Using panel data encompassing 282 cities across China from 2010 to 2022, this study delves into the ramifications of government environmental concern on green technological innovation, with a focus on intellectual property (IP) protection. It elucidates the mechanisms through which government environmental attention promotes green technological innovation, examining the effects of Research and Development (R&D) investment, resource allocation and talent aggregation. The findings indicate that government environmental attention markedly enhances green technological innovation. However, the impact of this attention significantly varies across diverse urban typologies, being more pronounced in second-tier metropolises, non-resource–reliant cities and municipalities governed by three-tier fiscal systems. Furthermore, the moderation effect test indicates that IP protection considerably enhances the efficacy of government environmental attention on green technological innovation. Further scrutiny suggests that optimal outcomes in IP protection are achieved when the average tenure of local government officials ranges from 39.64 to 43.55 months, particularly when these officials have an environmental background. In addition, in-depth analysis reveals that the effects of R&D investment, resource allocation and talent agglomeration constitute the pivotal transmission mechanisms through which government environmental attention stimulates green technological innovation. The conclusions of this study not only enrich the theoretical foundation for green technological innovation by government environmental attention but also provide empirical validation for exploring pathways through which IP protection can empower it.
With the deepening of the global sustainable development concept, green technology innovation, as the core driving force for promoting high-quality economic development and achieving the ‘dual carbon’ goals, has increasingly gained considerable attention from policymakers and the academic community. Green technology innovation refers to the process of technological R&D (Research and Development) and application aimed at reducing resource consumption, alleviating environmental pollution or enhancing ecological efficiency, encompassing multiple fields, such as clean energy, energy conservation and emission reduction, pollution control and recycling. Compared with traditional technological innovation, green technology innovation not only exhibits the general characteristics of technological progress but also shows considerable ‘double externality’ owing to its inherent connection with ecological environment governance. On the one hand, its positive environmental externality means that the social benefits exceed the private benefits. Conversely, the easy imitation of technological achievements results in knowledge spill-overs, which weaken the return expectations of innovation subjects (Rennings, 2000).
Against this backdrop, the common problems of high energy consumption, high emissions and low efficiency faced by traditional industries have become a key bottleneck that restricts sustainable development. Green technology innovation is considered to be an important path to break through this dilemma. In recent years, the Chinese government has attached great importance to green technology innovation and placed it at the core of the national development strategy. At the end of 2022, the National Development and Reform Commission and the Ministry of Science and Technology jointly issued the ‘Plan for Improving the Market-Oriented Green technology innovation System from 2023 to 2025’. It clearly proposed to build an institutional environment to support the development of green technologies and the formation of an innovation ecosystem with the collaborative participation of multiple entities by strengthening market mechanisms, optimising resource allocation and integrating innovation chains. However, owing to the characteristics of large investment, long cycles, high risk and strong externalities in green technology innovation, its development largely depends on external institutional support. In particular, the government needs to play a key role in offering policy guidance, allocating resources and providing institutional guarantees (Wang et al., 2023).
How can the government effectively guide green technology innovation? Majority of existing studies focus on specific policy tools, such as the intensity of environmental regulations, the scale of fiscal subsidies or tax incentives (Han et al., 2024). These studies have yielded rich insights into the ‘implementation effects’ of policies. However, they generally overlook the ‘antecedent mechanism’ of policy formation, that is, why and how the government prioritises environmental protection issues. In other words, the ‘intensity’ of fiscal subsidies or environmental regulations is itself a result rather than a starting point; their introduction often presupposes the government’s strategic attention to specific issues. To this end, this study introduces the concept of ‘government environmental attention’ as the core explanatory variable to capture the government’s strategic emphasis on environmental protection and its priority in resource allocation within the complex governance structure. ‘Government environmental attention’ does not refer to the public’s or media’s general attention. Rather, it specifically refers to the institutionalised, ongoing attention given by government decision-makers to environmental protection issues in the policy-making and implementation process, manifested through formal administrative actions and reflecting a sense of priority. This concept is derived from the ‘scarcity of attention’ theory proposed by Herbert Simon, which posits that decision-makers, limited by cognitive resources, must selectively allocate attention to process vast amounts of information (Simon, 1976). Jones and Olken (2005) further applied this theory to the field of public policy, emphasising that the formation of the policy agenda is essentially the result of the government’s attention allocation. Compared with outcome-based indicators, such as ‘policy intensity’ or ‘financial investment’, ‘government attention’ is more proactive and strategic and can demonstrate the internal logic of policy priority formation. In the Chinese context, as the policy agenda is deeply influenced by high-level guidance and the administrative system, government environmental attention can be quantified through observable administrative actions, such as the frequency of environmental protection keywords in policy texts, number of leadership instructions on environmental protection, adjustment of special environmental protection financial budgets and establishment and upgrading of environmental protection institutions (Zheng et al. 2017). Therefore, it is a measurable institutional resource. The concept of ‘attention’ captures the crucial transition from ‘an issue entering the decision-making vision’ to ‘resource allocation with a bias’, thereby addressing the deficiency in existing research that extensively focuses on policy tools while disregarding the agenda-setting mechanism.
However, does an increase in government environmental attention necessarily translate into actual achievements in green technology innovation? The existing literature has not provided a systematic answer to this question. On the one hand, attention, as a political signal, can push environmental protection issues onto the policy agenda and promote investment in resources. Conversely, without effective institutional support, this ‘attention dividend’ may be difficult to convert into sustainable technological outputs. Particularly in the field of green technology, its knowledge-intensive nature and high imitability make innovation achievements highly vulnerable to the ‘free-rider’ problem. The expected return on enterprises’ R&D investment largely depends on the effectiveness of intellectual property (IP) protection (Hu et al., 2025). Therefore, whether the mobilisation effect of government attention can be realised depends on the presence of an institutional environment that protects innovation returns.
The ambiguity of the aforementioned issues leads to three types of fragmentation in existing studies. First, a fragmentation exists between policy formation and implementation, and attention allocation is overlooked as a precondition for policy introduction. Second, a fragmentation exists between political signals and institutional capacity, failing to recognise the complementary association between attention and property rights protection. Third, a fragmentation exists between unified policies and regional differences, lacking tests of the institutional interaction effect at the spatial level. Therefore, this study aims to systematically explore the following interrelated topics: First, can increased government environmental attention effectively promote green technology innovation? Second, through which mechanisms does this promoting effect occur? In particular, does IP protection play a moderating role in this process? Third, in regions with various foundations of IP systems, such as IP pilot cities and non-pilot cities, are there significant differences in the impact of government environmental attention on green technology innovation?
Therefore, the marginal contributions of this study are mainly reflected in the following aspects: First, it breaks with the traditional analytical paradigm that reduces government behaviour to specific policy tools. It proposes that government environmental attention is a pre-institutional signal for green innovation and reveals its leading role in policy agenda setting and priority resource allocation. Therefore, it shifts the research perspective from the policy implementation effect to the policy generation logic and reconstructs the theoretical starting point for the association between government intervention and green innovation. Second, it goes beyond research limitations by examining political mobilisation or institutional design in isolation and identifying the institutional complementarity mechanism between government attention and IP protection. The former releases strategic guidance and stimulates policy potential, whereas the latter consolidates enterprises’ expectations by reducing innovation uncertainty. The interaction between the two realises a transformation from political promotion to continuous innovation, enriching the application of the institutional complementarity theory in the context of sustainable development. Third, it breaks through the one-size-fits-all policy evaluation framework. Comparison of the heterogeneous responses of IP pilot cities and non-pilot areas shows the regulatory boundary of the institutional environment on the effectiveness of attention. It proposes that the effectiveness of green governance depends on the degree of alignment between attention inputs and institutional foundations, providing a new theoretical proposition for understanding the spatial logic of policy interaction.
Literature reviewAt present, many scholars have conducted extensive research on green development in the context of achieving carbon peaking and carbon neutrality. Their research mainly focuses on the R&D paths of green and low-carbon technologies and products, aiming to tap into the huge potential of the green and low-carbon market (Wu et al., 2025). Although existing studies have yielded some results in areas such as policy tools, corporate behaviour and technological innovation mechanisms, the overall framework still suffers from scattered topics, insufficient theoretical integration and weak critical reflection. There is an urgent need to construct a more systematic and explanatory analytical perspective.
First, at the policy level, government subsidies, as the core means to incentivise green technology innovation, have attracted considerable attention. Many empirical studies have demonstrated that fiscal subsidies can effectively alleviate the financial constraints on enterprise R&D and reduce the marginal cost of green technology development, thereby increasing the output of green patents (Liu et al., 2025). However, majority of these studies remain at the descriptive level of the ‘input–output’ linear logic, ignoring the negative effects, such as resource misallocation, rent-seeking behaviour and ‘policy dependence’, that subsidies may cause (Morris et al., 2012). For example, in some regions, enterprises engage in ‘strategic innovation’ or ‘symbolic emission reduction’ to obtain subsidies, thereby reducing the quality of green innovation (Cheng et al. 2025). This suggests that solely relying on fiscal incentives makes it difficult to sustain high-quality green technology innovation, and it is urgent to introduce additional structural and institutional governance mechanisms.
Second, although research on government governance capacity has expanded to some extent, it remains weak. Using a game model, Eeckhout and Jovanovic (2002) demonstrated that the choice of government intervention mode markedly affects the willingness to cooperate between technology companies and green start-ups: static intervention tends to weaken cooperation motivation, whereas dynamic intervention exerts a stronger incentive effect. This finding shows the crucial role of policy flexibility in fostering interaction within the innovation ecosystem. However, this study is limited to theoretical modelling, fails to account for differences across real-world institutional environments and does not fully address the practical constraints of governance elements, such as local governments’ implementation capacity and regulatory transparency. Similarly, Deng et al. (2019) proposed, based on a Stackelberg game model that there is an inverted U-shaped relationship between political competition and corporate green technology innovation, indicating that moderate political incentives can enhance innovation. However, this conclusion is based on idealised assumptions. It fails to adequately address the complex tension between the promotion logic of local officials and environmental performance assessment in the Chinese context (Tang et al., 2021; Wang and Lei, 2020). Overall, current research on government governance mainly focuses on single-policy tools or incentive mechanisms, lacking a systematic evaluation of the overall effectiveness of the governance system, and rarely incorporates the allocation of government attention into the core of the analysis.
Third, the environmental attention of senior executives at the enterprise level has emerged as a new research direction in recent years. Xiao et al. (2024) and Li et al. (2022) found that managers with higher environmental awareness are more likely to promote green strategic investment and technological innovation. This type of research enriches the explanation of the driving factors of green innovation from the perspective of organisational behaviour. However, its limitation lies in its excessive reliance on individual psychological characteristics or leadership styles (Hao et al., 2019), which can easily lead to the ‘heroic leadership’ narrative and overlook the structural shaping effect of the institutional environment on corporate decision-making. Moreover, the attention of senior executives may be driven by external policy signals and regulatory pressure (Hu et al., 2023). If the pre-impact of government attention is neglected, such research is likely to fall into the endogeneity dilemma, and elucidating the causal chain is difficult.
Finally, IP protection, as an important part of institutional supply, has been considered to be a ‘catalyst’ for green technology innovation in recent years. Liu et al. (2024) found that IP protection can indirectly support green innovation by reducing financing constraints and promoting R&D investment. Yu et al. (2025) further pointed out that cities with IP courts have enhanced judicial protection, increased the level of investment agglomeration and information technology support and thus stimulated green technology R&D. From the perspective of environmental performance, Zhang et al. (2024a) confirmed that the IP pilot policy has significantly reduced haze pollution in pilot cities, with the mechanism mainly attributed to the green innovation effect and the optimisation of resource allocation. These studies provide strong evidence of the association between the institutional environment and technological innovation. However, it is noteworthy that the existing analyses mostly focus on the average effect of policy implementation (Sweet and Maggio, 2015) and pay less attention to the moderating effects of unbalanced regional development, differences in law enforcement capabilities and enterprise heterogeneity. Moreover, the ‘double-edged sword’ effect of IP protection in the diffusion of green technologies—promoting innovation on the one hand and potentially creating technological monopolies and hindering the popularisation of technologies on the other—has not been fully explored.
In conclusion, although existing studies have investigated the driving factors of green technology innovation from multiple dimensions, including government subsidies, governance mechanisms, corporate attention and institutional environment, the following notable deficiencies remain: First, the research perspectives are fragmented, lacking an integrated framework that takes government attention, a macro-governance variable, as the core explanatory mechanism. Second, there is a lack of critical analysis. Most studies tend to confirm the effectiveness of policies but pay insufficient attention to policy limitations, unintended consequences and the complexity of institutional interactions. Third, the scope of the literature coverage is rather narrow, neglecting the integrated application of theories, such as institutional theory and innovation ecosystem theory. This study aims to fill the aforementioned gaps. By integrating institutional theory and innovation ecosystem theory, this study constructs an analytical framework to examine how government environmental attention affects regional green technology innovation and to analyse its specific mechanisms of action.
Theoretical analysis and research hypothesisGovernment environmental attention and green technology innovationIn promoting regional low-carbon transformation, local governments face trade-offs among multiple governance goals. They need to not only take on the responsibilities of economic growth and improving people’s livelihoods (Song et al., 2020) but also respond to the policy requirements for ecological and environmental protection. This multi-tasking structure causes their attention resources to be scattered across different policy areas, thereby limiting their sustained focus on green development.
However, in recent years, the central government has strengthened the top-down environmental accountability mechanism through systems such as environmental inspections and environmental interviews (Chi et al., 2024), inducing considerable vertical pressure. Simultaneously, the awakening of public environmental awareness and the increasing demand for environmental public goods have constituted horizontal social pressure (Zhang et al., 2024a). The dual-pressure mechanism jointly encourages local governments to allocate more attention to environmental governance. Such a reallocation not only reflects adjustments to public organisations’ policy agenda priorities but also aligns with the ‘pressure-response’ logic in multi-level governance theory (Xiao et al., 2025).
The increase in government environmental attention markedly promotes green technology innovation through policy guidance and institutional supply. Based on the Porter hypothesis (Ambec et al., 2013), the government forces enterprises to adopt ‘creative responses’ by setting strict environmental regulatory standards, prompting them to actively explore clean production technologies. Meanwhile, the government implements incentive policies, such as tax incentives, R&D subsidies and green credit (Wang et al., 2021a), which effectively lowers the marginal cost of enterprises’ green R&D and increase their willingness to invest in innovation, reflecting the application of the institutional incentive theory in environmental policies.
Furthermore, the government invests in the development of basic scientific research systems and public technology platforms, providing knowledge spill-overs and infrastructure support for green technology breakthroughs, which is consistent with the emphasis on the government’s role in knowledge accumulation in endogenous growth theory (Yeo and Lee, 2020). By establishing green market access and certification mechanism, the government creates a market environment conducive to green product competition. It guides the allocation of resources to low-carbon industries (Zhangv et al., 2024). In the context of open innovation, local governments also introduce, absorb and re-innovate advanced international green technologies through international cooperation channels, thereby achieving technological catch-up and local integration.
Based on the aforementioned analysis, the following hypothesis can be posited.
Hypothesis 1 Government environmental attention positively influences green technological innovation.
The Solow growth model highlights that technological progress is the main driver of long-term economic growth. The endogenous growth theory further argues that technological innovation is endogenously determined by the institutional environment, policy incentives and market structure (Bajo-Rubio, 2000). On this basis, government environmental attention can influence enterprises’ innovation decisions through public policy tools.
Specifically, the government sends clear policy signals to the market by formulating stringent environmental protection regulations and providing breaks, such as R&D subsidies and tax incentives, to direct resources towards green technologies (Han et al., 2024a). However, policy orientation alone is insufficient to fully simulate continuous green innovation. A well-established institutional guarantee is also warranted to ensure that innovation benefits are not eroded. At this time, IP protection, as an important part of the institutional environment, plays an important regulatory role.
According to the theory of public goods, knowledge is characterised by non-excludability and non-rivalry. Without effective protection, the ‘free-riding’ problem will arise, suppressing enterprises’ R&D investment (Eeckhout and Jovanovic, 2002). Therefore, a sound IP system grants innovators temporary monopolies, enabling them to obtain reasonable returns from green technology patents, thereby enhancing enterprises’ willingness to engage in high risk, long-cycle R&D activities (Shavell and Van Ypersele, 2001). Furthermore, an effective IP enforcement system can kerb the production of counterfeit and shoddy products and illegal replication, reduce the cost of safeguarding innovators’ rights and further consolidate their dominant market position (Yang et al., 2024).
This policy orientation facilitates the formation of a positive cycle. That is, a robust IP protection system enhances enterprises’ willingness to innovate, and government environmental attention provides the necessary external support. The two complement each other and jointly promote green technology innovation and application. Moreover, market competition will be more inclined towards technology than price, which further encourages enterprises to pursue technological progress, particularly technological innovations that enhance energy efficiency and reduce emissions, ultimately promoting society’s transformation towards a sustainable development model. The specific mechanism of action is depicted in Fig. 1.
Grounded in the aforementioned discourse, the following hypotheses are posited.
Hypothesis 2 In cities with strong IP protection, government environmental attention exerts a more significant promoting effect on green technological innovation.
(1) Effect of R&D investment
Government environmental attention is reflected not only in policy-making but also in promoting green technology innovation through increased R&D investment. A research report by the Organisation for Economic Cooperation and Development indicated that R&D investment in green technology is one of the key factors in achieving sustainable development goals (Eid et al., 2024). This investment directly promotes technological progress in areas such as clean energy, pollution control and efficient resource utilisation and indirectly stimulates the private sector’s enthusiasm to invest in green technology.
Based on the green Solow growth model, in the context of a green economy, technological progress is more directed towards the development of environmentally friendly technologies (Chen et al., 2023). Government R&D investment markedly reduces the uncertainty and cost of enterprises’ green R&D through knowledge spill-over and risk-sharing mechanisms (Huang et al., 2024), thereby promoting subsequent private sector investment. This mechanism is particularly prominent in the Chinese institutional context. The government-led science and technology resource allocation system offers guidance to fiscal science and technology expenditures (Song et al., 2025). Through major national science and technology projects as well as key R&D programmes, it guides universities, research institutions and enterprises in conducting collaborative research aligned with national strategic goals.
Furthermore, under pressure to perform, local governments also tend to intensify the incentive effect of central R&D funds by providing supporting funds, tax rebates and project support, thereby forming an innovation linkage mechanism of ‘central guidance–local response–enterprise participation’ (Li et al., 2024). Taking the key project of ‘Causes and Governance of Air Pollution’ as an example, the central government’s financial investment has increased the level of industrial support funds and self-raised funds from enterprises several times, markedly enhancing regional green technology supply capacity. Therefore, government R&D investment not only directly promotes green technological progress but also leverages a wider range of social innovation resources through institutional embeddedness mechanisms.
In light of these considerations, H3a is posited.
H3a Government environmental attention promotes the development of green technological innovation through the effect of R&D investment.
(2) Resource allocation effect
Based on institutional theory, effective institutional arrangements play a pivotal role in promoting green technology innovation and kinetic energy transformation by optimising the resource allocation mechanism (Webb et al., 2013). In China’s institutional system, the operation of this mechanism depends not only on the top-level design of central policies but also on local governments’ implementation logic in resource allocation. The implementation of policy tools, such as fiscal subsidies and tax incentives, is often channelled through local governments’ performance evaluation systems, leading to an uneven resource distribution across regions and enterprises. Although this top-down resource allocation model can quickly focus on key areas, it may also lead to problems such as ‘policy rent-seeking’ or ‘emphasising subsidies while neglecting performance’, thereby weakening incentive efficiency (Cai et al., 2017).
In this context, based on the ‘environmental regulation–innovation incentive’ mechanism emphasised by the Porter hypothesis, appropriate and forward-looking regulatory standards can compel enterprises to overcome path dependence and implement process upgrades and product innovations (Zhang et al., 2020). In recent years, the Chinese government has created stable expectations for enterprises by setting new energy vehicle promotion targets, implementing the ‘dual-credit’ policy and eliminating outdated production capacity, thereby stimulating their R&D investment in core areas such as battery technology and electric drive systems. Meanwhile, the government has markedly reduced transaction costs and uncertainties in the innovation process and accelerated the transition of technology from the laboratory to the market by obtaining green technology R&D funds, promoting collaborative research between universities and enterprises and building technology transfer platforms.
However, the government’s role is not limited to being a resource provider; it should also maintain market order. Over-reliance on administrative means for resource allocation may result in rent-seeking behaviour or the formation of implicit guarantees for specific enterprises, distorting the market competition mechanism. Therefore, only by ensuring fair rules and transparent information and by enabling various market players to equally compete under a unified regulatory environment can the institutional dividends be truly released, thereby achieving a dual improvement in resource allocation efficiency and green innovation performance (Hou et al., 2025).
Based on this, H3b is proposed.
H3b IP protection promotes green technological innovation by influencing resource allocation.
(3) Aggregation effect of science and technology talents
The government has created a favourable development environment for the green industry by formulating and implementing environmental protection policies and by effectively facilitating the regional agglomeration of scientific and technological talent (Long et al., 2023). In China’s institutional system, local governments, as the key entities for policy implementation and resource allocation, actively build talent hubs in the greenfield through various measures, such as talent introduction programmes, scientific research funding and industrial park construction. This policy orientation not only enhances the career development prospects of the green industry but also enhances its attractiveness to high-quality labour. According to the theory of human capital mobility, individuals tend to gather in fields where they can maximise their knowledge value and gain room for growth (Liu et al., 2023). The government’s continuous investment in environmental protection projects provides talents with stable development platform and innovation opportunities.
The agglomeration of scientific and technological talents further promotes green technology innovation through multiple mechanisms. First, as knowledge and skill carriers, the spatial concentration of talents strengthens the integration ability of innovation elements. In the innovation clusters formed under policy guidance, frequent interactions among individuals result in the rapid spread of tacit knowledge, thereby inspiring green technology breakthroughs (Xue et al., 2022). Second, the increase in the number of talents directly expands regional technological reserves and R&D capabilities, allowing enterprises to more efficiently accumulate knowledge and overcome complex technological challenges (Luo et al., and Zhao, 2024). More importantly, the talent-dense environment promotes the formation of interdisciplinary and cross-institutional cooperation networks. With the support of the collaborative platforms created by local governments, researchers can share the latest achievements and exchange practical experiences, creating a continuously interactive innovation ecosystem. This knowledge spill-over effect arising from agglomeration markedly improves the quality and transformation efficiency of green technology R&D (Liu et al., 2021).
Based on this, H3c is proposed.
H3c IP protection promotes green technological innovation by fostering talent agglomeration.
This study utilises 2010–2022 data from Chinese prefecture-level cities to construct a model for examining the impact of government environmental attention on green technological innovation and the mechanism underlying this impact, using the approach described by Yang Zhen (2024).
Among them, model (1) is the benchmark regression model, where lnGreenit denotes the explained variable, representing green technology innovation, and Attentionit denotes the explanatory variable, representing government environmental attention. Models (2) and (3) are mechanism test models, where M1it denotes the mechanism variable, representing R&D investment, resource allocation and talent agglomeration. Model (4) is a multi-period difference-in-difference model to test the moderating effect, where M2it denotes the moderating variable, representing the pilot cities for intellectual property rights (IPR). Controlsit refers to a series of control variables; Yeart and Cityi represent year and city fixed effects, respectively; and εit denotes random disturbance terms. Subscripts i and t represent cities and years, respectively.
Variable selection and measurement(1) Explained variables
Green technology innovation (lnGreen) encompasses environmentally conscious technologies dedicated to resource conservation, energy efficiency enhancement, emission reduction and pollution prevention. These innovations serve as a testament to the advancement of eco-friendly technology in any given region. The quantity of green patents mainly comprises counts of patent filings and approvals, with filings commonly used as a metric to gauge the intensity of such innovation. Consequently, this study uses the number of green patent applications, subsequently transformed into its natural logarithm, as the criterion for evaluating the level of green technology innovation within a specific region.
(2) Core explanatory variables
Government environmental attention (Attention): To measure government environmental attention, this study draws on the government work reports of sample cities from 2010 to 2022. We use Python to scrape text data and, following the keyword classification of Bao and Liu (2022), count the frequencies of words associated with environmental protection. The ratio of the frequency of environmental protection–related words to the total number of words in the report is used as a proxy variable to quantify the degree of local government environmental attention issues. This method has the advantages of objectivity and comparability.
The government work report is an authoritative policy document that comprehensively reflects the key points of governance. In addition, the ratio of word frequencies can effectively control for differences in report length and capture the dynamic changes in attention allocation. Existing research has confirmed that text analysis can reveal policy preferences (Wang et al., 2021b). This study follows this approach, identifying the priority of environmental issues through the distribution of high-frequency words and providing a quantitative basis for understanding local governments’ motivations in environmental governance.
(3) Adjustment variables
The policy pilot project to develop a model city for IPR serves as a vanguard in IPR reform, offering an ideal policy experimental setting to examine the influence of IPR protection effectiveness on green innovation in model cities. Therefore, this study treats the construction of such a model city as a quasi-natural experiment, evaluating the intensity of IPR protection through the interaction effect of city-specific variables (treat) and time-indicator variables (time) before and after policy implementation. Specifically, model cities are assigned treat = 1 and serve as the experimental group, whereas non-model cities are assigned treat = 0 and serve as the control group. In addition, the periods before and after policy implementation are denoted by time = 0 and time = 1, respectively.
(4) Mechanism variable
The selection of R&D indicators involves the use of the natural logarithm of the city’s general science budget expenditures as a measure. The efficiency of resource allocation (Resource), using the Theil index to evaluate the rationality of industrial allocation. The natural logarithm of the number of R&D personnel measures the talent concentration (Personnel).
(5) Control variables
The level of economic development (lnEconomic) is quantified by the logarithm of GDP per capita. The urbanisation process (Urban) is evaluated based on the proportion of urban residents in the total population. The level of marketisation (Market) is assessed using Fan Gang’s marketisation index. The degree of openness to the outside world (Open) is reflected as the ratio of total import and export value to GDP. The green carbon sequestration capacity (lnCarbon) is represented by the logarithm of the green coverage area in the urban built-up area.
Data descriptionTo ensure data quality, this study excluded observations with missing or abnormal key variables. It adopted interpolation to handle missing values for a small number of cities, thereby constructing a complete sample set covering 282 cities in China from 2010 to 2022. The National Intellectual Property Administration compiles the official list of IP model cities. Simultaneously, the number of green technology patent applications was extracted from the China National Research Data Service Platform. The rest of the data were mainly collected from the ‘China Urban Statistical Yearbook’, statistical announcements released by local governments at various levels and related online statistical resources. Descriptive statistics for all variables are presented in Table 1.
Descriptive statistics of variables.
This section examines the impact of government environmental attention on green technology innovation based on model (1). Table 2 presents the regression results. The results indicate that at the 1% significance level, government environmental attention markedly promotes green technology innovation, and the conclusion remains robust after controlling for variables and including two-way fixed effects, supporting H1. This finding suggests that government environmental attention issues can effectively guide the allocation of innovation resources to the green field, highlighting the pivotal role of policy orientation in promoting technological transformation.
Benchmark regression.
Note: The values enclosed in parentheses represent standard errors. *, ** and *** denote significance at the 10%, 5% and 1% levels, respectively, as depicted in the table provided below.
Compared with existing studies that mostly focus on environmental regulatory tools themselves (Peng et al., 2021), this study approaches the topic from the perspective of attention allocation, demonstrating the driving mechanism of the government’s priority-setting for green technology innovation and enriching the theoretical dialogue in environmental governance and innovation research. According to the innovation diffusion theory, the government’s investment of attention can be considered as a strong signal (Zhu and Zhao, 2018), which helps enhance the social recognition of green technologies and reduce innovation costs and risks through various channels, such as fiscal incentives and institutional guarantees.
Meanwhile, this result also echoes the core proposition of institutional theory: the value orientation of institutional actors can shape organisational behaviour and promote technological paradigm change. This study confirms that the government, not only as a rule-maker but also as a guide for innovation direction, plays an irreplaceable role in creating a green innovation ecosystem. Future policies should further strengthen the continuity of attention, optimise the incentive mechanism and deepen government–enterprise collaboration to consolidate and expand the institutional dividends of green technology development.
Robustness check and endogeneity analysisTo ensure the stability of the conclusions derived from the benchmark regression analysis, this study employs the following methods to confirm the robustness, with Column (4) of the benchmark regression serving as the foundational model. The specific methods are delineated as follows. Initially, the regression model is substituted: recognising that the benchmark analysis involves the use of a fixed effects model, this section uses a fixed Tobit model for verification, with the detailed verification outcomes presented in Column (1) of Table 3. Subsequently, the dependent variable is replaced, and the clustering for standard errors is altered: to accurately gauge the activity of green technological innovation, the number of green patent authorisations per million individuals is used as the core indicator, aiming to alleviate the influence of the time lag from patent application to authorisation on the assessment of immediate technological innovation levels. Furthermore, during the conduct of benchmark regressions, this study clusters standard errors at the city level. To further examine the stability of the benchmark regression results, this section clusters standard errors at the year level, positing that samples within the same year exhibit homoscedasticity, whereas years differ in heteroscedasticity, thereby challenging the assumption that the variance of random error terms of all samples is uniform and accentuating the disparities between different years. The corresponding analytical outcomes are presented in Column (2) of Table 3. Lastly, samples from municipalities are excluded: The four major municipalities, Beijing, Tianjin, Shanghai and Chongqing, were also designated as IP pilot cities during the research period. However, owing to the substantial differences in economic scale and resource allocation between municipalities and prefecture-level cities, this study omits these four cities and concentrates on the panel data analysis of other prefecture-level cities to uphold the rigour of the analysis. The related test results are enumerated in Column (3) of Table 3. Based on these test outcomes, the positive and negative directions and significance levels of the explanatory variables are consistent with those of the benchmark regression, thereby substantiating that the benchmark regression results are commendably robust.
Robustness test and endogeneity analysis results.
Based on the public policy implementation theory, the process from formulation to actual execution and then to observable effects of government environmental policies usually involves a time cycle (Robichau and Lynn Jr, 2009). Considering the term cycle of government officials, policy continuity and enforcement may be influenced, further delaying the time when policy effects become apparent. In addition, there is a delay in enterprises’ responses to government environmental directives via technological innovation, as this involves multiple stages, from R&D investment and technology development to commercial application. Therefore, the selection of data that are lagged by two or more periods can better capture this temporal delay while reducing the possibility that current government environmental attention directly results in increased innovation activities, thereby effectively mitigating endogeneity bias. This method meets the requirements of instrument variable relevance and exclusivity, ensuring the accuracy of causal inference. Thus, this study selects a two-period lagged government environmental attention (IV) as the instrumental variable for current government environmental attention and employs the two-stage least squares method for model estimation. Column (4) of Table 3 presents the results of the first-stage regression, which reveals a significant positive correlation between the instrument variable (IV) and government environmental attention (Attention), thereby substantiating the replaceability of IV for Attention. Column (5) of Table 3 presents the outcomes of the second-stage regression, demonstrating that even after controlling for the instrument variable, government environmental attention continues to exert a significant positive influence on green technological innovation. In conjunction with the econometric test results, the LM test achieves 1% significance level, affirming the identifiability of the selected instrumental variable. The Wald F-statistic value surpasses the critical value, thereby passing the weak instrumental variable test and confirming the robustness of the instrumental variable.
Analysis of heterogeneity(1) Heterogeneity of city tiers
From an administrative standpoint, owing to differences in city tiers, urban centres significantly vary in economic resources, talent pools, market demand, policy support and other dimensions. These elements influence the effectiveness of the government’s environmental focus in fostering green technological innovation. Consequently, adhering to the most recent urban-scale classification standard issued by the State Council in 2014, this study categorises 282 sample cities into three groups, namely, megacities (with a permanent resident population of 5 million or more), major cities (between 3 and 5 million) and medium-sized cities (less than 3 million), and conducts grouped regression analysis. The specific findings are detailed in Table 4, Columns (1) to (3). The research findings suggest that the promoting effect of government environmental attention on green technology innovation is most significant in type-II large cities but weakens in higher-level cities. This result may seem contrary to the intuition that ‘more resources lead to stronger capabilities’, but in fact, it reflects the non-linear relationship between urban governance efficiency and development impetus.
Heterogeneity analysis results.
Type-II large cities are generally in the crucial stage of rapid urbanisation as well as industrial transformation and upgrading. Local governments in these cities have a strong sense of urgency for development and high policy implementation capabilities. With a moderate administrative level and a short policy transmission chain, their environmental attention can be more efficiently translated into specific policy tools, precisely motivating enterprises’ green R&D. Simultaneously, these cities have not yet established a highly rigid interest structure and path dependence, featuring high institutional flexibility and being more receptive to and promoting emerging technological changes. Contrarily, although megacities and type-I large cities have more abundant financial, human and scientific research resources, their large-scale governance and diverse policy goals result in scattered attention, and other priorities can easily dilute environmental protection. Furthermore, these high-level cities have complex industrial structures and numerous vested-interest groups. Green transformation faces greater coordination costs and resistance, thereby reducing policy response efficiency. Therefore, despite their superior resource endowments, institutional inertia and goal conflicts weaken the actual conversion effect of environmental attention.
Therefore, unified environmental governance standard and incentive mechanism for all cities should be avoided. For type-II large cities, environmental governance powers should be further decentralised to support them in the exploration of differentiated green innovation support policies, such as establishing local special funds for green technologies, streamlining environmental protection approval processes and building regional innovation consortia, so as to give full play to their advantages of flexible governance and rapid response.
(2) Heterogeneity of resource endowments
Cities that rely on natural resources often follow a single and intensive path of economic growth, focusing on high energy consumption and primary processing, which can easily result in long-term structural rigidity and path dependence (Goumagias et al., 2022). This development model may consume excessive capital and human resources in resource-intensive industries, thereby increasing factor costs, and be detrimental to green transformation, leaving cities facing the ‘resource curse’ dilemma (Zhou et al., 2023). In light of this, this study, in response to the spirit of the 2013 State Council’s ‘National Resource-Based Cities Sustainable Development Plan’, classifies the 282 cities under examination into resource- and non-resource-dependent categories and conducts grouped regression analysis, as shown in Columns (4) and (5) of Table 4. The regression results indicate that the promoting effect of government environmental attention on green technology innovation is markedly stronger in non-resource than in resource-dependent cities. This difference is rooted in the fundamental disparities between the two types of cities in terms of development paths, industrial structures and innovation ecosystems.
Resource-dependent cities have traditionally relied on natural resource exploitation, resulting in a single economic structure dominated by high-energy–consuming industries, such as energy, metallurgy and chemical engineering. This structure gives rise to the ‘resource curse’ effect. The resource industry offers substantial profits, attracting a large amount of capital and talent, which limits the availability of resources needed for green technology R&D. Meanwhile, vested-interest groups strongly prefer maintenance of the status quo and hold a negative or even resistant attitude towards environmental protection policies, weakening the effectiveness of policy implementation. In addition, resource-dependent cities generally face problems such as weak innovation capabilities and imbalanced human capital structure, lacking the innovation infrastructure to support green technology incubation and diffusion. Even if the government increases its attention to environmental protection, its policy signals can hardly break through the lock-in effect of the existing structure.
Contrarily, non-resource-dependent cities lack traditional growth drivers and are more inclined to view green innovation as a strategic breakthrough towards sustainable development. These cities have a diversified industrial structure, with a relatively high proportion of the service industry and high-tech industries, and possess stronger risk-bearing capacity and innovation adaptability. In such cities, government environmental attention can be more easily integrated with development goals, such as industrial upgrading, investment promotion and urban brand building, thereby forming a policy interaction that effectively guides enterprises to increase their investment in green R&D.
Therefore, to address the transformation dilemma of resource-dependent cities, it is necessary to go beyond single-factor environmental protection incentives and develop a ‘structural transformation’ policy system. The central government should provide special financial support for these cities to eliminate backward production capacity, cultivate successor industries and promote the establishment of cross-regional green technology cooperation networks to address the shortage of local innovation capabilities. Simultaneously, environmental regulations and resource tax reforms should be strengthened to force traditional enterprises to undergo green upgrading.
(3) Fiscal grade heterogeneity
The financial tier of different cities will influence their development and economic conditions. At present, 27 secondary fiscal cities in our country remit taxes to the central government but not to the province, thereby enjoying greater fiscal autonomy and greater flexibility in financial allocation. This allows these cities to more quickly respond to and support the central government’s policy orientation on environmental protection and green development, thereby providing more financial support and policy incentives for green technological innovation. Contrarily, tertiary fiscal cities, which must remit taxes to the national and provincial fiscal authorities, face greater restrictions on the promotion of green technological innovation. Therefore, this study further analyses the behaviour patterns of local governments under different fiscal systems and their impact on promoting green technological innovation. The results are presented in Table 4, Columns (6) and (7). The results indicate that although government environmental attention markedly promotes green technology innovation in second- and third-level fiscal cities, the effect is more pronounced in third-level cities.
Based on the multi-task agency and resource dependence theories, third-level fiscal cities face dual assessment pressures from the central and provincial governments. The effectiveness of environmental governance has become an important indicator of local governments’ capabilities. In this context, increasing environmental attention is not only about fulfilling responsibilities but is also an important means to demonstrate governance effectiveness to higher-level authorities and to seek policy support and resource allocation preferences. Therefore, local governments are more motivated to effectively implement environmental protection policies, establishing a ‘pressure-response’ mechanism. Moreover, owing to relatively limited fiscal resources, third-level cities are more inclined to leverage social capital via innovative means, such as the PPP model, to participate in environmental governance. This helps enhance the efficiency of fund utilisation and promotes the application of green technologies.
Therefore, the vertical fiscal incentive mechanism should be optimised. While maintaining the necessary regulatory pressure, the sustainable investment capacity of grassroots governments should be improved. Special transfer payments for green innovation in third-level fiscal cities could be considered or a ‘performance-appropriation’ linkage mechanism could be established to encourage long-term investment in green technology R&D and prevent environmental governance from becoming a short-term perfunctory measure.
Test of the moderating effectAgainst the backdrop where local governments are gradually shifting their focus from public affairs to environmental protection and placing greater emphasis on environmental governance, IP protection can often play a more pivotal role in urban green technology innovation. Moreover, as the ‘spokespersons’ of local governments, leading officials’ opinions are reflected in various environmental decisions and government governance outcomes. The impact of leading officials on environmental protection governance depends not only on changes in the officials themselves but also on local officials’ characteristics. This is because characteristics, such as an official’s tenure, personal background and values, may influence the continuity of policies. In view of this, based on model (4), this study further examines the moderating role of IP protection in the influence of government environmental attention on green technology innovation and determines whether differences exist in this moderating effect under different official characteristics. Among them, the official tenure is calculated by adding the tenure of the secretary of the municipal Party committee of each prefecture-level city, multiplied by 0.6, and the mayor’s tenure, multiplied by 0.4. Officials’ personal backgrounds and values are grouped by whether they have an environmental protection background.
The results of the moderating effect of IP protection are presented in Column (1) of Table 5. The results suggest that IP protection exerts a significant positive moderating effect on the influence of government environmental attention on green technology innovation. This shows that policy attention alone is insufficient to fully release the potential of green innovation. It also requires a strong institutional support, particularly the protection of property rights for innovation achievements, to effectively encourage enterprises to engage in green technology R&D, thereby validating H2.
Heterogeneity of moderating effects: tenure and environmental background of officials.
To further verify the optimal tenure period for officials in promoting IP protection and green technology innovation, this study divides officials’ tenure at the 25th, 50th and 75th percentiles and conducts another regression analysis. The results are shown in Columns (2)–(5) of Table 5. The findings indicate that when local government officials’ tenure falls within the 20%–50% time frame, IP protection can effectively promote the green technology innovation effect of government environmental attention. After matching the sample data, the optimal tenure range for municipal government leaders is 39.64–43.55 months. This discovery shows the importance of the ‘policy stability period’ for the institutional interaction effect. That is, officials need to eliminate uncertainties in the initial adaptation stage before entering the short-term behaviour period leading up to their transfer. Therefore, they are more willing to promote the green innovation strategy that requires long-term investment and to cooperate with institutional construction, such as IP protection, to establish policy interaction.
Finally, this study conducts a grouped regression based on whether local leading officials have an environmental protection background during the sample period. The results are presented in Columns (6) and (7) of Table 5. The results suggest that when local leading officials have an environmental protection background, IP protection amplifies the positive impact of government environmental attention on green technology innovation. This is because officials with an environmental protection background may have more professional knowledge and understanding of environmental and sustainable development issues. Officials with environmental protection experience, whether professional or work, generally have a deeper understanding of sustainable development issues. They can more accurately recognise the value of green technologies and their long-term importance for environmental governance. Meanwhile, they often have a broader network of resources in the environmental protection field, which provides access to cutting-edge information, connections with professional institutions and cross-departmental collaboration, thereby enabling a more effective transformation of environmental attention into substantive support for innovation policies. More importantly, such officials are more likely to regard IP protection as the core mechanism for incentivising green innovation and to proactively promote enhancement in relevant systems to achieve a positive interaction between attention allocation and institutional supply.
The above research results expand understanding of the collaborative mechanism between environmental governance and innovation incentives, highlighting that achieving institutional effectiveness is inseparable from the initiative and trait matching of local government entities. In practice, the research provides a clear path for optimising the local green innovation policy mix. That is, while increasing environmental attention, IP protection should be simultaneously strengthened, and attention should be paid to the continuity and professionalism of cadre allocation to maximise governance effectiveness.
Test for transmission mechanismThis study delves deeper into the impact mechanism of governmental environmental focus on green technological innovation, exploring enhanced R&D investment effects, resource allocation efficiencies and talent convergence interaction, as elucidated through models (2) and (3) in Table 6.
Examination of the transmission mechanism.
First, as regards the effect of R&D investment, Columns (1) and (2) show that government environmental attention considerably promotes enterprises’ R&D expenditures, which in turn has a direct positive impact on green technology innovation. H3a is thus validated. This finding suggests that when the government includes environmental protection in its priority agenda, it will use policy tools, such as fiscal subsidies, tax incentives and green credit, to encourage enterprises to increase their R&D efforts. Under China’s environmental governance system centred on ‘central guidance–local implementation’, local governments often proactively introduce supporting policies to achieve the environmental protection assessment targets set by higher-level authorities, resulting in a resource tilt towards green technology projects. This not only mitigates the financial constraints of enterprise innovation but also reduces R&D risks, effectively stimulating their innovation willingness. This mechanism confirms the core view of the resource-based theory: external institutional support can be transformed into strategic resources for enterprises, thereby enhancing their innovation capabilities. Thus, strengthening the stability and predictability of environmental protection policies is crucial for promoting breakthroughs in green technology.
Second, as regards the resource allocation effect, the results in Columns (3) and (4) indicate that government environmental attention helps optimise resource allocation, thereby promoting green technology innovation. H3b is thus validated. The Chinese government plays a pivotal role in economic development and exhibits strong regulatory capabilities, particularly in land supply, project approvals and energy quotas. Local governments achieve structural adjustment of factor resources by restricting the expansion of high-pollution industries and guiding capital into green industries. However, this mechanism also harbours risks. If policy implementation extensively relies on administrative means, it may result in an imbalance in resource allocation across regions or in ‘policy arbitrage’ behaviour. That is, enterprises make symbolic environmental protection investments rather than substantial innovations to obtain resources. In addition, the problem of redundant construction under ‘green competition’ may occur in some regions, resulting in wasted resources. Therefore, in the future, attention should be paid to the interaction between market mechanisms and policy guidance to enhance resource allocation efficiency and avoid the negative effects of a one-size-fits-all approach.
Finally, in terms of the talent agglomeration effect, Columns (5) and (6) confirm that government environmental attention markedly attracts high-skilled talents to gather in the field of green technology innovation, thereby validating H3c. In China, local governments create a talent ecosystem conducive to green innovation by establishing green industrial parks, offering household registration for talent, providing housing subsidies and supporting research funding. This ‘policy signal’ enhances career development prospects in environmental protection and attracts talent from fields such as environmental engineering, new energy and materials science. According to the human capital theory, talent agglomeration can not only enhance local knowledge spill-overs but also form an innovation network to accelerate technological iteration. However, it should be noted that current talent policies tend to be concentrated in first-tier cities and developed regions, exacerbating brain drain in less-developed areas and further widening the gap in regional green innovation capabilities.
The above research systematically identifies and empirically tests the specific channels of policy transmission in the institutional context, highlighting that the government is not only a rule-maker but also a resource allocator and a shaper of the innovation ecosystem. This finding provides a theoretical basis and practical inspiration for improving green governance policies: only by coordinating policy incentives, market mechanisms and regional coordination can the goal of sustainable development be achieved.
Conclusions and policy recommendationsResearch conclusionsThe study examines 282 prefecture-level cities in China from 2010 to 2022, focusing on IP protection to understand how government environmental attention influences green technological innovation. This approach provides a new perspective on how external factors can drive green innovation. The article delves into three dimensions to analyse the mechanism: R&D investment, resource allocation and talent concentration. The goal is to elucidate the internal logic behind how government environmental attention contributes to green technological innovation. The findings indicate that government environmental attention markedly enhances green technological innovation. This conclusion holds even after robustness tests and endogeneity analysis.
In addition, when considering factors such as city rank, resource endowment and fiscal rank from a heterogeneous perspective, the impact varies across cities. Specifically, the effect is greater in type-II large cities, non-resource–based cities and cities with a fiscal rank of three. Moderating effect tests show that IP protection considerably enhances the impact of government environmental attention on green technological innovation. Further exploration reveals that differences in officials’ tenure, personal environmental backgrounds and values affect the extent of the promoting effect. For example, when the average tenure of local chief officials is between 39.64 and 43.55 months and the officials have an environmental background, IP protection yields better results. The study further analyses the transmission mechanisms through which government environmental attention influences green technological innovation. The results indicate that R&D investment, resource allocation and talent concentration are key mechanisms driving green technological innovation.
Policy recommendationsBased on the research findings, this study proposes the following policy recommendations.
First, strengthen top-level design and organisational guarantees to enhance the institutionalisation level of government environmental attention. The research finds that government attention is a key factor influencing policy effectiveness. Therefore, an inter-departmental environmental protection coordination agency should be established, coordinated by the central government and involving local cooperation. This agency should be directly responsible for the highest level of decision-making, break down departmental barriers and promote the coordinated implementation of environmental and development policies. The agency should be responsible for strategic planning, supervision, evaluation and resource allocation to ensure that environmental protection issues remain a top priority on the government’s agenda. Concurrently, environmental protection performance should be incorporated into the official assessment system, supplemented by systematic training to enhance local governments’ understanding and implementation ability of green development.
Second, improve the IP protection system to amplify the incentive effect of government attention on green technology innovation. The research demonstrates that the IP system is an important regulatory mechanism for transforming government environmental attention into green innovation achievements. In other words, without an effective property-right protection, merely attracting policy attention is difficult to stimulate market players’ innovation motivation. Therefore, a multi-dimensional framework for green IP protection, encompassing patents, trademarks, copyrights and related rights, must be developed. In particular, the efficiency of patent examinations and the intensity of law enforcement for green technology patents should be improved. Local governments should formulate differentiated support policies tailored to local innovation characteristics, such as establishing a fast-track for green patents, reducing registration fees and providing rights-protection assistance. Meanwhile, the successful experience of IP demonstration cities can be learned from and the ‘green IP + industrial incubation’ model promoted in key counties.
Third, focus on supporting key resources and develop a trinity innovation support system of ‘fund-park-talent’. To leverage the mediating effects of R&D investment, resource allocation and talent agglomeration, it is recommended to focus resources on creating several national-level green technology innovation demonstration zones, integrating special funds, science and technology parks and high-end talent policies. Specifically, establish large-scale green technology R&D guidance funds that cover the entire cycle from basic research to commercialisation; build technology parks integrating R&D, pilot testing and transformation in key areas, providing shared facilities and one-stop services; and simultaneously promote in-depth integration among the government, industries, universities, research institutions and users. Encourage enterprises and universities to jointly establish joint laboratories, implement ‘order-based’ talent cultivation and introduce special plans to attract leading green technology talents locally and abroad. This recommendation responds to the finding in the research that ‘resource investment and talent agglomeration enhance the policy transmission effect’, highlighting the approach of using key points to drive the whole and making targeted efforts to avoid resource dispersion.
Research limitations and prospectsThis study has yielded certain results in exploring the impact of government environmental attention on green technology innovation and its mechanism of action. However, there are still limitations that can be further explored in the future. First, in terms of variable measurement, government environmental attention is mainly measured by the frequency of words in the government work report. Although this method is objective and traceable, there is a risk that the formal expressions do not reflect the actual governance actions. In the future, multi-dimensional indicators, such as environmental protection fiscal expenditure, environmental administrative penalty data or the frequency of on-site investigations by officials, can be integrated for triangulation verification to enhance the accuracy and comprehensiveness of the measurement. Second, this study focuses on the moderating effect of IP protection. However, green technology innovation may also be influenced by multiple external factors, such as the regional institutional environment, public environmental protection demands and media supervision. In the future, a wider range of institutional and social context variables can be further incorporated to expand the explanatory power of the research framework. Finally, although the analysis of the mechanism of action is conducted along three paths—R&D investment, resource allocation and talent agglomeration—the dynamic relationships and collaborative mechanisms among the various effects remain unclear. In the future, system dynamics or dynamic panel models can be used to more deeply reveal the temporal relationships and non-linearities between the transmission of government attention and the agglomeration of innovation elements, thereby providing more precise theoretical support for policy optimisation.
Ethical approvalThe research included in the current submission does not involve human or animal subjects, or involves pathological reports, etc.
Consent to participateAll authors agree to participate in this study.
Consent for publicationAll authors agree to the publication of this research in the journal. Manuscript is approved by all authors for publication. The authors declare no competing interests.
Declaration of artificial intelligence applicationsThis manuscript does not involve the use of artificial intelligence.
Data availabilityData will be made available on request.
CRediT authorship contribution statementLi Yue: Writing – review & editing, Supervision, Software, Resources, Project administration, Funding acquisition. Liang Han: Writing – review & editing, Writing – original draft, Software, Methodology, Data curation.
No conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all authors for publication.
This work was supported by the Research on the Impact of Market Entities' Behaviors on Gansu Power Market against the Backdrop of New-Type Power System.