The Sinicization of Growth Pole Theory: Evidences from National New Areas in China's Mainland

The Sinicization of Growth Pole Theory: Evidences from National New Areas in China's Mainland | 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 The Sinicization of Growth Pole Theory: Evidences from National New Areas in China's Mainland Zhibao Wang, Yi Zheng, Lijie Wei, Ping Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9093969/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 As the important growth poles, national new areas are key to regional economy. In order to argue the Sinicization of growth pole theory, we investigate the radiative driving effects of China’s 19 national new areas on their hinterland by a multi-period double difference method with bidirectional fixed effects. Overall, China's national new areas have a positive radiative driving effect on their hinterland. In terms of marginal effects, general expenditure in Binhai New Area shows a cubic relationship with its hinterland, while which shows a cubic relationship with population scale, consumption capacity, or economic development in other national new areas. Population scale in most of national new areas has a positive marginal effect on economy in their hinterland. Through policies, innovation, and markets, these national new areas have produced significant positive spatial spillover effects and proximity effects on their hinterland. Growth pole theory The radiation driven effect The spatial spillover effects The multi-period double difference model National new area China's mainland Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction As comprehensive functional areas, national new areas undertake major strategic tasks in China. As mentioned in The Action Plan for Promoting High Quality Construction of National New Areas released in 2024, national new areas have played an important demonstration and driving role in gathering industrial clusters with strong competitiveness. Since Pudong New Area was established in 1992, 19 national new areas have been successively done in China. China's national new areas has gone through three stages, namely: the exploration period (1992–2009); the functional upgrading period (2010–2013); the intensive development period (2014-present). Except for Xiong'an New Area, GDP was about 5.5 trillion RMB 1 in other 18 national new areas in 2021, accounting for China’s 4.80%. We can see that national new areas have gradually become powerful growth engines driving regional development. So, we select them as samples to investigate their radiation effects. This helps to comprehensively measure their spillover effectiveness, and provides empirical support for regional policy adjustments. Meanwhile, this is also a theoretical reconstruction of growth pole theory, namely: Sinicization. The initial growth pole, namely: "developmental poles" (Perroux, 1955 ) came from the concept of "magnetic pole" in Physics. Boudeville's geographical interpretation first shifted the Perroux's concept of growth poles from abstract economic space to geographic space (Boudeville, 1961 ) with a particular emphasis on the special location of "city-periphery", and thus derived the basic idea of his regional growth pole strategy (Hermansen, 1969 ). However, Boudeville overly concretized and geographized economic space, transforming it into a general regional space, thus transforming it into what Perroux's called "mediocre" geographical space, ignoring the original abstraction and globalization of economic space (Hermansen, 1969 ). Myradal (1957) supplemented the operational mechanism of growth poles, also emphasized the role of government (Barber, 2008 ; Sheppard, 2017 ). This is the essence of Myrdal's growth pole theory (Friedmann, 1966 ; Myrdal, 1996 ). The "Geographical Dual Sector Model" (Lewis, 1954 ) pointed out the negative effects of growth poles, namely: the "backward effect", which compensated for another deficiency of Perroux's growth pole theory. And then, the "Geographical Dual Economic Structure" (Myradal, 1957) also pointed out their negative effects, namely: the "echo effect", which compensated for another deficiency of Perroux's growth pole theory (Plummer & Sheppard, 2006 ). Correspondingly to Myradal's "echo effect" and "diffusion effect", Hirschman ( 1958 ) referred to the favorable effects of growth poles (central cities) on their hinterland as the "trickle-down effect", while whose adverse effects were referred to as the "polarization effect" (Li & Pan, 2021 ). The growth pole has been proven to have a macro level impact on financial market (Aghion & Bolton, 1997 ), factor market (Beladi,1990), and capital market (Blackburn & Bose, 2003 ). However, accompanying this is constant questioning from society and academia about whether the "trickle-down effect" can be achieved (Richardson, 1997). Early research on growth poles mainly focused on qualitative analysis of their spatial and temporal distribution (Perroux, 1955 ; Boudeville, 1961 ), strategic positioning (Bere, 2014 ; Cheshmehzangi & Tang, 2022 ), policy characteristics (Lo & Salih, 1978 ; Friedmann, 1978 ), development models (Wojnicka-Sycz, 2013 ), development mechanisms (Cheshmehzangi &Tang, 2022 ), roles or functional configuration (Lo & Salih, 1978 ), and problems (Thomas, 1975 ) in target implementation during and after construction. The growth pole strategy has proved to be an important plan tool (Campbell, 1972 ; Thomas, 1975 ; Parr, 1999 ; Dranca, 2013 ; Wojnicka-Sycz, 2013 ; Guo, 2021 ). Specifically, previous studies have focused more on the spatial configuration (Lo & Salih, 1978 ) and the spillover effects (Parr, 1999 ; Wojnicka-Sycz, 2013 ; Cheshmehzangi & Tang, 2022 ), trickle down effects (Roberts, 1995 ), spread–backwash effects (Ke & Feser, 2010), multiplier and bolstering effects (Guo, 2021 ) of the planned poles based on the theory of spread and backwash (Richardson, 1976 ), as a regional development factor (Wojnicka-Sycz, 2013 ). Case studies at the macro level include the EU (Dranca, 2013 ; Kotlebová & Širaňová, 2014; Matzana et al., 2022 ), growth poles of the global economy (Popkova, 2020 ), China’s city clusters (Cheshmehzangi & Tang, 2022 ), China's strategy of Central Rise (Ke & Feser, 2010), and do Detroit (Tobler, 1970 ). Meanwhile, case studies at the micro level include Carajás's satellite boom town (Roberts, 1995 ), accelerated industrialization in major Southeast Asian countries (Lo & Salih, 1978 ), the Snow White gas field (Eikeland, 2014 ), inter-community development associations (IDA) in Romania (Bere, 2014 ), Siracusa (Graziano et al., 2023), Shanghai (Dobrescu & Dobre, 2015 ), Guizhou (Jiao et al., 2023 ), etc. Additionally, Adekunle Ajasin University (Eludoyin, 2010 ) and Delta State University (Ojeh & Origho, 2012 ) are special growth poles. The growth pole theory was introduced to China in the 1980s. China’s scholars have proposed the "Gradient Transfer Theory" (Xia et al., 1983 ) and the "Point-Axis Theory" (Lu, 1989 ). These theories are the Sinicization of growth pole theory, which have promoted the adjustment of China's regional development strategy from balanced development to unbalanced development. Chinese studies have confirmed that national new areas can significantly drive economic growth in their hinterland (Feng & Wang, 2021 ), whose driving effects are sustainable. Additionally, national new areas can enhance urban ecological (Wang et al., 2022 ), energy and environmental efficiency (Wu, 2023 ). In summary, present empirical studies have made beneficial explorations on various growth poles, while there are also some limitations. Firstly, most studies focus on analyzing the effects of growth poles on their hinterland, while whose measurement dimension is relatively single and cannot comprehensively examine their utility. Secondly, existing studies have only focused on whether growth poles have a promoting effect on their hinterland, with little attention paid to the relative degree or the measurement of their effects. Thirdly, the data used is mainly at urban level. However, new growth poles are often established across administrative regions. Therefore, it is difficult to accurately measure the effects of growth poles, based on city data with short time spans. Thus, we comprehensively investigate the overall effects of 19 national new areas on their hinterland during 2010–2022 by a multi-period double difference method with bidirectional fixed effects, and evaluate the marginal effects, spatial spillover effects, and proximity effects between each new area and its hinterland by linkage effects. Through the analysis of their spillover effectiveness, our findings contribute to a deeper understanding of the actual situation in China's national new areas, provide reference for economic growth in other regions, and can further promote the growth pole theory. The rest of our paper is organized as follows. We describe method and data in Section 2 . In Section 3 , we investigate and discuss the overall effects of 19 national new areas on their hinterland. And then, we evaluate the Sinicization of growth pole theory in Section 4 . Finally, Section 5 describes our main conclusions, policy implications and prospects. 2. Methodology and Data 2.1. Main methods Currently, most existing studies regard national new areas as a location-oriented policy (Wang et al., 2022 ; Liu et al, 2023 ), and choose a double difference model to evaluate their own economic status or driving effects on their hinterland. However, due to some limits (e.g. omitted variables, sample selection bias, and measurement errors), the double difference model cannot fully handle endogeneity. Additionally, there are temporal heterogeneities in the establishment of national new areas. And then, there is no unified policy implementation node. So, we measure the spillover effects and radiative driving effects of national new areas on their hinterland by a multi-period double difference model with bidirectional fixed effects. The specific model is as follows: Y it = β 0 + β 1 did it + β 2 control it + ε it (1) Where, Y it is the economic effectiveness in each new area, reflecting GDP in new area i in year t . did it is an interactive item. If new area i is established in year t , then did it =1 starting from that year; otherwise, did it =0. did it = treated × time . Among them, treated is an individual dummy variable, and time is a time dummy variable. If new area i is established, it belongs to the processing group, treated = 1; otherwise, treated = 0. control it is the control variable. ε it is the error term. β 0 is a constant term. β 1 , β 2 are the coefficients. β 1 represents the net policy effect of national new areas. β 1 > 0 indicates that national new areas can generate positive spillover effects; otherwise, they generate negative spillover effects. We conduct regression analysis on the relationship between growth poles and their hinterland by SPSS28.0. The specific multiple linear regression model is as follows: E i = α 0 + α 1 X i 1 + α 2 X i 2 + α 3 X i 3 + α 4 X i 4 + α 5 X i 5 + u i (2) Where, E i represents the sum of GDP in new area i and its hinterland. { X i 1 ,…, X i 5 } represents GDP, general public budget revenue, total retail sales of consumer goods in the whole society, total trade import and export volume, and permanent population in national new area i , respectively. α 0 is a constant term. { α 1 ,…, α 5 } is the regression coefficient. u i is the control variable. In order to measure the radiating effect of national new areas on their hinterland, we analyze their external functions based on the market principles of the Central Location Theory. We use Δ c to determine whether the national new area has been the ideal growth pole. The specific formulas are as follows: Δ c i =| c i - c 0 | (3) Δ c i '=| c i '- c 0 | (4) Where, c 0 is the ratio of the area of a regular hexagon to its circumference. We assume that the side length of a regular hexagon is 1km. So, c 0 is a constant, namely: c 0 = 6. c i is the ratio of the area of each national new area to its circumference before deformation, while c i ' is that after deformation. i ∈[1,18]. Δ c 's ideal value is 6. The larger or smaller Δ c , the more it indicates that the external functions of the new area have not achieved the expected results. 2.2. Study area China’s 19 national new areas are mainly concentrated in the eastern and central regions of China (Fig. 1 , Appendix 1a). In 2023, at the cost of about 0.2% of China's population and about 5.4% of China's area, GDP reached about 6.2 trillion RMB in national new areas, creating China’s economic output of 5.0%. From the perspective of regional functional positioning, prime mover industry is high-tech industry in national new areas, mainly including high-end service industry, strategic emerging industry, high-end manufacturing industry, etc. For example, the total output of strategic emerging industries accounts for over 50.8% of the total industrial output in Xiangjiang New Area. Two "200 billion RMB" pillar industries have been formed in Liangjiang New Area, including automobile and electronic information, where a total of 139 aerospace industry chain enterprises have also been introduced, with an industrial scale of 3.5 billion RMB. Since 2018, the average annual growth rate of strategic emerging industries has reached 16.3% in Xixian New Area (Appendix 1b). 2.3. Data We consider the establishment of national new areas as a quasi-natural experiment, with them as the experimental group (data from the main districts or counties where them are located before their establishment) and the main cities to which them belong as the control group. Considering the availability of data, we select a research period of 2010–2022. We designate the hinterland of Pudong New Area and Zhoushan Archipelago New Area as the Yangtze River Delta Urban Agglomeration. The hinterland of Binhai New Area is designated as Beijing-Tianjin-Hebei urban agglomeration, while which of Liangjiang New Area is designated as Chengdu-Chongqing urban agglomeration (Fig. 1 ). Additionally, due to the late establishment of Xiong'an New Area, its data is difficult to obtain. So, we only conduct qualitative analysis on it. GDP, general public budget revenue, total retail sales of consumer goods in the whole society, total trade import and export volume, and permanent population in national new area come from statistical yearbooks, national socioeconomic development statistical bulletins (Table 1 ). Partial data in new areas is supplemented from statistical yearbooks in provinces or cities they belong to. Missing data is obtained by averaging and interpolation methods. In order to eliminate the impact of data dimensionality, we standardize them. Table 1 Descriptive statistics of main indicators. Variables Indicators (Unit) Code N Mean SD Min Max Economic development GDP in national new area (10 8 CNY) GDP 1 187 2220.516 2533.481 18.980 13207.030 GDP in the hinterland (10 8 CNY) GDP 2 295 8886.075 19812.430 232.920 320735.000 Total GDP (10 8 CNY) GDP 3 196 60194.185 61320.679 4626.040 290288.800 Population scale Permanent population (10 4 persons) POP 627 2453.583 4733.812 25.990 29547.730 Consumption ability Total retail sales of consumer goods in the whole society (10 8 CNY) TTR 566 10010.995 17622.890 0.058 111463.640 General expenses General public budget revenue (10 8 CNY) GGR 565 12992.448 57173.879 9.625 493904.922 Foreign trade Total trade import and export volume (10 8 USD) TE 517 4863.650 7913.419 0.306 87463.100 Notes : According to the midpoint of the exchange rate between the Chinese yuan and the US dollar on January 18, 2024, namely: 1 USD = 7.1174 CNY. 3. Results and Discussion GDP continues to grow in Pudong New Area and Binhai New Area (Fig. 2 a), with a particularly significant increase in their economic speed. One main reason is that them was established earlier, especially the policy incentives undertaken by the earliest established Pudong New Area, which are not available in later new areas. Pudong New Area was established in the early stages of China's socialist market economy system construction (1992) and before joining the WTO (2001). When it was established, Pudong New Area was endowed with ten preferential policies of the National Economic and Technological Development Zone, nine preferential policies of the Special Economic Zone, and five unique functional policies. 2 Then, there were significant differences in the institutional environment compared to later new areas. GDP also shows an overall upward trend in other new areas (Fig. 2 b-d). This to some extent contributes to our study and lays the foundation for further testing their radiative effects, in order to verify the Sinicization of growth pole theory. 3.1. Regression results of benchmark model The results of the multi-period double difference model with bidirectional fixed effects are divided into three columns (Table 2 ). Among them, in the first column, we control for individual fixed effects. The results show that the dummy variable did 's coefficient β 1 =-0.241 and | β 1 |<0.330 (standard error) with an intercept term of 1.541. This indicates that the effect of growth poles on their hinterland is negative, but β 1 is not significant. So, the individual fixed effects model may not fully explain the construction effectiveness of growth poles. In the second column, we control for time fixed effects. The results show that β 1 = 0.320 and | β 1 |>0.165 (standard error) with an intercept term of 2.137. The significance of β 1 has been improved. This indicates that the fixed effects model better captures the construction effectiveness of growth poles. However, further exploration of other factors is still needed. In the third column, we simultaneously control for both individual and time fixed effects. The results show that β 1 = 0.374 and | β 1 |>0.163 (standard error) with an intercept term of 2.065. β 1 is significant in the bidirectional fixed effects model. This indicates that the bidirectional fixed effects model is more suitable, which can comprehensively evaluate the radiation driven effects of growth poles. Overall, the results may be closer to the actual situation in the third column. Because it takes into account individual and time dimensions more comprehensively, whose β 1 is more significant. Table 2 Benchmark regression results. Variables Model(1) Model(2) Model(3) did -0.241 0.320* 0.374** (0.330) (0.165) (0.163) POP 0.748** 0.369*** 0.298*** (0.335) (0.103) (0.099) TTR 0.010 0.073** 0.086** (0.339) (0.034) (0.034) GGR 0.536*** 0.443*** 0.618*** (0.931) (0.063) (0.055) TE 0.076** 0.074*** 1.37e-06 (0.032) (0.024) (3.81e-06) Individual fixed effect YES NO YES Time-fixed effect NO YES YES Constant 1.541 2.137*** 2.065*** (1.410) (0.490) (0.467) Notes : Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1. 3.2. Parallel trend test To evaluate the implementation of policy effectiveness by the double difference method, it is necessary to first satisfy the common trend assumption, namely: when there are no policy effects in national new areas, where economic trends of the experimental group and the control group show consistency. So, before conducting model regression, we conduct a parallel trend test (Fig. 3 ). Due to the limited data, we summarize the data from the 4 years before policy implementation in the pre_4th period and the 7 years after policy implementation in the post_7th period. Additionally, we select the pre_4th period as the base period. β 2 is not significant in each period before policy implementation. This indicates that there is no significant difference between the treatment group and the control group before policy implementation, and our samples passed the parallel trend test. Although β 2 is not significant in the 4 years after policy implementation. However, this does not affect the test results. This is due to the lag in policy effects, as the policies may not immediately take effect. The policy effects are cumulative. As time goes by, the policy effects gradually become apparent. Meanwhile, the policy effects are also influenced by factors such as regional or industrial heterogeneities. Additionally, in our study, there may be fluctuations and noise in the data. This may result in less significant policy effects in certain periods. With the increase of data and extension of period, these fluctuations and noise are gradually eliminated, and the policy effects are evident. Notes Solid dots represent regression coefficients. The short vertical line represents the upper and lower 95% confidence interval corresponding to the standard error. 3.3. Placebo tests The regression results of the benchmark model mentioned earlier indicate that as growth poles, national new areas have a positive spillover effect on their hinterland, which can also be understood as a positive correlation between the two. However, this conclusion may be influenced by other random factors. To further eliminate the influence of other non-observed omitted variables on the regression results, we conduct placebo tests on the randomized treatment group and the control group (Fig. 4 ). We randomly select one group of subjects from the sample area as the treatment group and another group of subjects as the control group. If national new areas have been established in n regions in a certain year, and when is fixed, we will randomly select n regions from the regions without national new areas in that year or before as the new processing group. And then, we reestimate the model (3) (Table 2 ) by new samples. So far, we have completed the first placebo test. After repeating the above process 500 times, β 0 (500 estimated coefficient values of did ) is obtained. By plotting the nuclear density distribution and p-value, β 0 ≈ 0 in most cases (Fig. 4 ), and follows a normal distribution. This indicates that most regression results are not significant. β 0 is located at the high tail of the false regression coefficient distribution in benchmark regression. In individual placebo tests, this is a low probability event. So, we can exclude the possibility that the baseline estimation results may be affected by unobservable factors. 4. The Sinicization of Growth Pole Theory The above empirical results indicate that there is an overall positive spillover relationship between national new areas and their hinterland. And then, we conduct multiple regression analysis to further investigate their quantitative relationship (Table 3 ) by SPSS 28.0. Among them, we lag the independent variables in Lanzhou New Area, Nansha New Area, Gui'an New Area, Xixian New Area, West Coast New Area, and Jinpu New Area by one period, do them in Binhai New Area and Tianfu New Area by two periods, and do them in Zhoushan Archipelago New Area by five periods. Their results are significant. Table 3 Multiple linear regression results. Hinterland Growth pole GGR TTR TE POP Constant Adjusted R 2 Hinterland range National new areas -0.025*** -0.001 0.891*** 0.031*** 0.088*** 1.108 0.986 Yangtze River Delta Urban Agglomeration Pudong New Area 0.334* 0.158* 0.744** 0.000 -6.818* 67.340 0.998 Beijing-Tianjin-Hebei Urban Agglomeration Binhai New Area -0.299*** 0.936*** 0.007 0.166** -0.223 5.664 0.993 Chengdu-Chongqing Urban Agglomeration Liangjiang New Area -0.009** -0.490** 0.306** 0.237*** 19.698*** -174.438 0.996 Yangtze River Delta Urban Agglomeration Zhoushan Archipelago New Area 0.093* -0.571 1.185*** 0.024 -0.528 8.886 0.996 Gansu Province Lanzhou New Area -0.260** -1.412** 2.822* 0.229 -3.363 21.702 0.989 Guangdong Province Nansha New Area 0.619*** -0.138 0.408* 0.202** -0.181* 3.708 0.998 Shaanxi Province Xixian New Area 0.347* -0.541 0.884 0.854 -22.839 184.177 0.991 Guizhou Province Gui'an New Area -0.012* -0.055 0.018 0.082** 8.512** -66.005 1 Shandong Province West Coast New Area 0.309** -0.146 0.822** -0.022 -2.669* 27.639 1 Liaoning Province Jinpu New Area 0.207** 0.072* 0.576*** 0.046 -5.312*** 46.678 0.999 Sichuan Province Tianfu New Area 0.061*** -0.113 0.522** 0.181*** 7.117** -59.294 0.999 Hunan Province Xiangjiang New Area 0.363 -0.020 -0.463 0.353 8.205 -63.680 0.990 Jiangsu Province Jiangbei New Area 0.293** -0.342 0.276 0.270** 2.995 -19.940 0.999 Fujian Province Fuzhou New Area -0.009 0.290 0.408 0.082 5.446 -41.755 1 Yunnan Province Dianzhong New Area 1.184* 0.204 0.292* 0.062 5.415* -48.322 1 Heilongjiang Province Harbin New Area -0.012 0.244 0.129 -0.017 -1.826 21.603 0.932 Jilin Province Changchun New Area 0.044*** -0.886*** -0.191** 0.582*** -0.975* 20.160 1 Jiangxi Province Ganjiang New Area 0.503 0.970 0.068 0.002 -8.113 66.782 0.993 Note : *** p < 0.01, ** p < 0.05, * p < 0.1. We can explain the results from three aspects. First is the time lag effect . As growth poles, the radiation driven effects of national new areas obviously delay. This delay effect may be due to the implementation of policies (Richardson, 1976 ), investments (Dranca, 2013 ), or other measures taking some time. Next is the cumulative effect . This means that new areas will ultimately have a significant effect after a lag period, rather than immediately appearing, such as the spread effects of GDP and employment growths (Ke & Feser, 2010). The last is regional interaction , including cooperation in resource sharing, policy coordination, and industrial chain connection (Campbell, 1972 ; Guo, 2021 ). Interacting with other factors, these new areas can still significantly affect their hinterland. Some studies have confirmed that national new areas can fully leverage the effects of growth poles and boom their hinterland (Feng & Wang, 2021 ). Therefore, we focus on analyzing the negative correlation between new areas and their hinterland. There are several reasons. First is the polarization effect . New areas can usually quickly gather many resources, including funds, talents, and technology (Popkova, 2020 ), while which are the opposite to their hinterland, leading to uneven regional resource allocation (Graziano et al., 2023). Resource loss, economic structure adjustment (Kotlebová & Širaňová, 2014; Popkova, 2020 ), and market are important to Fuzhou New Area, Harbin New Area, and Changchun New Area. Next is the external dependence . New areas are usually more open, attracting more foreign investment (Roberts, 1995 ; Dranca, 2013 ; Bere, 2014 ) and interregional trade (Dobrescu & Dobre, 2015 ), while the relatively low openness limits the expansion of resources and markets in whose hinterland. Market factors such as demand (Roberts, 1995 ) and competition are important to the relationship between new areas and their hinterland. Additionally, the transportation network (Cheshmehzangi & Tang, 2022 ) and infrastructure are generally more complete in new areas, facilitating the flow of resources and information, while the opposite is true in whose hinterland. Some new areas are currently going on economic restructure, where GDP growth will be affected by adjustments in some pillar industries (Thomas, 1975 ), thereby affecting whole regional economy (Kotlebová & Širaňová, 2014). The last is the scale effect . The scale effect is not significant in these newly established new areas. Because these new areas are relatively small in scale, which only have limited effects on the entire region. Another important reason is the heterogeneity in their leading industries (Guo, 2021 ), such as oil industry (Eikeland, 2014 ) and tourism (Matzana et al., 2022 ; Graziano, et al., 2023), etc. The growth pole effects will not spread within the overall region. Due to late establishment and extremely difficult data acquisition, we don't conduct relevant empirical analysis on Xiong'an New Area. However, based on the results of the multi-period double difference regression with bidirectional fixed effects, we speculate that Xiong'an New Area is likely to have a positive effect on its hinterland in the future. From the regression results of the benchmark model, we can see that as growth poles, there is an overall positive spillover relationship between national new areas and their hinterland. In order to investigate the specific relationship, we further measure their linkage effects, including the marginal effects, spatial spillover effects, and proximity effects. 4.1. The marginal effects The results show that there is indeed a significant positive correlation between most new areas and their hinterland, while there is also a significant negative correlation or a correlation but not significant relationship (Table 4 ). Except for Zhoushan Archipelago New Area, Nansha New Area, and Binhai New Area, the dominant factor is population scale in other new areas. Table 4 The quantitative relationship between growth poles and their hinterland. Relationship Growth poles Regression models Dominant factors Total New areas E = e 1.229 ( TTR 0.891 TE 0.031 POP 0.088 )/( GDP 0.025 GGR 0.001 ) TTR Significant positive correlation Pudong New Area E = e 103.76 ( GDP 0.334 GGR 0.158 TTR 0.744 )/ POP 6.818 POP Zhoushan Archipelago New Area E = e 65.511 ( GDP 0.093 TTR 1.185 TE 0.024 )/( GGR 0.571 POP 0.528 ) TTR Nansha New Area E = e 4.702 ( GDP 0.619 TTR 0.408 TE 0.202 )/( GGR 0.138 POP 0.181 ) GDP Xixian New Area E = e 278.331 ( GDP 0.347 TTR 0.884 TE 0.854 )/( GGR 0.541 POP 22.839 ) POP West Coast New Area E = e 29.898 ( GDP 0.309 TTR 0.822 )/( GGR 0.146 TE 0.022 POP 2.669 ) POP Jinpu New Area E = e 50.964 ( GDP 0.207 GGR 0.072 TTR 0.576 TE 0.046 )/ POP 5.312 POP Tianfu New Area E = e −37.37 ( GDP 0.061 TTR 0.522 TE 0.181 POP 7.117 )/ GGR 0.113 POP Jiangbei New Area E = e 16.351 ( GDP 0.293 TTR 0.276 TE 0.270 POP 2.995 )/ GGR 0.342 POP Dianzhong New Area E = e −42.408 TTR 0.292 TE 0.062 POP 5.415 GDP 1.184 GGR 0.204 POP Changchun New Area E = e 20.724 ( GDP 0.044 TE 0.582 )/( GGR 0.886 TTR 0.191 POP 0.975 ) POP Significant negative correlation Bianhai New Area E = e 7.694 ( GGR 0.936 TTR 0.007 TE 0.166 )/( GDP 0.299 POP 0.223 ) GGR Liangjiang New Area E = e −156.094 ( TTR 0.306 TE 0.237 POP 19.698 )/( GDP 0.009 GGR 0.490 ) POP Lanzhou New Area E = e 136.77 ( TTR 2.822 TE 0.229 )/( GDP 0.260 GGR 1.412 POP 3.363 ) POP Gui'an New Area E = e −62.986 ( TTR 0.018 TE 0.082 POP 8.512 )/( GDP 0.012 GGR 0.055 ) POP Not significantly positively correlated Xiangjiang New Area E = e −12.752 ( GDP 0.363 TE 0.353 POP 8.205 )/( GGR 0.020 TTR 0.463 ) POP Ganjiang New Area E = e 135.748 ( GDP 0.503 GGR 0.970 TTR 0.068 TE 0.002 )/ POP 8.113 POP Not significantly negatively correlated Fuzhou New Area E = e −34.704 ( GGR 0.290 TTR 0.408 TE 0.082 POP 5.446 )/ GDP 0.009 POP Harbin New Area E = e 27.578 ( GGR 0.244 TTR 0.129 )/( GDP 0.012 TE 0.017 POP 1.826 ) POP And then, we fit their hinterland and dominant factors in 15 new areas. There is a cubic relationship between the hinterland of Binhai New Area and its dominant factors, while the hinterland of other new areas shows a cubic relationship with their dominant factors (Fig. 5 a). Among them, for Lanzhou New Area, Jinpu New Area, Harbin New Area, and Changchun New Area (Fig. 5 b), there is a negative correlation between the dominant factors and hinterland, namely: the larger the population scale, the more the regional economy shows reverse growth. This indicates that population scale has a negative marginal effect on its hinterland, except for Zhoushan Archipelago New Area, Nansha New Area, and Binhai New Area. Notes x is the dominant factor. Among them, x Zhoushan Archipelago New Area is total retail sales of consumer goods in the whole society, x Nansha New Area is GDP, x Binhai New Area is the general public budget revenue, and x the remaining new areas is permanent population. 4.2. The spatial spillover effects and proximity effects Based on the regression results (Appendix 2) between new areas and their hinterland, we classify their spatial spillover or proximity effects into four categories, namely: α < 0; 0 ≤ α < 0.50; 0.50 ≤ α < 1.00, and α ≥ 1.00. Among them, α < 0 indicates that the new area has a negative spatial spillover effect on the city, while other three categories are opposite and its intensity continues to increase. There are negative spatial spillover effects or proximity effects between Gui'an New Area and its hinterland (Fig. 6 a). We summarize the reasons for this phenomenon as resource competition, talent loss, and uneven development. Except for Gui'an New Area, there is a negative spillover relationship between Jinpu New Area, West Coast New Area, Harbin New Area, and Changchun New Area and their hinterland (Fig. 6 b-e). The remaining new areas exhibit positive spatial spillover effects on their hinterland (Fig. 6 f-r). As regional growth poles, these new areas have attracted a large amount of capital (Dranca, 2013 ; Bere, 2014 ), technology (Popkova, 2020 ), and talent through their strong agglomeration effects, forming industrial agglomeration (Dobrescu & Dobre, 2015 ). This agglomeration not only enhances the competitiveness in new areas, but also transmits industry, technology, and management experience to their hinterland through diffusion effects, upgrading regional industrial structure. Meanwhile, the infrastructure construction (Wu et al., 2023 ) and public services (Bere, 2014 ) in new areas benefit their hinterland through radiation effects, improving their regional allocative efficiency. Benefited from the support of national policies, new areas enjoy tax incentives and other policies, which have also had a positive effect on their hinterland. The innovation driven and coordinated development strategy in new areas, as well as the expansion of market demand, has provided new opportunities for enterprises in their hinterland. Additionally, regional talent exchange and industrial chain extension have further promoted their hinterland. So, through agglomeration, diffusion, radiation effects, as well as policies, innovation, markets, and others, these national new areas have produced significant positive spatial spillover effects and proximity effects on their hinterland. And then, we transform the real “geographic space” into an ideal “economic space” by spatial friction, in order to realize the assumption of homogeneous plain and further refine the broader applicability of growth theory. Considering distance attenuation, which is very realistic, the straight-line distance ( l ) between the two is used to reflect spatial friction (1/ l ). The new spatial spillover effect ( α ') of national new areas is as follows: α '= α / l (5) According to geographical position and radiation effects, these new areas is very in line with the growth pole theory and serves their hinterland well, such as Gui'an New Area, Lanzhou New Area, Harbin New Area, and Liangjiang New Area (Fig. 6 a,d,h,j,l). Meanwhile, the original core cities have affected the radiation effects of new areas, such as West Coast New Area, Pudong New Area, and Binhai New Area (Fig. 6 c,f,g). Through the distorted map of α and α ' (Fig. 6 ), “geographic space” has once again been corrected, which better meets the basic requirements for cultivating growth poles. The mutual interference makes it difficult for us to accurately calculate the diffusion effect of the new area on its hinterland. As we know, as the original and higher-level growth poles, the original core cities (e.g. provincial capital cities) weaken the regional driving effect of secondary growth poles such as national new areas. This is not only an important manifestation of the Sinicization of growth pole theory, but also a new research direction for us in the future. Additionally, according to the external functions of national new areas (Table 5 ), we find that the external functions of Changchun New Area, Gui'an New Area, Lanzhou New Area, Harbin New Area, Xixian New Area and Dianzhong New Area are poor, which are all in the early construction stage. Meanwhile, Pudong New Area, Jiangbei New Area and West Coast New Area are the ideal growth poles. In short, the early construction stage of the new areas has a significant effect on the relationship between the two. Of course, the existing higher-level growth poles (core cities) also affect their relationship. Table 5 The external functions of national new areas. Growth poles Hinterland ∆ c ∆ c ' Harbin New Area Heilongjiang Province 16.747 19.71 Xixian New Area Shaanxi Province 14.187 15.611 Dianzhong New Area Yunnan Province 13.992 13.677 Ganjiang New Area Jiangxi Province 11.383 11.008 Xiangjiang New Area Hunan Province 9.434 10.722 Pudong New Area Yangtze River Delta Urban Agglomeration 6.409 9.071 Jiangbei New Area Jiangsu Province 6.491 8.676 Tianfu New Area Sichuan Province 15.089 8.579 Nansha New Area Guangdong Province 5.473 6.092 Fuzhou New Area Fujian Province 7.8 5.757 Liangjiang New Area Chengdu-Chongqing Urban Agglomeration 15.497 5.637 West Coast New Area Shandong Province 6.321 5.167 Binhai New Area Beijing-Tianjin-Hebei Urban Agglomeration 11.154 4.681 Zhoushan Archipelago New Area Yangtze River Delta Urban Agglomeration 6.409 4.194 Jinpu New Area Liaoning Province 6.936 3.462 Lanzhou New Area Gansu Province 19.475 0.613 Gui'an New Area Guizhou Province 10.708 0.417 Changchun New Area Jilin Province 12.838 0.236 5. Conclusions and Prospects 5.1. Main conclusions Through empirical analysis by a multi-period double difference model with bidirectional fixed effects, we conclude that there is an overall positive spillover relationship between national new areas and their hinterland. Subsequently, their specific relationship is measured for linkage effects from the perspectives of marginal effects, spatial spillover effects, and proximity effects. We obtain the following main conclusions. (1) In terms of marginal effects, the hinterland of Binhai New Area shows a cubic relationship with its dominant factors, while which of others does a cubic relationship. Among them, for Lanzhou New Area, Jinpu New Area, Harbin New Area, and Changchun New Area, there is a negative correlation, namely: the larger the population scale, the more the regional economy shows reverse growth. This indicates that population scale has a negative marginal effect on their hinterland. However, for others, the opposite is true. (2) In terms of spatial spillover effects and proximity effects, there is a negative spatial spillover effect or proximity effect between Gui'an New Area and its hinterland. This phenomenon can be attributed to resource competition, talent loss, and uneven development. Meanwhile, there is a negative spillover relationship between other new areas and some areas in their hinterland, such as Jinpu New Area, etc. However, the remaining new areas exhibit a positive spatial spillover effect on their hinterland. Overall, new aeras not only have a significant promoting effect on themself, but also have a profound impact on their hinterland. Through positive spatial spillover effects, new areas can promote their hinterland and whole regional economy. 5.2. Policy implications Based on the above analysis, we propose some policy inspirations. (1) For national new areas with a significant positive correlation with their hinterland ① While continuing to maintain their growth pole position, infrastructure connectivity should be enhanced in Pudong New Area and Jiangbei New Area, to promote regional development by strengthening regional coordination, industry-city integration, institutional innovation, attracting investment (Dranca, 2013 ; Bere, 2014 ), developing high-tech and modern service industries (Bere, 2014 ). ② Regional cooperation should be further strengthened in Zhoushan Archipelago New Area and Nansha New Area, to develop smart islands, logistics and tourism information service platforms (Matzana et al., 2022 ; Graziano et al., 2023), strengthen port logistics and trade, create international commodity distribution centers by integrated infrastructure construction (Wu et al., 2023 ), and share the benefits of marine economy (Balaji, 2024 ) with their hinterland by implementing a strategy driven by marine technology innovation. ③ Regional coordination should be deepened in Xixian New Area to achieve effective integration of resources and markets, and significantly promote its hinterland by utilizing its geographical advantages, strengthening connections (Campbell, 1972 ; Cheshmehzangi & Tang, 2022 ) and industrial complementarity with core cities such as Xi'an and Xianyang. ④ While strengthening the coordination of its functional areas and promoting equal development (Chao & Lin, 2020 ), technological innovation should also been done in West Coast New Area, to develop some industries such as marine and high-end manufacturing economy, enhance its exchanges and cooperation with countries such as Japan and South Korea, and create an international innovation economy leading zone with its advantages in free trade zones. ⑤ Jinpu New Area and Changchun New Area should fully leverage their core location advantages in the Northeast Asian Economic Circle, maintain strategic intervention capabilities (Wu & Zhang, 2022 ), increase external trade and FDI, promote industrial structure optimization and upgrading to achieve regional sustainable development. ⑥ As the provincial growth poles, high-tech industries should be vigorously developed in Tianfu New Area and Dianzhong New Area, to enhance their innovation capabilities (Thomas, 1975 ; Wojnicka-Sycz, 2013 ), while where logistics and transportation hubs should be developed to optimize regional transportation network (Cheshmehzangi & Tang, 2022 ) by their location advantages. (2) For national new areas with a significant negative correlation with their hinterland ① As an engine and a free trade experimental carrier, industrial structure (Thomas, 1975 ) in Binhai New Area should be continuously adjusted to achieve the reorganization of resources, better embed into regional economic networks by strengthening its connections (Matzana et al., 2022 ) with other cities and infrastructure construction (Wu et al., 2023 ), increasing innovation efforts, optimizing regional industrial layout, sharing talents and resources. ② Technological innovation (Wojnicka-Sycz, 2013 ) should be promoted in Liangjiang New Area, to cultivate endogenous driving forces, strengthen exchanges, and establish a win-win cooperation mechanism. ③ The connections (Matzana et al., 2022 ) with its hinterland should be strengthened to improve its economic spillover effects (Parr, 1999 ) in Lanzhou New Area through reasonable planning by relying on local characteristic industries, enhancing industrial synergy, and promoting rational resource flow (Wu et al., 2023 ). ④ Emphasizing the coordination and complementarity of policies, the "Two Cities and One District" model should continue to be developed in Gui'an New Area, to strengthen infrastructure connectivity, promote networked governance, and accelerate the realization of regional coordinated coexistence and coordinated development by transferring some industries and employment opportunities (Ke & Feser, 2010), implementing a balanced development strategy (Guo, 2021 ). (3) For national new areas with insignificant positive correlation with their hinterland ① Industrial clusters with core competitiveness should be cultivated and strengthened in Xiangjiang New Area, to drive the industrial synchronous development in its hinterland by promoting the optimization and upgrading of industrial structure (Thomas,1972). ② By developing industries with local characteristics and gathering more resources and investment (Dranca, 2013 ; Bere, 2014 ), unique economic advantages can be formed in Ganjiang New Area to promote balanced regional development. (4) For national new areas with insignificant negative correlation with their hinterland ① Unique advantages should be fully utilized in Fuzhou New Area to create a cutting-edge platform for communication (Popkova, 2020 ; Cheshmehzangi & Tang, 2022 ) with its hinterland, by strengthening deep cooperation in economic, social, cultural and other fields, promoting industrial coordination and sharing resource. ② The international logistics channels should be accelerated in Harbin New Area to create high-end services and factor aggregation platforms, by optimizing its radiation policies, encouraging industrial transfer and cooperation, and enhancing regional synergy and local self-development capabilities. 5.3. Research prospects Through empirical analysis and theoretical exploration, our study provides a new perspective for understanding and optimizing the relationship between growth poles and their hinterland, and achieves the Sinicization of growth pole theory. Meanwhile, our findings can provide empirical evidence for policymakers to design more targeted regional policies. Furthermore, our theoretical model and policy implications can help entrepreneurs or investors find more favorable opportunities. However, there also are some limitations. Firstly, due to the obtaining difficulties and limitations of data, some indicators are unable to be utilized, which may affect the generality of our results. Secondly, because of various dynamic factors, such as policy heterogeneities and market changes, the relationship between national new areas and their hinterland cannot fully be reflected. Finally, the interference of regional preexisting core cities on the radiative driving effects of national new areas is currently beyond our control. Future research will strive to more reasonably reveal the dynamic evolution of their relationship by expanding the scope of data and deepening theoretical models. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9093969","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607393718,"identity":"2cc32f61-a704-4637-84e3-59fa8169e858","order_by":0,"name":"Zhibao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIie3RvQrCMBDA8YNAXE6yNlR8hkAhU8FXaZduQsGlQwdFqYPoXN9CN0clEJeIq5uKq4uLOIkfq9LWzSE/Mt6fcByAZf0j8n6ADFa9Q5Ck1ZMG7yklDkZX/gh8oXTEjwNSPi7W5HiKUx95ZmQSdimw4SgoTHifel6uI2Ro5C5cNMAxm1lhwghIF6lCnr8SQ0E47eKEktrVxbtCsT/LOMxIecIISreePZOljqBSwvvY8aaTCHlXKScwGkt3Edv1/BRf/dbrlJdbkjbZcFycfMDfxi3LsqyvHqpfRqFs11FLAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Normal University","correspondingAuthor":true,"prefix":"","firstName":"Zhibao","middleName":"","lastName":"Wang","suffix":""},{"id":607393719,"identity":"da61cd91-099f-4e12-a9d2-753fcf5eeb85","order_by":1,"name":"Yi Zheng","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zheng","suffix":""},{"id":607393720,"identity":"383f0252-62a2-471d-ba99-68cdf913ae70","order_by":2,"name":"Lijie Wei","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lijie","middleName":"","lastName":"Wei","suffix":""},{"id":607393721,"identity":"5daa3141-e0ac-411e-8133-6a7405333906","order_by":3,"name":"Ping Wang","email":"","orcid":"","institution":"Shandong Province Yantai No. 11 Middle School","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-11 12:29:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9093969/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9093969/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104880856,"identity":"bb9218a7-a752-4fac-b89d-606ad698b114","added_by":"auto","created_at":"2026-03-18 09:13:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":650968,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution of China’s 19 national new areas.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9093969/v1/95e1bd549da0901808da606d.png"},{"id":104880857,"identity":"ad19f9e2-d7b3-44da-b0d0-f5a3bf86b649","added_by":"auto","created_at":"2026-03-18 09:14:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":219625,"visible":true,"origin":"","legend":"\u003cp\u003eGDP in China’s 18 national new areas.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9093969/v1/013d0182868b558b7c426520.png"},{"id":104880803,"identity":"bcccae8c-ae57-441f-bddb-3145e71fee57","added_by":"auto","created_at":"2026-03-18 09:13:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47436,"visible":true,"origin":"","legend":"\u003cp\u003eParallel trend test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: Solid dots represent regression coefficients. The short vertical line represents the upper and lower 95% confidence interval corresponding to the standard error.\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9093969/v1/4d3e53fa334f1f61a88e4c75.jpeg"},{"id":104880693,"identity":"af6fe32b-8ea7-4826-87f1-aece8b528b85","added_by":"auto","created_at":"2026-03-18 09:13:41","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":103968,"visible":true,"origin":"","legend":"\u003cp\u003ePlacebo test results.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9093969/v1/26213af3ba8c99ab4015f12c.jpeg"},{"id":104880817,"identity":"98e23107-3e16-4e20-b6f7-eefef71038e3","added_by":"auto","created_at":"2026-03-18 09:13:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":91415,"visible":true,"origin":"","legend":"\u003cp\u003eThe fitting relationship between dominant factors and their hinterland.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: \u003cem\u003ex\u003c/em\u003e is the dominant factor. Among them, \u003cem\u003ex\u003c/em\u003e\u003csub\u003eZhoushan Archipelago New Area\u003c/sub\u003e is total retail sales of consumer goods in the whole society, \u003cem\u003ex\u003c/em\u003e\u003csub\u003eNansha New Area\u003c/sub\u003e is GDP, \u003cem\u003ex\u003c/em\u003e\u003csub\u003eBinhai New Area\u003c/sub\u003e is the general public budget revenue, and\u003cem\u003e x\u003c/em\u003e\u003csub\u003ethe remaining new areas\u003c/sub\u003e is permanent population.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9093969/v1/2a855de967f9ea0a3f98e478.png"},{"id":104880933,"identity":"43ede958-f0a3-4e9d-9752-951d92dc24d6","added_by":"auto","created_at":"2026-03-18 09:14:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1633503,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial spillover effect of national new areas.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-9093969/v1/df5cc6c9f3287bc335dc95e4.png"},{"id":104880941,"identity":"c5a8ca6d-e2fa-40c4-86ba-4ede7bf86e20","added_by":"auto","created_at":"2026-03-18 09:14:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3662589,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9093969/v1/7bd1f14a-cb1b-4246-982a-b1cb6512e25a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Sinicization of Growth Pole Theory: Evidences from National New Areas in China's Mainland","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs comprehensive functional areas, national new areas undertake major strategic tasks in China. As mentioned in \u003cem\u003eThe Action Plan for Promoting High Quality Construction of National New Areas\u003c/em\u003e released in 2024, national new areas have played an important demonstration and driving role in gathering industrial clusters with strong competitiveness. Since Pudong New Area was established in 1992, 19 national new areas have been successively done in China. China's national new areas has gone through three stages, namely: the exploration period (1992\u0026ndash;2009); the functional upgrading period (2010\u0026ndash;2013); the intensive development period (2014-present). Except for Xiong'an New Area, GDP was about 5.5 trillion RMB\u003csup\u003e1\u003c/sup\u003e in other 18 national new areas in 2021, accounting for China\u0026rsquo;s 4.80%. We can see that national new areas have gradually become powerful growth engines driving regional development. So, we select them as samples to investigate their radiation effects. This helps to comprehensively measure their spillover effectiveness, and provides empirical support for regional policy adjustments. Meanwhile, this is also a theoretical reconstruction of growth pole theory, namely: Sinicization.\u003c/p\u003e \u003cp\u003eThe initial growth pole, namely: \"developmental poles\" (Perroux, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1955\u003c/span\u003e) came from the concept of \"magnetic pole\" in Physics. Boudeville's geographical interpretation first shifted the Perroux's concept of growth poles from abstract economic space to geographic space (Boudeville, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1961\u003c/span\u003e) with a particular emphasis on the special location of \"city-periphery\", and thus derived the basic idea of his regional growth pole strategy (Hermansen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). However, Boudeville overly concretized and geographized economic space, transforming it into a general regional space, thus transforming it into what Perroux's called \"mediocre\" geographical space, ignoring the original abstraction and globalization of economic space (Hermansen, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). Myradal (1957) supplemented the operational mechanism of growth poles, also emphasized the role of government (Barber, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Sheppard, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This is the essence of Myrdal's growth pole theory (Friedmann, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1966\u003c/span\u003e; Myrdal, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \"Geographical Dual Sector Model\" (Lewis, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1954\u003c/span\u003e) pointed out the negative effects of growth poles, namely: the \"backward effect\", which compensated for another deficiency of Perroux's growth pole theory. And then, the \"Geographical Dual Economic Structure\" (Myradal, 1957) also pointed out their negative effects, namely: the \"echo effect\", which compensated for another deficiency of Perroux's growth pole theory (Plummer \u0026amp; Sheppard, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Correspondingly to Myradal's \"echo effect\" and \"diffusion effect\", Hirschman (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1958\u003c/span\u003e) referred to the favorable effects of growth poles (central cities) on their hinterland as the \"trickle-down effect\", while whose adverse effects were referred to as the \"polarization effect\" (Li \u0026amp; Pan, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The growth pole has been proven to have a macro level impact on financial market (Aghion \u0026amp; Bolton, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), factor market (Beladi,1990), and capital market (Blackburn \u0026amp; Bose, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). However, accompanying this is constant questioning from society and academia about whether the \"trickle-down effect\" can be achieved (Richardson, 1997).\u003c/p\u003e \u003cp\u003eEarly research on growth poles mainly focused on qualitative analysis of their spatial and temporal distribution (Perroux, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1955\u003c/span\u003e; Boudeville, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1961\u003c/span\u003e), strategic positioning (Bere, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cheshmehzangi \u0026amp; Tang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), policy characteristics (Lo \u0026amp; Salih, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Friedmann, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), development models (Wojnicka-Sycz, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), development mechanisms (Cheshmehzangi \u0026amp;Tang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), roles or functional configuration (Lo \u0026amp; Salih, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), and problems (Thomas, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) in target implementation during and after construction. The growth pole strategy has proved to be an important plan tool (Campbell, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Thomas, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Parr, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Dranca, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wojnicka-Sycz, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Guo, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Specifically, previous studies have focused more on the spatial configuration (Lo \u0026amp; Salih, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) and the spillover effects (Parr, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Wojnicka-Sycz, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cheshmehzangi \u0026amp; Tang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), trickle down effects (Roberts, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), spread\u0026ndash;backwash effects (Ke \u0026amp; Feser, 2010), multiplier and bolstering effects (Guo, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) of the planned poles based on the theory of spread and backwash (Richardson, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1976\u003c/span\u003e), as a regional development factor (Wojnicka-Sycz, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Case studies at the macro level include the EU (Dranca, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kotlebov\u0026aacute; \u0026amp; Širaňov\u0026aacute;, 2014; Matzana et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), growth poles of the global economy (Popkova, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), China\u0026rsquo;s city clusters (Cheshmehzangi \u0026amp; Tang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), China's strategy of Central Rise (Ke \u0026amp; Feser, 2010), and do Detroit (Tobler, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). Meanwhile, case studies at the micro level include Caraj\u0026aacute;s's satellite boom town (Roberts, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), accelerated industrialization in major Southeast Asian countries (Lo \u0026amp; Salih, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1978\u003c/span\u003e), the Snow White gas field (Eikeland, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), inter-community development associations (IDA) in Romania (Bere, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Siracusa (Graziano et al., 2023), Shanghai (Dobrescu \u0026amp; Dobre, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Guizhou (Jiao et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), etc. Additionally, Adekunle Ajasin University (Eludoyin, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Delta State University (Ojeh \u0026amp; Origho, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) are special growth poles.\u003c/p\u003e \u003cp\u003eThe growth pole theory was introduced to China in the 1980s. China\u0026rsquo;s scholars have proposed the \"Gradient Transfer Theory\" (Xia et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) and the \"Point-Axis Theory\" (Lu, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). These theories are the Sinicization of growth pole theory, which have promoted the adjustment of China's regional development strategy from balanced development to unbalanced development. Chinese studies have confirmed that national new areas can significantly drive economic growth in their hinterland (Feng \u0026amp; Wang, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), whose driving effects are sustainable. Additionally, national new areas can enhance urban ecological (Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), energy and environmental efficiency (Wu, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, present empirical studies have made beneficial explorations on various growth poles, while there are also some limitations. Firstly, most studies focus on analyzing the effects of growth poles on their hinterland, while whose measurement dimension is relatively single and cannot comprehensively examine their utility. Secondly, existing studies have only focused on whether growth poles have a promoting effect on their hinterland, with little attention paid to the relative degree or the measurement of their effects. Thirdly, the data used is mainly at urban level. However, new growth poles are often established across administrative regions. Therefore, it is difficult to accurately measure the effects of growth poles, based on city data with short time spans.\u003c/p\u003e \u003cp\u003eThus, we comprehensively investigate the overall effects of 19 national new areas on their hinterland during 2010\u0026ndash;2022 by a multi-period double difference method with bidirectional fixed effects, and evaluate the marginal effects, spatial spillover effects, and proximity effects between each new area and its hinterland by linkage effects. Through the analysis of their spillover effectiveness, our findings contribute to a deeper understanding of the actual situation in China's national new areas, provide reference for economic growth in other regions, and can further promote the growth pole theory.\u003c/p\u003e \u003cp\u003eThe rest of our paper is organized as follows. We describe method and data in Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we investigate and discuss the overall effects of 19 national new areas on their hinterland. And then, we evaluate the Sinicization of growth pole theory in Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Finally, Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e5\u003c/span\u003e describes our main conclusions, policy implications and prospects.\u003c/p\u003e"},{"header":"2. Methodology and Data","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Main methods\u003c/h2\u003e \u003cp\u003eCurrently, most existing studies regard national new areas as a location-oriented policy (Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and choose a double difference model to evaluate their own economic status or driving effects on their hinterland. However, due to some limits (e.g. omitted variables, sample selection bias, and measurement errors), the double difference model cannot fully handle endogeneity. Additionally, there are temporal heterogeneities in the establishment of national new areas. And then, there is no unified policy implementation node. So, we measure the spillover effects and radiative driving effects of national new areas on their hinterland by a multi-period double difference model with bidirectional fixed effects. The specific model is as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eY\u003c/em\u003e \u003csub\u003e \u003cem\u003eit\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003edid\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003econtrol\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e (1)\u003c/p\u003e \u003cp\u003eWhere, \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the economic effectiveness in each new area, reflecting GDP in new area \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e. \u003cem\u003edid\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is an interactive item. If new area \u003cem\u003ei\u003c/em\u003e is established in year \u003cem\u003et\u003c/em\u003e, then \u003cem\u003edid\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e=1 starting from that year; otherwise, \u003cem\u003edid\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e=0. \u003cem\u003edid\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e =\u003cem\u003etreated\u003c/em\u003e\u0026times;\u003cem\u003etime\u003c/em\u003e. Among them, \u003cem\u003etreated\u003c/em\u003e is an individual dummy variable, and \u003cem\u003etime\u003c/em\u003e is a time dummy variable. If new area \u003cem\u003ei\u003c/em\u003e is established, it belongs to the processing group, \u003cem\u003etreated\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1; otherwise, \u003cem\u003etreated\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0. \u003cem\u003econtrol\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the control variable. \u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the error term. \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is a constant term. \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e are the coefficients. \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e represents the net policy effect of national new areas. \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0 indicates that national new areas can generate positive spillover effects; otherwise, they generate negative spillover effects.\u003c/p\u003e \u003cp\u003eWe conduct regression analysis on the relationship between growth poles and their hinterland by SPSS28.0. The specific multiple linear regression model is as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eE\u003c/em\u003e \u003csub\u003e \u003cem\u003ei\u003c/em\u003e \u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eα\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eα\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eα\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eα\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eα\u003c/em\u003e\u003csub\u003e4\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eα\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e5\u003c/sub\u003e+\u003cem\u003eu\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e (2)\u003c/p\u003e \u003cp\u003eWhere, \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the sum of GDP in new area \u003cem\u003ei\u003c/em\u003e and its hinterland. {\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e1\u003c/sub\u003e,\u0026hellip;,\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e5\u003c/sub\u003e} represents GDP, general public budget revenue, total retail sales of consumer goods in the whole society, total trade import and export volume, and permanent population in national new area \u003cem\u003ei\u003c/em\u003e, respectively. \u003cem\u003eα\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is a constant term. {\u003cem\u003eα\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e,\u0026hellip;,\u003cem\u003eα\u003c/em\u003e\u003csub\u003e5\u003c/sub\u003e} is the regression coefficient. \u003cem\u003eu\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the control variable.\u003c/p\u003e \u003cp\u003eIn order to measure the radiating effect of national new areas on their hinterland, we analyze their external functions based on the market principles of the Central Location Theory. We use Δ\u003cem\u003ec\u003c/em\u003e to determine whether the national new area has been the ideal growth pole. The specific formulas are as follows:\u003c/p\u003e \u003cp\u003eΔ\u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e=|\u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e-\u003cem\u003ec\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e| (3)\u003c/p\u003e \u003cp\u003eΔ\u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e'=|\u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e'-\u003cem\u003ec\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e| (4)\u003c/p\u003e \u003cp\u003eWhere, \u003cem\u003ec\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is the ratio of the area of a regular hexagon to its circumference. We assume that the side length of a regular hexagon is 1km. So, \u003cem\u003ec\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is a constant, namely: \u003cem\u003ec\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;6. \u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the ratio of the area of each national new area to its circumference before deformation, while \u003cem\u003ec\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e' is that after deformation. \u003cem\u003ei\u003c/em\u003e\u0026isin;[1,18]. Δ\u003cem\u003ec\u003c/em\u003e's ideal value is 6. The larger or smaller Δ\u003cem\u003ec\u003c/em\u003e, the more it indicates that the external functions of the new area have not achieved the expected results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study area\u003c/h2\u003e \u003cp\u003eChina\u0026rsquo;s 19 national new areas are mainly concentrated in the eastern and central regions of China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Appendix 1a). In 2023, at the cost of about 0.2% of China's population and about 5.4% of China's area, GDP reached about 6.2 trillion RMB in national new areas, creating China\u0026rsquo;s economic output of 5.0%. From the perspective of regional functional positioning, prime mover industry is high-tech industry in national new areas, mainly including high-end service industry, strategic emerging industry, high-end manufacturing industry, etc. For example, the total output of strategic emerging industries accounts for over 50.8% of the total industrial output in Xiangjiang New Area. Two \"200\u0026nbsp;billion RMB\" pillar industries have been formed in Liangjiang New Area, including automobile and electronic information, where a total of 139 aerospace industry chain enterprises have also been introduced, with an industrial scale of 3.5\u0026nbsp;billion RMB. Since 2018, the average annual growth rate of strategic emerging industries has reached 16.3% in Xixian New Area (Appendix 1b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data\u003c/h2\u003e \u003cp\u003eWe consider the establishment of national new areas as a quasi-natural experiment, with them as the experimental group (data from the main districts or counties where them are located before their establishment) and the main cities to which them belong as the control group. Considering the availability of data, we select a research period of 2010\u0026ndash;2022. We designate the hinterland of Pudong New Area and Zhoushan Archipelago New Area as the Yangtze River Delta Urban Agglomeration. The hinterland of Binhai New Area is designated as Beijing-Tianjin-Hebei urban agglomeration, while which of Liangjiang New Area is designated as Chengdu-Chongqing urban agglomeration (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, due to the late establishment of Xiong'an New Area, its data is difficult to obtain. So, we only conduct qualitative analysis on it.\u003c/p\u003e \u003cp\u003eGDP, general public budget revenue, total retail sales of consumer goods in the whole society, total trade import and export volume, and permanent population in national new area come from statistical yearbooks, national socioeconomic development statistical bulletins (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Partial data in new areas is supplemented from statistical yearbooks in provinces or cities they belong to. Missing data is obtained by averaging and interpolation methods. In order to eliminate the impact of data dimensionality, we standardize them.\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\u003eDescriptive statistics of main indicators.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eIndicators (Unit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEconomic development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP in national new area (10\u003csup\u003e8\u003c/sup\u003e CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2220.516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2533.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13207.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP in the hinterland (10\u003csup\u003e8\u003c/sup\u003e CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8886.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19812.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e232.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e320735.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal GDP (10\u003csup\u003e8\u003c/sup\u003e CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60194.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61320.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4626.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e290288.800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePermanent population (10\u003csup\u003e4\u003c/sup\u003e persons)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2453.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4733.812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e25.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29547.730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsumption ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal retail sales of consumer goods in the whole society (10\u003csup\u003e8\u003c/sup\u003e CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTTR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10010.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17622.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e111463.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral expenses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral public budget revenue (10\u003csup\u003e8\u003c/sup\u003e CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGGR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12992.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e57173.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e493904.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign trade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal trade import and export volume (10\u003csup\u003e8\u003c/sup\u003e USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4863.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7913.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87463.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eNotes\u003c/b\u003e: According to the midpoint of the exchange rate between the Chinese yuan and the US dollar on January 18, 2024, namely: 1 USD\u0026thinsp;=\u0026thinsp;7.1174 CNY.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cp\u003eGDP continues to grow in Pudong New Area and Binhai New Area (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), with a particularly significant increase in their economic speed. One main reason is that them was established earlier, especially the policy incentives undertaken by the earliest established Pudong New Area, which are not available in later new areas. Pudong New Area was established in the early stages of China's socialist market economy system construction (1992) and before joining the WTO (2001). When it was established, Pudong New Area was endowed with ten preferential policies of the National Economic and Technological Development Zone, nine preferential policies of the Special Economic Zone, and five unique functional policies.\u003csup\u003e2\u003c/sup\u003e Then, there were significant differences in the institutional environment compared to later new areas. GDP also shows an overall upward trend in other new areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-d). This to some extent contributes to our study and lays the foundation for further testing their radiative effects, in order to verify the Sinicization of growth pole theory.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Regression results of benchmark model\u003c/h2\u003e \u003cp\u003eThe results of the multi-period double difference model with bidirectional fixed effects are divided into three columns (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among them, in the first column, we control for individual fixed effects. The results show that the dummy variable \u003cem\u003edid\u003c/em\u003e's coefficient \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e=-0.241 and |\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e|\u0026lt;0.330 (standard error) with an intercept term of 1.541. This indicates that the effect of growth poles on their hinterland is negative, but \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e is not significant. So, the individual fixed effects model may not fully explain the construction effectiveness of growth poles. In the second column, we control for time fixed effects. The results show that \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.320 and |\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e|\u0026gt;0.165 (standard error) with an intercept term of 2.137. The significance of \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e has been improved. This indicates that the fixed effects model better captures the construction effectiveness of growth poles. However, further exploration of other factors is still needed. In the third column, we simultaneously control for both individual and time fixed effects. The results show that \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.374 and |\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e|\u0026gt;0.163 (standard error) with an intercept term of 2.065. \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e is significant in the bidirectional fixed effects model. This indicates that the bidirectional fixed effects model is more suitable, which can comprehensively evaluate the radiation driven effects of growth poles. Overall, the results may be closer to the actual situation in the third column. Because it takes into account individual and time dimensions more comprehensively, whose \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e is more significant.\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\u003eBenchmark regression results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \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\u003eModel(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel(3)\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\u003e\u003cem\u003edid\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.320*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.330)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.163)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.748**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.369***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.298***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.103)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.099)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTTR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.073**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.339)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.034)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eGGR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.536***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.443***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.618***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.931)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.063)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.055)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.076**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.074***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.81e-06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual fixed effect\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\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime-fixed effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.137***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.065***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.410)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.490)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.467)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eNotes\u003c/b\u003e: 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=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Parallel trend test\u003c/h2\u003e \u003cp\u003eTo evaluate the implementation of policy effectiveness by the double difference method, it is necessary to first satisfy the common trend assumption, namely: when there are no policy effects in national new areas, where economic trends of the experimental group and the control group show consistency. So, before conducting model regression, we conduct a parallel trend test (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Due to the limited data, we summarize the data from the 4 years before policy implementation in the pre_4th period and the 7 years after policy implementation in the post_7th period. Additionally, we select the pre_4th period as the base period. \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e is not significant in each period before policy implementation. This indicates that there is no significant difference between the treatment group and the control group before policy implementation, and our samples passed the parallel trend test. Although \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e is not significant in the 4 years after policy implementation. However, this does not affect the test results. This is due to the lag in policy effects, as the policies may not immediately take effect. The policy effects are cumulative. As time goes by, the policy effects gradually become apparent. Meanwhile, the policy effects are also influenced by factors such as regional or industrial heterogeneities. Additionally, in our study, there may be fluctuations and noise in the data. This may result in less significant policy effects in certain periods. With the increase of data and extension of period, these fluctuations and noise are gradually eliminated, and the policy effects are evident.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNotes\u003c/strong\u003e \u003cp\u003eSolid dots represent regression coefficients. The short vertical line represents the upper and lower 95% confidence interval corresponding to the standard error.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Placebo tests\u003c/h2\u003e \u003cp\u003eThe regression results of the benchmark model mentioned earlier indicate that as growth poles, national new areas have a positive spillover effect on their hinterland, which can also be understood as a positive correlation between the two. However, this conclusion may be influenced by other random factors. To further eliminate the influence of other non-observed omitted variables on the regression results, we conduct placebo tests on the randomized treatment group and the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We randomly select one group of subjects from the sample area as the treatment group and another group of subjects as the control group. If national new areas have been established in \u003cem\u003en\u003c/em\u003e regions in a certain year, and when is fixed, we will randomly select \u003cem\u003en\u003c/em\u003e regions from the regions without national new areas in that year or before as the new processing group. And then, we reestimate the model (3) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) by new samples. So far, we have completed the first placebo test. After repeating the above process 500 times, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (500 estimated coefficient values of \u003cem\u003edid\u003c/em\u003e) is obtained. By plotting the nuclear density distribution and p-value, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;0 in most cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and follows a normal distribution. This indicates that most regression results are not significant. \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is located at the high tail of the false regression coefficient distribution in benchmark regression. In individual placebo tests, this is a low probability event. So, we can exclude the possibility that the baseline estimation results may be affected by unobservable factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. The Sinicization of Growth Pole Theory","content":"\u003cp\u003eThe above empirical results indicate that there is an overall positive spillover relationship between national new areas and their hinterland. And then, we conduct multiple regression analysis to further investigate their quantitative relationship (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) by SPSS 28.0. Among them, we lag the independent variables in Lanzhou New Area, Nansha New Area, Gui'an New Area, Xixian New Area, West Coast New Area, and Jinpu New Area by one period, do them in Binhai New Area and Tianfu New Area by two periods, and do them in Zhoushan Archipelago New Area by five periods. Their results are significant.\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\u003eMultiple linear regression results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHinterland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGrowth pole\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGGR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTTR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eTE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAdjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHinterland range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNational new areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.025***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.891***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.088***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYangtze River Delta Urban Agglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePudong New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.334*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.158*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.744**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-6.818*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e67.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeijing-Tianjin-Hebei Urban Agglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinhai New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.299***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.936***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.166**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChengdu-Chongqing Urban Agglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiangjiang New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.009**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.490**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.306**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.237***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.698***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-174.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYangtze River Delta Urban Agglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZhoushan Archipelago New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.093*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.185***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGansu Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLanzhou New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.260**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.412**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.822*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuangdong Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNansha New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.619***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.408*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.202**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.181*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShaanxi Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXixian New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.347*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-22.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e184.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuizhou Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGui'an New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.082**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.512**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-66.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShandong Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWest Coast New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.309**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.822**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.669*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiaoning Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJinpu New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.207**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.576***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-5.312***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSichuan Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTianfu New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.522**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.181***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.117**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-59.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHunan Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXiangjiang New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-63.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJiangsu Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJiangbei New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.293**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.270**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-19.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFujian Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuzhou New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-41.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYunnan Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDianzhong New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.184*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.292*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.415*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-48.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeilongjiang Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHarbin New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-1.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJilin Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChangchun New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.886***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.191**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.582***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.975*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJiangxi Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGanjiang New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-8.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e66.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNote\u003c/b\u003e: *** 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 \u003cp\u003eWe can explain the results from three aspects. \u003cem\u003eFirst is the time lag effect\u003c/em\u003e. As growth poles, the radiation driven effects of national new areas obviously delay. This delay effect may be due to the implementation of policies (Richardson, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1976\u003c/span\u003e), investments (Dranca, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), or other measures taking some time. \u003cem\u003eNext is the cumulative effect\u003c/em\u003e. This means that new areas will ultimately have a significant effect after a lag period, rather than immediately appearing, such as the spread effects of GDP and employment growths (Ke \u0026amp; Feser, 2010). \u003cem\u003eThe last is regional interaction\u003c/em\u003e, including cooperation in resource sharing, policy coordination, and industrial chain connection (Campbell, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Guo, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Interacting with other factors, these new areas can still significantly affect their hinterland.\u003c/p\u003e \u003cp\u003eSome studies have confirmed that national new areas can fully leverage the effects of growth poles and boom their hinterland (Feng \u0026amp; Wang, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, we focus on analyzing the negative correlation between new areas and their hinterland. There are several reasons. \u003cem\u003eFirst is the polarization effect\u003c/em\u003e. New areas can usually quickly gather many resources, including funds, talents, and technology (Popkova, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while which are the opposite to their hinterland, leading to uneven regional resource allocation (Graziano et al., 2023). Resource loss, economic structure adjustment (Kotlebov\u0026aacute; \u0026amp; Širaňov\u0026aacute;, 2014; Popkova, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and market are important to Fuzhou New Area, Harbin New Area, and Changchun New Area. \u003cem\u003eNext is the external dependence\u003c/em\u003e. New areas are usually more open, attracting more foreign investment (Roberts, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Dranca, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bere, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and interregional trade (Dobrescu \u0026amp; Dobre, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while the relatively low openness limits the expansion of resources and markets in whose hinterland. Market factors such as demand (Roberts, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) and competition are important to the relationship between new areas and their hinterland. Additionally, the transportation network (Cheshmehzangi \u0026amp; Tang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and infrastructure are generally more complete in new areas, facilitating the flow of resources and information, while the opposite is true in whose hinterland. Some new areas are currently going on economic restructure, where GDP growth will be affected by adjustments in some pillar industries (Thomas, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), thereby affecting whole regional economy (Kotlebov\u0026aacute; \u0026amp; Širaňov\u0026aacute;, 2014). \u003cem\u003eThe last is the scale effect\u003c/em\u003e. The scale effect is not significant in these newly established new areas. Because these new areas are relatively small in scale, which only have limited effects on the entire region. Another important reason is the heterogeneity in their leading industries (Guo, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), such as oil industry (Eikeland, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and tourism (Matzana et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Graziano, et al., 2023), etc. The growth pole effects will not spread within the overall region.\u003c/p\u003e \u003cp\u003eDue to late establishment and extremely difficult data acquisition, we don't conduct relevant empirical analysis on Xiong'an New Area. However, based on the results of the multi-period double difference regression with bidirectional fixed effects, we speculate that Xiong'an New Area is likely to have a positive effect on its hinterland in the future. From the regression results of the benchmark model, we can see that as growth poles, there is an overall positive spillover relationship between national new areas and their hinterland. In order to investigate the specific relationship, we further measure their linkage effects, including the marginal effects, spatial spillover effects, and proximity effects.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. The marginal effects\u003c/h2\u003e \u003cp\u003eThe results show that there is indeed a significant positive correlation between most new areas and their hinterland, while there is also a significant negative correlation or a correlation but not significant relationship (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Except for Zhoushan Archipelago New Area, Nansha New Area, and Binhai New Area, the dominant factor is population scale in other new areas.\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\u003eThe quantitative relationship between growth poles and their hinterland.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth poles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegression models\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDominant factors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNew areas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e1.229\u003c/sup\u003e (\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.891\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.031\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e0.088\u003c/sup\u003e)/(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.025\u003c/sup\u003e\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.001\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTTR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eSignificant positive correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePudong New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e103.76\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.334\u003c/sup\u003e\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.158\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.744\u003c/sup\u003e)/\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e6.818\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZhoushan Archipelago New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e65.511\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.093\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e1.185\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.024\u003c/sup\u003e)/(\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.571\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e0.528\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTTR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNansha New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e4.702\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.619\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.408\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.202\u003c/sup\u003e)/(\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.138\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e0.181\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGDP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXixian New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e278.331\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.347\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.884\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.854\u003c/sup\u003e)/(\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.541\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e22.839\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWest Coast New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e29.898\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.309\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.822\u003c/sup\u003e)/(\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.146\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.022\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e2.669\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJinpu New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e50.964\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.207\u003c/sup\u003e\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.072\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.576\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.046\u003c/sup\u003e)/\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e5.312\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTianfu New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u0026minus;37.37\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.061\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.522\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.181\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e7.117\u003c/sup\u003e)/\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.113\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJiangbei New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e16.351\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.293\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.276\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.270\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e2.995\u003c/sup\u003e)/\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.342\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDianzhong New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u0026minus;42.408\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.292\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.062\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e5.415\u003c/sup\u003e\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e1.184\u003c/sup\u003e\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.204\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChangchun New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e20.724\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.044\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.582\u003c/sup\u003e)/(\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.886\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.191\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e0.975\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSignificant negative correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBianhai New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e7.694\u003c/sup\u003e(\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.936\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.007\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.166\u003c/sup\u003e)/(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.299\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e0.223\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eGGR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiangjiang New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u0026minus;156.094\u003c/sup\u003e(\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.306\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.237\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e19.698\u003c/sup\u003e)/(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.009\u003c/sup\u003e\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.490\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLanzhou New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e136.77\u003c/sup\u003e(\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e2.822\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.229\u003c/sup\u003e)/(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.260\u003c/sup\u003e\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e1.412\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e3.363\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGui'an New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u0026minus;62.986\u003c/sup\u003e(\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.018\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.082\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e8.512\u003c/sup\u003e)/(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.012\u003c/sup\u003e\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.055\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNot significantly positively correlated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXiangjiang New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u0026minus;12.752\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.363\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.353\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e8.205\u003c/sup\u003e)/(\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.020\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.463\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGanjiang New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e135.748\u003c/sup\u003e(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.503\u003c/sup\u003e\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.970\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.068\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.002\u003c/sup\u003e)/\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e8.113\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNot significantly negatively correlated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFuzhou New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003ee\u003c/em\u003e\u003csup\u003e\u0026minus;34.704\u003c/sup\u003e(\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.290\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.408\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.082\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e5.446\u003c/sup\u003e)/\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.009\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHarbin New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e=\u003cem\u003ee\u003c/em\u003e\u003csup\u003e27.578\u003c/sup\u003e(\u003cem\u003eGGR\u003c/em\u003e\u003csup\u003e0.244\u003c/sup\u003e\u003cem\u003eTTR\u003c/em\u003e\u003csup\u003e0.129\u003c/sup\u003e)/(\u003cem\u003eGDP\u003c/em\u003e\u003csup\u003e0.012\u003c/sup\u003e\u003cem\u003eTE\u003c/em\u003e\u003csup\u003e0.017\u003c/sup\u003e\u003cem\u003ePOP\u003c/em\u003e\u003csup\u003e1.826\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ePOP\u003c/em\u003e\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\u003eAnd then, we fit their hinterland and dominant factors in 15 new areas. There is a cubic relationship between the hinterland of Binhai New Area and its dominant factors, while the hinterland of other new areas shows a cubic relationship with their dominant factors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Among them, for Lanzhou New Area, Jinpu New Area, Harbin New Area, and Changchun New Area (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), there is a negative correlation between the dominant factors and hinterland, namely: the larger the population scale, the more the regional economy shows reverse growth. This indicates that population scale has a negative marginal effect on its hinterland, except for Zhoushan Archipelago New Area, Nansha New Area, and Binhai New Area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNotes\u003c/strong\u003e \u003cp\u003e \u003cem\u003ex\u003c/em\u003e is the dominant factor. Among them, \u003cem\u003ex\u003c/em\u003e\u003csub\u003eZhoushan Archipelago New Area\u003c/sub\u003e is total retail sales of consumer goods in the whole society, \u003cem\u003ex\u003c/em\u003e\u003csub\u003eNansha New Area\u003c/sub\u003e is GDP, \u003cem\u003ex\u003c/em\u003e\u003csub\u003eBinhai New Area\u003c/sub\u003e is the general public budget revenue, and \u003cem\u003ex\u003c/em\u003e\u003csub\u003ethe remaining new areas\u003c/sub\u003e is permanent population.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2. The spatial spillover effects and proximity effects\u003c/h2\u003e \u003cp\u003eBased on the regression results (Appendix 2) between new areas and their hinterland, we classify their spatial spillover or proximity effects into four categories, namely: \u003cem\u003eα\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0; 0\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003eα\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.50; 0.50\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003eα\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.00, and \u003cem\u003eα\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;1.00. Among them, \u003cem\u003eα\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0 indicates that the new area has a negative spatial spillover effect on the city, while other three categories are opposite and its intensity continues to increase. There are negative spatial spillover effects or proximity effects between Gui'an New Area and its hinterland (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). We summarize the reasons for this phenomenon as resource competition, talent loss, and uneven development. Except for Gui'an New Area, there is a negative spillover relationship between Jinpu New Area, West Coast New Area, Harbin New Area, and Changchun New Area and their hinterland (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb-e). The remaining new areas exhibit positive spatial spillover effects on their hinterland (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef-r). As regional growth poles, these new areas have attracted a large amount of capital (Dranca, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bere, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), technology (Popkova, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and talent through their strong agglomeration effects, forming industrial agglomeration (Dobrescu \u0026amp; Dobre, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This agglomeration not only enhances the competitiveness in new areas, but also transmits industry, technology, and management experience to their hinterland through diffusion effects, upgrading regional industrial structure. Meanwhile, the infrastructure construction (Wu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and public services (Bere, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) in new areas benefit their hinterland through radiation effects, improving their regional allocative efficiency.\u003c/p\u003e \u003cp\u003eBenefited from the support of national policies, new areas enjoy tax incentives and other policies, which have also had a positive effect on their hinterland. The innovation driven and coordinated development strategy in new areas, as well as the expansion of market demand, has provided new opportunities for enterprises in their hinterland. Additionally, regional talent exchange and industrial chain extension have further promoted their hinterland. So, through agglomeration, diffusion, radiation effects, as well as policies, innovation, markets, and others, these national new areas have produced significant positive spatial spillover effects and proximity effects on their hinterland.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnd then, we transform the real \u0026ldquo;geographic space\u0026rdquo; into an ideal \u0026ldquo;economic space\u0026rdquo; by spatial friction, in order to realize the assumption of homogeneous plain and further refine the broader applicability of growth theory. Considering distance attenuation, which is very realistic, the straight-line distance (\u003cem\u003el\u003c/em\u003e) between the two is used to reflect spatial friction (1/\u003cem\u003el\u003c/em\u003e). The new spatial spillover effect (\u003cem\u003eα\u003c/em\u003e') of national new areas is as follows:\u003c/p\u003e \u003cp\u003e \u003cem\u003eα\u003c/em\u003e'=\u003cem\u003eα\u003c/em\u003e/\u003cem\u003el\u003c/em\u003e (5)\u003c/p\u003e \u003cp\u003eAccording to geographical position and radiation effects, these new areas is very in line with the growth pole theory and serves their hinterland well, such as Gui'an New Area, Lanzhou New Area, Harbin New Area, and Liangjiang New Area (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea,d,h,j,l). Meanwhile, the original core cities have affected the radiation effects of new areas, such as West Coast New Area, Pudong New Area, and Binhai New Area (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,f,g). Through the distorted map of \u003cem\u003eα\u003c/em\u003e and \u003cem\u003eα\u003c/em\u003e' (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), \u0026ldquo;geographic space\u0026rdquo; has once again been corrected, which better meets the basic requirements for cultivating growth poles. The mutual interference makes it difficult for us to accurately calculate the diffusion effect of the new area on its hinterland. As we know, as the original and higher-level growth poles, the original core cities (e.g. provincial capital cities) weaken the regional driving effect of secondary growth poles such as national new areas. This is not only an important manifestation of the Sinicization of growth pole theory, but also a new research direction for us in the future.\u003c/p\u003e \u003cp\u003eAdditionally, according to the external functions of national new areas (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), we find that the external functions of Changchun New Area, Gui'an New Area, Lanzhou New Area, Harbin New Area, Xixian New Area and Dianzhong New Area are poor, which are all in the early construction stage. Meanwhile, Pudong New Area, Jiangbei New Area and West Coast New Area are the ideal growth poles. In short, the early construction stage of the new areas has a significant effect on the relationship between the two. Of course, the existing higher-level growth poles (core cities) also affect their relationship.\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\u003eThe external functions of national new areas.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth poles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHinterland\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e∆\u003cem\u003ec\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e∆\u003cem\u003ec\u003c/em\u003e'\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarbin New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHeilongjiang Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXixian New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShaanxi Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDianzhong New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYunnan Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGanjiang New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJiangxi Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXiangjiang New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHunan Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePudong New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYangtze River Delta Urban Agglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJiangbei New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJiangsu Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTianfu New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSichuan Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNansha New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuangdong Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuzhou New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFujian Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiangjiang New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChengdu-Chongqing Urban Agglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWest Coast New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShandong Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinhai New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeijing-Tianjin-Hebei Urban Agglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.681\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhoushan Archipelago New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYangtze River Delta Urban Agglomeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJinpu New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiaoning Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanzhou New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGansu Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGui'an New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuizhou Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChangchun New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJilin Province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.236\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"},{"header":"5. Conclusions and Prospects","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Main conclusions\u003c/h2\u003e \u003cp\u003eThrough empirical analysis by a multi-period double difference model with bidirectional fixed effects, we conclude that there is an overall positive spillover relationship between national new areas and their hinterland. Subsequently, their specific relationship is measured for linkage effects from the perspectives of marginal effects, spatial spillover effects, and proximity effects. We obtain the following main conclusions.\u003c/p\u003e \u003cp\u003e(1) In terms of marginal effects, the hinterland of Binhai New Area shows a cubic relationship with its dominant factors, while which of others does a cubic relationship. Among them, for Lanzhou New Area, Jinpu New Area, Harbin New Area, and Changchun New Area, there is a negative correlation, namely: the larger the population scale, the more the regional economy shows reverse growth. This indicates that population scale has a negative marginal effect on their hinterland. However, for others, the opposite is true.\u003c/p\u003e \u003cp\u003e(2) In terms of spatial spillover effects and proximity effects, there is a negative spatial spillover effect or proximity effect between Gui'an New Area and its hinterland. This phenomenon can be attributed to resource competition, talent loss, and uneven development. Meanwhile, there is a negative spillover relationship between other new areas and some areas in their hinterland, such as Jinpu New Area, etc. However, the remaining new areas exhibit a positive spatial spillover effect on their hinterland. Overall, new aeras not only have a significant promoting effect on themself, but also have a profound impact on their hinterland. Through positive spatial spillover effects, new areas can promote their hinterland and whole regional economy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Policy implications\u003c/h2\u003e \u003cp\u003eBased on the above analysis, we propose some policy inspirations.\u003c/p\u003e \u003cp\u003e(1) For national new areas with a significant positive correlation with their hinterland\u003c/p\u003e \u003cp\u003e① While continuing to maintain their growth pole position, infrastructure connectivity should be enhanced in Pudong New Area and Jiangbei New Area, to promote regional development by strengthening regional coordination, industry-city integration, institutional innovation, attracting investment (Dranca, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bere, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), developing high-tech and modern service industries (Bere, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). ② Regional cooperation should be further strengthened in Zhoushan Archipelago New Area and Nansha New Area, to develop smart islands, logistics and tourism information service platforms (Matzana et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Graziano et al., 2023), strengthen port logistics and trade, create international commodity distribution centers by integrated infrastructure construction (Wu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and share the benefits of marine economy (Balaji, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) with their hinterland by implementing a strategy driven by marine technology innovation. ③ Regional coordination should be deepened in Xixian New Area to achieve effective integration of resources and markets, and significantly promote its hinterland by utilizing its geographical advantages, strengthening connections (Campbell, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Cheshmehzangi \u0026amp; Tang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and industrial complementarity with core cities such as Xi'an and Xianyang. ④ While strengthening the coordination of its functional areas and promoting equal development (Chao \u0026amp; Lin, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), technological innovation should also been done in West Coast New Area, to develop some industries such as marine and high-end manufacturing economy, enhance its exchanges and cooperation with countries such as Japan and South Korea, and create an international innovation economy leading zone with its advantages in free trade zones. ⑤ Jinpu New Area and Changchun New Area should fully leverage their core location advantages in the Northeast Asian Economic Circle, maintain strategic intervention capabilities (Wu \u0026amp; Zhang, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), increase external trade and FDI, promote industrial structure optimization and upgrading to achieve regional sustainable development. ⑥ As the provincial growth poles, high-tech industries should be vigorously developed in Tianfu New Area and Dianzhong New Area, to enhance their innovation capabilities (Thomas, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Wojnicka-Sycz, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), while where logistics and transportation hubs should be developed to optimize regional transportation network (Cheshmehzangi \u0026amp; Tang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) by their location advantages.\u003c/p\u003e \u003cp\u003e(2) For national new areas with a significant negative correlation with their hinterland\u003c/p\u003e \u003cp\u003e① As an engine and a free trade experimental carrier, industrial structure (Thomas, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) in Binhai New Area should be continuously adjusted to achieve the reorganization of resources, better embed into regional economic networks by strengthening its connections (Matzana et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with other cities and infrastructure construction (Wu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), increasing innovation efforts, optimizing regional industrial layout, sharing talents and resources. ② Technological innovation (Wojnicka-Sycz, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) should be promoted in Liangjiang New Area, to cultivate endogenous driving forces, strengthen exchanges, and establish a win-win cooperation mechanism. ③ The connections (Matzana et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with its hinterland should be strengthened to improve its economic spillover effects (Parr, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) in Lanzhou New Area through reasonable planning by relying on local characteristic industries, enhancing industrial synergy, and promoting rational resource flow (Wu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). ④ Emphasizing the coordination and complementarity of policies, the \"Two Cities and One District\" model should continue to be developed in Gui'an New Area, to strengthen infrastructure connectivity, promote networked governance, and accelerate the realization of regional coordinated coexistence and coordinated development by transferring some industries and employment opportunities (Ke \u0026amp; Feser, 2010), implementing a balanced development strategy (Guo, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e(3) For national new areas with insignificant positive correlation with their hinterland\u003c/p\u003e \u003cp\u003e① Industrial clusters with core competitiveness should be cultivated and strengthened in Xiangjiang New Area, to drive the industrial synchronous development in its hinterland by promoting the optimization and upgrading of industrial structure (Thomas,1972). ② By developing industries with local characteristics and gathering more resources and investment (Dranca, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bere, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), unique economic advantages can be formed in Ganjiang New Area to promote balanced regional development.\u003c/p\u003e \u003cp\u003e(4) For national new areas with insignificant negative correlation with their hinterland\u003c/p\u003e \u003cp\u003e① Unique advantages should be fully utilized in Fuzhou New Area to create a cutting-edge platform for communication (Popkova, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cheshmehzangi \u0026amp; Tang, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with its hinterland, by strengthening deep cooperation in economic, social, cultural and other fields, promoting industrial coordination and sharing resource. ② The international logistics channels should be accelerated in Harbin New Area to create high-end services and factor aggregation platforms, by optimizing its radiation policies, encouraging industrial transfer and cooperation, and enhancing regional synergy and local self-development capabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.3. Research prospects\u003c/h2\u003e \u003cp\u003eThrough empirical analysis and theoretical exploration, our study provides a new perspective for understanding and optimizing the relationship between growth poles and their hinterland, and achieves the Sinicization of growth pole theory. Meanwhile, our findings can provide empirical evidence for policymakers to design more targeted regional policies. Furthermore, our theoretical model and policy implications can help entrepreneurs or investors find more favorable opportunities. However, there also are some limitations. Firstly, due to the obtaining difficulties and limitations of data, some indicators are unable to be utilized, which may affect the generality of our results. Secondly, because of various dynamic factors, such as policy heterogeneities and market changes, the relationship between national new areas and their hinterland cannot fully be reflected. Finally, the interference of regional preexisting core cities on the radiative driving effects of national new areas is currently beyond our control. Future research will strive to more reasonably reveal the dynamic evolution of their relationship by expanding the scope of data and deepening theoretical models.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhibao Wang: Conceptualization, Methodology, Visualization, Investigation, Writing - Review \u0026amp; Editing. Yi Zheng: Data Curation, Investigation, Writing - Review \u0026amp; Editing. Lijie Wei: Writing - Review \u0026amp; Editing. Ping Wang: Data Curation, Supervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAghion, P., \u0026amp; Bolton, P. (1997). 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[In Chinese].\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Data sources: \u003cem\u003eThe National New Area Research Report (2022\u0026ndash;2023)\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.cn/guoqing/2009-09/21/content_2752431.htm\u003c/span\u003e\u003cspan address=\"https://www.gov.cn/guoqing/2009-09/21/content_2752431.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003cdiv id=\"Par33\" class=\"Para\"\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mp.weixin.qq.com/s/XXC-lSCbv3b41WcCSISmAQ?\u003c/span\u003e\u003cspan address=\"https://mp.weixin.qq.com/s/XXC-lSCbv3b41WcCSISmAQ?\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/div\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Growth pole theory, The radiation driven effect, The spatial spillover effects, The multi-period double difference model, National new area, China's mainland","lastPublishedDoi":"10.21203/rs.3.rs-9093969/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9093969/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs the important growth poles, national new areas are key to regional economy. In order to argue the Sinicization of growth pole theory, we investigate the radiative driving effects of China\u0026rsquo;s 19 national new areas on their hinterland by a multi-period double difference method with bidirectional fixed effects. Overall, China's national new areas have a positive radiative driving effect on their hinterland. In terms of marginal effects, general expenditure in Binhai New Area shows a cubic relationship with its hinterland, while which shows a cubic relationship with population scale, consumption capacity, or economic development in other national new areas. Population scale in most of national new areas has a positive marginal effect on economy in their hinterland. Through policies, innovation, and markets, these national new areas have produced significant positive spatial spillover effects and proximity effects on their hinterland.\u003c/p\u003e","manuscriptTitle":"The Sinicization of Growth Pole Theory: Evidences from National New Areas in China's Mainland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 09:11:45","doi":"10.21203/rs.3.rs-9093969/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":"5c137fb6-7688-43a9-81de-832d1df3fae8","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T12:38:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 09:11:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9093969","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9093969","identity":"rs-9093969","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}