; Giuntella et al., 2022 ; Lappegård et al., 2022 ; Ma & Ding, 2023 ), this study controls the level of economic development, financial development level, industrial structure level, education level, employment level, unemployment rate, urbanization rate, and savings level. 3.3 Data 3.3.1 Data sources Since the 2008 global financial crisis, digital technologies like AI have started to integrate deeply into the economy (Chen et al., 2022 ; Chen, 2023 ), with AI gradually entering large-scale commercial applications. China has a vast unevenly developed territory. Consequently, for Chinese scholars, studying China's issues at the city level is a preferred approach. The data from the China City Statistical Yearbook have been updated through 2020. Therefore, this study conducted an empirical analysis of China's city data from 2008 to 2020. The data were processed as follows: (1) missing samples were eliminated, and (2) To eliminate the influence of outliers, this study performed a 1% Winsorised tail reduction on other continuous variables, except for those subjected to the natural logarithm. A total of 3,456 year-city observations were obtained. The count of AI patent applications and authorizations was sourced from the National Intellectual Property Administration. We used AI as a keyword to extract other data from the China City Statistical Yearbook. In addition, taking the natural logarithm eliminates the influence of outliers. To eliminate the influence of outliers, this study performs a 1% Winsorised tail reduction on other continuous variables except the natural logarithm. 3.3.2 Summary Statistics Table 2 presents the descriptive statistics of the variables. As Table 2 illustrates, the average city savings level ( SAVE ) in China is 3.6086, with a minimum of 0.3998 and a maximum of 16.3033. This variation aligns with the fundamental national condition of uneven development. Furthermore, the average AI development level ( AI ) is 0.3662, ranging from a minimum of 0.0000 to a maximum of 9.1395. The gap between the two is also large, which is consistent with China's basic national conditions of unbalanced development. Table 2 Summary Statistics Variables Obs Mean Std.Dev. Min Max PGRW 3,456 0.3083 5.4430 -21.5496 36.0927 rPGRW 3,456 5.8765 0.6955 2.9226 8.1362 AI 3,456 1.2150 1.6111 0.0000 9.4356 rAI 3,456 0.7232 1.1642 0.0000 7.6416 PGDP 3,456 5.9000 0.7441 3.5688 8.4008 INDS 3,456 2.2764 0.1425 1.8312 2.6431 EDUC 3,456 0.1833 0.2308 0.0000 1.1410 EMPL 3,456 3.5818 0.8383 1.4375 6.8945 UNEM 3,456 0.0593 0.0341 0.0080 0.2149 FSIZ 3,456 8.3774 32.8696 0.0021 190.3869 SAVE 3,456 0.5787 0.1297 0.2089 0.8100 4. Results 4.1 Univariate analysis Figure 4 presents a scatter plot and univariate regression line depicting the relationship between the population growth rate and AI development level. As shown in the figure, there is a clear positive linear relationship between AI and population growth rate. The figure indicates that as the level of AI development improves, the population growth rate increases. Following this, a multivariate regression analysis was performed. 4.2 Multivariate regression Using PGRW as the dependent variable, and AI and rAI as independent variables, the fixed effects model FE estimated Eq. ( 1 ) without including control variables. The results are shown in columns (1) and (2) of Table 3 . Upon adding all control variables, Eq. ( 1 ) was re-estimated, yielding the results in columns (3) and (4) of Table 3 . Columns (1)–(4) of Table 3 show that the coefficients of of AI development level are significantly positive at the 1% or 10% significance level, and AI can promote population growth. The empirical results support hypothesis H1. This conclusion is consistent with the previous theoretical analyses. AI can alleviate time poverty, improve the public environment and fertility conditions, and improve farmers' production and living conditions, thus promoting population growth. The natural logarithm is used to represent growth rates (Zhao & Zhang, 2018 ), and the level of AI development can be expressed as the natural logarithm of the number of AI patent applications or grants plus one, respectively. Accordingly, as shown in columns (3) and (4) of Table 3 , when the AI development level doubles, the population growth rate increases by 0.2357 and 0.7189 percentage points, respectively. Columns (1)–(4) of Table 3 show that: firstly, the coefficient of education level is significantly negative at the 1% significance level, and an improvement in education level reduces the population growth rate (Tavares, 2010 ; Cygan-Rehm & Maeder, 2013 ; Doepke et al., 2022 ). These findings align with the results of previous theoretical analyses. Secondly, the coefficient of the employment level is significantly positive at the 1% or 5% significance level, and an improvement in the employment level promotes population growth (Myrskylä et al., 2009 ; Lappegård et al., 2022 ; Ma & Ding, 2023 ). This is consistent with previous theoretical analyses. Thirdly, the coefficient of economic development is significantly negative at the 1% significance level, and economic development reduces the fertility rate (Myrskylä et al., 2009 ), thus inhibiting population growth. Fourthly, the coefficient of financial development is significantly positive at the 1% level. Financial development can alleviate people's financing constraints, provide credit funds for production and life, increase happiness, promote fertility (Yang & Xie, 2022 ), and promote population growth. Fifthly, the coefficient of the industrial structure level is significantly negative at the 1% significance level, and upgrading the industrial structure reduces the fertility rate. The reason may be that China's tertiary industries, such as accommodation and catering, wholesale and retail, and residential services, need to continuously pay attention to and serve customers, and work long hours (Ma & Ding, 2023 ), thus generating ‘time poverty’ and reducing fertility. Lastly, the coefficient of the savings level is significantly negative at the 1% or 5% significance level, suggesting that higher savings levels are not conducive to population growth. One possible reason for this is that savings might contribute to economic development, which can influence fertility rates. Table 3 FE estimation results of Eq. ( 1 ) (1) (2) (3) (4) Variables PGRW PGRW PGRW PGRW AI 0.9690*** 0.2357* (0.1700) (0.1264) rAI 1.9223*** 0.7189*** (0.2895) (0.2144) PGDP -20.9813*** -20.6212*** (1.7725) (1.8049) FSIZ 0.1247*** 0.1225*** (0.0203) (0.0202) INDS -13.1327*** -12.5043*** (2.7446) (2.8019) EDUC -11.3495*** -11.7217*** (2.6224) (2.6958) EMPL 1.3656*** 1.1528** (0.4838) (0.4869) UNEM -4.2521 -4.4732 (3.5208) (3.4879) CITY 4.4854 3.7371 (3.0626) (3.1087) SAVE -8.6234*** -7.6468** (3.0180) (3.1048) Constant 0.7049*** 0.7169*** 143.0908*** 140.1874*** (0.1129) (0.1038) (12.7666) (13.0635) City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Observations 3,456 3,456 3,456 3,456 R-squared 0.0502 0.0749 0.4613 0.6166 N 283 283 283 283 Note: Robust standard errors in parentheses; * p < 0.01, ** p < 0.05, *** p < 0.1 4.3 Robustness Checks Table 3 shows that the coefficients for the AI development level are significantly positive, which indicates the reliability of the conclusions to a certain extent. Further robustness checks were conducted, including endogenous treatment, modifications to population growth rate measurements, and variations in estimation methods. 4.3.1 Endogenous treatment Previous theoretical analyses and empirical tests have shown that AI significantly affects population growth. Conversely, the influence of population growth on AI is considered to be more limited. Therefore, it is difficult to establish a two-way causal relationship between AI and population growth. However, the number of AI patent applications and authorizations were retrieved by using AI as the keyword in the National Intellectual Property Administration. Keyword retrieval inevitably introduces deviations that may lead to measurement errors in the assessment of AI development levels, potentially causing endogeneity. Digital technology entrepreneurship is an entrepreneurial activity aimed at digital technology innovation and applications such as AI, blockchain technology, cloud computing, big data, and the Internet of Things. The entrepreneurial process is also the process of implementing entrepreneurial plans for new ventures (Gartner, 1988 ; Low & MacMillan, 1988 ). Under Chinese law, every enterprise must obtain pre-registration approval that determines its business scope and is limited to conducting activities within this scope. Accordingly, the terms “AI”, “blockchain”, “cloud computing”, “big data”, and “Internet of Things” were used as keywords to identify enterprises registered with the State Administration for Market Regulation that operate within these domains. Cities were used as the unit of analysis, following the methodology of McDougall & Robinson Jr. ( 1990 ) and Zahra & Bogner ( 2000 ). Utilizing the method of Li & Zhang ( 2007 ), the number of enterprises established from 2008 to 2020 in each city with activities in “AI”, “blockchain”, “cloud computing”, “big data”, and "the Internet of Things" was calculated, yielding the count of digital technology startups, denoted as ABCDI. Then, drawing on the methodologies of Laeven & Levine ( 2007 , 2009 ), Faccio et al. ( 2011 ), and Chen et al. ( 2022 , 2023 ), we calculated the mean value of the number of digital technology startups in other cities for the same year, with a one-period lag, to derive L.ivAI as an instrumental variable. The number of digital technology startups in other cities is unlikely to directly affect a city's birth rate. Thus, L.ivAI satisfies the exogeneity conditions. Technological innovation has a demonstration effect, and digital technology entrepreneurship in other cities promotes AI development, which may affect the development of AI in this city. Therefore, the number of digital technology startups in other cities is related to the level of AI development in the city, and L.ivAI meets the conditions. Employing L.ivAI as the instrumental variable, the instrumental variable method (IV) was utilized to re-estimate Eq. ( 1 ). The Cragg–Donald F statistic of the weak instrumental variable test is 108.865, which is much larger than the critical value of 16.38 under 10% bias; i.e., the hypothesis of weak instrumental variables was rejected, and L.ivAI is an effective instrumental variable. Using PGRW as the dependent variable, L.ivAI as the instrumental variable, and AI and rAI as independent variables, the instrumental variable method (IV) was employed to re-estimate Eq. ( 1 ). The results are presented in Columns (1) and (2) of Panel A in Table 4 . To mitigate endogeneity concerns with the control variables, all such variables were lagged by one period, and IV estimation was applied to re-estimate Eq. ( 1 ). The results are shown in Columns (3) and (4) of Table 4 Panel A. From Table 4 , Panel A, the coefficient of the AI is significantly positive at the 1% significance level. Excluding endogeneity, AI can promote population growth, and the conclusion that H1 is supported is robust. Table 4 Estimated results of robustness checks Panel A (1) (2) (3) (4) Variables PGRW PGRW PGRW PGRW AI 4.9201*** 13.8175*** (1.3195) (2.5876) rAI 4.0759*** 11.3745*** (1.0347) (1.8975) City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Control Yes Yes Yes Yes Observations 3,131 3,131 3,131 3,131 R-squared 0.1893 0.3741 -2.1502 -0.7668 N 282 282 282 282 Panel B (1) (2) (3) (4) Variables rPGRW rPGRW PGRW PGRW AI 0.0134*** 0.2357** (0.0021) (0.1061) rAI 0.0187*** 0.7189*** (0.0030) (0.1268) City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Control Yes Yes Yes Yes Observations 3,456 3,456 3,456 3,456 R-squared 0.6166 0.6197 N 283 283 283 283 Note: All the instrumental variables have passed the validity test. 4.3.2 Changing the measurement of population growth With the dependent variable replaced by rPGRW , Eq. ( 1 ) was re-estimated using the Fixed Effects (FE) model, treating AI and rAI as the independent variables. The results are presented in Columns (1) and (2) of Panel B of Table 4 . Columns (1) and (2) demonstrate that AI contributes to population growth, affirming the robust support for Hypothesis H1. 4.3.3 Changing the estimation method To mitigate bias from the estimation method, the Maximum Likelihood Estimation (MLE) approach was employed to re-estimate Eq. ( 1 ). The findings are displayed in Columns (3) and (4) of Panel B in Table 4 . According to Columns (3) and (4), the robustness of the conclusion, establishing Research Hypothesis H1, is confirmed. In summary, in the case of controlling endogeneity and changing the measurement and estimation methods of population growth, AI is shown to promote population growth, confirming that Hypothesis H1 is robustly supported. 5. Regulation Mechanism 5.1 Regulation effect on the educational level Section 2 demonstrated that education level could mitigate the inhibitory effects of AI on population growth. In this analysis, PGRW is used as the dependent variable, with AI and rAI as independent variables, and the education level ( EDUC ) serving as the adjustment variable for estimating Eq. ( 2 ). The results are shown in Columns (1) and (2) in Table 5 . The dependent variable, PGRW , was replaced with rPGRW , and Eq. ( 2 ) was re-estimated in the aforementioned order. The results are shown in Columns (3) and (4) in Table 5 . As indicated in Table 5 , although the coefficient for the AI is not significant, the interaction terms between AI and the education level are significantly positive at the 1% significance level. The partial derivatives relating to the AI development level suggest that AI's contribution to population growth is enhanced with improvements in the education level. Additionally, the coefficient of education level is significantly negative at the 1% significance level, while the cross term of AI and education level is significantly positive at the 1% significance level. The regression results from Columns (1)–(4) in Table 5 yield the partial derivatives for the education level ( EDUC ) as follows: ∂PGRW/∂EDUC=-15.7791 + 1.3080*AI, ∂PGRW/∂EDUC=-15.7461 + 1.8802*rAI, ∂rPGRW/∂EDUC=-0.3662 + 0.0314*AI, ∂rPGRW/∂EDUC =-0.3443 + 0.0354*rAI. These four partial derivative formulas show that the marginal impact of the education level on the population growth rate weakens with an increase in AI development level (Fig. 5 ). Therefore, hypothesis H2 holds. Table 5 Estimates of the Moderating Effect of Educational Level (1) (2) (3) (4) Variables PGRW PGRW rPGRW rPGRW AI -0.1439 0.0042 (0.1520) (0.0030) EDUC×AI 1.3080*** 0.0314*** (0.3564) (0.0063) EDUC×rAI 1.8802*** 0.0354*** (0.4960) (0.0087) rAI 0.1208 0.0074 (0.2646) (0.0045) EDUC -15.7791*** -15.7461*** -0.3662*** -0.3443*** (2.9149) (2.8648) (0.0626) (0.0616) City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Control Yes Yes Yes Yes Observations 3,456 3,456 3,456 3,456 R-squared 0.4661 0.4716 0.6264 0.6275 N 283 283 283 283 5.2 Regulation effect on the employment level Section 2 demonstrated that the employment level can enhance AI's role in promoting population growth. In this analysis, PGRW is used as the dependent variable, with AI and rAI as independent variables, and the employment level ( EMPL ) serving as the adjustment variable to estimate Eq. ( 2 ). The results are shown in Columns (1) and (2) of Table 6 . Following the aforementioned order, the dependent variable was replaced with rPGRW , and Eq. ( 2 ) was re-estimated. The results are shown in Columns (3) and (4) in Table 6 . According to Table 6 , despite the AI coefficient being not significant, the interaction terms between AI and employment level are significant at the 1%, 5%, and 10% levels. The partial derivatives relating to AI development level indicate that AI's contribution to population growth increases with improvements in the employment level. Additionally, the coefficient of the employment level is significantly positive in most cases, and the cross-term of AI and employment level is significantly positive at the 1%, 5%, and 10% significance levels. Regarding the regression results from Columns (1)–(4) in Table 6 , the partial derivatives for the employment level ( EMPL ) were derived as follows: ∂PGRW/∂EMPL = 0.8689 + 0.2297*AI, ∂PGRW/∂EMPL = 0.2297*rAI , given the non-significance of EMPL 's coefficient in Column (1), it is treated as zero, resulting in: ∂rPGRW/∂EMPL = 0.2297 + 0.0056*AI, ∂rPGRW/∂EMPL = 0.0640 + 0.0074*rAI. From these four partial derivative formulas, this indicates that the employment level's marginal impact on the population growth rate intensifies as the AI development level increases (Fig. 6 ). Therefore, H3 holds. Table 6 Estimated results of regulation effect on employment level (1) (2) (3) (4) Variables PGRW PGRW rPGRW rPGRW AI -0.7258 -0.0100 (0.4977) (0.0084) EMPL×AI 0.2297* 0.0056*** (0.1181) (0.0021) EMPL×rAI 0.3730** 0.0074** (0.1721) (0.0029) rAI -0.9055 -0.0134 (0.7611) (0.0119) EMPL 0.8689* 0.6491 0.0634*** 0.0640*** (0.5124) (0.4991) (0.0088) (0.0087) City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Control Yes Yes Yes Yes Observations 3,456 3,456 3,456 3,456 R-squared 0.4629 0.4680 0.6199 0.6232 N 283 283 283 283 6. Time heterogeneity AI promotes population growth by alleviating time poverty, enhancing the public environment and fertility conditions, and boosting the production and living conditions of farmers. As a technology, AI has a continuous diffusion process. Over time, the application of AI has become increasingly extensive, and its role in alleviating time poverty, improving the public environment and fertility conditions, and improving farmers’ production and living conditions have been on the rise. The role of AI in promoting population growth has also expanded over time. Thus, we hypothesize that AI's role in promoting population growth exhibits time heterogeneity, increasing progressively over time. To test our hypothesis, PGRW was selected as the dependent variable and AI as the independent variable. The independent variable was then successively delayed by 0 to 3 periods, and Eq. ( 1 ) was re-estimated using the Fixed Effects (FE) model. The results are presented in Panel A of Table 7 . The dependent variable was replaced with rPGRW . Eq. ( 1 ) was re-estimated using the FE model, with results displayed in Panel B of Table 7 . Subsequently, the independent variable was replaced with rAI, and Eq. ( 1 ) was re-estimated following the previously mentioned sequence. The results are presented in Panel C and Panel D of Table 7 . Table 7 demonstrates that both the independent variables and their lagged terms are significantly positive at the 1% or 10% significance levels, and AI can promote population growth. Secondly, with an increase in the number of lag periods, the coefficient of the independent variables increased. These empirical findings corroborate the hypothesis that AI's role in promoting population growth not only exhibits time heterogeneity but also intensifies over time. Table 7 Estimation results of time heterogeneity Panel A (1) (2) (3) (4) Variables PGRW PGRW PGRW PGRW AI 0.2357* (0.1264) L.AI 0.3982*** (0.1459) L2.AI 0.4757*** (0.1839) L3.AI 0.7324*** (0.1876) City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Control Yes Yes Yes Yes Observations 3,456 3,130 2,845 2,570 R-squared 0.4613 0.4756 0.4939 0.5076 N 283 283 283 282 Panel B (1) (2) (3) (4) Variables rPGRW rPGRW rPGRW rPGRW AI 0.0134*** (0.0021) L.AI 0.0126*** (0.0021) L2.AI 0.0149*** (0.0023) L3.AI 0.0161*** (0.0023) City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Control Yes Yes Yes Yes Observations 3,456 3,130 2,845 2,570 R-squared 0.6166 0.6101 0.6285 0.6405 N 283 283 283 282 Panel C (1) (2) (3) (4) Variables PGRW PGRW PGRW PGRW rAI 0.7189*** (0.2144) L.rAI 0.9506*** (0.2105) L2.rAI 0.9251*** (0.2276) L3.rAI 1.0388*** (0.2683) City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Control Yes Yes Yes Yes Observations 3,456 3,130 2,845 2,570 R-squared 0.4613 0.4756 0.4939 0.5076 N 283 283 283 282 Panel D (1) (2) (3) (4) Variables rPGRW rPGRW rPGRW rPGRW rAI 0.0187*** (0.0030) L.rAI 0.0193*** (0.0030) L2.rAI 0.0209*** (0.0031) L3.rAI 0.0232*** (0.0033) City FE Yes Yes Yes Yes Year FE Yes Yes Yes Yes Control Yes Yes Yes Yes Observations 3,456 3,130 2,845 2,570 R-squared 0.6166 0.6101 0.6285 0.6405 N 283 283 283 282 7. Discussion AI can automate simple and complex labor (Acemoglu et al., 2018), thereby improving social production efficiency (Haseeb et al., 2019 ) and shortening working hours. This increase in leisure time boosts the desire to reproduce (Pan, 2018 ), which in turn promotes population growth. Additionally, the public environment inhibits fertility (Aitken, 2022 ). This is associated with increased exposure to chemicals directly or indirectly derived from fossil fuels (Skakkebæk et al., 2022). AI uses big data for analysis to understand the market’s environmental needs, public environmental conditions, prominent areas of public environmental problems, and concentrated distribution areas of pollution sources more accurately. Through the optimal allocation of resources, such as capital, technology, and equipment, it can achieve energy conservation and emission reduction and improve the public environment (Zhang & Li, 2021 ), thereby promoting population growth. Our findings indicate that AI plays a significant role in fostering population growth, aligning with existing research on AI's influence on fertility rates. Our research indicates that the level of education can reduce the population growth rate. This finding aligns with the prevailing literature on population fertility. Education improves people's ability to work, particularly for women. Women with higher levels of education encounter greater opportunity costs when exiting the labor market to raise children, compared to their less-educated counterparts (Tavares, 2010 ). Higher levels of education, especially for women, undermine their desire to have children over time (Cygan-Rehm & Maeder, 2013 ). In societies with higher education levels, the implied costs of childrearing are greater, leading parents to opt for fewer children, focusing on improving child quality (Doepke et al., 2022 ). Similarly, our research demonstrates that higher employment levels can facilitate population growth. This finding aligns with the prevailing literature on population fertility. People's judgments of their futures affect their desire for fertility. Future economic uncertainty is an important factor that affects fertility desire (Lappegård et al., 2022 ). Negative economic scenarios significantly reduce fertility intentions, whereas positive scenarios enhance them (Lappegård et al., 2022 ). Rising employment rates and better employment prospects can create a positive economic environment, thereby enhancing the willingness to have children and promoting population growth. Employment can promote occupational well-being (Ma & Ding, 2023 ). The latter can increase the desire for fertility (Yang & Xie, 2022 ) and promote population growth. 8. Conclusion, enlightenment, and outlook 8.1 Conclusion This study examined the impact of AI on population growth. The impact of AI on population growth was analyzed theoretically from two perspectives: direct impact and regulatory mechanisms. This was followed by empirical testing of the two-way fixed effects of year and city, utilizing a sample from Chinese cities spanning 2008 to 2020. It was found that AI can promote population growth in China. With the doubling of the AI development level, the population growth rate increases by 0.2357 percentage points. Furthermore, AI was found to mitigate the negative impact of education on population growth and enhance the positive influence of employment. Additionally, the influence of AI on population growth was observed to exhibit temporal heterogeneity, intensifying over time. 8.2 Enlightenment Our findings have several theoretical and practical implications for future research. First, these findings offer new insights into promoting high-quality development in the Chinese economy. As the world's largest developing country, the high-quality growth of China's economy plays a crucial role in the welfare of the Chinese people and people worldwide. The population serves as a major driving force behind economic growth. This study has found that AI can promote population growth, which is conducive to China's economic growth. Therefore, Chinese cities should vigorously promote AI development. While promoting economic development with AI, they can also promote population growth and provide human resources for economic development. Second, these findings hold significance for developing countries striving for high-quality economic development. As global efforts to reduce carbon emissions continue, achieving high-quality economic development has become increasingly critical for developing countries. AI can be used in environmental governance (Zhang & Li, 2021 ) to achieve low-carbon development. Simultaneously, AI can promote population growth, thereby providing human capital for economic development. Therefore, developing countries should actively promote AI development to achieve high-quality economic development. Third, these findings also carry implications for economic development in developed countries. Developed countries are experiencing a decline in population growth, and economic development is limited by human capital. This study found that AI can promote population growth. Thus, developed countries need not fear that AI-induced machine substitution will lead to lower birth rates; instead, they should vigorously promote the development of AI to enhance economic development. 8.3 Outlook This study has examined the impact of AI on population growth. The following aspects are worthy of further study: The first is the transmission mechanism of AI affecting population growth. 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Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYFACHiA2YJBjY28/QJIWA2M+njMJpGhhMEicJ+FgQJwG3Rm5Bz/zFPxJb5NgSGD4UbGNsBazG3nJ0jwGBrlt0o0HGHvO3CZGS44ZM1iLzIEEZsY2ErSks0kkGJCmJYEELWfeGEvOMTA2bAMG8kHi/HI8x/DDmz9y8vLt7Qcf/KggQguDQAKCfYAI9UDAT6S6UTAKRsEoGMEAACOONupmmqgSAAAAAElFTkSuQmCC","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Yihua","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-05-20 01:40:56","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6702628/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6702628/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83275465,"identity":"2f8935d5-592c-4043-9aea-84a16d13bb90","added_by":"auto","created_at":"2025-05-22 08:54:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":378030,"visible":true,"origin":"","legend":"\u003cp\u003eLogical diagram of AI affecting population growth in China\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6702628/v1/b2dd0797c62707b7f877d67b.png"},{"id":83276575,"identity":"a33cf7b2-0df6-42b1-936b-5322df3a3c19","added_by":"auto","created_at":"2025-05-22 09:10:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":207741,"visible":true,"origin":"","legend":"\u003cp\u003eLogical diagram of the moderating effect of AI on education in China's population growth\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6702628/v1/1700e891870ebb72eda7bc4a.png"},{"id":83275715,"identity":"9124dab5-639d-4f7e-a512-edb648ef7653","added_by":"auto","created_at":"2025-05-22 09:02:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":217789,"visible":true,"origin":"","legend":"\u003cp\u003eLogical diagram of the effect of AI on employment and the adjustment of population growth in China.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6702628/v1/b8efec11be6cdf0ef4b5fb52.png"},{"id":83275720,"identity":"c8ccaef8-25fb-4b8d-972a-594c92884c75","added_by":"auto","created_at":"2025-05-22 09:02:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":174139,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot and univariate regression line of population growth rate and AI development level\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6702628/v1/3a76789edea3f719494c0cf8.png"},{"id":83275461,"identity":"da793baa-e447-4889-b10e-51c0ebf613a1","added_by":"auto","created_at":"2025-05-22 08:54:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66051,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram of the Marginal Effect of AI on Weakening the Impact of Education and Reducing Population Growth\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6702628/v1/8916507ac4d7d3501ff1c62b.png"},{"id":83275466,"identity":"4f2b16dd-ec7d-4003-86f4-883614ada99e","added_by":"auto","created_at":"2025-05-22 08:54:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":66943,"visible":true,"origin":"","legend":"\u003cp\u003eMarginal effects diagram of AI strengthening employment and promoting population growth\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6702628/v1/3e37b3e7d6a25856abfbb1bb.png"},{"id":83276803,"identity":"4d7a74a6-7676-4441-a00d-d8aa72b2d245","added_by":"auto","created_at":"2025-05-22 09:18:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2603402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6702628/v1/abc0cb82-1c55-4f5c-8f5a-11a9ea73b5d3.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eArtificial intelligence and population growth: evidence from China\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past half-century, many countries have witnessed a steep decline in their populations (Aitken, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Decreasing population growth will not only lead to rapid population decline but also cause a high degree of population aging, leading to a series of social problems (Wu, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Artificial intelligence (AI) automates tasks through algorithm development (Frey \u0026amp; Osborne, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), impacts every aspect of people's lives (Haseeb et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), promotes capital to replace labor (Acemoglu \u0026amp; Restrepo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and raises concerns about people becoming increasingly replaceable (Acemoglu et al., 2018; Acemoglu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This implies that AI may decrease people's desire to reproduce, thereby reducing population growth. \u0026ldquo;Time poverty\u0026rdquo; inhibits people's desire to reproduce. AI can promote population growth by improving social production efficiency (Haseeb et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and alleviating time poverty. Scholars globally have developed two generations of models to study birth rates and population growth (Doepke et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While China has emerged as a key player in global AI (Kshetri, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the impact of AI on China's population growth remains unexplored to date.\u003c/p\u003e \u003cp\u003eFor Chinese scholars, studying the country\u0026rsquo;s problems at the city level is often the preferred approach. China is a vast country with a large gap in city development. First of all, this paper takes \"AI\" as the keyword, extracts the number of AI patent applications and the number of grants in each city from the State Intellectual Property Office, adds one to the number of AI patent applications and the number of grants in each city, and then takes the natural logarithm to get \u003cem\u003eAI\u003c/em\u003e and \u003cem\u003erAI\u003c/em\u003e, which are proxy variables of AI development level. Taking the population growth rate of each city as the proxy variable for the \u003cem\u003ePGRW\u003c/em\u003e, the regression analysis of the natural logarithm of the variables can represent the variable growth rate. This study uses the natural logarithm of the total population of each city to obtain the \u003cem\u003erPGRW\u003c/em\u003e as another proxy variable for population growth rate. To empirically test the impact of AI on the population growth rate, this study uses the city and year two-way fixed effects model, with the city population growth rate as the dependent variable and the city AI development level as the independent variable. Then, the interaction terms of city education level, employment level, and city AI development level were used to empirically study the weakening effect of AI on education, reducing the population growth rate, and exploring the strengthening effect of AI on employment, thereby increasing the population growth rate. Finally, we analyze the regional heterogeneity of financially developed and underdeveloped, high-risk, and low-risk areas.\u003c/p\u003e \u003cp\u003eBased on the above research design, we find that: First, AI can promote the population growth of Chinese cities. When AI development level doubles, the population growth rate increases by 0.2357 percentage points. AI shortens the working hours of Chinese people, improves the environment, and weakens the transfer of the rural labor force, which is conducive to promoting the growth of city populations in China. Second, education reduces the birth rate and is not conducive to population growth. AI can improve quality of life and reduce the opportunity costs of women's fertility, thus weakening the adverse impact of education on population growth. Furthermore, employment promotes population growth. China's unique social system weakens the substitution effect of AI on the labor force, which can increase the remuneration of workers, thus strengthening the positive effect of employment on population growth. Third, the role of AI in promoting population growth exhibits temporal heterogeneity, which increases over time.\u003c/p\u003e \u003cp\u003eOur research contributes in several ways. First, it enriches the literature on population growth. Existing literature examines population growth from cultural, economic, and social perspectives. In terms of culture, late marriage (Greenhaus \u0026amp; Beutell, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1985\u003c/span\u003e), childless culture (Stegen et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the pursuit of freedom and equality (Poston Jr. \u0026amp; Trent, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) reduce the population growth rate. Economically, factors like development (Lappeg\u0026aring;rd et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), industrial structure (Skakkeb\u0026aelig;k et al., 2022; Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), resident income (Lee, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Keller \u0026amp; Utar, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and house prices (Ge \u0026amp; Zhang, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) influence population growth. For example, most industrialized regions have low fertility rates and slow population growth (Skakkeb\u0026aelig;k et al., 2022). In general, these include working hours (Greenhaus \u0026amp; Beutell, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), environment (Aitken, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), education (Cygan-Rehm \u0026amp; Maeder, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu \u0026amp; Raftery, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Doepke et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and employment (Alderotti et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Van Wijk et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, Doepke et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that higher levels of education across society mean higher costs for having children and that parents choose to have fewer children overall instead of higher-quality children. Scholars have conducted extensive research on the factors influencing population growth; however, to the best of our knowledge, the literature does not cover the impact of AI on China's population growth. Thus, this study expands the literature by examining AI's effects on population growth. Second, this study further contributes to the literature on AI's social impact. Existing literature has conducted extensive research from both economic and individual aspects, drawing a series of useful conclusions. On the economic side, AI promotes investment growth (Chen et al., 2016), thereby promoting economic growth (Chen et al., 2016; Agrawal et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kshetri, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AI also provides new methods that are conducive to technological innovation (Cockburn et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Based on the necessary supervision, AI can promote the sustainable development of the economy (Vinuesa et al., 2020). The impact of AI on financial services may be transformative, but it remains complex and uncertain (Bholat \u0026amp; Susskind, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the personal side, AI automates routine tasks based on clear rules (Frey \u0026amp; Osborne, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), eliminates existing jobs, adds new jobs, prefers highly skilled individuals (Yang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), eliminates low-end skilled jobs, and generates new highly skilled jobs (Acemoglu \u0026amp; Restrepo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with both job elimination and job creation effects (Acemoglu et al., 2018). This profoundly affects individual employment (Haseeb et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although scholars have conducted extensive research on the social impact of AI, its impact on population growth has not yet been studied.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e clarifies our testable hypothesis. Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the econometric models, variables, and data. Section \u003cspan refid=\"Sec18\" class=\"InternalRef\"\u003e4\u003c/span\u003e discusses the direct effects of AI on population growth. Section \u003cspan refid=\"Sec25\" class=\"InternalRef\"\u003e5\u003c/span\u003e tests the moderating effects of education and employment levels. Section \u003cspan refid=\"Sec28\" class=\"InternalRef\"\u003e6\u003c/span\u003e tests the time heterogeneity. Section \u003cspan refid=\"Sec29\" class=\"InternalRef\"\u003e7\u003c/span\u003e provides a discussion. Finally, this study is summarised.\u003c/p\u003e"},{"header":"2. Testable Hypotheses","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Direct influence\u003c/h2\u003e \u003cp\u003eGiven the existing findings, we posit that AI can reduce working hours, enhance environmental quality, and mitigate the migration of the rural labor force, thereby fostering population growth.\u003c/p\u003e \u003cp\u003eFirst, AI can mitigate time poverty, thereby promoting population growth. Firstly, it extends fertility opportunities, further promoting population growth. The work-family conflict theory holds that people's energy and time are limited, and the distribution of work and family will inevitably be biased (Greenhaus \u0026amp; Beutell, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Therefore, \u0026lsquo;time poverty\u0026rsquo; restricts the reproductive intention of both men and women, and individuals with greater time autonomy are more likely to have a strong intention to reproduce (Chen, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This means that reducing people's working hours can increase their desire to reproduce and thus promote population growth. Since 2008, AI's integration into the economic and financial sectors has deepened (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). AI has automated tasks previously performed by workers (Acemoglu et al., 2018) and has improved social production efficiency (Haseeb et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This can reduce people's working hours, thereby increasing their desire for fertility and promoting population growth (Pan, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Secondly, AI reduces rates of singleness, thereby promoting population growth. Regular overtime work leaves little leisure time for social interactions, potentially reducing individuals' attractiveness to potential partners (Zhang \u0026amp; Shi, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In other words, long working hours have been a cause of increased social singleness. Business work limits people's social lives and their desire for love. An increase in working hours reduces the time and energy devoted to love (Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), delaying or even reducing the time for young people to marry and have children. AI has improved social production efficiency (Haseeb et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), compressed people's working hours, reduced the rate of singles, increased fertility, and promoted population growth. Thirdly, it improves the efficiency and quality of continuing education and promotes population growth. China's urban and rural labor supply exceeds demand, and working workers, such as job seekers, face huge market competition pressure (Zhang \u0026amp; Shi, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To avoid the \u0026ldquo;survival of the fittest\u0026rdquo; scenario, workers often dedicate significant time to education for skill improvement. AI promotes the transformation of educational services from mobile terminals to smart terminals (Luo \u0026amp; Song, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), enabling tailored education programs for employed individuals, which improves the quality and efficiency of education, reduces education time, and increases workers\u0026rsquo; leisure time and fertility willingness, thereby promoting population growth.\u003c/p\u003e \u003cp\u003eSecond, AI can improve the public environment and fertility conditions, thereby promoting population growth. Firstly, by enhancing the public environment, AI contributes to population growth. Public environments inhibit fertility (Aitken, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This association stems from increased exposure to chemicals, directly or indirectly derived from fossil fuels (Skakkeb\u0026aelig;k et al., 2022). In other words, the deterioration of the public environment reduces fertility and is not conducive to population growth. In addition, concerns about the adverse effects of the population on the environment make it easier to accept the concept of childlessness in modern society (Stegen et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Aitken, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is not conducive to population growth. By analyzing big data, AI can gain a more accurate understanding of the market's environmental needs, public environmental conditions, prominent areas of public environmental problems, concentrated distribution areas of pollution sources, etc. Through the optimal allocation of resources such as capital, technology, and equipment, energy conservation and emission reduction can be achieved, and the public environment can be improved (Zhang \u0026amp; Li, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As a result, these improvements promote population growth. Secondly, by improving the working environment, AI also fosters population growth. AI can automate simple repetitive tasks (Frey \u0026amp; Osborne, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). China is a developing country. China performs several dangerous, heavy, and harsh tasks. Most of these tasks are simple and repetitive. AI automation of these tasks improves the working environment. For instance, the use of bionic aerial robots to replace human aerial workers can significantly improve their working conditions. AI can liberate people from dangerous, heavy, and harsh work tasks and improve their working environments (Pan, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Improving the work environment can increase people's happiness, thereby increasing their desire to reproduce and promoting population growth (Yang \u0026amp; Xie, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Thirdly, AI enhances fertility conditions further, thereby promoting population growth. AI is widely used in healthcare (Yu et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Utilizing diverse healthcare data, both structured and unstructured, AI assists in diagnosing a range of major diseases (Jiang et al., 2017). Additionally, by leveraging healthcare data, AI facilitates pregnancy testing, enhances maternal and fetal health, reduces pregnancy-related risks, and thereby promotes population growth. This indicates that AI can improve fertility conditions and increase the willingness to reproduce, thereby promoting population growth.\u003c/p\u003e \u003cp\u003eThird, AI can improve farmers\u0026rsquo; production and living conditions and promote population growth. Firstly, it promotes the return of migrant workers and population growth. Some three decades ago, the acceleration of China's industrialization led to large-scale population movements in China (Chen et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); a large number of farmers began moving to cities, leading to large-scale migration (Yin et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To support the elderly, one partner in a rural couple often works in urban areas, while the other remains in the countryside. This separation reduced the cohabitation time of young rural couples. In China, farmers have historically been crucial to national fertility rates. During the One Child Policy era, rural couples were often dubbed as the \u0026ldquo;super army\u0026rdquo; for fertility. AI has changed the labor demand structure of enterprises (Yang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and eliminated low-end skilled workers (Acemoglu \u0026amp; Restrepo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Due to limited education, among other factors, migrant workers, often in low-skilled jobs, have been displaced by AI, leading to their return to rural areas. This increases the time young farming couples spend together, enhancing their relationship, increasing the probability of rural women's pregnancy, and promoting population growth. Secondly, it enhances farmers' income, thereby further promoting population growth. China is predominantly an agricultural country, where farmers primarily earn from agricultural production. AI has penetrated all areas of the economy, including agricultural production (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Through the application of AI, agricultural production has become more efficient, leading to an increase in farmers\u0026rsquo; income. Income is an important variable that affects fertility decision-making (Giuntella et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). An increase in farmers' income creates a positive scenario that increases their fertility willingness (Lappeg\u0026aring;rd et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and promotes population growth. Thirdly, AI's role in increasing farmers' leisure time further supports population growth. It takes a lot of time for people to give birth and raise their children (Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Engaged in agriculture, farmers spend much of their time working, and with China's aging population issue intensifying, the burden of agricultural farming falls predominantly on young couples. AI can automate simple repetitive tasks (Frey \u0026amp; Osborne, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Given that much of agricultural work involves simple, repetitive tasks, AI can automate these processes, significantly reducing the need for manual labor. Therefore, AI can provide farmers with more time to give birth to and raise their children. This can increase the farmers\u0026rsquo; desire for fertility and promote population growth.\u003c/p\u003e \u003cp\u003eIn summary, AI can promote population growth by alleviating time poverty, enhancing the public environment and fertility conditions, and boosting farmers\u0026rsquo; production and living conditions (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, this study proposes the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1: AI can promote population growth.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Regulation mechanism\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 The regulating mechanism of education\u003c/h2\u003e \u003cp\u003eOn the one hand, education reduces fertility and inhibits population growth. Firstly, education increases the explicit costs of childbearing and child-rearing. As the largest developing country, China has faced enormous employment pressure in its process of rapid economic development. To improve their children's employment competitiveness and avoid being eliminated from a fiercely competitive environment, Chinese people are increasingly hoping to improve their children's level of education. For example, as undergraduate degrees have become more common, Chinese parents increasingly support their children in pursuing master's and doctoral studies. The societal improvement in education levels means that parents must invest more to elevate their children's education, enhancing their employment competitiveness. A societal increase in education levels leads to higher child-rearing costs, prompting parents to opt for fewer children to focus on enhancing their children's quality of life (Doepke et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Secondly, education increases the opportunity cost of having children. Education improves people's ability to work, particularly for women. Women with more education face higher opportunity costs of leaving the labor market and raising children than those with less education (Tavares, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). An increase in people's education level, especially for women, will weaken fertility desire through the value of time (Cygan-Rehm \u0026amp; Maeder, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), thus inhibiting population growth. Thirdly, education compresses the timeframe for childbirth. Improvement in the education level of society as a whole means that people have to spend more time on education. Fertility and parenting require considerable time (Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To receive more education, people must delay childbirth and marriage. Delaying childbearing and marriage reduces the number of children within the same period, resulting in a slowdown in population growth. Late marriage is also a direct cause of low fertility (Jones, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This also inhibited population growth.\u003c/p\u003e \u003cp\u003eOn the other hand, AI can weaken the negative effect of education on population growth. Firstly, it can lower the explicit cost of education. AI generates new positions, such as data administrators and analysts (Acemoglu et al., 2018). Furthermore, it has prompted enterprises to implement digital transformation, and the demand for technical, service-oriented, and highly skilled employees has increased significantly (Yang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Both data administrators and analysts, as well as technical, service-oriented, highly skilled employees\u0026mdash;all considered applied talents\u0026mdash;benefit from on-the-job training, or \u0026ldquo;learning by doing\u0026rdquo; rather than traditional school education. This may decrease the inclination of Chinese parents to unilaterally pursue higher education levels for their children, thereby lowering the direct costs associated with education. Secondly, AI can reduce the opportunity cost of education-induced fertility. The deep integration of digital technologies, such as AI, and the economy has produced a digital economy (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that has infused flexible employment with new vitality. The digital economy has transformed flexible employment from a passive choice to a career that many young people, women, college students, and other groups voluntarily or actively choose. Some flexible self-employment models can bring inner satisfaction and autonomy to practitioners and attract more workers to choose this form of employment (Qi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This enables women, particularly during their childbearing years, to opt for flexible employment and participate in the labor market. This approach not only offers inner satisfaction, mitigating loneliness and irritability during childbirth but also reduces the opportunity costs of childbirth. Thirdly, AI makes up for the crowding out of childbearing time by education. Intelligent information technologies, like AI and big data, have enhanced data mining, analysis, utilization, and various smart educational services (Yu, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It has transformed teaching from mobile terminals to intelligent terminals (Luo \u0026amp; Song, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), revolutionizing educational access and enabling learning during fragmented times from anywhere, at any time. Therefore, AI can alleviate the time conflict between education and fertility and compensate for the crowding out of fertility time through education.\u003c/p\u003e \u003cp\u003eIn summary, while advancements in social education levels may inhibit population growth, AI can mitigate this effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, this study proposes the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2: AI can weaken the inhibitory effect of education on population growth.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 The regulating mechanism of education employment\u003c/h2\u003e \u003cp\u003eOn the one hand, employment can promote population growth. Firstly, employment fosters professional satisfaction and increases the desire for fertility. According to Maslow's hierarchy of needs, self-actualization holds the highest level among people's needs. Employment generates income not only to meet lower-level needs. In employment, people can get a high sense of achievement by completing more difficult work tasks. The greater the task difficulty, the greater the sense of achievement after completion. The acquisition of a sense of achievement can meet employees\u0026rsquo; esteemed needs. Therefore, employment can promote occupational well-being (Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which can increase the desire to reproduce (Yang \u0026amp; Xie, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and promote population growth. Secondly, employment creates positive economic conditions, thereby increasing reproductive willingness. People's judgments of their futures also affect their desire to reproduce. Economic uncertainty regarding the future is a significant factor influencing reproductive desires (Lappeg\u0026aring;rd et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Negative economic conditions result in a marked decline in the intention to have children, whereas positive conditions foster an increase in such intentions (Lappeg\u0026aring;rd et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rising employment rates and better employment prospects can create a positive economic environment, thereby enhancing the willingness to have children and promoting population growth. Thirdly, employment increases family income and provides child-rearing protection. \"Wangzi Chenglong\" is a traditional deep-rooted concept of the Chinese people. The vast majority of Chinese people would opt not to have children if their wealth was insufficient to support their children's healthy development. Over time in China, an increasing number of women are choosing to remain employed after marriage, leading many urban families to hire nannies for child care. Consequently, this trend raises the domestic service costs for Chinese couples (Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Good economic conditions can increase the desire for improved fertility and fertility rates (Myrskyl\u0026auml; et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, employment contributes to an increase in household income, boosts fertility desires, and promotes population growth.\u003c/p\u003e \u003cp\u003eOn the other hand, AI can strengthen the positive effect of employment on the population growth rate. Firstly, it enhances professional satisfaction. AI automates simple and repetitive work tasks (Frey \u0026amp; Osborne, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and while eliminating these simple and repetitive jobs, it will also create relatively challenging high-skilled jobs (Acemoglu \u0026amp; Restrepo, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and improve the technical content of positions that are not eliminated. Employees need to continually enhance their skills to meet AI-driven job requirements. After improving their business skills and completing job tasks, people gain a sense of achievement and experience greater professional happiness. Secondly, AI can strengthen positive economic conditions. The deep integration of digital technologies such as AI and the economy has created a digital economy (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which has spawned emerging flexible employment such as \u0026ldquo;Sunday engineers\u0026rdquo;, \"Didi\", and live commerce. As of 2021, the number of individuals in flexible employment in China was expected to have reached approximately 200\u0026nbsp;million. These emerging flexible divisions of labor, large demand, and broad development space have provided people with a large number of employment opportunities (Qi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Upon facing unemployment, traditional employees could opt for flexible employment options. This means that emerging flexible employment can provide a buffer for traditional employment, thus enhancing the positive economic scenario for traditional employees due to employment. Thirdly, AI increases leisure time while ensuring family income. AI can improve social production efficiency (Haseeb et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For employees, AI can improve work efficiency and reduce working hours. Thus, AI alleviates employees' time poverty while safeguarding their family income, boosting fertility desires, and promoting population growth.\u003c/p\u003e \u003cp\u003eIn summary, an improvement in the social employment level promotes population growth, and AI has a strengthening effect on this promotion (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Therefore, this study proposes the following hypothesis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3: AI can strengthen the role of employment in promoting population growth.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Models, Variables, and Data","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Model\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Model of direct impact\u003c/h2\u003e \u003cp\u003eIn the study of issues specific to China, Chinese scholars prioritize empirical analysis at the city level. This study examines the effects of AI on population growth at the city level. To this end, this study draws on existing literature (Alderotti et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Giuntella et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lappeg\u0026aring;rd et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and designs the year and city two-way fixed effect model:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{PGRW}_{it}={\\alpha\\:}_{0}+{\\beta\\:}_{1}\\ast\\:{AI}_{it}+\\eta\\:\\ast\\:X+{\\alpha\\:}_{i}+{\\lambda\\:}_{t}+{\\epsilon\\:}_{it}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere i and t are the subscripts of city and year respectively; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{I}\\:\\)\u003c/span\u003e\u003c/span\u003eis to capture the city fixed effect; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\lambda\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003eis the capture year fixed effect, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\epsilon\\:}_{it}\\:\\)\u003c/span\u003e\u003c/span\u003e is the random error term. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{PGRW}_{it}\\:\\)\u003c/span\u003e\u003c/span\u003eis the dependent variable (the population growth rate of the city i in year t). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{AI}_{it}\\:\\)\u003c/span\u003e\u003c/span\u003erepresents an independent variable, namely, the AI development level in city i in year t, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e is its coefficient; if it is significantly positive, then AI can promote population growth. \u003cem\u003eX\u003c/em\u003e is the control variable, as detailed below.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Model of regulation mechanism\u003c/h2\u003e \u003cp\u003eTo test the regulation mechanism, we designed the following model:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{PGRW}_{it}={\\alpha\\:}_{0}+{\\beta\\:}_{1}\\ast\\:{AI}_{it}+{\\beta\\:}_{2}\\ast\\:{AI}_{it}\\ast\\:{ADJ}_{it}+{\\beta\\:}_{3}\\ast\\:{ADJ}_{it}+\\eta\\:\\ast\\:X+{\\alpha\\:}_{i}+{\\lambda\\:}_{t}+{\\epsilon\\:}_{it}\\:\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{ADJ}_{it}\\)\u003c/span\u003e \u003c/span\u003eis the moderating variable, namely, the education level (\u003cem\u003eEDUC\u003c/em\u003e) or employment level (\u003cem\u003eEMPL\u003c/em\u003e) of the city i in year t. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{AI}_{it}\\ast\\:{ADJ}_{it}\\)\u003c/span\u003e\u003c/span\u003e is the cross-term of AI development level and moderating variables. If \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e is significantly positive, AI can weaken the inhibitory effect of education on population growth or strengthen the promoting effect of employment on population growth. \u003cem\u003eX\u003c/em\u003e is a control variable, the same as in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Variables\u003c/h2\u003e \u003cp\u003eBased on the literature, the independent, dependent, regulatory, and control variables used in this study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable description\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymbol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVariable Definition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePopulation rate of increase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation increments in year t / total population at the beginning of year t * 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMa \u0026amp; Ding (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLn (population at the end of year t / population at the beginning of year t)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZhao \u0026amp; Zhang (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAI development level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLn (1\u0026thinsp;+\u0026thinsp;number of patent applications for AI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHuang et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e); Xu et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003erAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLn (1\u0026thinsp;+\u0026thinsp;number of AI patents granted)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eControl variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic development Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePGDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLn (real per capita gross domestic product [GDP]), with 2008 as the base period.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGiuntella et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); Lappeg\u0026aring;rd et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinancial development level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFSIZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLoan balance/GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGe \u0026amp; Zhang (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustrial structure level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eINDS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe added value of tertiary industry/secondary industry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMa \u0026amp; Ding (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeducational level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEDUC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of college students/total population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLiu \u0026amp; Raftery (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Doepke et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployment level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEMPL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLn (urban employment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlderotti et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployment rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eUNEM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnemployment/urban employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMa \u0026amp; Ding (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrbanization rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCITY\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUrban population/total population\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMa \u0026amp; Ding (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSavings level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSAVE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSavings deposits/balance of various deposits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Dependent variables\u003c/h2\u003e \u003cp\u003eThe dependent variable in this study is the population growth rate (\u003cem\u003ePGRW\u003c/em\u003e). \u003cem\u003ePGRW\u003c/em\u003e is obtained by calculating \u0026ldquo;the total population increment divided by the population at the beginning of the year, multiplied by 100\u0026rdquo;, serving as the proxy variable for the population growth rate. In addition, the natural logarithm can be used to represent the growth rate (Zhao \u0026amp; Zhang, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This study takes the natural logarithm of the ratio of the total population at the end of the year to the total population at the beginning of the year to get \u003cem\u003erPGRW\u003c/em\u003e, which is another proxy variable for the population growth rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Independent variables\u003c/h2\u003e \u003cp\u003eThe independent variable in this paper is the AI development level (\u003cem\u003eAI\u003c/em\u003e). Referring to the existing literature (Huang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), this study takes the natural logarithm of the number of patents, adds one to the number of AI patent applications in each city, and then takes the natural logarithm to obtain AI, which is used as a proxy variable for AI development level. \u003cem\u003erAI\u003c/em\u003e is obtained by adding the number of AI patents granted in each city and then taking the natural logarithm, which is another proxy variable of the AI development level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Control Variables\u003c/h2\u003e \u003cp\u003eReferring to the existing literature (Alderotti et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Giuntella et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lappeg\u0026aring;rd et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), this study controls the level of economic development, financial development level, industrial structure level, education level, employment level, unemployment rate, urbanization rate, and savings level.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Data sources\u003c/h2\u003e \u003cp\u003eSince the 2008 global financial crisis, digital technologies like AI have started to integrate deeply into the economy (Chen et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), with AI gradually entering large-scale commercial applications. China has a vast unevenly developed territory. Consequently, for Chinese scholars, studying China's issues at the city level is a preferred approach. The data from the China City Statistical Yearbook have been updated through 2020. Therefore, this study conducted an empirical analysis of China's city data from 2008 to 2020. The data were processed as follows: (1) missing samples were eliminated, and (2) To eliminate the influence of outliers, this study performed a 1% Winsorised tail reduction on other continuous variables, except for those subjected to the natural logarithm. A total of 3,456 year-city observations were obtained.\u003c/p\u003e \u003cp\u003eThe count of AI patent applications and authorizations was sourced from the National Intellectual Property Administration. We used AI as a keyword to extract other data from the China City Statistical Yearbook. In addition, taking the natural logarithm eliminates the influence of outliers. To eliminate the influence of outliers, this study performs a 1% Winsorised tail reduction on other continuous variables except the natural logarithm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Summary Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the descriptive statistics of the variables. As Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates, the average city savings level (\u003cem\u003eSAVE\u003c/em\u003e) in China is 3.6086, with a minimum of 0.3998 and a maximum of 16.3033. This variation aligns with the fundamental national condition of uneven development. Furthermore, the average AI development level (\u003cem\u003eAI\u003c/em\u003e) is 0.3662, ranging from a minimum of 0.0000 to a maximum of 9.1395. The gap between the two is also large, which is consistent with China's basic national conditions of unbalanced development.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd.Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-21.5496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.0927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.8765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.9226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.1362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.2150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.4356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.6416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePGDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.5688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.4008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eINDS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.2764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.8312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.6431\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEDUC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.1410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEMPL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.5818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.4375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.8945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUNEM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.2149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFSIZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.3774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.8696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e190.3869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSAVE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Univariate analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents a scatter plot and univariate regression line depicting the relationship between the population growth rate and AI development level. As shown in the figure, there is a clear positive linear relationship between AI and population growth rate. The figure indicates that as the level of AI development improves, the population growth rate increases. Following this, a multivariate regression analysis was performed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Multivariate regression\u003c/h2\u003e \u003cp\u003eUsing \u003cem\u003ePGRW\u003c/em\u003e as the dependent variable, and \u003cem\u003eAI\u003c/em\u003e and \u003cem\u003erAI\u003c/em\u003e as independent variables, the fixed effects model FE estimated Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) without including control variables. The results are shown in columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Upon adding all control variables, Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was re-estimated, yielding the results in columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eColumns (1)\u0026ndash;(4) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that the coefficients of of AI development level are significantly positive at the 1% or 10% significance level, and \u003cem\u003eAI\u003c/em\u003e can promote population growth. The empirical results support hypothesis H1. This conclusion is consistent with the previous theoretical analyses. AI can alleviate time poverty, improve the public environment and fertility conditions, and improve farmers' production and living conditions, thus promoting population growth. The natural logarithm is used to represent growth rates (Zhao \u0026amp; Zhang, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and the level of AI development can be expressed as the natural logarithm of the number of AI patent applications or grants plus one, respectively. Accordingly, as shown in columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, when the AI development level doubles, the population growth rate increases by 0.2357 and 0.7189 percentage points, respectively.\u003c/p\u003e \u003cp\u003eColumns (1)\u0026ndash;(4) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that: firstly, the coefficient of education level is significantly negative at the 1% significance level, and an improvement in education level reduces the population growth rate (Tavares, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Cygan-Rehm \u0026amp; Maeder, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Doepke et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These findings align with the results of previous theoretical analyses. Secondly, the coefficient of the employment level is significantly positive at the 1% or 5% significance level, and an improvement in the employment level promotes population growth (Myrskyl\u0026auml; et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lappeg\u0026aring;rd et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This is consistent with previous theoretical analyses. Thirdly, the coefficient of economic development is significantly negative at the 1% significance level, and economic development reduces the fertility rate (Myrskyl\u0026auml; et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), thus inhibiting population growth. Fourthly, the coefficient of financial development is significantly positive at the 1% level. Financial development can alleviate people's financing constraints, provide credit funds for production and life, increase happiness, promote fertility (Yang \u0026amp; Xie, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and promote population growth. Fifthly, the coefficient of the industrial structure level is significantly negative at the 1% significance level, and upgrading the industrial structure reduces the fertility rate. The reason may be that China's tertiary industries, such as accommodation and catering, wholesale and retail, and residential services, need to continuously pay attention to and serve customers, and work long hours (Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), thus generating \u0026lsquo;time poverty\u0026rsquo; and reducing fertility. Lastly, the coefficient of the savings level is significantly negative at the 1% or 5% significance level, suggesting that higher savings levels are not conducive to population growth. One possible reason for this is that savings might contribute to economic development, which can influence fertility rates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFE estimation results of Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9690***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2357*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9223***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7189***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.2895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e 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\u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFSIZ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1247***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1225***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0202)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eINDS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.1327***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-12.5043***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.7446)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.8019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEDUC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.3495***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.7217***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.6224)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.6958)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEMPL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3656***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.1528**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.4838)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.4869)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUNEM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.2521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.4732\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.5208)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.4879)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCITY\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.7371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.0626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.1087)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSAVE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.6234***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.6468**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.0180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.1048)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7049***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7169***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143.0908***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e140.1874***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1038)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(12.7666)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(13.0635)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Robust standard errors in parentheses; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Robustness Checks\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the coefficients for the AI development level are significantly positive, which indicates the reliability of the conclusions to a certain extent. Further robustness checks were conducted, including endogenous treatment, modifications to population growth rate measurements, and variations in estimation methods.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e4.3.1 Endogenous treatment\u003c/h2\u003e \u003cp\u003ePrevious theoretical analyses and empirical tests have shown that AI significantly affects population growth. Conversely, the influence of population growth on AI is considered to be more limited. Therefore, it is difficult to establish a two-way causal relationship between AI and population growth. However, the number of AI patent applications and authorizations were retrieved by using AI as the keyword in the National Intellectual Property Administration. Keyword retrieval inevitably introduces deviations that may lead to measurement errors in the assessment of AI development levels, potentially causing endogeneity.\u003c/p\u003e \u003cp\u003eDigital technology entrepreneurship is an entrepreneurial activity aimed at digital technology innovation and applications such as AI, blockchain technology, cloud computing, big data, and the Internet of Things. The entrepreneurial process is also the process of implementing entrepreneurial plans for new ventures (Gartner, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Low \u0026amp; MacMillan, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Under Chinese law, every enterprise must obtain pre-registration approval that determines its business scope and is limited to conducting activities within this scope. Accordingly, the terms \u0026ldquo;AI\u0026rdquo;, \u0026ldquo;blockchain\u0026rdquo;, \u0026ldquo;cloud computing\u0026rdquo;, \u0026ldquo;big data\u0026rdquo;, and \u0026ldquo;Internet of Things\u0026rdquo; were used as keywords to identify enterprises registered with the State Administration for Market Regulation that operate within these domains. Cities were used as the unit of analysis, following the methodology of McDougall \u0026amp; Robinson Jr. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) and Zahra \u0026amp; Bogner (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Utilizing the method of Li \u0026amp; Zhang (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), the number of enterprises established from 2008 to 2020 in each city with activities in \u0026ldquo;AI\u0026rdquo;, \u0026ldquo;blockchain\u0026rdquo;, \u0026ldquo;cloud computing\u0026rdquo;, \u0026ldquo;big data\u0026rdquo;, and \"the Internet of Things\" was calculated, yielding the count of digital technology startups, denoted as ABCDI. Then, drawing on the methodologies of Laeven \u0026amp; Levine (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Faccio et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and Chen et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we calculated the mean value of the number of digital technology startups in other cities for the same year, with a one-period lag, to derive \u003cem\u003eL.ivAI\u003c/em\u003e as an instrumental variable.\u003c/p\u003e \u003cp\u003eThe number of digital technology startups in other cities is unlikely to directly affect a city's birth rate. Thus, \u003cem\u003eL.ivAI\u003c/em\u003e satisfies the exogeneity conditions. Technological innovation has a demonstration effect, and digital technology entrepreneurship in other cities promotes AI development, which may affect the development of AI in this city. Therefore, the number of digital technology startups in other cities is related to the level of AI development in the city, and \u003cem\u003eL.ivAI\u003c/em\u003e meets the conditions. Employing \u003cem\u003eL.ivAI\u003c/em\u003e as the instrumental variable, the instrumental variable method (IV) was utilized to re-estimate Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Cragg\u0026ndash;Donald F statistic of the weak instrumental variable test is 108.865, which is much larger than the critical value of 16.38 under 10% bias; i.e., the hypothesis of weak instrumental variables was rejected, and \u003cem\u003eL.ivAI\u003c/em\u003e is an effective instrumental variable.\u003c/p\u003e \u003cp\u003eUsing \u003cem\u003ePGRW\u003c/em\u003e as the dependent variable, \u003cem\u003eL.ivAI\u003c/em\u003e as the instrumental variable, and \u003cem\u003eAI\u003c/em\u003e and \u003cem\u003erAI\u003c/em\u003e as independent variables, the instrumental variable method (IV) was employed to re-estimate Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results are presented in Columns (1) and (2) of Panel A in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. To mitigate endogeneity concerns with the control variables, all such variables were lagged by one period, and IV estimation was applied to re-estimate Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results are shown in Columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Panel A. From Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Panel A, the coefficient of the \u003cem\u003eAI\u003c/em\u003e is significantly positive at the 1% significance level. Excluding endogeneity, AI can promote population growth, and the conclusion that H1 is supported is robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated results of robustness checks\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.9201***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.8175***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.3195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.5876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.0759***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3745***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.0347)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.8975)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.1502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.7668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0134***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2357**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0187***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7189***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.1268)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: All the instrumental variables have passed the validity test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e4.3.2 Changing the measurement of population growth\u003c/h2\u003e \u003cp\u003eWith the dependent variable replaced by \u003cem\u003erPGRW\u003c/em\u003e, Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was re-estimated using the Fixed Effects (FE) model, treating \u003cem\u003eAI\u003c/em\u003e and \u003cem\u003erAI\u003c/em\u003e as the independent variables. The results are presented in Columns (1) and (2) of Panel B of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Columns (1) and (2) demonstrate that AI contributes to population growth, affirming the robust support for Hypothesis H1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e4.3.3 Changing the estimation method\u003c/h2\u003e \u003cp\u003eTo mitigate bias from the estimation method, the Maximum Likelihood Estimation (MLE) approach was employed to re-estimate Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The findings are displayed in Columns (3) and (4) of Panel B in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. According to Columns (3) and (4), the robustness of the conclusion, establishing Research Hypothesis H1, is confirmed.\u003c/p\u003e \u003cp\u003eIn summary, in the case of controlling endogeneity and changing the measurement and estimation methods of population growth, AI is shown to promote population growth, confirming that Hypothesis H1 is robustly supported.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"5. Regulation Mechanism","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Regulation effect on the educational level\u003c/h2\u003e \u003cp\u003eSection \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrated that education level could mitigate the inhibitory effects of AI on population growth. In this analysis, \u003cem\u003ePGRW\u003c/em\u003e is used as the dependent variable, with \u003cem\u003eAI\u003c/em\u003e and \u003cem\u003erAI\u003c/em\u003e as independent variables, and the education level (\u003cem\u003eEDUC\u003c/em\u003e) serving as the adjustment variable for estimating Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results are shown in Columns (1) and (2) in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The dependent variable, \u003cem\u003ePGRW\u003c/em\u003e, was replaced with \u003cem\u003erPGRW\u003c/em\u003e, and Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was re-estimated in the aforementioned order. The results are shown in Columns (3) and (4) in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, although the coefficient for the AI is not significant, the interaction terms between AI and the education level are significantly positive at the 1% significance level. The partial derivatives relating to the AI development level suggest that AI's contribution to population growth is enhanced with improvements in the education level. Additionally, the coefficient of education level is significantly negative at the 1% significance level, while the cross term of AI and education level is significantly positive at the 1% significance level.\u003c/p\u003e \u003cp\u003eThe regression results from Columns (1)\u0026ndash;(4) in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e yield the partial derivatives for the education level (\u003cem\u003eEDUC\u003c/em\u003e) as follows: \u0026part;PGRW/\u0026part;EDUC=-15.7791\u0026thinsp;+\u0026thinsp;1.3080*AI, \u0026part;PGRW/\u0026part;EDUC=-15.7461\u0026thinsp;+\u0026thinsp;1.8802*rAI, \u0026part;rPGRW/\u0026part;EDUC=-0.3662\u0026thinsp;+\u0026thinsp;0.0314*AI, \u0026part;rPGRW/\u0026part;EDUC =-0.3443\u0026thinsp;+\u0026thinsp;0.0354*rAI. These four partial derivative formulas show that the marginal impact of the education level on the population growth rate weakens with an increase in AI development level (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Therefore, hypothesis H2 holds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of the Moderating Effect of Educational Level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1520)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEDUC\u0026times;AI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3080***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0314***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.3564)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEDUC\u0026times;rAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8802***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0354***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.4960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0087)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.2646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0045)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEDUC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-15.7791***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-15.7461***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3662***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.3443***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(2.9149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2.8648)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0616)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Regulation effect on the employment level\u003c/h2\u003e \u003cp\u003eSection \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrated that the employment level can enhance AI's role in promoting population growth. In this analysis, \u003cem\u003ePGRW\u003c/em\u003e is used as the dependent variable, with \u003cem\u003eAI\u003c/em\u003e and \u003cem\u003erAI\u003c/em\u003e as independent variables, and the employment level (\u003cem\u003eEMPL\u003c/em\u003e) serving as the adjustment variable to estimate Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results are shown in Columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Following the aforementioned order, the dependent variable was replaced with \u003cem\u003erPGRW\u003c/em\u003e, and Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was re-estimated. The results are shown in Columns (3) and (4) in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, despite the AI coefficient being not significant, the interaction terms between AI and employment level are significant at the 1%, 5%, and 10% levels. The partial derivatives relating to AI development level indicate that AI's contribution to population growth increases with improvements in the employment level. Additionally, the coefficient of the employment level is significantly positive in most cases, and the cross-term of AI and employment level is significantly positive at the 1%, 5%, and 10% significance levels.\u003c/p\u003e \u003cp\u003eRegarding the regression results from Columns (1)\u0026ndash;(4) in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the partial derivatives for the employment level (\u003cem\u003eEMPL\u003c/em\u003e) were derived as follows: \u003cem\u003e\u0026part;PGRW/\u0026part;EMPL\u0026thinsp;=\u0026thinsp;0.8689\u0026thinsp;+\u0026thinsp;0.2297*AI, \u0026part;PGRW/\u0026part;EMPL\u0026thinsp;=\u0026thinsp;0.2297*rAI\u003c/em\u003e, given the non-significance of \u003cem\u003eEMPL\u003c/em\u003e's coefficient in Column (1), it is treated as zero, resulting in: \u003cem\u003e\u0026part;rPGRW/\u0026part;EMPL\u0026thinsp;=\u0026thinsp;0.2297\u0026thinsp;+\u0026thinsp;0.0056*AI, \u0026part;rPGRW/\u0026part;EMPL\u0026thinsp;=\u0026thinsp;0.0640\u0026thinsp;+\u0026thinsp;0.0074*rAI.\u003c/em\u003e From these four partial derivative formulas, this indicates that the employment level's marginal impact on the population growth rate intensifies as the AI development level increases (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Therefore, H3 holds.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated results of regulation effect on employment level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.7258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.4977)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEMPL\u0026times;AI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2297*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0056***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEMPL\u0026times;rAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3730**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0074**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1721)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0029)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.9055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.7611)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0119)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEMPL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8689*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0634***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0640***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.5124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.4991)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0087)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Time heterogeneity","content":"\u003cp\u003eAI promotes population growth by alleviating time poverty, enhancing the public environment and fertility conditions, and boosting the production and living conditions of farmers. As a technology, AI has a continuous diffusion process. Over time, the application of AI has become increasingly extensive, and its role in alleviating time poverty, improving the public environment and fertility conditions, and improving farmers\u0026rsquo; production and living conditions have been on the rise. The role of AI in promoting population growth has also expanded over time. Thus, we hypothesize that AI's role in promoting population growth exhibits time heterogeneity, increasing progressively over time.\u003c/p\u003e \u003cp\u003eTo test our hypothesis, \u003cem\u003ePGRW\u003c/em\u003e was selected as the dependent variable and AI as the independent variable. The independent variable was then successively delayed by 0 to 3 periods, and Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was re-estimated using the Fixed Effects (FE) model. The results are presented in Panel A of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The dependent variable was replaced with \u003cem\u003erPGRW\u003c/em\u003e. Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was re-estimated using the FE model, with results displayed in Panel B of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Subsequently, the independent variable was replaced with rAI, and Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was re-estimated following the previously mentioned sequence. The results are presented in Panel C and Panel D of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e demonstrates that both the independent variables and their lagged terms are significantly positive at the 1% or 10% significance levels, and AI can promote population growth. Secondly, with an increase in the number of lag periods, the coefficient of the independent variables increased. These empirical findings corroborate the hypothesis that AI's role in promoting population growth not only exhibits time heterogeneity but also intensifies over time.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimation results of time heterogeneity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel A\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2357*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL.AI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3982***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1459)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL2.AI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4757***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1839)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL3.AI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7324***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.1876)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0134***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL.AI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0126***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL2.AI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0149***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL3.AI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0161***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7189***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.2144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL.rAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9506***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.2105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL2.rAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9251***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.2276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL3.rAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0388***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.2683)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.4613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003erPGRW\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0187***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL.rAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0193***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL2.rAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0209***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eL3.rAI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0232***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0033)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCity FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"7. Discussion","content":"\u003cp\u003eAI can automate simple and complex labor (Acemoglu et al., 2018), thereby improving social production efficiency (Haseeb et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and shortening working hours. This increase in leisure time boosts the desire to reproduce (Pan, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which in turn promotes population growth. Additionally, the public environment inhibits fertility (Aitken, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is associated with increased exposure to chemicals directly or indirectly derived from fossil fuels (Skakkeb\u0026aelig;k et al., 2022). AI uses big data for analysis to understand the market\u0026rsquo;s environmental needs, public environmental conditions, prominent areas of public environmental problems, and concentrated distribution areas of pollution sources more accurately. Through the optimal allocation of resources, such as capital, technology, and equipment, it can achieve energy conservation and emission reduction and improve the public environment (Zhang \u0026amp; Li, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), thereby promoting population growth. Our findings indicate that AI plays a significant role in fostering population growth, aligning with existing research on AI's influence on fertility rates.\u003c/p\u003e \u003cp\u003eOur research indicates that the level of education can reduce the population growth rate. This finding aligns with the prevailing literature on population fertility. Education improves people's ability to work, particularly for women. Women with higher levels of education encounter greater opportunity costs when exiting the labor market to raise children, compared to their less-educated counterparts (Tavares, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Higher levels of education, especially for women, undermine their desire to have children over time (Cygan-Rehm \u0026amp; Maeder, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In societies with higher education levels, the implied costs of childrearing are greater, leading parents to opt for fewer children, focusing on improving child quality (Doepke et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, our research demonstrates that higher employment levels can facilitate population growth. This finding aligns with the prevailing literature on population fertility. People's judgments of their futures affect their desire for fertility. Future economic uncertainty is an important factor that affects fertility desire (Lappeg\u0026aring;rd et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Negative economic scenarios significantly reduce fertility intentions, whereas positive scenarios enhance them (Lappeg\u0026aring;rd et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rising employment rates and better employment prospects can create a positive economic environment, thereby enhancing the willingness to have children and promoting population growth. Employment can promote occupational well-being (Ma \u0026amp; Ding, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The latter can increase the desire for fertility (Yang \u0026amp; Xie, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and promote population growth.\u003c/p\u003e"},{"header":"8. Conclusion, enlightenment, and outlook","content":"\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e8.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis study examined the impact of AI on population growth. The impact of AI on population growth was analyzed theoretically from two perspectives: direct impact and regulatory mechanisms. This was followed by empirical testing of the two-way fixed effects of year and city, utilizing a sample from Chinese cities spanning 2008 to 2020. It was found that AI can promote population growth in China. With the doubling of the AI development level, the population growth rate increases by 0.2357 percentage points. Furthermore, AI was found to mitigate the negative impact of education on population growth and enhance the positive influence of employment. Additionally, the influence of AI on population growth was observed to exhibit temporal heterogeneity, intensifying over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e8.2 Enlightenment\u003c/h2\u003e \u003cp\u003eOur findings have several theoretical and practical implications for future research. First, these findings offer new insights into promoting high-quality development in the Chinese economy. As the world's largest developing country, the high-quality growth of China's economy plays a crucial role in the welfare of the Chinese people and people worldwide. The population serves as a major driving force behind economic growth. This study has found that AI can promote population growth, which is conducive to China's economic growth. Therefore, Chinese cities should vigorously promote AI development. While promoting economic development with AI, they can also promote population growth and provide human resources for economic development. Second, these findings hold significance for developing countries striving for high-quality economic development. As global efforts to reduce carbon emissions continue, achieving high-quality economic development has become increasingly critical for developing countries. AI can be used in environmental governance (Zhang \u0026amp; Li, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to achieve low-carbon development. Simultaneously, AI can promote population growth, thereby providing human capital for economic development. Therefore, developing countries should actively promote AI development to achieve high-quality economic development. Third, these findings also carry implications for economic development in developed countries. Developed countries are experiencing a decline in population growth, and economic development is limited by human capital. This study found that AI can promote population growth. Thus, developed countries need not fear that AI-induced machine substitution will lead to lower birth rates; instead, they should vigorously promote the development of AI to enhance economic development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e8.3 Outlook\u003c/h2\u003e \u003cp\u003eThis study has examined the impact of AI on population growth. The following aspects are worthy of further study: The first is the transmission mechanism of AI affecting population growth. While we studied the moderating effects of education and employment levels, this study has not yet examined the transmission paths through which AI affects population growth. This is a limitation of this study and a direction for future research. The second is that country heterogeneity affects population growth. We used Chinese city data to study the impact of AI on population growth. Fertility is closely related to economic and social development (Myrskyl\u0026auml; et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Heterogeneity among countries may influence how AI impacts population growth. This is an additional direction for future research.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcemoglu \u0026amp; Daron \u0026amp; Restrepo \u0026amp; Pascual (2018). \u0026quot;The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment.\u0026quot; American Economic Review.\u003c/li\u003e\n\u003cli\u003eAcemoglu, D. \u0026amp; D. H. Autor \u0026amp; J. Hazell \u0026amp; P. 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Zhang (2018). \u0026quot;Threshold Effect and Empirical Test of Tourism on Poverty Alleviation--Based on Provincial Panel Data in Western China.\u0026quot; Finance \u0026amp; Trade Economics 39(05): 130-145.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"artificial intelligence, population growth, education, employment","lastPublishedDoi":"10.21203/rs.3.rs-6702628/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6702628/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArtificial intelligence (AI) significantly impacts all aspects of human life; promoting population growth is a global issue. How will AI affect population growth? This paper investigates the impact of AI on population growth using a city and year two-way fixed effects model, analyzing data from Chinese cities from 2008 to 2020. The results demonstrate that AI facilitates population growth in China. Specifically, a one-fold increase in AI development is associated with a 0.2357 percentage point increase in the population growth rate. Furthermore, AI is found to reduce the negative impact of education on population growth and enhance employment's positive impact on population growth. The influence of AI on population growth also shows temporal variation, with its impact increasing over time. This study offers novel insights into the socio-economic effects of AI, providing valuable references for policymakers seeking to harness AI to promote population growth.\u003c/p\u003e","manuscriptTitle":"Artificial intelligence and population growth: evidence from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 08:54:17","doi":"10.21203/rs.3.rs-6702628/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6101bdc-0284-4f44-8cb7-6b47c7e66563","owner":[],"postedDate":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-22T08:54:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-22 08:54:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6702628","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6702628","identity":"rs-6702628","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}