Introduction

Achieving sustainable development depends on the development and diffusion of innovative and environmentally sound technologies (Jaffe, 2012). Technology diffusion not only promotes cumulative innovation but also improves sustainability by avoiding wasted R&D resources due to the duplication of investments. Knowledge gained from outside sources can contribute to productivity gains to a greater extent than internal research and development (Audretsch and Belitski, 2020; Corey, 2014). However, inventions have remained highly concentrated geographically over the past decade, with inventors in the top ten countries accounting for almost 90% of global inventions (Probst et al., 2021). Moreover, knowledge diffusion is geographically localised in the sense that the benefits of spillovers decline with distance (Jaffe et al., 1993; Keller, 2002), and cannot be fully expressed in a codified form (Catalini, 2018; Atkin et al., 2022). Therefore, the promotion of sustained development should not be limited to stimulating the internal R&D of innovative agents but should also focus on breaking down the geographic localisation of technology diffusion.

Green technology diffusion is influenced by environmental regulations, geographical distances, firm attributes, expected returns, technological complexity and relevance, and public demand for consumption (Losacker, 2022). Local financial markets play a critical role in facilitating technology diffusion, especially in the early stages of diffusion (Comin and Nanda, 2019). However, traditional financing tends to select credit clients based on their assets and profitability, leading to the financial exclusion of technology diffusion with development potential. Fortunately, green digital finance has gradually become one of the main financial development models in various countries, driven by carbon neutrality targets. BNP Paribas has put green and low-carbon transformation at the centre of its 2022–2025 development strategy, which is based on the theme of ‘Growth, Technology, Sustainability’. Mitsubishi UFJ Group has adopted ‘sustainable management, digitalisation, growth and new challenges’ as its three major development themes for 2022–2025. The Financial Technology Development Plan (2022–2025), issued by the People’s Bank of China, proposes to “utilise digital technology in green finance and enhance green financial risk management capabilities”.

Green digital finance may be understood as an intended application of digital finance or fintech towards achievement of the sustainable development goals (UNEP, 2018). Compared with traditional finance, green digital finance is expected to reduce financing costs, alleviate financing constraints, and guide the flow of funds toward green projects, thereby promoting technological innovation and diffusion. Digital technologies make more data available more cheaply, more quickly and more accurately to better inform financial decision-making; as well as encourage inclusion and unlock innovation. From the perspective of enhancing information transfer, green digital finance can reduce the cost of resource matching by promoting cooperation and information-sharing among enterprises and between financial institutions and enterprises. Moreover, the environmental information disclosure required by green digital finance may alleviate information asymmetry, which is conducive to technology spillovers. Wang and Wang (2021) found that green finance could promote green innovation in local and neighbouring provinces, suggesting green technology diffusion through spatial synergy. Therefore, it’s well worth examining whether green digital finance could overcome the shortcomings of traditional financing and promote the diffusion of technology among regions.

However, existing studies have several shortcomings. First, they focus on the impact of green digital finance on technological innovation rather than on technology diffusion. Given the significance of technology diffusion in sustainable development, particularly in developing countries, it is necessary to examine the impact of green digital finance on technology diffusion. Second, most studies only focus on the effect of administrative boundaries or geographic distances alone when examining technology spillovers. Singh and Marx (2013) distinguished between the different effects of administrative boundaries and geographic distances. It implies that both the effects of administrative boundaries and geographic distances should be taken into account when examining technology diffusion. Third, the impact of green digital finance on technology diffusion remains uncertain. Green digital finance may make the localisation trend more pronounced, which is closely related to the strength of patent protection. The spatial effect of green digital finance on technology diffusion may change with the increase in distance between cities due to the increase of information transmission costs and local protectionism.

To fill these research lacunas, this study explores the causal relationship between green digital finance and technology diffusion. The conceptual framework of this study is shown in Fig. 1. The marginal contributions include the following three aspects. First, compared to related studies on green digital finance and green innovation, it further investigates whether green digital finance can break down administrative boundaries and geographic distances. In the current era of green digital finance, technology diffusion is equally necessary and essential for technological innovation in environmental and climate governance. Second, it extends the classic spatial Dubin model with a dual-weighted boundary and distance at the city-pair level. Thus, this model could capture both the effects of administrative boundaries and geographic distances on technology diffusion. Third, this study examines mechanisms by which green digital finance affects technology diffusion. Green digital finance fosters a digital economy and promotes market integration, which may contribute to technical cooperation and knowledge sharing.

Fig. 1: Conceptual framework.
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This figure shows the research procedure, data, and research methods used in the study.

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Literature review and theoretical hypotheses

Green digital finance and technology diffusion

Technology diffusion is localised and usually located close to where innovations are developed (Keller, 2002; Dewald and Truffer, 2012), and technology tends to spread among geographically proximate partners (Han and Seo, 2023). The multi-dimensional proximity theory suggests that geographical, cognitive, and institutional proximity can effectively promote technological diffusion among institutions, cities, regions, and countries (Boschma et al., 2015). Technology diffusion depends on the geographical proximity of the innovator (Losacker et al., 2023), because knowledge involved in technology tends to be spatially sticky (Balland and Rigby, 2017). Lengyel et al. (2020) also suggested that spatial proximity plays a more important role in complex technologies, such as green technologies. However, Ocampo-Corrales et al. (2021) showed that knowledge flows for green technologies (i.e., renewable energy technologies), indicated by patent citations, are not localised, but rather span large distances.

However, green digital finance may have the following advantages to break through localisation barriers and facilitate technology diffusion over traditional finance. First, the digitalisation of finance includes an ecosystem of technologies such as big data, artificial intelligence, mobile platforms, and blockchain, thereby eliminating the obstacles geographical distances pose to technology diffusion (Abramo et al., 2020; Anacka and Lechman, 2023). Global village theory argues that new Internet technology helps break down communication barriers between individuals and organisations, and digital technologies are useful in lowering the coordination costs associated with distance (Forman et al., 2005). Therefore, green digital finance utilises digital technologies to enhance the flow of financial resources and to facilitate technical cooperation and knowledge sharing (Xu et al., 2023). Second, green financing is essentially policy-driven and environmentally focused (Zhang et al., 2019), which broadens the financing channels and alleviates financing constraints for green projects. It allows financial institutions to innovate financial instruments according to the green capital needs of enterprises, which stimulates green technology innovation and diffusion (Balsmeier et al., 2017; Peng et al., 2021). Moreover, green digital finance requires environmental information disclosure, encouraging technology cooperation and information-sharing among enterprises, between financial institutions and enterprises, and between financial institutions and financial institutions. Based on the above analysis, the following hypothesis is proposed:

Hypothesis 1: Green digital finance overcomes localisation barriers and promotes technology diffusion.

Green digital finance, digital economy, and technology diffusion

Green digital finance fosters a digital economy with the development and integration of digitalisation and greening. It provides digital technology, application scenarios, data elements and efficient industrial interaction modes, thus contributing to digitalisation upgrading in a multi-dimensional way. By simplifying the programmed and complicated green financial service process under the traditional financial system, green digital finance expands the scope of financial services, improves the efficiency of capital allocation, and strongly supports digital transformation (Yu et al., 2022). Moreover, it stimulates new innovative and entrepreneurial activities across traditional industry boundaries, such as embracing networks, building ecosystems and communities, and integrating digital assets, thus accelerating the formation of new industrial patterns. On this basis, the digital economy will further encourage technology innovation and knowledge sharing.

First, the level of digitisation, such as industry digitalisation intensity, may have a significant impact on technology diffusion. Digitisation boosts cashless transactions, e-banking, digital fund transfers, etc., which help minimise process risks and reduce travel time and transaction costs (Chatterjee, 2020). This effectively breaks down barriers to interregional factor mobility, and facilitates frequent learning exchanges and knowledge sharing among R&D personnel, thus promoting technology diffusion. In addition, the relative digital intensity of different regions may play an important role in determining whether innovation adopters in the region will use locally developed technologies or whether adopters will rely on technologies developed in other regions, thereby affecting technology diffusion.

Second, the innovative capability of digitisation mediates the relationship between green digital finance and technology diffusion. It would reconfigure the distributed business model, and determine the cooperation of innovative subjects in the distributed innovation network (Hui et al., 2023), thus influencing the diffusion of technology. A strong inventive capacity may produce a radiation effect that effectively promotes technological innovation in neighbouring regions. However, regions with less digital innovation capacity are more dependent on technology transfer from other regions (Losacker et al., 2023). Besides, the promotion effect of the digital economy on green technological innovation will cause imitation and learning in geographically neighbouring regions or regions with similar economic conditions (Fagerberg and Verspagen, 2002).

Third, the digital economy can contribute to technological diffusion by alleviating communication costs and information asymmetry. Poor information quality or asymmetric information environment may lead to underinvestment in innovation activities, and prevent the effective transfer of innovative technology (Amin et al., 2023; Mariotti et al., 2010). Digital technology penetrates the process of information collection, transmission, analysis, and application, expanding the sources of information, enhancing the ability to analyse information, and mitigating information asymmetry (Sun et al., 2022). Therefore, the digital economy makes more data available more cheaply, quickly and accurately, thus reducing the cost of matching information and promoting technology diffusion (Francis et al., 2009; Liu et al., 2018). Hence, this study proposes the following hypothesis:

Hypothesis 2: Green digital finance promotes technology diffusion by fostering a digital economy through the level of digitisation, innovative capability and communication cost.

Green digital finance, market integration, and technology diffusion

Green digital finance can scale sustainable finance, drive environmentally sustainable growth, and promote market integration (Hui et al., 2023), thereby expanding the scale and quality of trade. First, green digital finance advances more resource-efficient consumption and production patterns, unlocks new sources of finance, and enables new business models in environmentally friendly sectors. It makes full use of the radiation and decentralisation functions of growth poles to rationalise resource allocation and reduce regional disparities (Huang et al., 2022). Second, green digital finance increases the scale of external financing for disadvantaged enterprises under information friction. It provides a signalling mechanism for exporting enterprises excluded by traditional finance to obtain financing, and reduces financing costs (Jagtiani and Lemieux, 2018). Third, green trade barriers comprise a series of green requirements and restrictions on product exports. Green digital finance would promote green innovation and technology, thus meeting environmental requirements and overcoming green trade barriers (Wang et al., 2023).

Trade is an important pathway for technology diffusion (Halleck-Vega et al., 2018), which is reflected in both trade volume and trade intensity (Fracasso and Vittucci Marzetti, 2015). Lowering trade barriers, especially in developing countries, can trigger faster diffusion of technology (Fadly and Fontes, 2019). First, trade enables firms to obtain high-tech products from other regions, which greatly increases the likelihood of technology spillovers (Feng et al., 2019; Vujanović et al., 2022). Second, trade in intermediate products reduces enterprises’ costs, allowing them to invest more in innovation activities, thus promoting technology diffusion (Xu and Chiang, 2005; Ayerst et al., 2023). Third, trade in final products increases the competition faced by enterprises, forcing them to increase innovation and productivity levels (Bloom et al., 2016), thus facilitating technology diffusion. Hence, this study proposes the following hypothesis:

Hypothesis 3: Green digital finance facilitates technology diffusion by promoting market integration.

Research design

Sample data

This study compiles a dataset of 189 Chinese cities from 2002 to 2015 to examine whether and how green digital finance affects technology diffusion across cities, covering 35,532 ‘citing city-cited city’ pair observations every year. The main data used in this study are available from the Annual Financial Reports of Chinese Listed Companies released by the China Stock Market and Accounting Research (CSMAR) database, the China Industry Business Performance Database released by the Express Professional Superior data platform, the Chinese Industrial Enterprise Database released by the Bureau of Statistics of China, and the China City Statistical Yearbook released by the Bureau of Statistics of China (Table 1).

Table 1 Descriptive statistics.
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Empirical model

This study applied the classic spatial Dubin model to the city-pair level by adding two different spatial weights. Compared to the classic model, this extended city-level dual-weighted spatial model highlights and disentangles spatial correlations among various variables, especially in different spatial connections (Atella et al., 2014). Technology diffusion is the process through which patent knowledge spreads from one city to another. In general, administrative boundaries and geographic distances have different spatial spillover effects on technology diffusion (Singh and Marx, 2013; von Graevenitz et al., 2022; Perri and Santangelo, 2023). Technology diffusion is significantly affected by the geographical boundaries in bordering cities. In non-bordering cities, technology diffusion is significantly affected by distance. Related studies on patent knowledge have only focused on one sole spatial influence, which does not really reflect how technology diffusion occurs between cities today. Therefore, this study adds both within-contiguity and outside-distance matrices to the classic spatial Dubin model, which can explore spatial correlations among various spatial variables.

The extended spatial models proceed as follows: t represents the year, i represents the citing city, j represents the cited city, −i represents any other city except the citing city i itself, and −j represents any other city except the cited city j itself. (1) TDi,j,t is the dependent variable representing the technology diffusion from cited city j to citing city i in year t. (2) GDFi,j,t is the independent variable representing the green digital finance level between citing city i and cited city j in year t. (3) Xi,j,t represents a set of control variables. (4) W−i,−j,t represents the dual-weighted spatial correlation between citing city i and cited city j in year t. W1 is the within-contiguity matrix defined by the queen contiguity connection. W2 is the external distance matrix defined by the inverse geographic distance. (5) \({W}_{1}^{GD{F}_{-i,-j,t}}\) and \({W}_{2}^{GD{F}_{-i,-j,t}}\) represent the spatial influence of the green digital finance level in year t. (6) \({W}_{1}^{T{D}_{-i,-j,t}}\) and \({W}_{2}^{T{D}_{-i,-j,t}}\) represent the spatial influence of the technology diffusion situation in year t. (7) δf represents the potential multiple fixed effects. (8) μi,j,t is the spatial disturbance error term, including the unobservable spatial error Wμ and normal distribution error εi,j,t. (9) The remaining parameters are corresponding coefficients to be estimated, among which β is the most interesting and focused.

$$T{D}_{i,j,t}=\alpha +\beta GD{F}_{i,j,t}+{W}_{-i,-j,t}+\gamma {X}_{i,j,t}+{\delta }_{f}+{\mu }_{i,j,t}$$
(1)
$${W}_{-i,-j,t}={\rho }_{1}{W}_{1}^{GD{F}_{-i,-j,t}}+{\omega }_{1}{W}_{1}^{T{D}_{-i,-j,t}}+{\rho }_{2}{W}_{2}^{GD{F}_{-i,-j,t}}+{\omega }_{2}{W}_{2}^{T{D}_{-i,-j,t}}$$
(2)
$${\mu }_{i,j,t}=\lambda {W}_{\mu }+{\varepsilon }_{i,j,t}$$
(3)
$${\varepsilon }_{i,j,t}\sim N(0,{\sigma }_{i,j,t}^{2})$$
(4)

Dependent variable: technology diffusion

As technology diffusion is a relatively dynamic process, this study constructs a patent citation network at the city level to measure how new technology is invented and created using current achievements. Following related emerging studies on patent networks (Baruffaldi and Simeth, 2020; Losacker, 2022), we match patent-citing and patent-cited information at the city level. Because all patent information in China is released and managed by the State Intellectual Property Office, ‘citing city-cited city’ pair data can only be obtained from 2002 to 2015 through multiple databases and platforms (Tong et al., 2018; Liu et al., 2021). By constructing this patent citation network, technology diffusion is measured using two patent citation forms: patent citation existence (TD1i,j,t) and patent citation number (TD2i,j,t). The existence of patent citations is a dummy variable. If existing patent citation information is available, its value is 1. On the other hand, the patent citation number is an actual value reflecting cumulative citations over the past three years.

Independent variable: green digital finance

Green digital finance represents how a traditional brown economy can be transformed into a sustainable economy and is a comprehensive reflection of green transformation and digital innovation (Feng et al., 2022; Hyun, 2022; Paradise, 2022). Studies on how to measure green digital finance are still lacking, especially those that consider the development and application of digital technology (Lv et al., 2021; Khera et al., 2022). Following previous studies, we adopt principal component analysis to estimate this new multi-dimensional variable from six aspects: green digital credit, green digital investment, green digital insurance, green digital bonds, green digital support, and green digital funds. First, this study obtains the original city-level green finance data from the China City Statistical Yearbook, including green credit, green investment, green insurance, green bonds, green support, and green funds.Footnote 1 Second, the industrial digitalisation intensity will be matched to the city-level green economic data. By doing that, six green finance data will be adjusted and transferred into the green digital aspect.Footnote 2 Last, the principal component analysis will be used to estimate the final green digital finance variable.

Other control variables

First, the single city-level control variables include gross domestic product (GDPi,j,t), secondary industry output (STRi,j,t), fixed-asset investment (FAi,j,t), foreign direct investment (FDIi,j,t), R&D investment (RDi,j,t), financial deposits (FINi,j,t), Internet population (INTi,j,t), and patent output (PATi,j,t). All control variables are used twice in the empirical analyses below. One is for the citing city (such as GDP1i,j,t), and the other is for the cited city (such as GDP2i,j,t). Second, the ‘citing city-cited city’ pair control variables include the province-level boundary (BORi,j,t) and the high-speed rail between cities (HSRi,j,t). These two control variables are only used once in the empirical analyses below because they already have city-city pair information. All control variables are in natural logarithmic form and have been handled by related economic deflators, if possible and applicable.

Empirical results

Benchmark analysis

Table 2 presents the benchmark results for green digital finance and technology diffusion. All standard errors allowed for clustering at the target ‘citing city-cited city’ level observations with the cited city, city, and year-fixed effects added. In Columns (1)–(3), technology diffusion is measured by the existence of patent citations (TD1i,j,t). In Columns (4)–(6), technology diffusion is measured by the patent citation number (TD2i,j,t). Except for this difference, Columns (1)–(3) and (4)–(6) maintain a consistent analysis process. In Columns (1) and (4), the dual-weighted spatial model adopts a city-level within-contiguity matrix and a city-level outside-distance matrix. In Columns (2) and (5), the city-level within-contiguity matrix and province-level outside-distance matrix were adopted for the substitution analysis. In Columns (3) and (6), the city-level within-contiguity, city-level outside-distance, and province-level outside-distance matrices are constrained simultaneously. To better understand this, all estimated coefficients were adjusted and transferred into the final total spillover effect form according to the spatial matrices.

Table 2 Effect of green digital finance on technology diffusion, benchmark.
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In comparison, Column (1) is sufficient to indicate the overall spatial spillover effect of green digital finance on technology diffusion. For every 1% increase in green digital finance, the overall patent citation probability can be improved by 16.89%, which is equivalent to an almost 43% variation calculated by its standard error. Moreover, for the number of patent citations in Column (4), the overall promoting effect of 1% green digital finance is 28.06%. Statistically, both a 16.89% increase in the average patent citation probability and a 28.06% increase in the average patent citation number illustrate the significant and considerable stimulus effects of green digital finance, especially spatial diffusion across cities. In this sense, the testable Hypothesis 1 is supported by Table 2.

Technology diffusion duration

Considering the lagging effect of patent output and patent citation, this study further recalculates technology diffusion using its cumulative value over five and ten years. In Table 3, columns (1) and (3) adopt the five-year cumulative value of technology diffusion, and columns (2) and (4) adopt the ten-year cumulative value of technology diffusion. The results show that, even with lagging technology diffusion as the dependent variable, the stimulating effect of green digital finance on patent citations remains significantly positive. Slight changes in the re-estimated coefficients did not affect the benchmark conclusions. To some extent, there is also a slight cumulative phenomenon during the spatial promoting effect of green digital finance on technology diffusion.

Table 3 Effect of green digital finance on technology diffusion, robustness.
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Green digital finance dimension

Since green digital finance is a relatively multi-dimensional and comprehensive index, this study then uses its six sub-indexes to re-estimate the benchmark results. In Table 4, green digital credit, green digital investment, green digital insurance, green digital bonds, green digital support, and green digital funds will be adopted gradually in columns (1)–(6) and in columns (7)–(12). By comparison, the stimulating effects of green digital finance on technology diffusion are more evident among three sub-indexes: green digital credit, green digital investment, and green digital support. Considering green and digital transformation, these three sub-dimensions are the emerging essential issues in the Chinese economy. Therefore, their estimated coefficients are relatively considerable than the others.

Table 4 Effect of green digital finance on technology diffusion, robustness.
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Instrument variable method

Considering the potential two-way causal relationship between green digital finance and technology diffusion across the sample cities, the instrumental variable method is adopted, as shown in Table 5. Related studies show that Hangzhou City is the origin of the Chinese Internet economy, thus making it the leading city in the green digital economy (Feng et al., 2022; Zhao et al., 2023). Therefore, this study applies ‘the spherical distance from the sample city to Hangzhou City’ as the instrument variable (HZi,j,t). On one hand, as a predetermined exogenous geographical spherical distance, it has no direct connection with city-level technology diffusion. On the other hand, other cities must learn how to develop a green economy from Hangzhou City. Therefore, there is a correlation between green financing and this instrument variable. The results below show that the ‘Hangzhou City spherical distance’ is an acceptable and effective instrumental variable in both patent citation existence and number, indicating no significant two-way causal relationship issue.

Table 5 Effect of green digital finance on technology diffusion, robustness.
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Further discussion

Underlying mechanism

To better understand how green digital finance can and will affect technology diffusion across different cities, this study uses three variables as potential mechanism pathways: the digital economy and market integration. In Table 6, the interaction term analysis is applied to conduct all mechanism estimations.

Table 6 Effect of green digital finance on technology diffusion, mechanism.
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First, the mechanism effects of interregional trade are discussed in Columns (1)–(3) and Columns (6)–(8). The digitalisation of production factors and technical experience is necessary and essential to break geographical boundaries and communication distances, thereby achieving technology diffusion across cities. As for economic transformation, the level of digitisation is expressed by industry digitalisation intensity (DITi,j,t) in Columns (1) and (6). The interaction term results show that green digital finance can better increase technology diffusion across cities by the higher level of digitisation. For economic upgrading, industry digitalisation innovation (DINi,j,t) is applied in Columns (2) and (7). The results also suggest that digitalisation innovation can have a better mechanism effect than digitalisation intensity and optical cables laid. It implies that new digital technologies play more important roles in the new green economic era. The communication cost can be reduced and thus measured by the amount of optical cables laid (OCLi,j,t) in Columns (3) and (8). The results verify that more optical cables laid can alleviate communication costs and information asymmetry, thus increasing technology transfer and exchange. In conclusion, the testable Hypotheses 2 is supported by the mechanism analysis shown in Table 6.

Second, the mechanism effect of market integration is analysed in Columns (4)–(5) and Columns (9)–(10). In Columns (4) and (9), market integration is measured using the actual trading flows between cities. In Columns (5) and (10), market integration is measured by the actual trading intensity between cities. As the interaction term results show, regardless of whether it is a trading flow or trading intensity form, interregional trade can enhance the stimulating effect of green digital finance on technology diffusion. As the main pathway of economic communication across cities, interregional trade allows various production factors to flow among different regions, sectors, industries, and enterprises, thus facilitating technical learning and experience exchange. By enabling green and digital technologies through interregional trade, green digital finance can promote technology diffusion across cities. Therefore, the testable Hypotheses 3 is also supported by the mechanism analysis shown in Table 6.

Technology diffusion direction

In addition, this study explored the direction of technology diffusion using dummy interaction term analysis, as shown in Table 7. Without loss of generality, cities in Beijing, Tianjin, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong, and Hainan Provinces are regarded as the eastern region. Other cities are regarded as the central-west region. As the results below show, during the process of green digital finance promoting technology diffusion, Columns (1) and (5) show how east-region cities cite central-west-region cities, Columns (2) and (6) show how east-region cities cite east-region cities, Columns (3) and (7) show how central-west-region cities cite east-region cities, and Columns (4) and (8) show how central-west-region cities cite central-west-region cities.

Table 7 Effect of green digital finance on technology diffusion, heterogeneity.
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Consider patent citations as an example. In contrast, the estimated coefficients in Columns (2) and (3) are more evident than those in the other two columns. These differences imply the potential technology diffusion direction: the diffusion effect from east-region cities is better than that from central-west cities. Even a sample city in the east-region cites more patents from other cities in the east region. Judging from the economic development stage, the ‘reform and opening up’ strategy made the East region economically prosperous, subtly creating more cutting-edge new technologies. In the current green digital era, cutting-edge technologies have played a strong diffusion role in achieving balanced and sustainable regional development.

Conclusions and policy implications

Conclusions

Based on the extended dual-weighted spatial Dubin model, this study explores the causal relationship between green digital finance and technology diffusion using panel data of 35,532 ‘citing city-cited city’ pair observations every year from 2002 to 2015. Through detailed empirical experience, this study examines the overall spatial diffusion effect, potential mechanisms, and heterogeneous differences in various ways. These conclusions remain robust after several estimations, including changing the independent and dependent variables, adopting different spatial weights, and using instrumental variables. The main conclusions are as follows.

  1. (1)

    For every 1% increase in green digital finance, the overall patent citation probability and number improved by 16.89% and 28.06%, respectively. Statistically, both the 16.89% increase in the average patent citation probability and the 28.06% increase in the average number of patent citations illustrate the significant spatial stimulus effects of green digital finance, particularly spatial diffusion across cities.

  2. (2)

    Digital economy and market integration are two effective mechanism pathways during the stimulating process between green digital finance and technology diffusion across different cities. Regarding the digital economy, digitalisation innovation can have a better mechanism effect than digitalisation intensity and optical cables laid. Regarding the market integration, whether it is a trading flow or trading intensity form, both can enhance this positive effect.

  3. (3)

    Considering the direction of technology diffusion, it was found that the diffusion effect from east-region cities was better than that from central-west cities. Even the sample city in the east-region cites more patents from other cities in the east region. In addition, for different technology-diffusion durations, a slight cumulative phenomenon is revealed: for different green digital finance dimensions, green credit, green investment, and green support have a better stimulating effect on technology diffusion.

Policy implications

First, green financial policies and technological innovation policies should be synergised to give full play to the role of green digital finance in promoting technology diffusion. Indicators of technological innovation and technology diffusion should be incorporated into green financial standards, and green digital finance should be guided to support technological innovation and technology diffusion projects. The construction of provincial- and municipal-level reward systems should be guided toward patented technologies linked to green financial policies, and cross-regional technological cooperation and exchange should be strengthened.

Second, transmission channels should be optimised, and the diffusion effect of green digital finance on technology should be promoted. The construction of a unified large market should be promoted, trade costs should be reduced, smooth trade channels should be emphasised, and the scale and quality of the trade in green products and services should be improved. There is a need to enhance the integration of green digital finance and digital technology, strengthen the construction of transportation and information infrastructure, and provide basic infrastructure services for the diffusion of green digital finance and technology.

Third, the ability of technologically underdeveloped areas to receive and apply technology to support regional development must be cultivated. Green and development finance should be guided towards the cultivation and support of R&D capacity in technologically backward regions, and the ability of these regions to receive technology should be improved.

Limitations and future plans

To a certain extent, this study also has some limitations. Although this study analyses the spatial diffusion effect of green digital finance on patent technology at the meso-city level, it does not accurately assess the diffusion effect of green patent technology. We will explore this subtle research point in our future research by obtaining new data.