Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3, 1349-1359 2025 Publisher: Learning Gate DOI: 10.55214/25768484.v9i3.5529 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 17 January 2025; Revised: 24 February 2025; Accepted: 26 February 2025; Published: 17 March 2025 * Correspondence: wangpeixu0506@163.com The impact mechanism of green technology innovation on urban green development: Evidence from Central China Wang Peixu1*, Zunirah Mohd Talib2, Omar b. Abdul Rashid3, Ooi Boon Keat4 1Management and Science University, 40100,Shah Alam, Selangor, Malaysia; wangpeixu0506@163.com (W.P.). 2Postgraduate Centre, Management and Science University, 40100,Shah Alam, Selangor, Malaysia; zunirah@msu.edu.my (Z.M.T.). 3Postgraduate Centre, Management & Science University, Seksyen 13, 40100 Shah Alam, Selangor, Malaysia; omar@msu.edu.my (O.B.A.R.). 4School of Education and Social Science,Management and Science University，40100,Shah Alam, Selangor, Malaysia; bkooi@msu.edu.my (O.B.K.). Abstract: In order to further promote urban green development in Central China, the research uses panel data from 80 cities in Central China from 2011 to 2022 as a sample and employs the linear regression method, mediating effect, and threshold effect to empirically test the impact mechanism of green technology innovation on urban green development in Central China. The results show that: first, green technology innovation significantly promotes urban green development, and the conclusion still holds when considering endogeneity. Second, the regional heterogeneity test found that the differences in resource endowments among provinces lead to significant differences in the impact of green technology innovation on urban green development. Third, the mediating effect found that digital inclusive finance and industrial structure upgrading both play partial mediating roles between green technology innovation and urban green development. Fourth, the threshold effect found that urban environmental infrastructure investment has a positive single threshold effect between green technology innovation and urban green development. This paper not only enriches the research on urban green development at the academic level but also provides reference value for the sustainable development of cities in other developing countries and regions. The objective of this study is to explore how to promote the green development of cities in Central China from the perspective of green technology innovation and to provide a reference for other developing countries and regions. In this topic, the main concepts and theories that underpin the research are presented. First, the concept of sustainable development lays the main tone of this study. Second, Schumpeter's innovation theory provides theoretical support for establishing the connection between green technology innovation and urban green development. The methodology adopted for this research comprises the entropy method, panel multiple linear regression model, mediation effect model, and threshold effect model. The original data used in this study are authoritative secondary data from the official statistical yearbooks of various provinces in Central China. The results obtained revealed that green technology innovation has a significant positive promoting effect on urban green development in Central China, and digital inclusive finance and upgrading of industrial structure have significant partial mediating roles in the process of green technology innovation promoting urban green development in Central China. The threshold effect found that urban environmental infrastructure investment has a significant single threshold effect in the process of green technology innovation promoting urban green development in Central China. In the discussion section, these results are contextualized in light of the theoretical framework, highlighting the implications and relationships identified. Possible discrepancies and limitations of the study are also considered in this section. The practical and theoretical implications of this research are https://orcid.org/0000-0002-4367-561X https://orcid.org/0000-0001-8002-1685 1350 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1349-1359, 2025 DOI: 10.55214/25768484.v9i3.5529 © 2025 by the authors; licensee Learning Gate discussed, providing insights into how the results can be applied or influence practices in the field of urban green development. First, this study provides new ideas for policymakers in urban green development in Central China. Second, this study provides a reference for urban green development in other developing countries and regions. These implications could encompass green technology, green lifestyle, green production, and green industry, etc. This study contributes to the literature by considering new ideas for green technology innovation, as well as the intermediary effect of digital inclusive finance and the threshold effect of green investment. The relevance and value of this research are evidenced by the fact that green technology innovation, digital inclusive finance, and green investment provide new ideas for policymakers and enterprises for urban green development. Keywords: Digital inclusive finance, Green technology innovation, Green development, Upgrading of industrial structure. 1. Introduction With the increasing destruction of natural resources and environmental pollution around the world, ecological problems are increasingly restricting urban development. Green development is considered an effective way to achieve a win-win situation between economy and environment, which means that development and environmental protection are compatible. Urban green development referred to in this article highlights the concept of "sustainable development" and emphasizes the possibility of achieving coordinated development of economy, society and environment [1]. Green development has become the fundamental driving force for sustainable cities. How to improve urban green development has become the focus of academic attention. In the current era, the implementation of an innovation-led development strategy requires technological transformation to support a systematic response to ecological pollution without giving up social development and economic growth. Schumpeter [2] innovation theory confirms that economic development would be stimulated by innovation [2]. Then most scholars believed that green technology innovation is an important driving force for achieving green development [3, 4]. With the increasing shortage of resources and severity of environmental pollution problems, the focus of academic research has gradually shifted to how to achieve regional development driven by technological innovation in an environmentally friendly way. This study selects Central China as the research sample. Central China is a developing region in China with a strong industrial and agricultural base and a dense population. Therefore, it is very important to study the role of green technology innovation in Central China in achieving urban green development, and it also has important reference significance for other developing countries and regions. 2. Theoretical Framework The world is currently facing multiple environmental crises, such as global warming, extreme weather and severe pollution. Green development is oriented to achieve harmony between man and nature, emphasizing the overall coordination of economic, social and environmental systems [5]. It is an economic development model that takes into account both ecological sustainability and development goals, and has become a hot topic in academia. In this context, green technology innovation is considered to be a key means to achieve a green development model, by significantly reducing the environmental effects of fossil energy consumption, to improve green total factor productivity and achieve sustainable development goals Dam, et al. [6]. Obobisa and Ahakwa [7] found that green technology innovation significantly improved the carbon emission efficiency of European countries. Feng, et al. [8] believed that technological innovation is a significant factor in promoting regional green growth. However, there is currently a lack of direct research on the relationship between green technology innovation and urban green development. Therefore, the study takes Central China as the background and proposes the first hypothesis: H1: Green technology innovation positively affects urban green development in Central China. 1351 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1349-1359, 2025 DOI: 10.55214/25768484.v9i3.5529 © 2025 by the authors; licensee Learning Gate The essence of digital inclusive finance is the combination of intelligent digital technology innovation and traditional finance. Through innovative technical support, it provides the public with personalized, low-cost, sustainable and comprehensive financial services. Many scholars have studied the green effects of digital inclusive finance. Digital inclusive finance can promote regional green growth [9] improve regional green development efficiency [9] and reduce regional carbon emissions [10]. At present, there is no literature in the academic community that studies the mediating effect of digital inclusive finance between green technology innovation and urban green development. However, Xie, et al. [11] and Qin, et al. [12] found that financial technology innovation can indirectly promote green economic growth and improve the green environment index through green finance. Therefore, this study proposes the second hypothesis. H2: Digital inclusive finance plays a mediating role in the impact of green technology innovation on urban green development in Central China. Industrial structure upgrading refers to the transformation of a country's economic growth mode, including the transformation from labor-intensive growth mode to capital-intensive and knowledge- intensive growth mode, and the transformation of economic growth momentum from factor-driven to investment-driven and innovation-driven. Existing scholars have found that industrial structure upgrading often drives regional green development, including reducing carbon dioxide emissions [13] improving regional green development efficiency [14] and promoting green economic growth [15]. Among the driving factors of industrial structure upgrading, technological innovation is undoubtedly the most critical, because technological progress caused by technological innovation affects the direction and speed of industrial development [3] and is an important driving force for promoting industrial intensive and large-scale development [16]. However, the academic community has not directly studied the transmission mechanism of industrial structure upgrading between green technological innovation and green development of Central China cities. Therefore, this study proposes the third hypothesis. H3: Industrial structure upgrading plays a mediating role in the impact of green technology innovation on urban green development in Central China. Urban environmental infrastructure investment in this study belongs to macro-government green investment, which is conducive to promoting the development of circular economy, reducing environmental pollution and preventing natural disasters, and is a necessary government fiscal means to achieve urban green development. Tan and Zhou [17] and Hao, et al. [18] found that green investment has a nonlinear threshold effect on environmental pollution and carbon emission intensity. Huang and Chen [19] used government environmental governance investment as a proxy indicator and found that green investment has a threshold effect on environmental quality. However, there is no literature in the academic community on the threshold effect of green investment in the process of green technology innovation affecting urban green development. Therefore, this study proposes the fourth research hypothesis. H4: Green investment plays a threshold effect in the impact of green technology innovation on urban green development in Central China. 3. Methodology 3.1 Data Sources Central China includes 6 provinces, including 82 cities. The data of Xiangxi and Enshi were seriously missing, so they were excluded. Finally, 80 cities in Central China were used as samples for this study. This paper uses panel data of 80 cities in Central China from 2011 to 2022, and the original data comes from China Statistical Yearbook, Shanxi Statistical Yearbook, Henan Statistical Yearbook, Hubei Statistical Yearbook, Hunan Statistical Yearbook, Jiangxi Statistical Yearbook and Anhui Statistical Yearbook. For few missing data, interpolation method is used to fill in. 3.2. Variables Dependent variable: Urban green development (UGD). Urban green development is a comprehensive concept. This study establishes an urban green development index system from three aspects: government management, enterprise development, and residents' lives. The urban green 1352 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1349-1359, 2025 DOI: 10.55214/25768484.v9i3.5529 © 2025 by the authors; licensee Learning Gate development level score calculated by the entropy method is used as a proxy for the dependent variable. Figure 1 shows the indicator framework of it. Figure 1. Original Indicators System for Urban Green Development Independent variable: Green technology innovation (GTI). In this study, the sum of green invention patents and green new utility patents will be used as a proxy indicator of green technology innovation. Control variables: First, the level of opening up to the outside world (OPEN). This study uses the proportion of foreign direct investment to GDP as a proxy indicator of opening up. Second, the level of education (EDU). This study uses the proportion of education expenditure to GDP as a proxy indicator of education level. Third, population density (PD). Population density is directly related to the pressure of urban development. In this paper, population density is measured by the number of urban populations per square kilometer. Fourth, the level of urban economy (UECO). This study uses the per capita GDP growth rate as a proxy indicator of urban economic level, which can better reflect the dynamic quality of urban economic. Mediating variables: First, digital financial inclusion (DFI). The digital financial inclusion data uses the Peking University Digital Financial Inclusion Index released by the Peking University Digital Finance Research Center. Second, upgrading of industrial structure (UIS). This study uses the Theil Index as a proxy indicator to measure industrial structure upgrading.As the formula of Thayer Index shows, TL is the Thayer Index, wi is the ratio of the output value of industry i to GDP, and ei is the ratio of the number of employees in industry i to the total number of employees: )log( n 1 i i i i e w wTL = = 1353 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1349-1359, 2025 DOI: 10.55214/25768484.v9i3.5529 © 2025 by the authors; licensee Learning Gate 3.3. Entropy Method "Entropy" is a term coined by German physicist R.J.E. Clausius in 1850 to indicate the uniformity of energy distribution in space, which belongs to the category of thermodynamics. Later, this method was cited by other disciplines. The larger the entropy, the greater the degree of dispersion of the indicator, and the greater the impact of the indicator on the comprehensive evaluation. It can reduce the influence of human subjectivity on the evaluation results and make the evaluation results more realistic. In this study, the entropy method is used to measure the UGD index. 3.4. The Panel Regression Model The study would use the panel regression model to prove the direct impact of GI on UGD. Its general formula is as follows, where β0 represents the intercept term of the Y axis, β1 to β5 represent the coefficients of the independent variable, and ε represents the error term. 3.5. Mediating Effect Model The formula of mediating effect model is following. In the above formula: a represents the direct effect of the independent variable on the mediating variable; b represents the direct effect of the mediating variable on the dependent variable; c and c' represent the direct effect of the independent variable on the dependent variable; e1, e2 and e3 represent error terms. 3.6. Threshold Effect Model The threshold effect refers to the situation that when an economic parameter reaches a specific value or a specific range, it would cause another economic parameter to undergo a sudden change or qualitative change. The basic formula of the threshold effect is as follows: In the above formula, β1 and β2 represent different threshold intervals, the influence coefficient of GTI on UGD; GTIt represents the threshold variable; γ represents the threshold value. 4. Results and Discussions 4.1. Test of Correlation and Multicollinearity Table 1 shows the results of the correlation test and multicollinearity test of the main variables. The dependent variable urban green development and the core independent variable green technology innovation are significantly positive at the 10% level, which preliminarily verifies the correlation between green technology innovation and urban green development. At the same time, the multicollinearity test results show that the VIF values of each variable are less than 5, which indicates that there is no multicollinearity problem in the model.  ++++++= UECOPDEDUOPENGTIUGD 543210 3 3 2 2 1 ' ' c ebUISGTIcUGD ebDFIGTIcUGD eaGTIUIS eaGTIDFI eGTIUGD ++= ++= += += += )( )( 2t 1t   ＞tt tt GTIGTIUGD GTIGTIUGD ++= ++= 1354 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1349-1359, 2025 DOI: 10.55214/25768484.v9i3.5529 © 2025 by the authors; licensee Learning Gate Table 1. Test of Correlation and Multicollinearity UGD GTI OPEN EDU PD UECO UGD 1.000 GTI 0.058* 1.000 OPEN 0.028 0.230*** 1.000 EDU -0.007 -0.310*** -0.128** 1.000 PD -0.385*** 0.353*** 0.174*** -0.235*** 1.000 UECO 0.157*** -0.015 0.02 -0.007 0.109*** 1.000 VIF 1.26 1.19 1.13 1.07 1.02 4.2. Benchmark Panel Regression This section analyzes the direct impact of GTI on UGD in Central China. To improve the robustness of the regression results, this paper adopts ordinary least squares (0LS), random effects regression (REs) and fixed effects regression (FEs). The empirical results shown in Table 4.2 show that the regression estimation coefficients of GTI on UGD in the three equations are all significantly positive. And the conclusions of the three models are consistent regardless of whether the control variables are included. In particular, the regression results of the random effects model show that for every unit increase in GTI, the level of industrial pollution emissions increases by 0.012 units. The above results strongly prove the role of GTI in promoting UGD in Central China, thus proving Hypothesis 1. Regarding control variables, the effect of the level of opening up to the outside world on UGD is not significant. The effect of education on UGD is significantly negative, and the possible reason is that Central China cannot attract talents well, resulting in the loss of human resources. The effect of population density on UGD is significantly negative, which just proves that the higher the population density, the more unfavorable it is for urban green development. The effect of urban economy on UGD is significantly positive, which shows that the economic growth of Central China cities has provided good financial support and promoted urban green development. Table 2. Results of Benchmark Panel Regression OLS FE RE GTI 0.004* (0.002) 0.014*** (0.002) 0.009*** (0.003) 0.010*** (0.003) 0.008*** (0.003) 0.012*** (0.002) OPEN 0.002* (0.001) 0.001 (0.001) 0.001 (0.001) EDU -0.004* (0.002) -0.011*** (0.004) -0.007** (0.003) PD -0.073*** (0.004) 0.031 (0.068) -0.071*** (0.009) UECO 0.012*** (0.002) 0.011*** (0.002) 0.011*** (0.001) Constant 0.551*** (0.011) 0.924*** (0.028) 0.525*** (0.014) 0.342 (0.409) 0.531*** (0.014) 0.939*** (0.056) N 960 960 960 960 960 960 R2 0.002 0.238 0.013 0.090 0.013 0.425 F Value/Wald Chi2 3.25 60.79 11.15 14.10 9.84 123.44 4.3. Robustness Test To ensure the robustness of the empirical results, this paper mainly uses three methods to conduct robustness tests. First, this study excludes half of the sample period and samples of provincial capital cities. There are significant differences between the green development of provincial capital cities and 1355 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1349-1359, 2025 DOI: 10.55214/25768484.v9i3.5529 © 2025 by the authors; licensee Learning Gate other ordinary cities, and this difference may affect the empirical results. Second, this study replaces the core explanatory variables. This study cites the number of green patent authorizations as a new proxy variable for green technology innovation, named GTIX. Third, this study shrinks the variables to reduce the impact of extreme values on the overall data analysis. Fourth, in order to eliminate the endogeneity problem, this study selects the independent variable with a lag of one period, named LGTI, as an instrumental variable for regression with Two-Stage Least Squares Method. Table 3. Results of Robustness Test Reducing sample size Replacing independent variable Winding data GTI 0.014*** (0.003) 0.010*** (0.003) GTIX 0.015*** (0.003) OPEN 0.000 (0.001) 0.000 (0.001) -0.001 (0.003) EDU -0.017*** (0.005) -0.003 (0.004) -0.009** (0.004) PD 0.146 (0.101) 0.014 (0.067) 0.077 (0.073) UECO 0.010*** (0.002) 0.011*** (0.002) 0.011*** (0.002) Constant -0.349 (0.599) 0.404 (0.403) 0.063 (0.437) N 518 960 960 R2 0.476 0.097 0.214 F 12.20 18.76 11.27 The regression results of the robustness test shown in Table 3 show that after reducing the sample size, replacing the explanatory variables and shrinking the data, the direct impact of green technology innovation on the green development of central China cities is still significant. The endogeneity analysis results shown in Table 4 show that after introducing instrumental variables, the direct impact of green technology innovation on the green development of Central China cities is still significant. The above results verify the robustness of the empirical model of this study. Table 4. Results of Endogeneity Test The First Step The Second Step LGTI 0.388*** (0.035) GTI 0.067*** (0.023) OPEN -0.006 (0.013) -0.004 (0.005) EDU -0.007 (0.024) 0.012 (0.010) PD 1.015*** (0.375) 0.015 (0.124) UECO -0.003 (0.006) 0.007** (0.003) Anderson-Rubin Wald value 10.07*** Kleibergen-Paap rk LM value 37.76*** Cragg-Donald Wald F value 166.63 Weak Identification Test Critical Value at 10% Level 16.38 1356 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1349-1359, 2025 DOI: 10.55214/25768484.v9i3.5529 © 2025 by the authors; licensee Learning Gate 4.4. Regional Heterogeneity Test The above conclusions confirm that GTI has a significant role in promoting urban green development in Central China. However, due to the vast territory of Central China and the uneven development of cities in various provinces, this study will conduct regression analysis on the six provinces in Central China. As shown in Table 5, the results show that: First, in Henan Province, Hunan Province and Anhui Province, the impact of GTI on urban green development is significantly positive. Second, in Shanxi Province, the impact of GTI on urban green development is significantly negative. The possible reason is that Shanxi Province is underdeveloped and the level of green technology innovation is in the primary stage, so promoting urban green development by developing green technology innovation is counterproductive. Third, in Hubei Province and Jiangxi Province, the impact of GTI on urban green development is not significant. The possible reason is that Hubei and Jiangxi are already in the late stage of urban green development, with a high green development index, so the role of GTI on urban green development is not obvious. Table 5. Results of Regional Heterogeneity Test SHANXI HENAN HUBEI HUNAN JIANGXI ANHUI GTI -0.096*** (0.008) 0.052*** (0.006) 0.002 (0.005) 0.015** (0.007) -0.001 (0.008) 0.029*** (0.006) OPEN 0.011**（0.005） -0.001 (0.001) -0.019** (0.009) -0.006 (0.006) 0.005 (0.013) 0.001 (0.005) EDU 0.035***（0.008） -0.034*** (0.010) -0.042*** (0.013) -0.030** (0.012) -0.009 (0.011) -0.010 (0.011) PD 0.448（0.292） 0.183* (0.109) 0.245 (0.210) -0.003 (0.192) 0.099 (0.325) -0.163 (0.100) UECO -0.002（0.002） 0.014*** (0.004) 0.005 (0.006) -0.007 (0.006) 0.012 (0.008) 0.014*** (0.005) Constant -1.585（1.587） -0.876 (0.716) -0.783 (1.231) 0.642 (1.120) 0.056 (1.863) 1.418** (0.645) N 132 204 144 156 132 192 R2 0.624 0.426 0.247 0.074 0.338 0.648 F Value 38.47 27.05 4.10 2.19 1.12 14.23 4.5. Mediating Effect Results This section will test the mediating role of digital inclusive finance and industrial structure upgrading in the process of GTI promoting urban green development in Central China with Bootstrap Method. The mediation effect regression results shown in Table 6 show that for the mediating variables DFI and UIS, the direct effect and the mediating effect are both positive and significant, and there is no 0 value in the confidence interval. This means that digital inclusive finance and industrial structure upgrading play a partial mediating role in the process of green investment affecting urban green development in Central China. Table 6. Results of Bootstrap Method for Mediating Effect Mediating Variables Effect Regression Results [95% conf.interval] DFI _bs_1: r (Mediating Effect) 0.005*** (0.002) [0.002 , 0.008] _bs_2: r (Direct Effect) 0.009*** (0.003) [0.004 , 0.014] UIS _bs_1: r (Mediating Effect) 0.004*** (0.001) [0.002 , 0.005] _bs_2: r (Direct Effect) 0.010*** (0.002) [0.006 , 0.015] 1357 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1349-1359, 2025 DOI: 10.55214/25768484.v9i3.5529 © 2025 by the authors; licensee Learning Gate 4.6. Threshold Effect Results In this study, urban environmental infrastructure investment would be used as a threshold variable and named UEI. In Table 7, after 100 times of Bootstrap repeated sampling, preliminary verification found that when UEI is as the threshold variable, only the single threshold effect is significant. Therefore, it is necessary to re-verify single-threshold regression separately. Table 7. Verification for Threshold Type for UEI Threshold Type Bootstrap F-Statistic P-value Crit10 Crit5 Crit1 Single Threshold 100 23.29 0.030 19.377 21.381 27.686 Double Threshold 100 4.52 0.960 18.814 22.538 30.564 Triple Threshold 100 6.25 0.860 16.980 19.630 26.057 Table 8. Retesting and Separately Estimating of Single Threshold Effect for UEI Threshold Type Bootstrap F Statistic P-value Crit10 Crit5 Crit1 Threshold value estimation [95% conf. Interval] Single Threshold 50 23.29 0.040 19.329 23.052 33.057 2.168 [ 2.133, 2.176] Retesting and Separately Estimating the Single Threshold Effect for UEI. As seen from Table 8 ,the single threshold effect of UEI is significant, and the single threshold value is 2.168. Single Threshold Effect Regression Results for UEI as Threshold Variable. Table 9 shows the single threshold effect regression results for UEI as threshold variable. First, when the logarithm of UEI is less than 2.168, the coefficient of GTI on UGD is 0.005. Second, when the logarithm of UEI is greater than or equal to 2.168, the coefficient of GTI on UGD is 0.012. It can be understood that the increase in urban residents' income would promote green consumption, thereby giving rise to a series of green and environmentally friendly products, and promoting the green economy and green development of cities. Table 9. Results of Threshold Effect Regression. Variables GTI (UEI＜2.168) GTI (UEI≧2.168) OPEN EDU PD UECO Constant R2 F Threshold Regressio n Results 0.005* (0.003) 0.012*** (0.003) 0.001 (0.001) -0.012*** (0.004) 0.036 (0.067) 0.010*** (0.002) 0.319 (0.405) 0.096 15.38 Source: Based on Empirical Analysis by Stata.17.0. 5. Conclusion Green technology innovation is an organic combination of environmental protection concepts and emerging technologies, and is a key tool for achieving green development in the future. This study uses the panel data of cities in Central China to test the impact mechanism of green technology innovation on urban green development through multiple empirical models. The results show that: First, green technology innovation has a significant promoting effect on urban green development in Central China, and the conclusion still holds when considering endogeneity. Second, the regional heterogeneity test found that the differences in resource endowments among provinces lead to huge differences in the impact of green technology innovation on urban green development. Third, the mediating effect found that digital inclusive finance and industrial structure upgrading are important transmission paths for green technology innovation to promote urban green development in Central China. Fourth, the heterogeneity test found that based on resource endowment conditions, the degree of influence between 1358 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 3: 1349-1359, 2025 DOI: 10.55214/25768484.v9i3.5529 © 2025 by the authors; licensee Learning Gate green technology innovation and urban green development in the six provinces of Central China is different. Fifth, the threshold effect test found that as a green investment, urban environmental infrastructure investment plays a single threshold effect in the process of green technology innovation affecting urban green development. The above research conclusions not only provide ideas for urban green development in Central China, but also have reference significance for other regions and developing countries. Based on it, this article puts forward the following policy recommendations: First, the Central China government should continue to strengthen support for green technology innovation and reduce the innovation costs of enterprises by strengthening tax exemptions, R&D subsidies, etc. At the same time, strengthen the control of industry green technology standards, improve the green patent review mechanism and efficiency, and encourage the transformation of technological achievements. In addition, the government should promote cooperation between local enterprises and universities, research institutes and international platforms, and focus on key technological innovation. Second, the central China government should accelerate the promotion of industrial structure upgrading, clarify the future direction of industrial development, gradually eliminate backward industries, develop new energy and energy-saving and environmental protection industries, and promote low-carbon transformation of high-energy-consuming industries. Third, Central China belongs to a developing region, and its digitalization level needs to be improved. Therefore, the central China government should further consolidate the basic support of digital inclusive finance, clarify the key service objects by improving digital infrastructure and formulating special plans for digital inclusive finance, and help the green and inclusive development of cities. Fourth, the government should formulate a green investment strategic plan and clarify the mandatory proportion of green investment. And strengthen the construction of green infrastructure, such as low-carbon transportation networks, ecological restoration projects, etc. Fifth, differentiated policy measures should be formulated according to local conditions. Since green technology innovation and urban green development vary greatly among provinces and cities, governments in central China should formulate targeted policy plans. For example, in ecologically fragile areas, support for green technology innovation should be strengthened, while in areas with better ecological benefits, environmental assessment and green technology innovation audits need to be strengthened. The limitations of this study depend on data availability. The first, since the original data is secondary data from the official statistical yearbooks of various provinces, it is limited to 2011 to 2011, and the sample size is relatively small. Second, there are countless indicators that constitute the urban green development system, but due to academic research, this article only obtained a dozen research indicators, so it cannot fully represent the true level of urban green development. In the future, researchers can consider increasing the time period to explore the relationship between the two under long-term effects, or take other countries and regions as research objects to further expand the research results. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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