
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12653-9
10.1016/j.heliyon.2024.e36622
e36622
Research Article
Dynamic simulation of policy-driven green technology innovation networks: Digital empowerment and collaborative efficiency
Li Jing ab
Li Guilong liguilong8881@163.com
cd⁎
Xie Jiaping ed
Zhang Guangsi d
a School of Management, Fudan University, Shanghai, 200433, China
b School of Economics and Management, Shanghai University of Political Science and Law, Shanghai, 201701, China
c Business School, Central University of Finance and Economics, Beijing, 100081, China
d School of Business Administration, Xinjiang University of Finance and Economics, Urumqi, 830012, China
e College of Business, Shanghai University of Finance and Economics, Shanghai, 200433, China
⁎ Corresponding author. Business School, Central University of Finance and Economics, Beijing, 100081, China. liguilong8881@163.com
22 8 2024
30 8 2024
22 8 2024
10 16 e3662225 5 2024
9 8 2024
20 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
To delve into the intricate dynamics of green innovation, it is imperative to establish a policy-driven green innovation network and optimize its multi-entity collaborative mechanism. Given the dynamic complexity of a technological innovation network composed of multiple entities, this paper examines the interactions among four subsystems based on system dynamics (SD) simulation: resource input, innovation performance, policy-driven, and digital empowerment subsystem. Furthermore, we analyze the combined effects of policy-driven initiatives and the role of digital platforms in facilitating innovation efficiency based on empirical evidence. The results indicate that: (1) Government can effectively promote green development by enforcing stronger environmental regulations, such as increasing carbon trading price, while enhancing the emission reduction efficiency of innovative products. (2) Increased per capita R&D investment, along with financial, tax, fiscal incentives for innovation investment, will increase the rate of innovation achievements. (3) Government should strengthen talent policy during anticipated increases in talent numbers and reduce the intensity of introductions during expected declines. (4) By implementing incentive policies to develop S&T platforms, government can broaden innovation network cooperation, promotes resource aggregation, and leverages multi-entity cohort effects.

Keywords

Green technology innovation
S&T platform
System dynamics
Policy-driven
==== Body
pmc1 Introduction

1.1 Background

Research indicates that promoting the advancement and industrialization of green technology is a vital strategy for achieving low-carbon transitions in industries. This includes the development of eco-friendly materials, renewable energy systems, and sustainable manufacturing processes [1]. Green technologies are instrumental in reducing energy consumption and pollution throughout all phases of the product life-cycle, from design and production to consumption and recycling, encompassing both green product and process innovation [2,3]. The rapid expansion of China's industrial manufacturing sector has significantly driven economic growth. However, this growth has also intensified challenges such as resource depletion and environmental degradation. The traditional development model, dependent on extensive resource investments and demographic advantages, is no longer sustainable. Consequently, enhancing green technology innovation and optimizing industrial transformation efficiency have become primary objectives for government and industry stakeholders. To enable the high-quality transformation and development of China's industries in line with the “double carbon” goal set by the Chinese State Council in 2021, as outlined in the “Guiding Opinions on Accelerating the Establishment and Improvement of a Green, Low-carbon and Circular Development Economic System,” it is imperative for the government to integrate economic and environmental considerations [4]. Embracing the principles of green development, policymakers must enhance efforts to reduce emissions and enhance efficiency in industrial manufacturing.

China's green technology innovation faces dual challenges from both external and internal factors. Externally, substantial pressure comes from initiatives like the United States' Investment Risk Assessment Law and Export Control Reform Law, which restrict China's access to key technologies, necessitating a stronger focus on independent technological innovation. Internally, despite increasing state financial support for green industries and corporate technological innovation, the outcomes have been underwhelming. According to the Ministry of Finance of the People's Republic of China and the National Bureau of Statistics, significant investments were made in 2023, including $33.9 billion in industrial pollution control and $472 billion in fiscal science and technology expenditures. Meanwhile, the number of valid green patents showed steady growth from 74,000 in 2016 to 218,000 in 2022, with an average annual growth rate of 4.9 % [5]. Despite these efforts, China ranked 154th among 180 countries and regions in the Environmental Performance Index of Yale University in2023.1 This disparity indicates that the free-market mechanism fail to align with green market demands, discouraging enterprises from investing in green technology innovation. The current situation of green technology innovation is primarily hindered by: (1) The lack of comprehensive policy frameworks that support innovation; (2) The difficulties in fostering collaboration among entities, such as governments, enterprises, research institutions, and network platforms. Therefore, the innovation system needs improvement, and incentive or regulatory policies should be reasonably implemented.

On the one hand, the Chinese government has introduced several policies to facilitate green innovation, including financial support, tax incentives, talent policy, and digital development policy. While numerous studies have verified the effectiveness of individual policies, insufficient attention has been paid to the combined impact of these policy portfolios. Additionally, the implementation of these policies is directly and indirectly influenced by various factors, such as market conditions, environmental factors, and resource availability. On the other hand, enhancing green technology innovation requires not only policy support but also collaborative efforts across multiple entities within the green technology industry. The government must take the lead in establishing collaborative networks involving joint scientific research institutes, enterprises, and intermediary service agencies focused on green innovation. However, several development challenges persist. The stakeholders within the innovation network often lack consistent goals, hindering effective coordination and the translation of innovations into industrial applications. For instance, enterprises may struggle with inadequate innovation efficiency and limited resources despite having better market insight. Conversely, research institutions may possess rich innovation resources but overlook market demand and innovation commercialization. Additionally, technology and innovation (S&T) platforms, which serve as intermediary service organizations, face difficulties in aligning the interests of all entities and delivering efficient services.

1.2 Research motivation and contributions

To develop a green innovation ecosystem, we suggest that the entities involved in multi-entity collaborative network, including government, enterprise, research institute, and digital platform, implement cooperative strategies after addressing the following issues: (1) To increase green innovation efficiency, how should the government incentivize enterprises, research institutes, platforms, and other relevant entities to cooperate? (2) In the green innovation network, which factors will significantly influence entities' collaboration, and how? (3) How will policy-driven mechanisms and digital empowerment influence the efficiency of green technology innovation networks?

To analyze this dynamic and intertwined ecosystem, system dynamic (SD) model has been widely used as a reliable tool [6,7]. SD model helps to study how policies, structure, inflow, outflow, and time delay correlated with each entity [8]. Therefore, we adopt the logic of SD model to develop a theoretical model that captures the structure of the green innovation system, including the resource input, innovation performance, policy-driven, and digital empowerment subsystems. Based on this model, we construct an innovation network model to analyze the key factors influencing green innovation efficiency. Furthermore, we optimize the collaborative efficiency of multi-entities and designs tailored, policy-driven approaches to effectively address various development scenarios. This study aims to provide policymakers with guidelines to enhance green innovation efficiency.

The main contribution of this paper are as follows: First, it elucidates the interaction dynamics, intensity, and influences among entities within the green technology innovation network. By examining the transmission paths and practical effects of policy implementation, this paper advances dynamic simulation research on multi-entity green innovation network. Second, it integrates multiple factors influencing innovation ecosystem, such as policy portfolio and digital platform. Compared with the previous literature that ignored the digital development of economy, we introduce a network platform to increase cooperative efficiency and design a cooperation model to enhance the industrialization efficiency of innovations. Third, this study investigates the different effects of policy implementation on green innovation based on empirical evidence, providing industry insights and policy guidance for green development.

The rest of the paper is arranged as follows. Section 2 provides a comprehensive literature review. Section 3 describe the innovation network and model establishment process. Section 4 presents the policy simulation and analysis of green innovation. Section 5 summarizes the paper and offers corresponding implications.

2 Literature review

2.1 Green innovation network and system dynamic model

Due to the inclusion of green product innovation, process innovation, and end-of-pipe treatment technology innovation [9], the green technology innovation network becomes a complex system that connects various entities. These entities include governments, enterprises, scientific research institutions, digital platforms, and intermediary institutions [10]. Generally, both “star” and “closed triangle” network configurations facilitate the formation of green innovation alliances [11]. The centrality, structural holes, and other characteristics of the green alliance network positively influence green innovation behavior and technology [12]. Previous research has demonstrated the impact of environmental regulations [13,14] and policy tools [15] on green technology innovation. Meanwhile, based on the development of the digital economy, network platform can positively impact collaboration and innovation efficiency [16]. However, existing studies primarily focus on the linear effects of environmental regulations and policy tools on green technology innovation, with less investigation into the dynamic impact of complex network systems involving multiple entities, such as government, enterprise, and digital platform.

The innovation network is a complex system [17], characterized by features such as multi-agent interactions, diverse objectives, self-organization, and self-adaptation. Given the advantages of system dynamics in analyzing complex system behaviors, optimization challenges within innovation networks can be addressed using system dynamics methods [18]. System dynamic models can visually stimulate a complex system of sustainability and innovation process, investigating the influence of network structures on innovation efficiency [19,20]. Driven by policy considerations, other studies examined the government's role in enhancing innovation efficiency and highlighted the strong policy sensitivity of technology innovation networks [21]. Consequently, the government should consider the interests of multiple stakeholders, implement policies in environmental, financial, and technological domains to regulate and support activities, and devise coordination mechanisms to optimize multi-agent cooperation models. This approach can harness innovation networks to facilitate green technology innovation.

2.2 Green innovation efficiency and policy

Numerous studies investigate the factors influencing innovation efficiency. First, human resource allocation is integral to green technological innovation. It mediates the relationship between digital technology application and green technology innovation performance [22]. Particularly for small and medium enterprises, effective human resource allocation enhances innovation capabilities through network collaboration [23]. Second, talent agglomeration is essential for improving green technology innovation network efficiency. Talent agglomeration exhibits competitive and scale effects, often leading to synergistic outcomes [24]. It stimulates the vitality of innovation and facilitates knowledge dissemination among diverse entities, thus boosting corporate green technology innovation [25,26]. Despite these insights, research on policy-driven green innovation network cooperation remains limited. There is a need for dynamic analysis of innovation cooperation intensity, synergy, and optimization pathways under varying policy contexts. Furthermore, comprehensive studies on policy-driven green technology innovation within the broader system perspective are lacking.

In the realm of technological innovation performance, existing research has predominantly concentrated on R&D investment and innovation performance, innovation capabilities and innovation diffusion [27]. In addition, green technology innovation offers dual benefits: mitigating environmental burdens while advancing economic technological modernization. Green technology serves as a pivotal component for boosting enterprise competitiveness, with an enterprise's green technology innovation capability holding significant importance [28]. Product innovation within green technology innovation impacts enterprise dynamic capabilities and subsequently influences competitive advantages [29]. Various factors influence the collaborative efficiency of technological innovation. The technological innovation environment and R&D investment are key factors affecting green technology innovation [30]. Others employ innovation value chain theory and system dynamics methodology, explored the green technology innovation process within large construction enterprises [31]. Their findings reveal the dynamic interactive relationships among external environmental factors, knowledge acquisition, and knowledge integration affecting green technology innovation.

Within the framework of the “dual carbon” objective, research indicates that government implementation of carbon emission reduction policies can foster corporate green technology innovation and drive circular economy development [32]. The institutional environment is an important factor affecting green technology innovation. Government policies directly promote green technology innovation, with policy quantity and effectiveness exerting a greater positive impact than policy execution. Effective adaptation to local contexts and harmonization of policy quantity, effectiveness, and execution are essential for cultivating a market-oriented environment conducive to green technology innovation [33]. In addition, environmental regulations play a crucial role in shaping green technology innovation achievements. Green finance and market-driven environmental regulations incentivize regional green technology innovation, whereas command-and-control regulations can stifle such innovation [34].

2.3 Research gap

The literature review highlights existing research on green technology innovation, identifying research gaps related to the combined effects of policy-driven initiatives and the role of digital platforms in facilitating innovation. Our study addresses these gaps by providing a detailed analysis of policy impacts and exploring the dynamics of multi-entity collaborations within green technology innovation networks. Table 1 compares our study with related research.Table 1 Comparison with the related studies.

Table 1Author(s)	Single policy	Policy portfolio	System dynamic	Green innovation	Digital Platform	
M. Yi et al. (2019) [15]	✓	-	-	✓	-	
W. Zhang et al. (2024) [16]	✓	-	-	✓	✓	
N. Wang et al. (2023) [31]	✓	-	✓	✓	-	
H. Abolghasemzadeh et al. (2024) [20]	✓	✓	✓	-	-	
E. Samara et al. (2012) [35]	✓	✓	✓	✓	-	
W. Wu et al. (2021) [36]	✓	✓	✓	✓	-	
Our study	✓	✓	✓	✓	✓	
Source of data: Compiled by author.

Our research contributes to the existing body of knowledge in two key aspects. Theoretically, we construct a system dynamics model to simulate the complex interactions within green innovation network, with an emphasis on policy-driven mechanisms and digital empowerment, particularly through S&T platform. This model provides a theoretical framework for understanding the intricate dynamics of green technology innovation. Practically, by discussing the impacts of various policies on innovation efficiency, including environmental regulatory policy, technological innovation policy, innovation talent policy, and S&T platform policy, we investigate the interaction dynamics and influences among entities within the innovation network. This analysis provides valuable insights into how different policies can optimize the collaborative efficiency of green innovation.

3 Green innovation ecosystem

3.1 Key factors and subsystems

In the context of the green innovation ecosystem, the efficiency of enterprise innovation and industrialization is influenced by policies, resources, the environment, the market, and other factors. The entities involved in the collaborative network includes: (1) Government: Responsible for implementing policies that promote green innovation. (2) Enterprise: Firms that develop, produce, and market green technologies and products. (3) Research Institution: Academic organizations conducting studies and developing new green technologies. (4) S&T Platform: Technology providers that offer platforms to facilitate collaboration and knowledge sharing among the entities. Before constructing the explanatory structural model for green technology innovation network, we refer to existing research [[36], [37], [38], [39], [40]] and delineate key factors impacting the efficiency of green innovation, as outlined in Table 2.Table 2 Key factors for green technology innovation.

Table 2Basic perspective	Factor categories	Contents	
System and environment	Entities	Technological innovation enterprises, universities, scientific research institutions, intermediaries, financial institutions and other entities.	
Resources	Innovation talents, capital investment, and experimental equipment.	
Environmental factors	Internal environment: resource agglomeration effect and group effect; external environment: innovation policy and environmental regulation.	
Impact path	Direct impact	Technology, human capital, and innovation investment funds.	
Indirect impact	Infrastructure, social environment, digital empowerment level, network collaboration degree, and macro policies.	
Structure and function	Entities	Initiator, implementer, and achiever of technological innovation and industrial transformation.	
Support entities	Not directly involved in green innovation but providing support to innovation entities, such as S&T platforms.	
Market condition	A network composed of the consumer market, industry, universities, research institutions and financial institutions.	
Source of data: Compiled by author.

Based on these key factors in Table 2, we find that the innovation system should be analyzed from the perspective of both the internal mechanism and the external environment, making it a nonlinear and complex system [36]. Therefore, employing a system dynamics model to study the green innovation system is feasible. We analyze the structural relationships among key factors with the goal of enhancing the industrialization efficiency of innovation outcomes, driven by government policies and centered around digital S&T platforms.

This study employs system dynamics methods to model the complex innovation network, decomposing it into four subsystems: (1) Policy-driven subsystem: This subsystem captures the impact of various policies on green technology innovation, including environmental regulations, innovation policies, platform policies, and talent policies. (2) Resource input subsystem: This subsystem is centered around the input of resources necessary for innovation, specifically technological innovation talents and innovation capital investment. (3) Digital empowerment subsystem: This subsystem emphasizes the role of digital technologies and networks in empowering green technology innovation, focusing on the scale of S&T network cooperation. (4) Innovation performance subsystem: This subsystem measures the output of innovation activities, focusing on technological innovation achievements, new product sales, and environmental pollution levels. The critical influencing factors of each subsystem are summarized in Fig. 1. In this framework, the S&T platform serves as a medium for resource aggregation, while government policies act as the primary driver enhancing collaborative effects among multiple stakeholders.Fig. 1 Subsystem division of green technology innovation network.

Fig. 1

Due to the negative impacts of knowledge externality and technology spillover on enterprises’ investment in green innovation, government policies and supervision are necessary. Thus, we focus on the policy-driven subsystem and investigate how different policies, such as environmental regulation, financial policy, talent policy, and innovation policy, affect the optimal operation of other subsystems. To better analyze the role of key factors and streamline the system dynamics model, we conduct targeted simulations by focusing on core factors and excluding secondary variables. Drawing from research [41], our study categorizes the endogenous key variables and exogenous auxiliary variables that influence system behavior. Environmental factors, including various government policies and regulations and changes in the development of S&T platforms, are treated as auxiliary variables and introduced into the model as constants or table functions. Additionally, we primarily consider the green innovation of the manufacturing industry, assuming steady economy growth without major economic fluctuations.

3.2 Variables and causal relationships

Based on previous theoretical framework of the green innovation network and the interactive relationships among the four subsystems, we identify the level variables (L1-L6) and exogenous influencing variables (A1-A5). The variables within four subsystems are illustrated in Table 3.Table 3 Variables in green technology innovation network subsystems.

Table 3Subsystem	Type of variable	Variable	
Resource input subsystem	level variables	The number of technological innovation talents (L1), the amount of innovation capital investment (L2).	
influencing variables	The introduction rate of innovative talents, the impact factors of capital investment, the impact factors of new product sales, the attracting factors for talents of the green industry, the impact factors of the amount of innovation achievements, the average annual funding increase rate of innovative talents, the impact factors of policy adjustments.	
Innovation performance subsystem	level variables	Amount of technological innovation achievements (L3), new product sales (L4), the degree of relative pollution of the environment (L5).	
influencing variables	The increase in innovation achievements per capita, the impact factor of per capita R&D funding, the impact factors of the scale of industry-university-research cooperation, the conversion rate of technological innovation achievements, the random factor of market fluctuations, the impact factor of consumer preference, the level of emissions reduction per unit of new products, the impact factor of policy adjustment.	
Digital empowerment subsystem	level variables	Scale of S&T network cooperation (L6).	
influencing variables	The level of multi-entity cooperation in innovation networks, the impact factors of the development of S&T platforms (A1), the driving force of green technology innovation, the impact factors of policy regulation, multi-entity cooperation group effect, resource agglomeration effect.	
Policy-driven subsystem	influencing variables	The impact factors of environmental regulation (A2), the impact factors of technological innovation policies (A3), the impact factors of financial policies (A4), the impact factors of innovative talent policies (A5).	
Source of data: Compiled by author.

Based on Table 2, we use Vensim software to construct a causal relationship among the variables, depicted in Fig. 2. We use the level variables to describe the accumulation effect of the system, while the influencing variables play intermediary roles among the level variables. This causal loop diagram consists of various causal chains that generate multiple positive and negative feedback loops, thereby shaping a green technology innovation network system through interactive dynamics.Fig. 2 Causal loop diagram of green innovation system.

Fig. 2

In the resource input subsystem, we examine the influence of innovative talents and capital investment on green innovation networks, considering factors such as policy portfolio, the amount of innovation achievements, and new product sales. Specifically, there are 37 causal loops centered around the number of technological innovation talents (L1) and 30 loops focused on the amount of innovation capital investment (L2). Within the innovation performance subsystem, we investigate the impact of policy adjustments, industry-university-research cooperation, innovation input, and achievement transformation efficiency on innovation output using core variables such as the amount of technological innovation achievements (L3), new products sales (L4), and the degree of relative pollution of the environment (L5). Notably, L3 and L4 each have 31 loops associated with them, while L5 is involved in 30 loops. In the digital empowerment subsystem, we explore the scale of S&T innovation network cooperation (L6) as the core variable, studying its effects on multi-entity cooperation dynamics, resource aggregation, and platform development alongside the relative level of environmental pollution. A total of 26 causal loops are influenced by factors including agglomeration effects and the evolution of S&T platforms. Furthermore, we regulate the system based on the polarity and interaction of each feedback loop to identify an innovation mechanism that promotes technological innovation and achievement transformation efficiency.

3.3 Data and formula

Data simulations are employed to analyze the effects of changes in different influencing factors on system output. This study employs a simulation period spanning 16 years from 2014 to 2030, with a simulation time step of one year (Time Step = 1 year). The testing period from 2014 to 2019 is used to validate the consistency of simulation results with real data, while the prediction period from 2020 to 2030 utilizes historical data to forecast the development trends of each variable. Data sources used in the simulation include the “China Statistical Yearbook,” “China Environmental Statistical Yearbook,” “China Science and Technology Statistical Yearbook,” and “China Industrial Statistical Yearbook.” To calculate the formula, we complement missing data using interpolation and regression analysis methods. To represent green innovation, we referred to literature [36,[42], [43], [44]] and selected indicators from industrial enterprises above designated size, primarily including sectors such as steel, petrochemicals, machinery, automobiles, power equipment, and electronics. These enterprises are significant sources of carbon emissions and have been mandated by the Chinese government to invest more in green technology following the 2019 “Dual-carbon target”. Indicators reflecting changes in level variables are presented in Table 4.Table 4 Indicator data corresponding to level variables.

Table 4Level variables	Indicator selection (industrial enterprises above designated size)	
Number of technological innovation talents	Full-time equivalent of R&D personnel (10,000 person/year).	
Amount of innovation capital investment	R&D investment from industrial enterprises (hundred million yuan).	
Amount of technological innovation achievements	Number of valid invention patents.	
New product sales	Sales revenue of new products (hundred million yuan).	
Relative level of environmental pollution	It contains 7 indicators and is comprehensively evaluated using the analytic hierarchy process, as shown in Table 5.	
Scale of S&T innovation network cooperation	Number of scientific research and development institutions (number), number of R&D institutions of colleges and universities (number).	
Source of data: Compiled by author.

The level variable, relative environmental pollution, encompasses various aspects, including water pollution, air pollution, and solid waste pollution. This variable is used as an indicator of the industry's green development status. To comprehensively assess the degree of environmental pollution, this study draws from existing literature [43,44] and identifies seven key pollution emission indicators: total wastewater emissions, ammonia nitrogen emissions, chemical oxygen demand emissions, sulfur dioxide emissions, industrial solid emissions, industrial waste gas emissions, and smoke and dust emissions. Each indicator is standardized and weighted to quantify the degree of environmental pollution effectively. The results are presented in Table 5.Table 5 The degree of relative environmental pollution.

Table 5Year	2014	2015	2016	2017	2018	2019	
Relative level of environmental pollution	1.72	1.76	1.70	1.65	1.61	1.54	

3.4 Stock and flow model of subsystems

3.4.1 L1 simulation model

In addition to key influencing factors, qualitative analysis revealed that the number of technological innovation talents is indirectly influenced by the amount of innovation capital investment, new product sales, and relative level of environmental pollution. These impact factors often have lagging effects on the level variables. For example, as enterprises gain more profit from green innovation due to increased new product sales, they are likely to invest more in innovation talents in the subsequent period. Similarly, the enterprises will prefer green innovation talents when environment pollution worsens. Accordingly, a branch structure simulation model for L1 was developed, depicted in Fig. 3.Fig. 3 L1 branch structure simulation model.

Fig. 3

To ensure the alignment of system parameters with real-world conditions, parametric equations were constructed based on selected data and theoretical assumptions as follows.1) Initial value of L1, L1(2014) = 264.2 (10,000 person/year).

2) Change in innovative talents (R1) = change in historical technological innovation talents + STEP (1,2020) × (the introduction rate of innovation talents × the number of technological innovation talents × the impact factors of innovative talent policy × the impact factors of new product sales × the attracting factors for talents of the green industry × the impact factors of capital investment × random impact factors of personnel changes). STEP (1, 2020) represents a step function indicating the prediction year starts from 2020.

3) The change in historical technological innovation talents is determined by a table function equation that varies over time, as detailed in Table 6.Table 6 Changes in historical technological innovation talents (10,000 person/year).

Table 6Year	2014	2015	2016	2017	2018	
Changes	−0.4	6.4	3.4	24.5	17.1	

4) the introduction rate of innovation talent = 0.09, indicating that the company recruits talent annually with an intensity of 9 %; the impact factor of innovative talent policy = 1.5, signifying that talent policies have a certain promoting effect. Both are adjustable exogenous parameters.

5) The impact factor of new product sales = IF THEN ELSE(new product sales > DELAY1I(new product sales, TIME STEP, 0), 1.3, 0.7). This means that if the current period's sales exceed those of the previous period, the impact factor is 1.3 (a 30 % increase), otherwise it drops to 0.7.

6) the attracting factors for talents of the green industry = IF THEN ELSE(environmental relative pollution level > DELAY1I(environmental relative pollution level, TIME STEP, 0), 1.2, 0.8).

7) the impact factors of capital investment = IF THEN ELSE(the amount of innovation capital investment > DELAY1I(the amount of innovation capital investment, TIME STEP, 0), 1.4, 0.6).

8) The random impact factor of personnel changes = RANDOM UNIFORM (−1, 1, 1), which is a uniformly distributed random function generating a random number with a seed of 1 within the range of (−1, 1), representing random fluctuations in the number of innovative talents.

Then simulate L1 using the amount of innovation capital investment, new product sales, and environmental relative pollution level from 2014 as initial values, with L2 (2014) = 9254.3 billion yuan, L4 = 142895.3 billion yuan, and L5 = 1.72.

3.4.2 Simulation results of L1

Since it is a simulation prediction after 2019, the change in historical technological innovation talent remains constant after 2019. The change in technological innovation talents post-2019 is influenced by personnel shifts, exhibiting a random fluctuation trend. The number of technological innovation talents shows a cumulative increase trend, as depicted in Fig. 4. The simulation outcomes are consistent with the predicted behaviors, confirming the validity of the model.Fig. 4 Variable simulation curve of L1.

Fig. 4

According to Fig. 4, we find the following: (1) The number of technological innovation talents shows a cumulative increase over time. This trend is driven by the continuous input of capital investment and the recruitment of new talents, supported by favorable talent policies. (2) The fluctuations of the number of technological innovation talents reflect real-world dynamics where talent numbers can vary due to various unforeseen factors. (3) The increasing trend in technological innovation talents aligns with the expected results, demonstrating the model's effectiveness in capturing the dynamics of the subsystem. In summary, the simulation results for L1 highlight the importance of continuous capital investment, supportive policies, and market success in driving the accumulation of technological innovation talents.

Similarly, branch structure simulation models for L2 to L6 are established, with parameter formulas defined within each branch structure. Subsequent simulation analyses demonstrate the rationality, effectiveness, and operational feasibility of the green technology innovation network system proposed in this study. Detailed procedures can be found in the Appendix.

3.5 System flow diagram

Drawing upon the characteristics and causal feedback loops inherent in the green technology innovation network system, and analyzing the logical relationships among system variables, a comprehensive system dynamics simulation flow diagram was developed. This diagram encompasses 50 variables, comprising 6 level variables, 31 auxiliary variables, and the remainder as constants or table function variables. By interconnecting the four major subsystem structures and their causal loops, a unified and comprehensive system diagram was obtained, depicted in Fig. 5.Fig. 5 Stock and flow model of the green technology innovation network.

Fig. 5

Given the challenges of accurately replicating real network structures using system dynamics models, this study rigorously tests the model's validity before conducting sensitivity analysis on various influencing factors to ensure that prediction outcomes and policy simulations accurately reflect the operational dynamics of the green technology innovation network. This validation process includes the following specific steps.(1) Structural Suitability and Consistency Testing: The Vensim simulation software includes a detection module that executes simulations only if the model structure is deemed reasonable and dimensions are consistent. This ensures that the system behavior described by the model aligns with actual system behavior. Furthermore, based on extensive literature review, a rationality analysis is conducted on causal relationships, variable selection and parameter settings. After exploring multiple modeling approaches, the simulation model with the highest fit is selected for detailed analysis.

(2) Historical Testing: Utilizing parameter matching and trend alignment methods, real data on “the amount of innovation capital investment” from 2014 to 2019 is selected and compared with forecasted results. This comparison assesses the level of correspondence and fit between the two datasets, providing insights into the rationality of model assumptions and relationship settings between variables. The results of this analysis are presented in Table 7.Table 7 Historical test results of innovation capital investment.

Table 7Year	2014	2015	2016	2017	2018	2019	
Simulation value	9254.3	10013.9	10944.7	12013	12954.8	13971.1	
Actual value	9254.3	10165	11075.5	11989.7	12905.8	13835.9	
Error rate	0 %	−1.51 %	−1.20 %	0.19 %	0.38 %	0.97 %	

The results indicate that the errors between the actual values of innovation capital investment (R&D expenditures from 2014 to 2019) and the predicted values are all within 5 %, demonstrating a high degree of model fit to real-world conditions. This validates the reliability of the system simulation flow and the credibility of simulation analysis results for future predictions.

4 Policy-driven simulation analysis

To analyze the effectiveness of various policies on the level of green technology innovation, we simulate four policies as input variables: environmental regulation policy (A2), technological innovation policy (A3), innovative talent policy (A5), and S&T platform development policy (A1). Different policies exhibit varying degrees of driving force and often demonstrate nonlinear effects on green innovation efficiency, influenced by complex external environments. Therefore, systematic sensitivity analysis provides a basis for optimizing collaborative innovation mechanisms driven by policies [45,46]. This research employs controlled variables and gradient analysis to set multiple gradients for the analysis subjects while keeping other parameters fixed. It discusses the impact of individual policies on system outcomes under different implementation intensities, identifies various policy portfolios, and summarizes underlying reasons, aiming to identify rational pathways for optimizing government policies.

4.1 Environmental regulatory policy

In this section, we investigate how changes in environmental regulation policies affect system operation output. This policy primarily includes the carbon emission cap-and-trade regulation, which can motivate enterprises to invest more in green production and process. By adjusting the influencing factors of environmental regulation, such as increasing the carbon trading price, and assuming that the price floats with various gradients (Table 8), the effect of policy changes on the degree of environmental pollution is depicted in Fig. 6.Table 8 Environmental policy simulation scheme.

Table 8The impact factors of environmental regulation	Current policy	Gradient 1	Gradient 2	Gradient 3	
Value	1	1 + 20 %	1 + 50 %	1 + 100 %	

Fig. 6 The impact of environmental policy changes on system output.

Fig. 6

Fig. 6 illustrates that the environmental policies, such as carbon cap-and-trade regulations, can significantly influence environmental pollution levels. The pollution level with current policy strength is higher than the situation with gradient 1, indicating that strengthening environmental regulations can effectively mitigate the total pollution amount even with increases in new product sales. Simulation results demonstrate that over time, environmental regulatory policies play a crucial role in reducing pollution levels. As policy intensity rises compared to the baseline, the rate of pollution reduction increases, thereby enhancing environmental quality. Under the constraints of cap-and-trade policy, enterprises that effectively reduce emissions have the opportunity to sell their allowances to those with higher pollution levels, thereby increasing profits through green innovation. The enterprises have to purchase insufficient emission rights from the carbon trading market if its carbon emissions per unit of product exceed the government's emission quota. Conversely, if the enterprises successfully reduce its emissions below the mandated level, it has the opportunity to sell the surplus emission rights and gain more profit. Furthermore, the emission reduction per unit of new products correlates with the scale of scientific and technological collaboration networks and investment in innovative talent. When there is significant fluctuation in innovation investment, the emission reduction per unit product decreases, leading to an increase in pollution levels. Therefore, improving the unit emission reduction level of new products while strengthening the implementation of environmental regulations can effectively promote the green transformation of the industry.

4.2 Technological innovation policy

The technological innovation policy is the innovation-oriented strategy that encompasses a portfolio of critical financial, tax, and fiscal policies. When the government increases the proportion of industrial pollution governance investment in GDP (fiscal policy), the rate of environmental tax (tax policy), or the green credit interest rate (financial policy), it can encourage enterprises to invest more in green innovation. In addition to the current policy, three policy schemes are introduced to investigate the impacts of policy portfolio on system output, representing various policy implementation strengths, in Table 8. We examine the trend of changes in the green innovation level under technological innovation policy scenarios, as illustrated in Fig. 7.Fig. 7 Impact of technological innovation policy on system output.

Fig. 7

The results presented in Fig. 7 demonstrate that variations in the technological innovation policy portfolio significantly impact the volume of technological innovation achievements. Compared with the current policy, the implementation of financial, tax, and fiscal policies can gradually increase the amount of innovation achievements. When the environmental tax rate, credit interest rate, or industrial pollution governance investment increases, the green innovation level improves. This is because stronger innovation policies raise the cost of enterprises' environmentally harmful behaviors. However, this increase in achievements does not always follow a linear trend; instead, it exhibits a phased fluctuation pattern influenced by fluctuations in per capita R&D expenditure. Increased policy intensity broadens the range of fluctuations and amplifies the impact of other factors on innovation achievements. Therefore, the government needs to tailor the innovation policy to the actual situation. For example, the government can strengthen the implementation of technological innovation support policies while increasing per capita R&D investment. Additionally, changes in the scale of S&T innovation networks also affect the amount of innovation achievements. When the effects of multi-entity cohort dynamics and resource agglomeration strengthen, the promoting effect of capital investment levels on innovation achievements is significantly magnified. Thus, as governments intensify their efforts in technological innovation policy portfolio, they should also enhance incentive policies for S&T innovation networks.

4.3 Innovation talent policy

This section delves deeper into the effects of alterations in innovation talent policies on system output. The talent policy primarily includes various government and enterprise subsidies for green innovative talents, such as increased project funding and establishment of scientific research teams. As the strength of the innovation talent policy increases, the R&D efficiency of researchers will accordingly improve. Using the same gradient span of policy influence factor settings as presented in Table 8, we examine its impact on both the quantity of innovation talents and the change in innovation achievements, as shown in Fig. 8.Fig. 8 The impact of changes in innovative talent policies on system output.

Fig. 8

Fig. 8 depicts that the quantity of innovative talents shows random fluctuations influenced by factors such as innovative talent policies, random personnel changes, levels of innovation investment, and new product sales. As talent policies gradually strengthen, the magnitude of these fluctuations also increases. Additionally, when the government intensifies innovative talent policies, the impact on the amount of innovation achievements is similar, potentially amplifying the fluctuation amplitude of system output results. Under gradient 2 and 3 policy scenarios, the trends in innovation outcomes before and after 2025 diverge from the baseline trend, while under gradient 1 policy intensity, the trend remains consistent. As talent policy intensity increases, it may expand the growth rate of talent numbers but also accelerates their rate of decline. This is because the technological talents are instrumental in helping enterprises conduct research and development of green products, thereby promoting green technology innovation. However, the process from the initiation of research to the output and transformation of innovations is lengthy. Consequently, there is a certain delay period in the investment of R&D talents. Therefore, government should adjust policies promptly based on different scenarios: intensifying policy efforts when predicting an upward trend in talent numbers and reducing talent acquisition efforts when anticipating a downward trend.

4.4 S&T innovation platform policy

The S&T innovation platform, functioning as an intermediary within the technological innovation network, enhances collaborative innovation efficiency among various actors such as enterprises, universities, research institutes, financial institutions, and intermediaries. Additionally, it expands the scale of network cooperation, thereby leveraging greater resource aggregation effects. This section analyzes the impact of S&T platform development policies on system output, including financial support and tax incentives. The gradient settings of the influencing factors align with those in Table 8, and the system output results are depicted in Fig. 9.Fig. 9 The impact of the development of S&T platforms on system output.

Fig. 9

From Fig. 9 and SD model, we find that the scale of innovation network cooperation is influenced by several factors, including the advancement of S&T platforms, the impetus for green technology innovation, and the extent of multi-entity collaboration. As environmental pollution levels decrease over time, the drive for technological innovation gradually increases. In this context, introducing additional policies to foster the development of S&T platforms broadens the scope of cooperation within innovation networks, thereby enhancing resource aggregation and leveraging multi-entity cohort effects more effectively. Additionally, improving the development level of S&T platforms enlarges the innovation network scale and encourages greater participation from entities in cooperative innovation systems. Therefore, the government's implementation of more lenient incentive policies for S&T platform development can stimulate innovation initiatives among various entities, boost demand within the innovation market, and accelerate the industry's green transformation and development.

4.5 Implications for policy implementation

According to the simulation results of policy-driven analysis, targeted policy interventions, such as increased carbon trading prices, R&D funding, latent subsidy, and tax incentives, significantly enhance the efficiency of green technology innovation. These findings underscore the importance of a balanced policy portfolio and the active engagement of all network entities. Specifically, S&T platform can facilitate collaboration and information sharing among network participants and accelerate the commercialization of green technologies. Effective collaboration among government, enterprise, and research institutions based on S&T platforms leads to more successful innovation output, highlighting the importance of communication channels and resource sharing.

Based on the simulation results, we recommend that policymakers focus on creating a balanced mix of financial incentives, talent support, carbon constraints, and R&D funding to foster green technology innovation. Additionally, policies should encourage the development and utilization of digital platforms to enhance collaboration among stakeholders. Enterprises should actively engage with research institutions and leverage digital tools to streamline their innovation processes. Research institutions should prioritize projects that align with policy goals and industry needs. In the future, research should explore the long-term effects of different policy scenarios on green technology innovation and investigate the role of emerging digital technologies, such as artificial intelligence and block chain, in enhancing collaboration and innovation efficiency.

5 Conclusion

To advance green technology innovation and facilitate the green transformation of industries, this study employs system dynamics simulation to construct a technological innovation network model centered on government policy portfolios and S&T platforms. This model aims to optimize the collaborative efficiency of multiple entities and design tailored policy-driven approaches for various development scenarios. By examining the interaction, intensity, and impact of entities within the green technology innovation network based on industry realities, four subsystems are established: (1) Resource investment subsystem: Focuses on the key factors of technology talents and capital investment on green innovation; (2) Innovation performance subsystem: Investigates the factors of innovation achievement, new product sales, and pollution level; (3) Policy-driven subsystem: Focuses on the influence of different policies on optimizing the operation of other subsystems, considering environmental regulation, talent policy, innovation policy and S&T platform policy. (4) Digital empowerment subsystem: Explores the role of S&T platforms in enhancing collaborative innovation efficiency among various actors, expanding network cooperation, and leveraging resource aggregation effects. Subsequently, a comprehensive SD model for the entire process of technological innovation is developed. This model utilizes industrial data for simulations to analyze key factors influencing multi-entity technological innovation cooperation and identify optimal pathways for collaborative innovation mechanisms among entities. Through model simulations, the study ultimately proposes policies and regulations aimed at fostering cooperation enthusiasm among entities. These recommendations provide policy guidance for the green transformation and development of industries. The main findings are summarized as follows.(1) Government can mitigate environmental pollution by implementing stricter environmental regulations, such as increasing carbon emission trading prices. However, significant fluctuations in technological innovation investment due to random factors can exacerbate pollution. Thus, government can effectively promote the green transformation and development of industries by enforcing stronger environmental regulations while enhancing the emission reduction efficiency of innovative products.

(2) Increased per capita R&D investment enables the government to boost innovation outputs by enhancing support policies for technological innovation. Financial, tax, and fiscal policies gradually increase the rate of innovation achievements, although these rates exhibit phased fluctuation patterns influenced by per capita R&D expenditure. Concurrently, introducing development incentive policies for S&T platforms can drive resource agglomeration and cooperative cohort effects, thereby amplifying the positive impact of investment on innovation achievements.

(3) Strengthening innovative talent policies impacts both the quantity of innovation talents and achievements of innovation, with fluctuations influenced by policy intensity, personnel changes, innovation investment levels, and new product sales. Timely adjustments to talent policies based on different scenarios are recommended. The government should adapt these policies based on projected changes in talent availability: strengthen policies during anticipated increases in talent numbers and reduce the intensity of introductions during expected declines.

(4) By implementing incentive policies to develop S&T platforms, the government can broaden innovation network cooperation, promotes resource aggregation, and leverages multi-entity cohort effects. These incentive policies for S&T platform development stimulate innovation initiatives and accelerate green transformation.

The study contributes significantly to the understanding of green technology innovation by elucidating the interaction dynamics, intensity, and influences among entities within the green technology innovation network. It provides a governance framework for the innovation network mechanism, advances dynamic simulation research on multi-entity green innovation networks, and identifies key factors influencing multi-entity green innovation collaboration. The research also designs a cooperation model to enhance the industrialization efficiency of innovations, conducts sensitivity analysis on government policy implementation, and offers industry insights and policy guidance for green transformation and development. However, the constructed model relies on specific parameter configurations, introducing a degree of simplification to reality. Future research should enhance the model by incorporating a broader range of factors influencing green technology innovation. Additionally, given the rapid evolution of technology and market landscapes, future research should focus on formulating dynamic policy frameworks capable of prompt adjustments based on real-time feedback.

Funding statement and Acknowledgement.

This research was supported by the following projects: the Major Project of the 10.13039/501100012325 National Social Science Fund of China (20&ZD060 ); the Youth Project of the National Social Science Fund of China (22CGL020 ); the 10.13039/100009110 Natural Science Foundation of Xinjiang Uygur Autonomous Region (2022D01B119 ); the Humanities and Social Sciences Base Fund of Xinjiang Uygur Autonomous Region General Higher Education (XJEDU2022P072 ); and the Special Fund for Basic Scientific Research Business Expenses of Central Universities (CXJJ-2022-395 ).

Data availability statement

This work proceeds within a theoretical and mathematical approach. We do not analyze or generate any datasets. The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

CRediT authorship contribution statement

Jing Li: Writing – review & editing, Writing – original draft, Visualization, Software, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization. Guilong Li: Writing – original draft, Validation, Supervision, Methodology, Formal analysis. Jiaping Xie: Supervision, Project administration, Funding acquisition. Guangsi Zhang: Visualization, Validation, Project administration, Funding acquisition, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Simulation models were developed to simulate the branch structure of key flow-level variables L2-L6, as illustrated in Figs. A1-A5. Assumptions for each parameter variable were made based on yearbook data and theoretical hypotheses.Fig. A1 Simulation Model of Branch Structure for L2

Fig. A1

Fig. A2 Simulation Model of Branch Structure for L3

Fig. A2

In the L2 branch structure model system, the change in innovation capital investment is calculated as follows:

The amount of change in innovation investment = historical innovation capital investment + STEP (1, 2020) × average annual funding growth rate for innovative talents × the impact factors of the amount of innovation achievements × the number of technological innovation talents × the impact factors of new product sales × the impact factors of financial policy.

Here are the specific values for the parameters:

The historical innovation capital investment follows a table function: (2014, 759.6), (2015, 930.8), (2016, 1068.3), (2017, 941.8), (2018, 1016.3).

The average annual funding growth rate for innovative talents is 8 %.

The initial value of the impact factors of financial policy is 5.

The impact factors of the amount of innovation achievements = IF THEN ELSE (the amount of technological innovation achievements > DELAY1I (the amount of technological innovation achievements, TIME STEP, 0), 1.1, 0.9).

The impact factors of new product sales = IF THEN ELSE (new product sales > DELAY1I (new product sales, TIME STEP, 0), 1.3, 0.7).

The initial value of the amount of innovation capital investment is 9254.3.

In the L3 branch structure model system, the amount of change in innovation achievements is calculated as follows:

The amount of change in innovation achievements = the amount of historical innovation achievements + STEP(1, 2020) * the impact factors of the scale of industry-university-research cooperation * the increase in innovation achievements per capita * the impact factors of R&D expenditure per capita * the number of technological innovation talents * the impact factors of technological innovation policy.

Here are the specific values for the parameters:

The amount of historical innovation achievements follows a table function: (2014, 7961.2), (2015, 23747.7), (2016, 16964.5), (2017, 5525.4), (2018, 14966.2).

The impact factors of the scale of industry-university-research cooperation is calculated as IF THEN ELSE (the scale of S&T innovation network cooperation > DELAY1I (the scale of S&T innovation network cooperation, TIME STEP, 0), 1.1, 0.9).

The initial value for the increase in innovation achievements per capita is 500.

The impact factors of R&D expenditure per capita = IF THEN ELSE (R&D expenditure per capita > DELAY1I (R&D expenditure per capita, 1, 0), 1.1, 0.9), R&D expenditure per capita = the amount of innovation capital investment/the number of technological innovation talents.

The initial value of the impact factors of technological innovation policy is 1.

The initial value of the amount of technological innovation achievements is 448,885.Fig. A3 Simulation Model of Branch Structure for L4

Fig. A3

Fig. A4 Simulation Model of Branch Structure for L5

Fig. A4

In the L4 branch structure model system, the amount of change in product sales is calculated as follows:

The amount of change in product sales = historical change in new product sales + the random factors of market fluctuations * the conversion rate of technological innovation achievements * the amount of technological innovation achievements, the impact factors of consumer preferences * STEP(1, 2020).

Here are the specific values for the parameters:

The historical change in new product sales follows a table function: (2014, 7961.2), (2015, 23747.7), (2016, 16964.5), (2017, 5525.4), (2018, 14966.2).

The random factors of market fluctuations = RANDOM UNIFORM(0.1, 0.9, 2).

The initial value of the impact factors of consumer preferences is 1.2.

The conversion rate of technological innovation achievements = IF THEN ELSE (the scale of S&T innovation network cooperation > DELAY1I (the scale of S&T innovation network cooperation, 1, 0), 1.2, 0.8).

The initial value of new product sales is 142,895.

In the L5 branch structure model system, the amount of change in the degree of contamination is calculated as follows:

The amount of change in the degree of contamination = historical environmental pollution level - IF THEN ELSE (the amount of change in product sales > DELAY1I (the level of emissions reduction per unit of new products, 1, 0), 0.1, −0.1) * the level of emissions reduction per unit of new products * the impact factors of environmental regulation.

Here are the specific values for the parameters:

The historical environmental pollution level follows a table function: (2014,0.04)，(2015,-0.06), (2016,-0.05), (2017,-0.04), (2018,-0.07).

The level of emissions reduction per unit of new products = IF THEN ELSE(factors of changes in emission reduction levels > DELAY1I(factors of changes in emission reduction levels, 1, 0), 1.1, −1.1).

Factors of changes in emission reduction levels = the scale of S&T innovation network cooperation * the amount of innovation capital investment/the number of technological innovation talents.

The initial value of the impact factors of environmental regulation is 1.Fig. A5 Simulation Model of Branch Structure for L6

Fig. A5

In the L6 branch structure model system, the degree of change in scale = change in the scale of historical industry-university-research cooperation + STEP(1, 2020) * the level of multi-entity cooperation in the innovation network * the impact factors of the development level of S&T innovation platforms * the driving force of green technology innovation.

Here are the specific values for the parameters:

Change in the scale of historical industry-university-research cooperation = change in the number of scientific research and development institutions * change in the number of R&D institutions in colleges and universities * the impact factors of financial and intermediary institutions * the peer effect of multi-entity cooperation.

The change in the number of scientific research and development institutions follows a table function: (2014,0.993), (2015,0.989), (2016,0.982), (2017,0.932), (2018,0.973).

The change in the number of R&D institutions in colleges and universities is represented by annual change rate values from a table function: (2014,1.103), (2015,1.113), (2016,1.146), (2017,1.087), (2018,1.129).

The initial values for the impact factors of financial and intermediary institutions, * the peer effect of multi-entity cooperation, and resource agglomeration effect are all set to 1.1.

The level of multi-entity cooperation in the innovation network = the impact factors of financial and intermediary institutions * the peer effect of multi-entity cooperation * resource agglomeration effect * IF THEN ELSE (the number of technological innovation talents > DELAY1I (the number of technological innovation talents, 1, 0), 1.2, 0.8).

The driving force of green technology innovation = IF THEN ELSE (Relative level of environmental pollution > DELAY1I (Relative level of environmental pollution, 1, 0), −1, 1).

The initial value of the impact factors of the development level of the S&T innovation platforms is set at 1.4.

The initial value of the scale of S&T innovation network cooperation is 1.

Finally, a comprehensive model is developed by integrating the L1-L6 branch structures to create a holistic system flow diagram for simulation and analysis.

1 2024 Environmental Performance Index - Environmental Performance Index. https://epi.yale.edu/measure/2024/EPI.
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References

1 Zhu L. Luo J. Dong Q. Zhao Y. Wang Y. Wang Y. Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: dynamic change and improvement path Technol. Forecast. Soc. Chang. 170 2021 120890 10.1016/j.techfore.2021.120890
2 Yin S. Liu L. Mahmood T. New trends in sustainable development for industry 5.0: digital green innovation economy Green.Low-Carbon.Econ. 00 00 2023 1 8 10.47852/bonviewGLCE32021584
3 Xie X. Huo J. Zou H. Green process innovation, green product innovation, and corporate financial performance: a content analysis method J. Bus. Res. 101 2019 697 706 10.1016/j.jbusres.2019.01.010
4 Chen L. Ye W. Huo C. James K. Environmental regulations, the industrial structure, and high-quality regional economic development: evidence from China Land 9 12 2020 517 10.3390/land9120517
5 China National Intellectual Property Administration Report on statistical analysis of green and low-carbon technology patents worldwide https://english.cnipa.gov.cn/art/2023/7/6/art_3262_186148.html 2023
6 Wen L. Wang A. System dynamics model of Beijing urban public transport carbon emissions based on carbon neutrality target Environ. Dev. Sustain. 25 11 2023 12681 12706 https://10.1007/s10668-022-02586-y
7 Liu X. Mao G. Ren J. Li R.Y.M. Guo J. Zhang L. How might China achieve its 2020 emissions target? A scenario analysis of energy consumption and CO2 emissions using the system dynamics model J. Clean. Prod. 103 2015 401 410 https://10.1016/j.jclepro.2014.12.080
8 Dong X. Li C. Li J. Huang W. Wang J. Liao R. Application of a system dynamics approach for assessment of the impact of regulations on cleaner production in the electroplating industry in China J. Clean. Prod. 20 1 2012 72 81 10.1016/j.jclepro.2011.08.014
9 Song M. Wang S. Zhang H. Could environmental regulation and R&D tax incentives affect green product innovation? J. Clean. Prod. 258 2020 120849 https://10.1016/j.jclepro.2020.120849
10 Su Y. Fan Q. Renewable energy technology innovation, industrial structure upgrading and green development from the perspective of China's provinces Technol. Forecast. Soc. Chang. 180 2022 121727 10.1016/j.techfore.2022.121727
11 Li H. Liu Q. Ye H. Digital development influencing mechanism on green innovation performance: a perspective of green innovation network IEEE Access 11 2023 22490 22504 10.1109/ACCESS.2023.3252912
12 Wang Z. Sun H. Ding C. Xin L. Xia X. Gong Y. Green innovation? A perspective from internal and external pressures of firms in China Sustainability 15 4 2023 3658 10.3390/su15043658
13 Yong F. Shao Z. Whether green finance can effectively moderate the green technology innovation effect of heterogeneous environmental regulation Int. J. Environ. Res. Public Health. 19 6 2022 3646 10.3390/ijerph19063646 35329333
14 Jiang Y. Wu Q. Brenya R. Wang K. Environmental decentralization, environmental regulation, and green technology innovation: evidence based on China Environ. Sci. Pollut. Res. 30 2023 28305 28320 https://doi:10.1007/s11356-022-23935-4
15 Yi M. Fang X. Wen L. Guang F. Zhang Y. The heterogeneous effects of different environmental policy instruments on green technology innovation Int. J. Environ. Res. Public Health. 16 23 2019 4660 10.3390/ijerph16234660 31766761
16 Zhang W. Ye S. Mangla S.K. Emrouznejad A. Song M. Smart platforming in automotive manufacturing for NetZero: Intelligentization, green technology, and innovation dynamics Int. J. Prod. Econ. 274 2024 109289 10.1016/j.ijpe.2024.109289
17 Sylvan Katz J. What is a complex innovation system? PLoS One 11 6 2016 e0156150 10.1371/journal.pone.0156150
18 Maruccia Y. Solazzo G. Vecchio P.D. Passiante G. Evidence from network analysis application to innovation systems and quintuple helix Technol. Forecast. Soc. Chang 161 2020 120306 10.1016/j.techfore.2020.120306
19 Honti G. Dörgő G. Abonyi J. Review and structural analysis of system dynamics models in sustainability science J. Clean. Prod. 240 2019 118015 https://10.1016/j.jclepro.2019.118015
20 Abolghasemzadeh H. Zekri E. Nasseri M. Regional-scale energy-water nexus framework to assess the GHG emissions under climate change and development scenarios via system dynamics approach Sust. Cities Soc. 111 2024 105565 10.1016/j.scs.2024.105565
21 Feldman M.P. Link A.N. Siegel D.S. Government's role in innovation The Economics of Science and Technology 2002 Springer Boston, MA 10.1007/978-1-4615-0981-3_6
22 Liu J. Wang Q. Wei C. Unleashing green innovation in enterprises: the transformative power of digital technology application, green human resource, and digital innovation networks Systems 12 1 2023 11 10.3390/systems12010011
23 Kesting P. Mueller S. Jorgensen F. Ulhoi J.P. Innovation and network collaboration: an HRM perspective Int. J. Technol. Manage. 56 2/3/4 2011 138 153 10.1504/IJTM.2011.042979
24 Zhou Y. Guo Y. Liu Y. High-level talent flow and its influence on regional unbalanced development in China Appl. Geogr. 91 2018 89 98 10.1016/j.apgeog.2017.12.023
25 Gonzalez-Ramos A.M. New orientation of human resources policies in Science and Technology (S&T): from brain drain to brain circulation and talent Papeles Poblac 20 82 2014 113 135 10.1146/annurev.me.03.020152.002041
26 Xue Q. Bai C. Xiao W. Fin-tech and corporate green technology innovation: impacts and mechanisms Manag. Decis. Econ. 43 8 2022 3898 3914 10.1002/mde.3636
27 Othman A. Assad F. Sohaib O. Enhancing innovative capability and sustainability of Saudi firms Sustainability 8 12 2016 1229 10.3390/su8121229
28 Li G. Wang X. Su S. Su Y. How green technological innovation ability influences enterprise competitiveness Technol. Soc. 59 2019 101136 10.1016/j.techsoc.2019.04.012
29 Qiu L. Jie X. Wang Y. Zhao M. Green product innovation, green dynamic capability, and competitive advantage: evidence from Chinese manufacturing enterprises Corp. Soc. Responsib. Environ. Manag. 27 1 2020 146 165 10.1002/csr.1780
30 Tian Y. Zhang K. Bipolar neutrosophic WINGS for green technology innovation Sci. Rep. 13 1 2023 19159 10.1038/s41598-023-46699-4
31 Wang N. Gong Z. Liu Z. Dynamic simulation of green technology innovation in large construction companies Environ. Sci. Pollut. Res. 30 53 2023 114452 114470 10.1007/s11356-023-30276-3
32 Zhang W. Li G. Guo F. Does carbon emissions trading promote green technology innovation in China? Appl. Energy 315 2022 119012 10.1016/j.apenergy.2022.119012
33 Wu G. Xu Q. Niu X. Tao L. How does government policy improve green technology innovation: an empirical study in China Front. Environ. Sci. 9 2022 799794 10.3389/fenvs.2021.799794
34 Fang Y. Shao Z. Whether green finance can effectively moderate the green technology innovation effect of heterogeneous environmental regulation Int. J. Environ. Res. Public Health. 19 6 2022 3646 10.3390/ijerph19063646 35329333
35 Samara E. Georgiadis P. Bakouros I. The impact of innovation policies on the performance of national innovation systems: a system dynamics analysis Technovation 32 2012 624 638 https://10.1016/j.technovation.2012.06.002
36 Wu W. Sheng L. Tang F. Zhang A. Liu J. A system dynamics model of green innovation and policy simulation with an application in Chinese manufacturing industry Sustain. Prod. Consump. 28 2021 987 1005 10.1016/j.spc.2021.07.007
37 Warfield J.N. On arranging elements of a hierarchy in graphic form IEEE Trans. Syst. Man. Cybern 2 1973 121 132 https://doi:10.1109/TSMC.1973.5408493
38 Zhang R. Wang Z. Tang Y. Zhang Y. Collaborative innovation for sustainable construction: the case of an industrial construction project network IEEE Access 8 2020 41403 41417 https://doi:10.1109/ACCESS.2020.2976563
39 Fu L. Fu S. Research on value decision of green innovation ecosystem from the perspective of trust transfer Pol. J. Environ. Stud. 33 1 2024 10.15244/pjoes/171668
40 Li Y. Sun Z. Green development system innovation and policy simulation in Tianjin based on system dynamics model Hum. Ecol. Risk Assess. 27 3 2021 773 789 10.1080/10807039.2020.1756739
41 Bala B.K. Arshad F.M. Noh K.M. System Dynamics: Modelling and Simulation 2017 Springer 10.1007/978-981-10-2045-2
42 Luo Y. Salman M. Lu Z. Heterogeneous impacts of environmental regulations and foreign direct investment on green innovation across different regions in China Sci. Total Environ. 759 2021 143744 10.1016/j.scitotenv.2020.143744
43 Li S. Zhou X. Wu Z. The heterogeneous policy driving efect of China's environmental protection industry based on the perspective of high-quality development Complex Syst. Complex Sci. 18 2 2021 66 80 10.13306/j.1672-3813.2021.02.008
44 Zheng S. How does environmental policy influence environmental quality? Based on provincial panel data China Soft Science 338 2 2019 49 61+92 https://doi:CNKI:SUN:ZGRK.0.2019-02-005
45 Kleijnen J.P.C. Sensitivity analysis and optimization of system dynamics models: regression analysis and statistical design of experiments Syst. Dyn. Rev. 11 4 1995 275 288 10.1002/sdr.4260110403
46 Uriona M. Grobbelaar S. Innovation system policy analysis through system dynamics modelling: a systematic review Sci. Public Policy. 46 1 2019 28 44 10.1093/scipol/scy034
