Exploring the Path of Green Innovation and High Quality Development of Influential Regional Enterprises - Based on the Analysis of Dynamic QCA Method and Matlab Sustainability Prediction

Exploring the Path of Green Innovation and High Quality Development of Influential Regional Enterprises - Based on the Analysis of Dynamic QCA Method and Matlab Sustainability Prediction | 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 Exploring the Path of Green Innovation and High Quality Development of Influential Regional Enterprises - Based on the Analysis of Dynamic QCA Method and Matlab Sustainability Prediction QIJAI LIU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4108767/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 Objective: To study the multifactor linkage effects behind the differences in the sustainable development of green innovation of local enterprises in the spatio-temporal dimension, so as to provide an important reference for the practice of sustainable development of green innovation of local enterprises. Methodology: An analytical framework for the sustainable development of green innovation of local enterprises is established, and the dynamic QCA method is applied to analyse the provincial-level panel data of China from 2012 to 2021, to explore the linkage effect of each factor on the time axis, and to explore the differences of multi-factors on the time axis. The experimental study also examined the spatial distribution of regional coverage in conjunction with different regional divisions in China. Findings The study found that different factors may have different influence effects in different contexts. Firstly, while market demand is an important influencing factor, its role relative to economic drivers and social environment factors may be relatively limited in some contexts. Second, high levels of foreign investment and demand for innovation in the service sector have a significant impact on green innovation in local firms. At the same time, with the growing consumer preference for green products, green preferences in market demand have also begun to become an important factor influencing firms' green innovation. Meanwhile, in the spatial dimension, the provincial coverage out of the obvious regional differences. Experimental significance The research on the sustainable development of green innovation of local enterprises needs to be combined with the actual situation in China, and the resource differences and characteristics of different regions make it necessary for local enterprises to combine with their own reality in the process of green innovation to achieve the linkage and complementarity of factors. This requires local governments to fully consider the actual situation of the region when formulating relevant policies, and promote green innovation according to local conditions. This experiment is the first attempt to use the joint application of dynamic QCA and Matlab for the study of green innovation in local enterprises, exploring the consistency in the longitudinal time dimension. factors dynamic QCA regional differences time dimension Matlab Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction China is a vast country with a large population, and its economic and technological development shows obvious geographical differences(Geary & Nyiawung, 2022 ; Tsui, 1993 ). Domestic and foreign scholars generally divide China into three major regions, namely, the eastern region, the central region, and the western region, for the purpose of research.As shown in Fig. 1 . As an important engine of China's economic development, the eastern region has been a leader in terms of GDP per capita and government expenditure on social security.As the eastern region has a more complete industrial system, so that it has a strong scientific and technological innovation in science and technology innovation(Wu, He, Zhuang, & Yi, 2020 ). Universities, research institutions and enterprises in the eastern region possess a large number of patents and technological achievements. They are also actively introducing advanced technologies from abroad and constantly promoting the development of scientific and technological innovation through digestion, absorption and re-innovation(Kostka, Zhang, & Shin, 2020 ; Shen, Li, & Tolbert, 2021 ). The central region is at the middle level of China in terms of economic output and government expenditure on social security. The central region has a better industrial base, but agriculture is still its dominant industry. In recent years, the central region has been actively promoting the strategies of industrialisation, urbanisation and agricultural modernisation, and accelerating the pace of upgrading and transforming the industrial structure(Jiang, Qian, & Wen, 2018 ). Although the western region is lagging behind economically, its government expenditure on social security is growing rapidly. This is mainly due to the fact that the western region is endowed with abundant resources and relatively low labour costs.The imperfect industrial system in the western region has led to its weak scientific and technological innovation capacity. As the Government has increased its support for the western region, enterprises in the western region have begun to pay attention to scientific and technological innovation, constantly improving their technological level and beginning to transform(Child & Tse, 2001 ). In recent years, the synergistic relationship between corporate green innovation and per capita output has also been widely studied. Many scholars have gradually reached a consensus on the view that corporate green innovation improves resource use efficiency, reduces production costs and increases per capita output. Sustainable development depends to a certain extent on the ability to produce green technologies(Song, Zhang, Sahut, & Rubin, 2023 ). Castellani et al. argue that green technologies make a positive contribution to per capita income(Castellani, Marin, Montresor, & Zanfei, 2022 ). In terms of local government funding for technological innovation, local government environmental policies have a significant impact on green innovation in regional firms, and local government R&D investment positively moderates this association(Chen, Yao, & Zhong, 2022 ). Lv,C,C et al. investigated the innovation efficiency of firms' green innovations in 30 provinces in China from 2003 to 2017, and statistically found that the environmental regime plays a positive moderating role between the financial structure and firms' green innovations(Lv, Shao, & Lee, 2021 ). By comparing the per capita output aspects of the regions, it was found that cities that overshoot their economic growth targets can have a significant dampening effect on green technological innovation in the regional service sector(F. Shen et al., 2021 ). A study of social security expenditures by regional governments found that the introduction of FDI can have a positive impact on regional green technology specialisation and per capita income(Paramati, Mo, & Huang, 2021 ). Regarding the new upgrading of green innovation in enterprises, some scholars have unfolded their research from the perspective of enterprises.Boajye et al. found that green innovation increases the cost of environmental governance, reduces investment in productive activities, and negatively affects the performance of enterprises(Boakye, Tingbani, Ahinful, & Nsor-Ambala, 2021 ). It is very interesting to note that the findings of Zhang et al. are completely opposite to those of Boajye et al. This may be due to the large development gap between the east and west of China(Q. Zhang & Ma, 2021 ). Other scholars have taken a social perspective. Romer and Lucas, in their theory of endogenous economic growth, argue that technological progress restructures industries and contributes to social development(Lucas Jr, 1988 ; Romer, 1986 ). The above studies have revealed the specific impact of per capita output value and government social security expenditure on enterprises' green technological innovation, respectively. However, they have not explored all three under the same theoretical framework. In addition, politics, business and social culture are all permeated with the influence on the social environment atmosphere, so the interaction between the three is inextricably linked to the influence of social environment factors(Li & Xu, 2023 ; J. Zhang, Huang, & He, 2023 ; N. Zhang et al., 2023 ). The social and environmental climate for enterprises can improve the sense of social responsibility and credibility of enterprises. By publicising the green production, environmental protection measures and sustainable development business philosophy of enterprises, it will guide the transformation of pollution-intensive enterprises to cleaner ones and achieve a major transformation from the secondary to the tertiary sector(Kuang & Lin, 2022 ). For consumers, the promotion of green concepts in the social environment can enhance their environmental awareness and increase their knowledge of and demand for green consumption. By publicising environmental protection concepts and promoting green products and services, it guides consumers to change their consumer attitudes and behaviours and promotes green technological innovation by enterprises(Hu & Zheng, 2023 ). This study incorporates economic factors such as per capita output, social environment factors such as government social security and market demand into the same framework. Drawing on the theory of synergy and complex systems, a composite factor single output analysis model is constructed. Using panel data provided by the National Bureau of Statistics of China (NBS) for 30 provinces (cities) over the period 2012–2021, a dynamic qualitative comparative analysis (QCA) method is employed. To reveal the causal mechanisms affecting the differences in the high-quality development of green innovation among regional enterprises on the longitudinal axis of time. And to deeply explore the differences in group preference among different regions. In order to overcome the shortcomings of linear regression and fuzzy set qualitative comparative analysis in dealing with the local practice context and factor linkage effects on the longitudinal axis of time. The experiment expects that this analytical framework can explain the path of green innovation and high-quality development of regional enterprises more comprehensively.This study aims to answer the following questions: is there a single factor that is necessary for the sustainability of a square business in the time dimension? Do these factors produce changes over time and exhibit time effects? In addition, in the spatial dimension, we will further explore whether the coverage of regional business grouping patterns shows regional differences? 2 Research Methodology and Data Construction 2.1 Theoretical framework construction The influence of enterprises on green technology innovation is mainly reflected in three dimensions: economic drive, social environment and market demand. As shown in Fig. 2 . Economic drivers include gross regional product per capita, the demand for green innovation in the regional service sector, and the number of foreign-invested enterprises.Pengwei et al. argue that an increase in per capita output means that demand for green products will be stimulated(Pengwei & Ji, 2023 ). The impact of higher per capita output on the upgrading of green technological innovation from the personal side, with the increase in residents' income, the individual's requirements for the quality of life will also increase accordingly, especially for the quality of the environment. Residents will pay more attention to environmental issues and participate more actively in environmental protection activities, thus promoting green innovation and upgrading(Peng, Xiaojie, & Shengkui, 2022 ). From the social aspect, as the overall disposable income of the population increases, the social demand for green products will also increase accordingly. This will prompt enterprises to more actively research and develop new green technologies, thus promoting green innovation and upgrading. Meanwhile, with the transformation and upgrading of China's economic structure, the service industry has become an important engine of economic growth. However, the traditional development model of the service industry is often accompanied by resource consumption and environmental pollution, which is contrary to China's goal of promoting the construction of an ecological civilisation.Therefore, there is an urgent need for the regional service industry to optimise and upgrade its industrial structure through green innovation, and to promote green and low-carbon economic development. For foreign-invested enterprises (FIEs), with the Chinese government increasing its environmental protection efforts and promoting the development of green industries, FIEs see the huge potential and business opportunities in China's green market. They hope to enhance their competitiveness in the Chinese market through green innovation. It is also in line with the global trend of sustainable development(Alvarado, Ponce, Criollo, Córdova, & Khan, 2018 ). The social environment and market demand have a far-reaching impact on the upgrading of enterprises' green technological innovation. The increase in the proportion of government social security expenditure reflects the state's emphasis on people's well-being and sustainable development, and provides strong support for enterprises' green technological innovation. The increase of aging population puts forward higher requirements for health and environmental protection products, which promotes the market demand for green innovation (Alvarez & Emery, 2000 ). Continuous promotion of urban greening construction not only improves the living environment of residents, but also provides a good application scenario for green technology innovation. The growing demand for green innovation from tourists has promoted the green transformation and upgrading of the tourism industry. Together, these factors push enterprises to increase investment in green technology innovation, enhance the green content of products and services, and achieve sustainable development (Keahey, 2021 ). 2.2 Dynamic QCA data construction Economic drivers mainly include three secondary conditions: gross regional product per capita, regional enterprises' demand for green innovation, and the number of foreign-invested enterprises. The three factors of GDP per capita, regional service industry's demand for green innovation, and the number of foreign-invested enterprises are particularly important when exploring in-depth the impact of economic drivers on enterprises' green innovation. As a key indicator to quantify the level of regional economic development, the increase of GDP per capita not only reflects the enhancement of the overall economic strength, but also provides the necessary material guarantee and market demand for green innovation activities. A higher GDP per capita in a region often means that the residents of the region have a stronger pursuit of a high quality of life, which drives the growth of demand for green innovative technologies and products.The per capita gross regional product in this paper adopts the public data of National Bureau of Statistics 2012–2021. At the same time the prosperity and development of the service sector and the number of foreign-invested enterprises play an important role in driving the demand for green innovation. The growth level of its indicators not only brings in the input of capital and technology, but also injects new vitality into the regional economy. The regional service industry's demand for green innovation, this paper refers to the research of statistion. The scholar believes that in the process of green innovation development of regional enterprises, the regional tertiary industry growth indicator is an important support for the sustainable development of local enterprises. Data on the number of foreign-invested enterprises comes from the provincial panel data published in the China Statistical Yearbook 2013–2020. When analysing the social and market environments in depth, factors such as the percentage of government social security expenditure, the level of ageing population, the demand for green innovations from tourists, and the greening rate of the city are of particular importance. The proportion of government social security expenditure not only reflects the government's emphasis on people's well-being, but also the cornerstone of social equity and stability. An increase in this indicator will help build a more harmonious social environment and provide a stable social foundation for economic development. The level of the aging population, on the other hand, reveals the trend of change in the social structure, with far-reaching impacts on the labour market, consumption patterns and many other aspects. Actively responding to aging is both a social challenge and a development opportunity. Demand for green innovation from the aging population is based on the number of people aged 65 and above (Population Sampling Survey) (persons)/Population Population Sampling Survey as a reference indicator. Source of data: Statistical Yearbook and National Bureau of Statistics. In addition, tourists' demand for green innovations and the greening rate of cities are directly related to the market environment. With the popularisation of the concept of green consumption, tourists' demand for green and innovative products and services is growing, which provides a broad space for the development of green industry. And the urban greening rate, as an important indicator of urban ecological environment, not only affects the quality of life of residents, but also directly relates to the sustainable development of the city. The above refers to the measurement method of Ma Liang, and the data are from China Statistical Yearbook. 2.3 Construct green technology innovation index prediction model Construct the loop cell structure in the green technology innovation index (LSTM memory network), which consists of three "gates" and one "cell state". Forget Gate: decides whether the cell state of the previous moment needs to be "forgotten". Input Gate*: determines whether the current input information is added to the cell state. Output Gate: decides what the output of the current moment is. Inside the recurrent cell of the LSTM network that builds the Green Technology Innovation Index, the interior consists of 4 layers of interconnected hierarchies. Among them, forgetting gates (ft), input gates (it), and output gates (ot) are dedicated to control the information flow. Establishing a time prediction model for green technology innovation in China using panel data of 30 provinces (cities) from 2012–2021 on the platform of the National Bureau of Statistics of China. Take the green technology innovation data as a sample and establish LSTM neural network. By making the input X as the year serial number, it can output the predicted green technology innovation value of China y. Firstly, establish the database x, y. X= [2012, 2013, 2014, 2015, 2016, ....…2021] Y= [0.943, 0.906, 0.866, 0.870, 0.840,......0.993] 3. data analysis and empirical results 3.1.Measurement method of green innovation of regional enterprises The entropy method measures the weight of each indicator layer in the composite system. If there are p provinces (cities), m indicators y years, Xɑβθ is the value of the βth indicator of the ɑth province in the θth year( ɑ = 1, 2, 3. . p; β = 1, 2, 3. . m, θ = 1, 2, 3. . y )。The formulae are as follows: 1. Indicator standardisation: Different indicators have different scales and units and therefore need to be standardised. If the indicator is positive, $$\begin{array}{c} {\text{Y}}_{\text{ɑ}{\beta }{\theta }}=\raisebox{1ex}{$\left({\text{X}}_{\text{ɑ}{\beta }{\theta }}-\text{min}\left({\text{X}}_{\text{ɑ}{\beta }{\theta }}\right)\right)$}\!\left/ \!\raisebox{-1ex}{$\left({\text{m}\text{a}\text{x}(\text{X}}_{\text{ɑ}{\beta }{\theta }})-\text{min}\left({\text{X}}_{\text{ɑ}{\beta }{\theta }}\right)\right)$}\right. \left(1\right) \end{array}$$ If the indicator is negative $${\text{Y}}_{\text{ɑ}{\beta }{\theta }}=\raisebox{1ex}{$\left({\text{m}\text{a}\text{x}(\text{X}}_{\text{ɑ}{\beta }{\theta }})-{\text{X}}_{\text{ɑ}{\beta }{\theta }}\right)$}\!\left/ \!\raisebox{-1ex}{$\left({\text{m}\text{a}\text{x}(\text{X}}_{\text{ɑ}{\beta }{\theta }})-\text{m}\text{i}\text{n}({\text{X}}_{\text{ɑ}{\beta }{\theta }})\right)$}\right. \left(2\right)$$ where min represents the minimum value and max represents the maximum value. 2. Calculation of characteristic proportions or contributions Z ɑβθ 。 $${\text{Z}}_{\text{ɑ}{\beta }{\theta }=}\frac{{\text{Y}}_{\text{ɑ}{\beta }{\theta }}}{\sum _{1}^{\text{p}}\sum _{1}^{\text{y}}{\text{Y}}_{\text{ɑ}{\beta }{\theta }}},\text{ɑ}=\text{1,2},3\dots \dots \text{p},{\theta }=1, 2, 3 . . . \text{y} \left(3\right)$$ 3. Calculation of entropy E β : $$\begin{array}{c}{\text{E}}_{{\beta }=}K\sum _{\text{a}=1}^{\text{p}}\sum _{{\theta }=1}^{\text{y}}{\text{Z}}_{\text{ɑ}{\beta }{\theta }}\text{ln}\left({\text{Z}}_{\text{ɑ}{\beta }{\theta }}\right),K=-\frac{1}{\text{ln}\left(\text{y}\text{p}\right)},{0\le \text{E}}_{{\beta }}\le 1 （4）\end{array}$$ 4. Calculate the information utility value of the βth indicator: $$\begin{array}{c}{\text{G}}_{{\beta }}=1-{\text{E}}_{{\beta }} \left(5\right)\end{array}$$ Determining the weights of evaluation indicators W β : $$\begin{array}{c}{\text{W}}_{{\beta }}=\frac{{\text{G}}_{{\beta }}}{\sum _{1}^{\text{m}}{\text{G}}_{{\beta }}} ,\beta = 1, 2, 3 . . . . . . m\#\left(6\right) \end{array}$$ 3.2 Constructing a Comprehensive Green Innovation Indicator System for Regional Enterprises Based on the panel data of each province in China, this paper refers to the research of Kang Yumei et al. and constructs the comprehensive green technology indicators from five dimensions, namely, the amount of patents granted, the share of technology market turnover, the government's investment in green technology and innovation, the urban unemployment rate, and the energy utilisation rate.(Yumei & Chengxing, 2023 )。The number of patents granted reflects how active a region or organisation is in technology development and innovation. A higher number of granted patents may imply that the region or organisation has a high level of innovative capacity and dynamism in green technologies. This lays the foundation for further development of green technologies. The ratio of technology market transactions reflects the activity of the technology market and the efficiency of the transformation of scientific and technological achievements. A higher technology market turnover ratio means that more green technology achievements have been applied and promoted. It helps to popularise and deepen green technology. Government expenditure on science and technology as a proportion of finance: This indicator reflects the importance the government attaches to science and technology and green technology. Government investment in science and technology and green technology can promote relevant research and development, and promote the progress and innovation of green technology. Urban registered unemployment rate can reflect the economic situation and employment. A healthy economic environment can provide more employment opportunities, thus attracting more talents to invest in green technology R&D and innovation.Electricity consumption per unit of GDP is an important criterion for measuring the efficiency of energy use in a region or organisation. By reducing electricity consumption per unit of GDP, energy consumption and environmental pollution can be reduced, and green and low-carbon development can be promoted, which is one of the important goals of green technology innovation. As shown in Table 1 . Table 1 Comprehensive evaluation index system of green innovation index for regional enterprises Objective level Criteria level Indicator layer Weights Properties Comprehensive Green Technology Indicators for Regional Enterprises Patent grants Patent Applications and Authorisations(item)/ Year-end Resident Population (10,000) 0.3331 Forward Percentage of Technology Market Transactions Technology market turnover (billion yuan) / GDP (billion yuan) 0.3205 Forward Government Investment in Science and Technology Innovation Share of Science and Technology Expenditure in Fiscal Expenditure 0.2303 Forward Urban Unemployment Rate Urban registered unemployment rate 0.0716 Forward Energy Utilisation Rate Electicity consumption per unit of GDP 0.0443 Negative Based on the entropy method and the comprehensive index of green technology innovation, this paper measures the development level of green technology innovation in each region of China from 2012 to 2021, as shown in Fig. 3 . It can be found from 2012 to 2021. The green technology innovation score of the eastern region ranges from 0.19 to 0.35, that of the central region ranges from 0.09 to 0.26, while that of the western region ranges from 0.09 to 0.17. The eastern region, with its higher level of economic development and better infrastructure, has always scored at a higher level in terms of green technological innovation. As shown in Fig. 3 , from the overall trend, China's green technology innovation index shows a gradual upward and steady development trend. The eastern region has been in the lead. However, it is worth noting that the inter-regional green STI gap between the central and western regions is gradually widening. This suggests that the western region may need more attention and investment in the development of green STI in China in order to narrow the regional gap. 3.3 Calibration In this paper, based on Boolean algebra theory and previous studies, the data were calibrated precisely to ensure the consistency and coverage of the analysis. The direct calibration method is used, and the 95% quartile, 50% quartile and 5% quartile are set as calibration anchor points, and the specific results are shown in Table 2 . Table 2 Calibration of variables Variable name Fully affiliated Intersections Completely unaffiliated Result Variables Green innovation of local enterprises (Y) 0.469 0.139 0.068 Conditional variables Per capita gross regional product(A) 116664 50242 28622 Percentage of government expenditure on social security(B) 45.215 40.4 33.88 Urban Green Coverage Rate(C) 0.194 0.134 0.084 Level of aging population(D) 0.159 0.108 0.072 Tourist demand for green innovation(E) 5.511 0.995 0.01 Number of foreign-invested enterprises(F) 79639.850 6698 737.8 Demand for innovation by regional service industries(G) 37660.94 9850.3 1542.95 3.4 Necessity Analysis of Individual Conditions According to the set theory of Boolean algebra and QCA design principles and applications, it is known that the smaller the adjustment distance of QCA panel data, the higher the consistency accuracy. However, the adjustment distance is not clearly defined in statistics.QCA experimental analysis needs to consider the data size and data inclusion, so the median value of the adjustment distance used in this experiment is 0.3.As shown in Table 3 , if per capita GDP (A), the proportion of government social security expenditures (B), the percentage of urban green coverage (C), the level of the aging population (D), the demand of tourists for green innovations (E ), number of foreign-invested enterprises (F), and regional enterprises' demand for innovation (G), the seven indicators have an adjusted distance to green innovation greater than 0.3, and coverage less than 0.5 requires researchers to further explore the necessity. Table 3 Analysis of the necessary conditions variant High level of local business green innovation (Y). Low level of local business green innovation (~ Y). Aggregate Consistency Aggregate coverage Inter-group consistency Intra-group consistency Aggregate Consistency Aggregate coverage Inter-group consistency Intra-group consistency A 0.85 0.84 0.10 0.24 0.47 0.56 0.52 0.52 ~A 0.56 0.46 0.18 0.45 0.87 0.88 0.11 0.21 B 0.75 0.69 0.06 0.38 0.58 0.65 0.28 0.47 ~B 0.62 0.55 0.18 0.40 0.73 0.78 0.09 0.40 C 0.65 0.61 0.20 0.42 0.63 0.72 0.29 0.39 ~C 0.71 0.61 0.25 0.35 0.86 0.70 0.15 0.39 D 0.77 0.71 0.20 0.30 0.54 0.61 0.36 0.49 ~D 0.85 0.51 0.37 0.41 0.75 0.80 0.19 0.32 E 0.61 0.66 0.50 0.41 0.51 0.67 0.45 0.56 ~E 0.69 0.54 0.29 0.32 0.74 0.70 0.19 0.34 F 0.77 0.79 0.06 0.37 0.88 0.61 0.15 0.60 ~F 0.62 0.49 0.04 0.43 0.83 0.81 0.08 0.30 G 0.78 0.75 0.07 0.40 0.50 0.60 0.27 0.55 ~G 0.85 0.49 0.20 0.47 0.79 0.81 0.07 0.35 average 0.7 0.6 0.2 0.4 0.7 0.7 0.2 0.4 By analysing the intergroup consistency and coverage of the corresponding variables (as shown in Tables 3 and 4 ). There are the following findings: firstly, in the process of analysing the necessary conditions, we did not find that any single factor can constitute the necessary conditions for green innovation of local enterprises alone. This means that green innovation of local enterprises is a complex process that requires multiple factors to work together to achieve it. Secondly, in the inter-group data with an adjusted distance greater than 0.3, we observe that in cases a, b, and c, the level of consistency across years does not reach 0.9. therefore the necessary relationship is not satisfied. Meanwhile, by plotting the scatterplot of coverage and consistency, we find that the coverage is mainly concentrated on the right y-axis, which passes the test of the non-essential condition. However, consistency does not pass the test of the non-essential condition. This further suggests that these factors may play a role in the green innovation process. However, they are not decisive and have their own research value. Table 4 Data between groups with adjusted distances greater than 0.3 Situation. Causal combination situations 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 a ~A and Y Intergroup consistency 0.68 0.68 0.66 0.63 0.59 0.58 0.54 0.50 0.47 0.41 Intergroup coverage 0.26 0.32 0.34 0.37 0.39 0.49 0.63 0.70 0.75 0.91 b ~F and Y Intergroup consistency 0.62 0.61 0.64 0.64 0.63 0.63 0.65 0.61 0.59 0.57 Intergroup coverage 0.31 0.37 0.38 0.41 0.44 0.50 0.59 0.62 0.67 0.74 c ~G and Y Intergroup consistency 0.72 0.73 0.71 0.68 0.61 0.54 0.50 0.50 0.48 0.44 Intergroup coverage 0.31 0.38 0.40 0.43 0.46 0.53 0.62 0.64 0.66 0.74 3.5 Configuration analysis results Table 5 Configuration truth table Conditional variables parameterisation1 Parameterisation2 Parameterisation3 Gross regional product per capita (A) ⊗ ⊗ ● Percentage of government expenditure on social security(B) ● ● ● Urban Green Coverage Rate(C) ⊗ ⊗ ● Level of aging population(D) ● ● Tourist demand for green innovation(E) ● ⊗ Number of foreign-invested enterprises(F) ● ● ● Demand for innovation by regional service industries(G) ● ● ● Consistency 0.838 0.827 0.845 Original Coverage 0.402 0.411 0.366 Unique Coverage 0.019 0.08 0.095 Inter-group consistency adjusted distance 0.011 0.012 0.012 Intra-group consistency-adjusted distance 0.026 0.029 0.024 Overall PRI 0.611 Overall Consistency 0.827 Overall Coverage 0.402 Note: ● and ⊗ indicate presence and absence of core; blank indicates that presence and absence are also possible. Table 5 shows that the consistency of grouping 1, grouping 2, and grouping 3 is 0.828, 0.827, and 0.845, respectively. The overall consistency is greater than 0.75. And the adjustment distance between intra-group and inter-group for individual grouping is less than 0.3. It shows that the aggregated consistency has a better explanatory strength. These three groupings can be regarded as sufficient conditions affecting the generation of sustainable green innovation in local enterprises. From the study of group state 1, we observe the influence of different factors on the green innovation of enterprises. Cohort 1 shows that economic drivers such as the number of foreign-invested firms and the demand for innovation in the regional service sector, as well as socio-environmental factors such as a high level of government social security expenditure and the level of ageing, are the main drivers of green innovation in firms, while market demand has a limited impact. Configuration 2 further emphasises the importance of high levels of foreign investment and service sector innovation demand. At the same time market demand for tourists' preference for green begins to emerge. By configuration 3, economic, social and market demand factors show a more balanced state. The economic drivers are GDP per capita, high level of foreign investment, and high level of regional demand for innovation in the service sector. The social environment factor is dominated by the high level of government social security expenditure share. Market demand is dominated by the urban greening coverage rate. These findings suggest that, in the context of China's geographical resource differences, localities should combine their own characteristics to achieve factor linkages in order to promote local firms' green innovation. It is worth noting that the multidimensional linkage model demonstrated in Grouping 3, although the study shows that the green innovation of local enterprises is influenced by multiple factors such as economic drive, social environment and market demand. However, this multidimensional linkage model still needs further in-depth exploration. The key lies in how enterprises balance supply and demand to achieve multi-dimensional power. Only by comprehensively considering economic, social and market demands can enterprises formulate a more reasonable and effective green innovation strategy. This is not only the key to enhance the competitiveness of enterprises, but also the way to realise the sustainable development of green innovation. Therefore, future research should pay more attention to the balance and synergy of enterprises under the effect of multidimensional factors, so as to promote the in-depth development of green innovation. 3.5 Between and within group results It was found that the adjusted distance of intergroup consistency for all 3 groupings was not greater than 0.3, indicating that there was no significant time effect. Further examination of its temporal changes revealed that the consistency levels of the 3 groupings showed a decline from 2012–2016. However, they collectively showed a period of rapid growth in 2016–2021. As shown in Fig. 4 . Among them, the fastest growth rate of intergroup consistency for group state 3 grows from 0.84 to 1.00. The reason for this is that our government intervention plays a crucial role. Checking government websites and local service platforms found that 60% more documents were released in 2016 to promote green development compared to 2015. Meanwhile, the government sends out strong intervention signals In terms of policy intervention: the Chinese government has introduced a series of policies to encourage green innovation and sustainable development, such as providing financial subsidies, tax incentives, and loan facilitation, in order to incentivise enterprises to increase their research and development in green technologies. Regulatory constraints: The government has strengthened the formulation and enforcement of environmental protection regulations, imposing strict limits on pollution emissions and energy consumption. Government in green procurement: As one of the largest consumers, the government has given priority to green products and services to encourage enterprises to actively develop green products and enhance their green innovation capability. Table 5 Geographical coverage Eastern China Central China Western China. Configuration 1 0.56 0.62 0.55 Configuration 2 0.38 0.46 0.38 Configuration 3 0.39 0.46 0.38 The range of intra- and inter-group consistency adjustment distances is almost the same, and the intra-group consistency adjustment distance is not greater than 0.3. The variability in the distribution of geographic coverage of the grouping models revealed in Table 5 . The explained cases of group states 2 and 3 are mainly concentrated in the central region, which may stem from the unique resource conditions and policy environment in these regions. However, histogram 1 shows stronger explanatory power in East, Central and West China, with a coverage of more than 0.5, indicating its universality. This shows that firms in different regions may be affected by different factors when facing green innovation and sustainability challenges. Such geographical differences may stem from the diversity of regional levels of economic development, market demand, resource distribution and policy orientations. For policy makers and entrepreneurs, an in-depth understanding of the geographical characteristics of the model can help develop more targeted strategies and measures to promote green transformation and sustainable development of enterprises. 3.6 Consistent prediction of sustainable development of green innovation of local enterprises Using MATLAB, the article constructed a consistency prediction model for the sustainable development of green innovation in local enterprises, as shown in Fig. 5 . The root mean square error (RMSE) of the model is 0.9, a value that indicates that the predictive accuracy of the model can be applied to consistency prediction.RMSE is a commonly used metric for assessing the predictive ability of a model, which measures the magnitude of the model's error by calculating the mean of the squared difference between the predicted value and the actual value. A lower RMSE value means that the model has a higher prediction accuracy. From the trend of the model's prediction graph, it can be observed that the overall trend of the consistency index of the sustainable development of green innovation of local enterprises shows a gradual decrease followed by a rapid growth trend. According to the prediction trend, China's progress in green technological innovation is gradually accelerating, and the consistency index of enterprises' sustainable development of green innovation will be maintained around 0.90. 4 Results This paper applies the dynamic QCA research method. Using the data cases of 30 provincial governments in China, it explores the influence effects of both supply and demand influencing factors on the sustainable development of green innovation of local enterprises. The core influencing factors affecting the sustainable development of local enterprises' green innovation and the interaction between them during 2012–2021 are revealed. These findings suggest that the influencing factors of local enterprises' sustainable development of green innovation are complex and diverse, and that different factors may have different influencing effects in different contexts. Firstly, although market demand is an important influencing factor. However, its role relative to economic drivers and social environment factors may be relatively limited in some cases. This suggests that when promoting green innovation in local enterprises, we cannot rely solely on the pull of market demand, but also need to take into account various factors such as the economic and social environment. Second, high levels of foreign investment and demand for innovation in the service sector have a significant impact on green innovation in local enterprises.This suggests that attracting foreign investment and promoting innovation in the service sector are important ways to enhance the green innovation capacity of local enterprises. At the same time, with the increasing preference of consumers for green products, green preferences in market demand have also begun to become an important factor influencing enterprises' green innovation. In addition, the resource differences and characteristics of different regions make it necessary for local enterprises to combine their own realities in the process of green innovation and realise the linkage and complementarity of factors. This requires local governments to take into full consideration the actual situation of the region when formulating relevant policies and promote green innovation according to local conditions. In the spatial dimension, the sustainable development of green innovation of local enterprises shows regional differences. This may be due to the differences in the level of economic development, industrial structure, policy environment and other factors in different regions. Therefore, when promoting green innovation, differentiated policy measures need to be formulated for the characteristics of different regions. Finally, by constructing a consistency prediction model, the study finds that China's progress in green technology innovation is gradually accelerating, and the consistency index of enterprises' sustainable development of green innovation will be maintained at a high level. This indicates that China's green innovation activities will become more active and stable in the future, providing strong support for sustainable development. This study still has limitations. Firstly, this study uses secondary public data and only explores the macro level. In the future, it is necessary to further combine qualitative interviews with participant observation to unveil the influence mechanism behind information disclosure at the micro level. Finally, this paper only focuses on national government data. Whether the findings can be extended to the sub-provincial level still needs to be further explored. Declarations It is hereby declared that there is no conflict of interest in this study, all data are sourced from publicly available data from the National Bureau of Statistics of China, the collected data are properly stored at Shinhan University, and this article is licensed under the Creative Commons Attribution 4.0 International License, which permits anyone to use, share, modify, distribute, and reproduce this article subject to certain conditions. If the material is not included in the Creative Commons Licence for this article, and your use is not intended to comply with statutory requirements or exceeds the scope of permitted use, you will need to obtain a licence directly from the copyright owner. Funding This study was conducted without receiving any external funding. Ethical Statement In the design and execution of this study, relevant ethical guidelines and regulatory requirements were strictly adhered to, to ensure that the research process would not cause any adverse effects on the participants, society or the environment. Therefore, we hereby declare that there are no ethical issues in this study. References Alvarado, R., Ponce, P., Criollo, A., Córdova, K., & Khan, M. K. (2018). Environmental degradation and real per capita output: New evidence at the global level grouping countries by income levels. Journal of Cleaner Production, 189 , 13-20. doi:https://doi.org/10.1016/j.jclepro.2018.04.064 Alvarez, R. C., & Emery, M. (2000). From action research to system in environments: A method. Systemic Practice and Action Research, 13 , 683-703. Boakye, D. J., Tingbani, I., Ahinful, G. S., & Nsor-Ambala, R. (2021). The relationship between environmental management performance and financial performance of firms listed in the Alternative Investment Market (AIM) in the UK. Journal of Cleaner Production, 278 , 124034. Castellani, D., Marin, G., Montresor, S., & Zanfei, A. (2022). Greenfield foreign direct investments and regional environmental technologies. Research Policy, 51 (1), 104405. doi:https://doi.org/10.1016/j.respol.2021.104405 Chen, Y., Yao, Z., & Zhong, K. (2022). Do environmental regulations of carbon emissions and air pollution foster green technology innovation: Evidence from China's prefecture-level cities. Journal of Cleaner Production, 350 , 131537. doi:https://doi.org/10.1016/j.jclepro.2022.131537 Child, J., & Tse, D. K. (2001). China's transition and its implications for international business. Journal of international business studies, 32 , 5-21. Geary, J., & Nyiawung, J. (2022). The impact of Chinese investments on western multinational enterprises’ work and employment practices: A consideration of institutional, political and dominance effects. human relations, 75 (5), 842-870. Hu, N., & Zheng, B. (2023). Natural resources, education, and green economic development. Resources Policy, 86 , 104053. Jiang, J., Qian, J., & Wen, Z. (2018). Social protection for the informal sector in urban China: institutional constraints and self-selection behaviour. Journal of Social Policy, 47 (2), 335-357. Keahey, J. (2021). Sustainable development and participatory action research: a systematic review. Systemic Practice and Action Research, 34 (3), 291-306. Kostka, G., Zhang, X., & Shin, K. (2020). Information, technology, and digitalization in China’s environmental governance. In (Vol. 63, pp. 1-13): Taylor & Francis. Kuang, Y., & Lin, B. (2022). Natural gas resource utilization, environmental policy and green economic development: Empirical evidence from China. Resources Policy, 79 , 102992. Li, H., & Xu, R. (2023). Impact of fiscal policies and natural resources on ecological sustainability of BRICS region: Moderating role of green innovation and ecological governance. Resources Policy, 85 , 103999. Lucas Jr, R. E. (1988). On the mechanics of economic development. Journal of monetary economics, 22 (1), 3-42. Lv, C., Shao, C., & Lee, C.-C. (2021). Green technology innovation and financial development: Do environmental regulation and innovation output matter? Energy Economics, 98 , 105237. doi:https://doi.org/10.1016/j.eneco.2021.105237 Paramati, S. R., Mo, D., & Huang, R. (2021). The role of financial deepening and green technology on carbon emissions: Evidence from major OECD economies. Finance Research Letters, 41 , 101794. doi:https://doi.org/10.1016/j.frl.2020.101794 Peng, H., Xiaojie, L., & Shengkui, C. (2022). Who is More Willing to Pay for Green Electricity? a Case Study of Anyang City, China. Journal of Resources and Ecology, 13 (2), 231-237. Pengwei, W., & Ji, Y. (2023). Tourists' Willingness to Pay Conservation Fees: The Case of Hulunbuir Grassland, China. Journal of Resources and Ecology, 14 (3), 656-666. Romer, P. M. (1986). Increasing returns and long-run growth. Journal of political economy, 94 (5), 1002-1037. Shen, F., Liu, B., Luo, F., Wu, C., Chen, H., & Wei, W. (2021). The effect of economic growth target constraints on green technology innovation. Journal of Environmental Management, 292 , 112765. doi:https://doi.org/10.1016/j.jenvman.2021.112765 Shen, X., Li, H., & Tolbert, P. S. (2021). Converging tides lift all boats: Consensus in evaluation criteria boosts investments in firms in nascent technology sectors. Organization Science . Song, Y., Zhang, Z., Sahut, J.-M., & Rubin, O. (2023). Incentivizing green technology innovation to confront sustainable development. Technovation, 126 , 102788. doi:https://doi.org/10.1016/j.technovation.2023.102788 Tsui, K.-y. (1993). Decomposition of China′ s regional inequalities. Journal of comparative Economics, 17 (3), 600-627. Wu, W., He, F., Zhuang, T., & Yi, Y. (2020). Stakeholder analysis and social network analysis in the decision-making of industrial land redevelopment in China: The case of Shanghai. International Journal of Environmental Research and Public Health, 17 (24), 9206. Yumei, K., & Chengxing, X. (2023). Financial Development, Innovation Inputs and High-Quality Economic Development - An Empirical Study Based on the Yangtze River Economic Belt. Journal of Cheung Kong University (Social Science Edition), 46 (02), 76-85. Zhang, J., Huang, R., & He, S. (2023). How does technological innovation affect carbon emission efficiency in the Yellow River Economic Belt: the moderating role of government support and marketization. Environmental Science and Pollution Research , 1-18. Zhang, N., Sun, J., Tang, Y., Zhang, J., Boamah, V., Tang, D., & Zhang, X. (2023). How Do Green Finance and Green Technology Innovation Impact the Yangtze River Economic Belt’s Industrial Structure Upgrading in China? A Moderated Mediation Effect Model Based on Provincial Panel Data. Sustainability, 15 (3), 2289. Zhang, Q., & Ma, Y. (2021). The impact of environmental management on firm economic performance: The mediating effect of green innovation and the moderating effect of environmental leadership. Journal of Cleaner Production, 292 , 126057. Additional Declarations No competing interests reported. Supplementary Files data.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4108767","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281708338,"identity":"522a7d6c-e20c-421a-af26-f3c4849ecc99","order_by":0,"name":"QIJAI LIU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYBACPmYgkcAjAWIwHmCoAPKYmRvwamGDaQExDjCcAWlhJKAFmXGAsQ3EJKSFncfwxgMZC3kgw+Aw77zaaP52oJYfFdvwOIzH2ALoMMM2ZpCWbcdzZxxmbGDsOXMbnxYzCaAWRqiWY7kNQC3MjG2EtdhDtMw5ljufWC2JEC0NNbkbCGthKwb5JbmNma3g4JxjB3I3ArUcxOcXfv7DG2/+7Kmz7QcyHrypqcudd/7wwQc/KnBrAQEJxh44+zCYPIBXPUgLww84u46Q4lEwCkbBKBiBAADGbk2AGaHzkAAAAABJRU5ErkJggg==","orcid":"","institution":"Shinhan University","correspondingAuthor":true,"prefix":"","firstName":"QIJAI","middleName":"","lastName":"LIU","suffix":""}],"badges":[],"createdAt":"2024-03-15 14:50:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4108767/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4108767/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53167857,"identity":"a7f6ecd3-c8ca-4e29-a018-382f8c4f7b3a","added_by":"auto","created_at":"2024-03-21 12:36:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":196648,"visible":true,"origin":"","legend":"\u003cp\u003eThree major regional divisions in China: eastern, central and western regions\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4108767/v1/640699733a47c06dee0e1413.png"},{"id":53167861,"identity":"bab2290d-a982-4435-92c7-d735266a9e55","added_by":"auto","created_at":"2024-03-21 12:36:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":125986,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical framework\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4108767/v1/042c07dab5b72667dbf2c304.png"},{"id":53167860,"identity":"329c841d-71c6-445b-acbd-bdae8a836093","added_by":"auto","created_at":"2024-03-21 12:36:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70593,"visible":true,"origin":"","legend":"\u003cp\u003eGreen Innovation Index for Regionntal Eerprises.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4108767/v1/2d4894250142629406f730a0.png"},{"id":53167856,"identity":"e8297784-feee-4e47-9c2f-486db6bef7bd","added_by":"auto","created_at":"2024-03-21 12:36:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":88056,"visible":true,"origin":"","legend":"\u003cp\u003eConfiguration consistency analysis\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4108767/v1/e125369eb05a5102ce9d2506.png"},{"id":53167859,"identity":"0c9bc506-520b-432c-a11a-d90363d18108","added_by":"auto","created_at":"2024-03-21 12:36:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":94710,"visible":true,"origin":"","legend":"\u003cp\u003eCoherent projections for the sustainable development of innovation\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4108767/v1/14e64430805e73dde8e50481.png"},{"id":55376345,"identity":"d4f9866f-eda5-41ad-8eb9-f6d464cc1aff","added_by":"auto","created_at":"2024-04-26 12:53:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1173015,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4108767/v1/88e546db-0f56-4c87-a52b-9b2029b1f9d7.pdf"},{"id":53167862,"identity":"c283f4ef-8aab-402c-b1c4-8348adbca099","added_by":"auto","created_at":"2024-03-21 12:36:11","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":201001,"visible":true,"origin":"","legend":"","description":"","filename":"data.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4108767/v1/4bc36b26d18ad6edac16e809.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Path of Green Innovation and High Quality Development of Influential Regional Enterprises - Based on the Analysis of Dynamic QCA Method and Matlab Sustainability Prediction","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eChina is a vast country with a large population, and its economic and technological development shows obvious geographical differences(Geary \u0026amp; Nyiawung, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tsui, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Domestic and foreign scholars generally divide China into three major regions, namely, the eastern region, the central region, and the western region, for the purpose of research.As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As an important engine of China's economic development, the eastern region has been a leader in terms of GDP per capita and government expenditure on social security.As the eastern region has a more complete industrial system, so that it has a strong scientific and technological innovation in science and technology innovation(Wu, He, Zhuang, \u0026amp; Yi, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Universities, research institutions and enterprises in the eastern region possess a large number of patents and technological achievements. They are also actively introducing advanced technologies from abroad and constantly promoting the development of scientific and technological innovation through digestion, absorption and re-innovation(Kostka, Zhang, \u0026amp; Shin, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shen, Li, \u0026amp; Tolbert, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The central region is at the middle level of China in terms of economic output and government expenditure on social security. The central region has a better industrial base, but agriculture is still its dominant industry. In recent years, the central region has been actively promoting the strategies of industrialisation, urbanisation and agricultural modernisation, and accelerating the pace of upgrading and transforming the industrial structure(Jiang, Qian, \u0026amp; Wen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although the western region is lagging behind economically, its government expenditure on social security is growing rapidly. This is mainly due to the fact that the western region is endowed with abundant resources and relatively low labour costs.The imperfect industrial system in the western region has led to its weak scientific and technological innovation capacity. As the Government has increased its support for the western region, enterprises in the western region have begun to pay attention to scientific and technological innovation, constantly improving their technological level and beginning to transform(Child \u0026amp; Tse, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, the synergistic relationship between corporate green innovation and per capita output has also been widely studied. Many scholars have gradually reached a consensus on the view that corporate green innovation improves resource use efficiency, reduces production costs and increases per capita output. Sustainable development depends to a certain extent on the ability to produce green technologies(Song, Zhang, Sahut, \u0026amp; Rubin, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Castellani et al. argue that green technologies make a positive contribution to per capita income(Castellani, Marin, Montresor, \u0026amp; Zanfei, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In terms of local government funding for technological innovation, local government environmental policies have a significant impact on green innovation in regional firms, and local government R\u0026amp;D investment positively moderates this association(Chen, Yao, \u0026amp; Zhong, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Lv,C,C et al. investigated the innovation efficiency of firms' green innovations in 30 provinces in China from 2003 to 2017, and statistically found that the environmental regime plays a positive moderating role between the financial structure and firms' green innovations(Lv, Shao, \u0026amp; Lee, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By comparing the per capita output aspects of the regions, it was found that cities that overshoot their economic growth targets can have a significant dampening effect on green technological innovation in the regional service sector(F. Shen et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A study of social security expenditures by regional governments found that the introduction of FDI can have a positive impact on regional green technology specialisation and per capita income(Paramati, Mo, \u0026amp; Huang, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Regarding the new upgrading of green innovation in enterprises, some scholars have unfolded their research from the perspective of enterprises.Boajye et al. found that green innovation increases the cost of environmental governance, reduces investment in productive activities, and negatively affects the performance of enterprises(Boakye, Tingbani, Ahinful, \u0026amp; Nsor-Ambala, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is very interesting to note that the findings of Zhang et al. are completely opposite to those of Boajye et al. This may be due to the large development gap between the east and west of China(Q. Zhang \u0026amp; Ma, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Other scholars have taken a social perspective. Romer and Lucas, in their theory of endogenous economic growth, argue that technological progress restructures industries and contributes to social development(Lucas Jr, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Romer, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1986\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe above studies have revealed the specific impact of per capita output value and government social security expenditure on enterprises' green technological innovation, respectively. However, they have not explored all three under the same theoretical framework. In addition, politics, business and social culture are all permeated with the influence on the social environment atmosphere, so the interaction between the three is inextricably linked to the influence of social environment factors(Li \u0026amp; Xu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; J. Zhang, Huang, \u0026amp; He, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; N. Zhang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The social and environmental climate for enterprises can improve the sense of social responsibility and credibility of enterprises. By publicising the green production, environmental protection measures and sustainable development business philosophy of enterprises, it will guide the transformation of pollution-intensive enterprises to cleaner ones and achieve a major transformation from the secondary to the tertiary sector(Kuang \u0026amp; Lin, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For consumers, the promotion of green concepts in the social environment can enhance their environmental awareness and increase their knowledge of and demand for green consumption. By publicising environmental protection concepts and promoting green products and services, it guides consumers to change their consumer attitudes and behaviours and promotes green technological innovation by enterprises(Hu \u0026amp; Zheng, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study incorporates economic factors such as per capita output, social environment factors such as government social security and market demand into the same framework. Drawing on the theory of synergy and complex systems, a composite factor single output analysis model is constructed. Using panel data provided by the National Bureau of Statistics of China (NBS) for 30 provinces (cities) over the period 2012\u0026ndash;2021, a dynamic qualitative comparative analysis (QCA) method is employed. To reveal the causal mechanisms affecting the differences in the high-quality development of green innovation among regional enterprises on the longitudinal axis of time. And to deeply explore the differences in group preference among different regions. In order to overcome the shortcomings of linear regression and fuzzy set qualitative comparative analysis in dealing with the local practice context and factor linkage effects on the longitudinal axis of time. The experiment expects that this analytical framework can explain the path of green innovation and high-quality development of regional enterprises more comprehensively.This study aims to answer the following questions: is there a single factor that is necessary for the sustainability of a square business in the time dimension? Do these factors produce changes over time and exhibit time effects? In addition, in the spatial dimension, we will further explore whether the coverage of regional business grouping patterns shows regional differences?\u003c/p\u003e"},{"header":"2 Research Methodology and Data Construction","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical framework construction\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe influence of enterprises on green technology innovation is mainly reflected in three dimensions: economic drive, social environment and market demand. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Economic drivers include gross regional product per capita, the demand for green innovation in the regional service sector, and the number of foreign-invested enterprises.Pengwei et al. argue that an increase in per capita output means that demand for green products will be stimulated(Pengwei \u0026amp; Ji, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The impact of higher per capita output on the upgrading of green technological innovation from the personal side, with the increase in residents' income, the individual's requirements for the quality of life will also increase accordingly, especially for the quality of the environment. Residents will pay more attention to environmental issues and participate more actively in environmental protection activities, thus promoting green innovation and upgrading(Peng, Xiaojie, \u0026amp; Shengkui, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). From the social aspect, as the overall disposable income of the population increases, the social demand for green products will also increase accordingly. This will prompt enterprises to more actively research and develop new green technologies, thus promoting green innovation and upgrading. Meanwhile, with the transformation and upgrading of China's economic structure, the service industry has become an important engine of economic growth. However, the traditional development model of the service industry is often accompanied by resource consumption and environmental pollution, which is contrary to China's goal of promoting the construction of an ecological civilisation.Therefore, there is an urgent need for the regional service industry to optimise and upgrade its industrial structure through green innovation, and to promote green and low-carbon economic development. For foreign-invested enterprises (FIEs), with the Chinese government increasing its environmental protection efforts and promoting the development of green industries, FIEs see the huge potential and business opportunities in China's green market. They hope to enhance their competitiveness in the Chinese market through green innovation. It is also in line with the global trend of sustainable development(Alvarado, Ponce, Criollo, C\u0026oacute;rdova, \u0026amp; Khan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe social environment and market demand have a far-reaching impact on the upgrading of enterprises' green technological innovation. The increase in the proportion of government social security expenditure reflects the state's emphasis on people's well-being and sustainable development, and provides strong support for enterprises' green technological innovation. The increase of aging population puts forward higher requirements for health and environmental protection products, which promotes the market demand for green innovation (Alvarez \u0026amp; Emery, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Continuous promotion of urban greening construction not only improves the living environment of residents, but also provides a good application scenario for green technology innovation. The growing demand for green innovation from tourists has promoted the green transformation and upgrading of the tourism industry. Together, these factors push enterprises to increase investment in green technology innovation, enhance the green content of products and services, and achieve sustainable development (Keahey, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Dynamic QCA data construction\u003c/h2\u003e \u003cp\u003eEconomic drivers mainly include three secondary conditions: gross regional product per capita, regional enterprises' demand for green innovation, and the number of foreign-invested enterprises. The three factors of GDP per capita, regional service industry's demand for green innovation, and the number of foreign-invested enterprises are particularly important when exploring in-depth the impact of economic drivers on enterprises' green innovation. As a key indicator to quantify the level of regional economic development, the increase of GDP per capita not only reflects the enhancement of the overall economic strength, but also provides the necessary material guarantee and market demand for green innovation activities. A higher GDP per capita in a region often means that the residents of the region have a stronger pursuit of a high quality of life, which drives the growth of demand for green innovative technologies and products.The per capita gross regional product in this paper adopts the public data of National Bureau of Statistics 2012\u0026ndash;2021. At the same time the prosperity and development of the service sector and the number of foreign-invested enterprises play an important role in driving the demand for green innovation. The growth level of its indicators not only brings in the input of capital and technology, but also injects new vitality into the regional economy. The regional service industry's demand for green innovation, this paper refers to the research of statistion. The scholar believes that in the process of green innovation development of regional enterprises, the regional tertiary industry growth indicator is an important support for the sustainable development of local enterprises. Data on the number of foreign-invested enterprises comes from the provincial panel data published in the China Statistical Yearbook 2013\u0026ndash;2020.\u003c/p\u003e \u003cp\u003eWhen analysing the social and market environments in depth, factors such as the percentage of government social security expenditure, the level of ageing population, the demand for green innovations from tourists, and the greening rate of the city are of particular importance. The proportion of government social security expenditure not only reflects the government's emphasis on people's well-being, but also the cornerstone of social equity and stability. An increase in this indicator will help build a more harmonious social environment and provide a stable social foundation for economic development. The level of the aging population, on the other hand, reveals the trend of change in the social structure, with far-reaching impacts on the labour market, consumption patterns and many other aspects. Actively responding to aging is both a social challenge and a development opportunity. Demand for green innovation from the aging population is based on the number of people aged 65 and above (Population Sampling Survey) (persons)/Population Population Sampling Survey as a reference indicator. Source of data: Statistical Yearbook and National Bureau of Statistics.\u003c/p\u003e \u003cp\u003eIn addition, tourists' demand for green innovations and the greening rate of cities are directly related to the market environment. With the popularisation of the concept of green consumption, tourists' demand for green and innovative products and services is growing, which provides a broad space for the development of green industry. And the urban greening rate, as an important indicator of urban ecological environment, not only affects the quality of life of residents, but also directly relates to the sustainable development of the city. The above refers to the measurement method of Ma Liang, and the data are from China Statistical Yearbook.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Construct green technology innovation index prediction model\u003c/h2\u003e \u003cp\u003eConstruct the loop cell structure in the green technology innovation index (LSTM memory network), which consists of three \"gates\" and one \"cell state\".\u003c/p\u003e \u003cp\u003eForget Gate: decides whether the cell state of the previous moment needs to be \"forgotten\".\u003c/p\u003e \u003cp\u003eInput Gate*: determines whether the current input information is added to the cell state.\u003c/p\u003e \u003cp\u003eOutput Gate: decides what the output of the current moment is. Inside the recurrent cell of the LSTM network that builds the Green Technology Innovation Index, the interior consists of 4 layers of interconnected hierarchies. Among them, forgetting gates (ft), input gates (it), and output gates (ot) are dedicated to control the information flow.\u003c/p\u003e \u003cp\u003eEstablishing a time prediction model for green technology innovation in China using panel data of 30 provinces (cities) from 2012\u0026ndash;2021 on the platform of the National Bureau of Statistics of China. Take the green technology innovation data as a sample and establish LSTM neural network. By making the input X as the year serial number, it can output the predicted green technology innovation value of China y. Firstly, establish the database x, y.\u003c/p\u003e \u003cp\u003eX= [2012, 2013, 2014, 2015, 2016, ....\u0026hellip;2021]\u003c/p\u003e \u003cp\u003eY= [0.943, 0.906, 0.866, 0.870, 0.840,......0.993]\u003c/p\u003e \u003c/div\u003e"},{"header":"3. data analysis and empirical results","content":"\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.1.Measurement method of green innovation of regional enterprises\u003c/h2\u003e\n \u003cp\u003eThe entropy method measures the weight of each indicator layer in the composite system. If there are p provinces (cities), m indicators y years, Xɑ\u0026beta;\u0026theta; is the value of the \u0026beta;th indicator of the ɑth province in the \u0026theta;th year(\u003cem\u003eɑ = 1, 2, 3. . p; \u0026beta;\u0026thinsp;=\u0026thinsp;1, 2, 3. . m, \u0026theta;\u0026thinsp;=\u0026thinsp;1, 2, 3. . y\u003c/em\u003e)。The formulae are as follows:\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e1. Indicator standardisation: Different indicators have different scales and units and therefore need to be standardised.\u003c/h3\u003e\n\u003cp\u003eIf the indicator is positive,\u003c/p\u003e\n\u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\begin{array}{c} {\\text{Y}}_{\\text{ɑ}{\\beta }{\\theta }}=\\raisebox{1ex}{$\\left({\\text{X}}_{\\text{ɑ}{\\beta }{\\theta }}-\\text{min}\\left({\\text{X}}_{\\text{ɑ}{\\beta }{\\theta }}\\right)\\right)$}\\!\\left/ \\!\\raisebox{-1ex}{$\\left({\\text{m}\\text{a}\\text{x}(\\text{X}}_{\\text{ɑ}{\\beta }{\\theta }})-\\text{min}\\left({\\text{X}}_{\\text{ɑ}{\\beta }{\\theta }}\\right)\\right)$}\\right. \\left(1\\right) \\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eIf the indicator is negative\u003c/p\u003e\n\u003cdiv id=\"Equb\"\u003e\n \u003cdiv id=\"FileID_Equb\" name=\"EquationSource\"\u003e$${\\text{Y}}_{\\text{ɑ}{\\beta }{\\theta }}=\\raisebox{1ex}{$\\left({\\text{m}\\text{a}\\text{x}(\\text{X}}_{\\text{ɑ}{\\beta }{\\theta }})-{\\text{X}}_{\\text{ɑ}{\\beta }{\\theta }}\\right)$}\\!\\left/ \\!\\raisebox{-1ex}{$\\left({\\text{m}\\text{a}\\text{x}(\\text{X}}_{\\text{ɑ}{\\beta }{\\theta }})-\\text{m}\\text{i}\\text{n}({\\text{X}}_{\\text{ɑ}{\\beta }{\\theta }})\\right)$}\\right. \\left(2\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere min represents the minimum value and max represents the maximum value.\u003c/p\u003e\n\u003ch3\u003e\u003cbr\u003e\u003c/h3\u003e\n\u003cdiv\u003e2. Calculation of characteristic proportions or contributions \u003cem\u003eZ\u003c/em\u003e\u003csub\u003e\u003cem\u003eɑ\u0026beta;\u0026theta;\u003c/em\u003e\u003c/sub\u003e。\u003c/div\u003e\n\u003cdiv id=\"Equc\"\u003e\n \u003cdiv id=\"FileID_Equc\" name=\"EquationSource\"\u003e$${\\text{Z}}_{\\text{ɑ}{\\beta }{\\theta }=}\\frac{{\\text{Y}}_{\\text{ɑ}{\\beta }{\\theta }}}{\\sum _{1}^{\\text{p}}\\sum _{1}^{\\text{y}}{\\text{Y}}_{\\text{ɑ}{\\beta }{\\theta }}},\\text{ɑ}=\\text{1,2},3\\dots \\dots \\text{p},{\\theta }=1, 2, 3 . . . \\text{y} \\left(3\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cbr\u003e\u003c/h3\u003e\n\u003cdiv\u003e3. Calculation of entropy \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/sub\u003e:\u003c/div\u003e\n\u003cdiv id=\"Equd\"\u003e\n \u003cdiv id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\begin{array}{c}{\\text{E}}_{{\\beta }=}K\\sum _{\\text{a}=1}^{\\text{p}}\\sum _{{\\theta }=1}^{\\text{y}}{\\text{Z}}_{\\text{ɑ}{\\beta }{\\theta }}\\text{ln}\\left({\\text{Z}}_{\\text{ɑ}{\\beta }{\\theta }}\\right),K=-\\frac{1}{\\text{ln}\\left(\\text{y}\\text{p}\\right)},{0\\le \\text{E}}_{{\\beta }}\\le 1 （4）\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e4. Calculate the information utility value of the \u0026beta;th indicator:\u003c/h3\u003e\n\u003cdiv id=\"Eque\"\u003e\n \u003cdiv id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$\\begin{array}{c}{\\text{G}}_{{\\beta }}=1-{\\text{E}}_{{\\beta }} \\left(5\\right)\\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eDetermining the weights of evaluation indicators \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/sub\u003e:\u003c/p\u003e\n\u003cdiv id=\"Equf\"\u003e\n \u003cdiv id=\"FileID_Equf\" name=\"EquationSource\"\u003e$$\\begin{array}{c}{\\text{W}}_{{\\beta }}=\\frac{{\\text{G}}_{{\\beta }}}{\\sum _{1}^{\\text{m}}{\\text{G}}_{{\\beta }}} ,\\beta = 1, 2, 3 . . . . . . m\\#\\left(6\\right) \\end{array}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.2 Constructing a Comprehensive Green Innovation Indicator System for Regional Enterprises\u003c/h2\u003e\n \u003cp\u003eBased on the panel data of each province in China, this paper refers to the research of Kang Yumei et al. and constructs the comprehensive green technology indicators from five dimensions, namely, the amount of patents granted, the share of technology market turnover, the government\u0026apos;s investment in green technology and innovation, the urban unemployment rate, and the energy utilisation rate.(Yumei \u0026amp; Chengxing, \u003cspan\u003e2023\u003c/span\u003e)。The number of patents granted reflects how active a region or organisation is in technology development and innovation. A higher number of granted patents may imply that the region or organisation has a high level of innovative capacity and dynamism in green technologies. This lays the foundation for further development of green technologies. The ratio of technology market transactions reflects the activity of the technology market and the efficiency of the transformation of scientific and technological achievements. A higher technology market turnover ratio means that more green technology achievements have been applied and promoted. It helps to popularise and deepen green technology. Government expenditure on science and technology as a proportion of finance: This indicator reflects the importance the government attaches to science and technology and green technology. Government investment in science and technology and green technology can promote relevant research and development, and promote the progress and innovation of green technology. Urban registered unemployment rate can reflect the economic situation and employment. A healthy economic environment can provide more employment opportunities, thus attracting more talents to invest in green technology R\u0026amp;D and innovation.Electricity consumption per unit of GDP is an important criterion for measuring the efficiency of energy use in a region or organisation. By reducing electricity consumption per unit of GDP, energy consumption and environmental pollution can be reduced, and green and low-carbon development can be promoted, which is one of the important goals of green technology innovation. As shown in Table \u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eComprehensive evaluation index system of green innovation index for regional enterprises\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eObjective level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCriteria level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicator layer\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWeights\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProperties\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eComprehensive Green Technology Indicators for Regional Enterprises\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatent grants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatent Applications and Authorisations(item)/ Year-end Resident Population (10,000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercentage of Technology Market Transactions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnology market turnover (billion yuan) / GDP (billion yuan)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGovernment Investment in Science and Technology Innovation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShare of Science and Technology Expenditure in Fiscal Expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Unemployment Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban registered unemployment rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eForward\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy Utilisation Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElecticity consumption per unit of GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBased on the entropy method and the comprehensive index of green technology innovation, this paper measures the development level of green technology innovation in each region of China from 2012 to 2021, as shown in Fig. \u003cspan\u003e3\u003c/span\u003e. It can be found from 2012 to 2021. The green technology innovation score of the eastern region ranges from 0.19 to 0.35, that of the central region ranges from 0.09 to 0.26, while that of the western region ranges from 0.09 to 0.17. The eastern region, with its higher level of economic development and better infrastructure, has always scored at a higher level in terms of green technological innovation. As shown in Fig. \u003cspan\u003e3\u003c/span\u003e, from the overall trend, China\u0026apos;s green technology innovation index shows a gradual upward and steady development trend. The eastern region has been in the lead. However, it is worth noting that the inter-regional green STI gap between the central and western regions is gradually widening. This suggests that the western region may need more attention and investment in the development of green STI in China in order to narrow the regional gap.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.3 Calibration\u003c/h2\u003e\n \u003cp\u003eIn this paper, based on Boolean algebra theory and previous studies, the data were calibrated precisely to ensure the consistency and coverage of the analysis. The direct calibration method is used, and the 95% quartile, 50% quartile and 5% quartile are set as calibration anchor points, and the specific results are shown in Table \u003cspan\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCalibration of variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable name\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFully affiliated\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntersections\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCompletely unaffiliated\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResult Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGreen innovation of local enterprises (Y)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eConditional variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer capita gross regional product(A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercentage of government expenditure on social security(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Green Coverage Rate(C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel of aging population(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTourist demand for green innovation(E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of foreign-invested enterprises(F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79639.850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e737.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemand for innovation by regional service industries(G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37660.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9850.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1542.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.4 Necessity Analysis of Individual Conditions\u003c/h2\u003e\n \u003cp\u003eAccording to the set theory of Boolean algebra and QCA design principles and applications, it is known that the smaller the adjustment distance of QCA panel data, the higher the consistency accuracy. However, the adjustment distance is not clearly defined in statistics.QCA experimental analysis needs to consider the data size and data inclusion, so the median value of the adjustment distance used in this experiment is 0.3.As shown in Table \u003cspan\u003e3\u003c/span\u003e, if per capita GDP (A), the proportion of government social security expenditures (B), the percentage of urban green coverage (C), the level of the aging population (D), the demand of tourists for green innovations (E ), number of foreign-invested enterprises (F), and regional enterprises\u0026apos; demand for innovation (G), the seven indicators have an adjusted distance to green innovation greater than 0.3, and coverage less than 0.5 requires researchers to further explore the necessity.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eAnalysis of the necessary conditions\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003evariant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eHigh level of local business green innovation (Y).\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eLow level of local business green innovation (~\u0026thinsp;Y).\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAggregate Consistency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAggregate coverage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInter-group consistency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntra-group consistency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAggregate Consistency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAggregate coverage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInter-group consistency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIntra-group consistency\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e~G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eaverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBy analysing the intergroup consistency and coverage of the corresponding variables (as shown in Tables \u003cspan\u003e3\u003c/span\u003e and \u003cspan\u003e4\u003c/span\u003e). There are the following findings: firstly, in the process of analysing the necessary conditions, we did not find that any single factor can constitute the necessary conditions for green innovation of local enterprises alone. This means that green innovation of local enterprises is a complex process that requires multiple factors to work together to achieve it. Secondly, in the inter-group data with an adjusted distance greater than 0.3, we observe that in cases a, b, and c, the level of consistency across years does not reach 0.9. therefore the necessary relationship is not satisfied. Meanwhile, by plotting the scatterplot of coverage and consistency, we find that the coverage is mainly concentrated on the right y-axis, which passes the test of the non-essential condition. However, consistency does not pass the test of the non-essential condition. This further suggests that these factors may play a role in the green innovation process. However, they are not decisive and have their own research value.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eData between groups with adjusted distances greater than 0.3\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSituation.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCausal combination situations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2021\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e~A and Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntergroup consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntergroup coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e~F and Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntergroup consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntergroup coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e~G and Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntergroup consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntergroup coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.5 Configuration analysis results\u003c/h2\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eConfiguration truth table\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConditional variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eparameterisation1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameterisation2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameterisation3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGross regional product per capita (A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercentage of government expenditure on social security(B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban Green Coverage Rate(C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLevel of aging population(D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTourist demand for green innovation(E)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026otimes;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of foreign-invested enterprises(F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDemand for innovation by regional service industries(G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e●\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOriginal Coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.366\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnique Coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInter-group consistency adjusted distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntra-group consistency-adjusted distance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall PRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall Consistency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall Coverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eNote: ● and \u0026otimes; indicate presence and absence of core; blank indicates that presence and absence are also possible.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan\u003e5\u003c/span\u003e shows that the consistency of grouping 1, grouping 2, and grouping 3 is 0.828, 0.827, and 0.845, respectively. The overall consistency is greater than 0.75. And the adjustment distance between intra-group and inter-group for individual grouping is less than 0.3. It shows that the aggregated consistency has a better explanatory strength. These three groupings can be regarded as sufficient conditions affecting the generation of sustainable green innovation in local enterprises. From the study of group state 1, we observe the influence of different factors on the green innovation of enterprises. Cohort 1 shows that economic drivers such as the number of foreign-invested firms and the demand for innovation in the regional service sector, as well as socio-environmental factors such as a high level of government social security expenditure and the level of ageing, are the main drivers of green innovation in firms, while market demand has a limited impact. Configuration 2 further emphasises the importance of high levels of foreign investment and service sector innovation demand. At the same time market demand for tourists\u0026apos; preference for green begins to emerge. By configuration 3, economic, social and market demand factors show a more balanced state. The economic drivers are GDP per capita, high level of foreign investment, and high level of regional demand for innovation in the service sector. The social environment factor is dominated by the high level of government social security expenditure share. Market demand is dominated by the urban greening coverage rate.\u003c/p\u003e\n \u003cp\u003eThese findings suggest that, in the context of China\u0026apos;s geographical resource differences, localities should combine their own characteristics to achieve factor linkages in order to promote local firms\u0026apos; green innovation. It is worth noting that the multidimensional linkage model demonstrated in Grouping 3, although the study shows that the green innovation of local enterprises is influenced by multiple factors such as economic drive, social environment and market demand. However, this multidimensional linkage model still needs further in-depth exploration. The key lies in how enterprises balance supply and demand to achieve multi-dimensional power. Only by comprehensively considering economic, social and market demands can enterprises formulate a more reasonable and effective green innovation strategy. This is not only the key to enhance the competitiveness of enterprises, but also the way to realise the sustainable development of green innovation. Therefore, future research should pay more attention to the balance and synergy of enterprises under the effect of multidimensional factors, so as to promote the in-depth development of green innovation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.5 Between and within group results\u003c/h2\u003e\n \u003cp\u003eIt was found that the adjusted distance of intergroup consistency for all 3 groupings was not greater than 0.3, indicating that there was no significant time effect. Further examination of its temporal changes revealed that the consistency levels of the 3 groupings showed a decline from 2012\u0026ndash;2016. However, they collectively showed a period of rapid growth in 2016\u0026ndash;2021. As shown in Fig. \u003cspan\u003e4\u003c/span\u003e. Among them, the fastest growth rate of intergroup consistency for group state 3 grows from 0.84 to 1.00. The reason for this is that our government intervention plays a crucial role. Checking government websites and local service platforms found that 60% more documents were released in 2016 to promote green development compared to 2015. Meanwhile, the government sends out strong intervention signals In terms of policy intervention: the Chinese government has introduced a series of policies to encourage green innovation and sustainable development, such as providing financial subsidies, tax incentives, and loan facilitation, in order to incentivise enterprises to increase their research and development in green technologies. Regulatory constraints: The government has strengthened the formulation and enforcement of environmental protection regulations, imposing strict limits on pollution emissions and energy consumption. Government in green procurement: As one of the largest consumers, the government has given priority to green products and services to encourage enterprises to actively develop green products and enhance their green innovation capability.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eGeographical coverage\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEastern China\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCentral China\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWestern China.\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConfiguration 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConfiguration 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConfiguration 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe range of intra- and inter-group consistency adjustment distances is almost the same, and the intra-group consistency adjustment distance is not greater than 0.3. The variability in the distribution of geographic coverage of the grouping models revealed in Table \u003cspan\u003e5\u003c/span\u003e. The explained cases of group states 2 and 3 are mainly concentrated in the central region, which may stem from the unique resource conditions and policy environment in these regions. However, histogram 1 shows stronger explanatory power in East, Central and West China, with a coverage of more than 0.5, indicating its universality. This shows that firms in different regions may be affected by different factors when facing green innovation and sustainability challenges. Such geographical differences may stem from the diversity of regional levels of economic development, market demand, resource distribution and policy orientations. For policy makers and entrepreneurs, an in-depth understanding of the geographical characteristics of the model can help develop more targeted strategies and measures to promote green transformation and sustainable development of enterprises.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.6 Consistent prediction of sustainable development of green innovation of local enterprises\u003c/h2\u003e\n \u003cp\u003eUsing MATLAB, the article constructed a consistency prediction model for the sustainable development of green innovation in local enterprises, as shown in Fig. \u003cspan\u003e5\u003c/span\u003e. The root mean square error (RMSE) of the model is 0.9, a value that indicates that the predictive accuracy of the model can be applied to consistency prediction.RMSE is a commonly used metric for assessing the predictive ability of a model, which measures the magnitude of the model\u0026apos;s error by calculating the mean of the squared difference between the predicted value and the actual value. A lower RMSE value means that the model has a higher prediction accuracy. From the trend of the model\u0026apos;s prediction graph, it can be observed that the overall trend of the consistency index of the sustainable development of green innovation of local enterprises shows a gradual decrease followed by a rapid growth trend. According to the prediction trend, China\u0026apos;s progress in green technological innovation is gradually accelerating, and the consistency index of enterprises\u0026apos; sustainable development of green innovation will be maintained around 0.90.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Results","content":"\u003cp\u003eThis paper applies the dynamic QCA research method. Using the data cases of 30 provincial governments in China, it explores the influence effects of both supply and demand influencing factors on the sustainable development of green innovation of local enterprises. The core influencing factors affecting the sustainable development of local enterprises' green innovation and the interaction between them during 2012\u0026ndash;2021 are revealed.\u003c/p\u003e \u003cp\u003eThese findings suggest that the influencing factors of local enterprises' sustainable development of green innovation are complex and diverse, and that different factors may have different influencing effects in different contexts. Firstly, although market demand is an important influencing factor. However, its role relative to economic drivers and social environment factors may be relatively limited in some cases. This suggests that when promoting green innovation in local enterprises, we cannot rely solely on the pull of market demand, but also need to take into account various factors such as the economic and social environment. Second, high levels of foreign investment and demand for innovation in the service sector have a significant impact on green innovation in local enterprises.This suggests that attracting foreign investment and promoting innovation in the service sector are important ways to enhance the green innovation capacity of local enterprises. At the same time, with the increasing preference of consumers for green products, green preferences in market demand have also begun to become an important factor influencing enterprises' green innovation. In addition, the resource differences and characteristics of different regions make it necessary for local enterprises to combine their own realities in the process of green innovation and realise the linkage and complementarity of factors. This requires local governments to take into full consideration the actual situation of the region when formulating relevant policies and promote green innovation according to local conditions. In the spatial dimension, the sustainable development of green innovation of local enterprises shows regional differences. This may be due to the differences in the level of economic development, industrial structure, policy environment and other factors in different regions. Therefore, when promoting green innovation, differentiated policy measures need to be formulated for the characteristics of different regions. Finally, by constructing a consistency prediction model, the study finds that China's progress in green technology innovation is gradually accelerating, and the consistency index of enterprises' sustainable development of green innovation will be maintained at a high level. This indicates that China's green innovation activities will become more active and stable in the future, providing strong support for sustainable development.\u003c/p\u003e \u003cp\u003eThis study still has limitations. Firstly, this study uses secondary public data and only explores the macro level. In the future, it is necessary to further combine qualitative interviews with participant observation to unveil the influence mechanism behind information disclosure at the micro level. Finally, this paper only focuses on national government data. Whether the findings can be extended to the sub-provincial level still needs to be further explored.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u0026nbsp;It is hereby declared that there is no conflict of interest in this study, all data are sourced from publicly available data from the National Bureau of Statistics of China, the collected data are properly stored at Shinhan University, and this article is licensed under the Creative Commons Attribution 4.0 International License, which permits anyone to use, share, modify, distribute, and reproduce this article subject to certain conditions. If the material is not included in the Creative Commons Licence for this article, and your use is not intended to comply with statutory requirements or exceeds the scope of permitted use, you will need to obtain a licence directly from the copyright owner.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp; \u0026nbsp;This study was conducted without receiving any external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u0026nbsp;\u003c/strong\u003e In the design and execution of this study, relevant ethical guidelines and regulatory requirements were strictly adhered to, to ensure that the research process would not cause any adverse effects on the participants, society or the environment. Therefore, we hereby declare that there are no ethical issues in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlvarado, R., Ponce, P., Criollo, A., C\u0026oacute;rdova, K., \u0026amp; Khan, M. K. 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The impact of environmental management on firm economic performance: The mediating effect of green innovation and the moderating effect of environmental leadership. \u003cem\u003eJournal of Cleaner Production, 292\u003c/em\u003e, 126057.\u003c/li\u003e\n\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":"factors, dynamic QCA, regional differences, time dimension, Matlab","lastPublishedDoi":"10.21203/rs.3.rs-4108767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4108767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjective: To study the multifactor linkage effects behind the differences in the sustainable development of green innovation of local enterprises in the spatio-temporal dimension, so as to provide an important reference for the practice of sustainable development of green innovation of local enterprises.\u003c/p\u003e\n\u003cp\u003eMethodology: An analytical framework for the sustainable development of green innovation of local enterprises is established, and the dynamic QCA method is applied to analyse the provincial-level panel data of China from 2012 to 2021, to explore the linkage effect of each factor on the time axis, and to explore the differences of multi-factors on the time axis. The experimental study also examined the spatial distribution of regional coverage in conjunction with different regional divisions in China.\u003c/p\u003e\n\u003cp\u003eFindings The study found that different factors may have different influence effects in different contexts. Firstly, while market demand is an important influencing factor, its role relative to economic drivers and social environment factors may be relatively limited in some contexts. Second, high levels of foreign investment and demand for innovation in the service sector have a significant impact on green innovation in local firms. At the same time, with the growing consumer preference for green products, green preferences in market demand have also begun to become an important factor influencing firms' green innovation. Meanwhile, in the spatial dimension, the provincial coverage out of the obvious regional differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExperimental significance The research on the sustainable development of green innovation of local enterprises needs to be combined with the actual situation in China, and the resource differences and characteristics of different regions make it necessary for local enterprises to combine with their own reality in the process of green innovation to achieve the linkage and complementarity of factors. This requires local governments to fully consider the actual situation of the region when formulating relevant policies, and promote green innovation according to local conditions. This experiment is the first attempt to use the joint application of dynamic QCA and Matlab for the study of green innovation in local enterprises, exploring the consistency in the longitudinal time dimension.\u003c/p\u003e","manuscriptTitle":"Exploring the Path of Green Innovation and High Quality Development of Influential Regional Enterprises - Based on the Analysis of Dynamic QCA Method and Matlab Sustainability Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 12:36:03","doi":"10.21203/rs.3.rs-4108767/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":"9868aa5b-e872-42e3-ac81-5579913d05ce","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-26T12:45:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 12:36:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4108767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4108767","identity":"rs-4108767","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}