The influencing factors of green technology innovation in renewable energy companies based on hyper-network

Abstract

Green technology innovation is a critical factor in ensuring the long-term stable development of renewable energy enterprises. Based on the super network theory, this paper constructs a network model of green technology innovation influencing factors of renewable energy enterprises, which includes the knowledge sub-network of green technology innovation of renewable energy enterprises, the research and development member sub-network of green technology innovation team of renewable energy enterprises and the policy sub-network of green technology innovation of renewable energy enterprises. It explores the mechanism of its influence on innovation in the preparation stage. Simulation analysis by Netlogo software concludes that innovation knowledge sharing, R&D membership, and innovation policy all have a significant positive impact on green technology innovation in renewable energy companies.

Introduction

With the further aggravation of environmental pollution and resource wastage, renewable energy plays an essential role in resource development, and renewable energy enterprises have emerged and have become an essential driving force for China’s economic development. At the same time, the state advocates and encourages renewable energy enterprises to develop new technologies, break through the original technical barriers, and realize innovation.

Renewable energy enterprises realize the technological transformation of the first task, which is to continue to implement the green concept towards the green development of the innovation direction (Jiang et al. 2022). Green technological innovation refers to the optimization and upgrading of traditional technologies in the context of ecological civilization in order to achieve the goals of saving resources and energy and avoiding or mitigating ecological pollution and damage. Its core pursuit is to provide new products, processes, and technologies through innovation in order to promote the sustainable development of the ecological environment. The impact of green technology innovation on renewable energy enterprises is more far-reaching, and the factors affecting green technology innovation can be summarized into two categories: one is the internal factors, and the other is the external factors; the internal factors include green orientation and green technology capability (Lian et al. 2022). The paper importantly discusses the internal factors: enterprise green technology innovation knowledge, innovative R&D personnel, and green technology innovation policy.

There are several main reasons for studying the influencing factors of green technology innovation in renewable energy companies. First, in today’s era, the Internet and big data make it easier and faster for companies to acquire, utilize, and share knowledge. It is easier for companies to initiate a series of innovative activities, which leads them to promote green technology innovation intentionally or unintentionally (Sun et al. 2024). Second, diversified technological and national knowledge, as well as relevant policymakers, are positively correlated with technological innovation. Access to crucial knowledge resources is a critical step for firms to be able to capitalize on the first development opportunities (Zeng et al. 2023; Hu et al. 2023a, b). Again, green technology innovation and renewable energy are effective in reducing carbon emissions; energy consumption utilizing second-generation panel technology effectively promotes green technology innovation (Gu 2024; Liu and Dong 2022; Li et al. 2023, 2024). Finally, appropriate environmental policies favor the development of green technology innovation (Wang and Wang 2024). Relevant economic policies and subsidies can incentivize enterprises to carry out green technology innovation (Li et al. 2022; Ma et al. 2023; Afum et al. 2023; Jiang and Liu 2024; Jiao et al. 2023). Green technology innovation has a catalytic effect on the development of renewable energy enterprises, which can improve the carbon emission performance of enterprises, alleviate the pressure brought by environmental problems, and thus improve the economic efficiency of enterprises (Wang et al. 2022, 2023; Tian et al. 2024; Qin et al. 2022; Zhang et al. 2023; Liu et al. 2024a, b). Therefore, it is of great significance to study the influencing factors of green technology innovation in renewable energy enterprises.

Previous studies on the influencing factors of green technology innovation in renewable energy enterprises are mainly based on empirical analysis or structural modeling, in which the amount of technological knowledge storage and the quality and quantity of technological innovation researchers affect the level of innovation (Huang et al. 2022; Liu et al. 2023; Chen et al. 2022; Rao et al. 2022; Zhu et al. 2023; Sun et al. 2022). Based on the content of green technological innovation (Feng et al. 2022; Lin and Jia 2020; Suki et al. 2022; Razzaq et al. 2021; Habiba et al. 2022; Obobisa et al. 2022), the relevant influencing factors of green technological innovation (Behera and Sethi 2022; Cao et al. 2022; Tasker and Scoones 2022; Cafarella et al. 2022; Malusà et al. 2022; Jorge et al. 2020), and the relevant influencing mechanisms of green technological innovation (Spurek et al. 2022; Zantow et al. 2022; Peng et al. 2019; Abbas and Sagsan 2019; Song et al. 2018; Diercks et al. 2019), hyper-networks have gradually appeared in the perspective and field of green technological innovation. Hypernetworks provide tools for studying other network relationships, and hypernetworks and hypergraphs have essential uses in the representation and operation of knowledge networks. With the development of global information technology and the expansion of channels connecting businesses, knowledge sharing through hypernetworks can facilitate business performance. In 1985, Sheffi first introduced the concept of splitting a complex network into multiple sub-networks in his study of urban transportation route networks by dividing the route pattern into two mechanisms, traffic service level, and traffic flow, which constrain each other in a balanced manner. Nagurney then clarified the concept of “Supernetwork” and its attributes, i.e., a network with multi-level, multi-dimensional, multi-attribute, coordination, and congestion characteristics, which is not only higher than the existing network but also beyond the existing network (Xi et al. 2019; Liu et al. 2024a, b). Scholars usually interpret it as a complex network consisting of multi-layered networks (network of networks). In other words, according to different research needs, a “supernetwork” decomposes the existing complex network with multiple attributes, layers, and dimensions into multiple sub-networks with a single attribute, layer, and dimension, which are interconnected and constrained by specific relationships. This kind of super network can present the changes of multiple attributes sub-networks more clearly and make the vast and messy complex network structure simple and straightforward (Pu et al. 2024; Suo and Guo 2017).

Therefore, this paper constructs a supernetwork model to analyze the role of influencing factors on the green technological innovation of renewable energy enterprises based on the supernetwork model and simulation results, based on the analysis of innovative members’ participation in the process of corporate knowledge-sharing under the influence of innovation policy.

Study design

Adopting Netlogo for hyper-network dynamic simulation, firstly, using the similarity characteristics of complex networks, we study the network abstraction of object system confrontation, and abstract the confrontation process into the relevant elements of dynamic evolution; secondly, according to the abstracted elements, we formulate the corresponding constraint rules of dynamic evolution to guide the behaviors and interactions of each node or activity in the simulation process (Gang et al. 2019); thirdly, based on the multi-subjective modeling function of Netlogo, we create the hyper-network model, and define the behaviors and interaction principles of each subject; lastly, we run the model and observe the process of its dynamic evolution (Hu et al. 2023a, b).

Innovation influence factor network model construction

The innovation influence factor network model constructed in this paper is a composite network composed of knowledge meta-network and innovation subject network, containing renewable energy enterprise green technology innovation knowledge sub-network, renewable energy enterprise green technology innovation policy sub-network, renewable energy enterprise green technology innovation team R&D member sub-network, the model exists three types of nodes and six types of edges, describing the association between innovation knowledge nodes, innovation policy, innovation team member nodes, nodes three kinds of heterogeneous nodes and the connection between each homogeneous node, as shown in Fig. 1.

Fig. 1

Member participation in knowledge sharing hyper-network model

The hyper-network in Fig. 1 represents $\:G\:=\:\left(V,E,W\right)$ where V is the set of nodes in the hyper-network and contains three types of nodes. $\:V\:=\:\left(K,P,S\right)$, K represents the set of green technology innovation knowledge nodes of renewable energy enterprises, P represents the set of policy nodes issued for green technology innovation knowledge sharing of renewable energy enterprises, S represents the set of green technology innovation member nodes in the enterprise organizations involved in green technology innovation knowledge sharing of renewable energy enterprises. The set contains six types of edges, $\:E\:=\:\left({E}_{K-K},{E}_{P-P},{E}_{S-S},{E}_{P-K},{E}_{S-K},{E}_{P-S}\right)$ corresponding to the edges in the network of green technology innovation knowledge of renewable energy enterprises, the edges in the network of green technology innovation policies of renewable energy enterprises, the edges in the network of green technology innovation members of renewable energy enterprises, and the mapping between green technology innovation knowledge of renewable energy enterprises and green technology innovation members of renewable energy enterprises, the mapping between green technology innovation knowledge of renewable energy enterprises and green technology innovation members of renewable energy enterprises, and the mapping between green technology innovation policies of renewable energy enterprises and green technology innovation members of renewable energy enterprises, respectively. Technology innovation knowledge and renewable energy enterprises green technology innovation policy, renewable energy enterprises green technology innovation knowledge and renewable energy enterprises green technology innovation members of the mapping and renewable energy enterprises green technology innovation policy and renewable energy enterprises green technology innovation members of the mapping; $\:W\:=\:\left\{\omega\:\left({E}_{K-K}\right),{\omega\:(E}_{P-P}),\omega\:({E}_{S-S}),{\omega\:(E}_{P-K}),{\omega\:(E}_{S-K}),{\omega\:(E}_{P-S})\right\}$, which are the weights of each super-edge respectively.

Renewable energy enterprise green technology innovation knowledge sub-network

The green technology innovation knowledge network of renewable energy enterprises is denoted as $\:{G}_{K}\:=\:\left(K,Q\left(k\right),{E}_{K-K},\omega\:\left({E}_{K-K}\right)\right)$, where $\:K\:=\:\left\{{k}_{1},{k}_{2}\cdots\:{k}_{n}\right\}$ is the set of green technology innovation knowledge nodes of renewable energy enterprises, $\:Q\:=\:\left\{Q\left({k}_{1}\right),Q\left({k}_{2}\right)\cdots\:Q\left({k}_{i}\right)\cdots\:Q\left({k}_{m}\right)\right\}$ is the storage of green technology innovation knowledge nodes, $\:0\le\:Q\left({k}_{i}\right)\le\:K$, K is a constant, $\:{E}_{K-K}$ is the edge in the green technology innovation knowledge network of renewable energy enterprises, $\:\omega\:\left({E}_{K-K}\right)$ is the weight of the edge in the green technology innovation knowledge network of renewable energy enterprises, and see Eqs. (1)-(3):

$$\:{E}_{K-K}\:=\:\left\{\left({k}_{i},{k}_{j}\right)\right\}\:i,j\:=\:\text{1,2}\cdots\:m$$

(1)

$$\:\left({k}_{i},{k}_{j}\right)\:=\:\left\{\begin{array}{cc}0&\:\text{K}\text{n}\text{o}\text{w}\text{l}\text{e}\text{d}\text{g}\text{e}\:{\text{k}}_{\text{i}}\:\text{h}\text{a}\text{s}\:\text{n}\text{o}\:\text{a}\text{s}\text{s}\text{o}\text{c}\text{i}\text{a}\text{t}\text{i}\text{o}\text{n}\:\text{w}\text{i}\text{t}\text{h}\:\text{k}\text{n}\text{o}\text{w}\text{l}\text{e}\text{d}\text{g}\text{e}\:{\text{k}}_{\text{j}}\\\:1&\:\text{K}\text{n}\text{o}\text{w}\text{l}\text{e}\text{d}\text{g}\text{e}\:{\text{k}}_{\text{i}}\:\text{h}\text{a}\text{s}\:\text{a}\text{s}\text{s}\text{o}\text{c}\text{i}\text{a}\text{t}\text{i}\text{o}\text{n}\:\text{w}\text{i}\text{t}\text{h}\:\text{k}\text{n}\text{o}\text{w}\text{l}\text{e}\text{d}\text{g}\text{e}\:{\text{k}}_{\text{j}}\end{array}\right.$$

(2)

$$\:\omega\:\left({E}_{K-K}\right)\:=\:\omega\:\left({k}_{i},{k}_{j}\right)\:=\:\left\{\begin{array}{cc}{\alpha\:}_{ij}&\:\left({k}_{i},{k}_{j}\right)\:=\:1\:and\:0<{\alpha\:}_{ij}\le\:1\\\:0&\:\left({k}_{i},{k}_{j}\right)\:=\:0\end{array}\:\right.$$

(3)

Where the larger $\:Q\left({k}_{i}\right)$ indicates the larger amount of renewable energy enterprise green technology innovation knowledge stored in renewable energy enterprise green technology innovation knowledge node $\:{k}_{i}$, and the larger $\:{\alpha\:}_{ij}$ indicates the closer correlation between $\:{k}_{i}$ and $\:{k}_{j}$.

Renewable energy enterprise green technology innovation member sub-network

The renewable energy enterprise green technology innovation member network is denoted as $\:{G}_{S}\:=\:(S,{E}_{S-S},\omega\:({E}_{S-S}\left)\right)$, where $\:S\:=\:\{{s}_{1},{s}_{2}{\cdots\:s}_{n}\}$ is the set of renewable energy enterprise green technology innovation member nodes in the renewable energy enterprise green technology innovation member network, $\:{E}_{S-S}$ is the edge in the renewable energy enterprise green technology innovation member network of the enterprise organization, $\:\omega\:\left({E}_{S-S}\right)$ is the weight of the edge in the renewable energy enterprise green technology innovation member network, and see Eqs. (4)-(6):

$$\:{E}_{S-S}\:=\:\left\{\left({s}_{i},{s}_{j}\right)\right\}\:i,j\:=\:\text{1,2}\cdots\:n$$

(4)

$$\:\left({s}_{i},{s}_{j}\right)\:=\:\left\{\begin{array}{cc}0&\:\text{m}\text{e}\text{m}\text{b}\text{e}\text{r}\:{s}_{i\:}\text{d}\text{o}\text{e}\text{s}\:\text{n}\text{o}\text{t}\:\text{i}\text{n}\text{t}\text{e}\text{r}\text{a}\text{c}\text{t}\:\text{w}\text{i}\text{t}\text{h}\:{s}_{j}\\\:1&\:\text{m}\text{e}\text{m}\text{b}\text{e}\text{r}\:{s}_{i}\:\text{h}\text{a}\text{s}\:\text{i}\text{n}\text{t}\text{e}\text{r}\text{a}\text{c}\text{t}\:\text{w}\text{i}\text{t}\text{h}\:{s}_{j}\end{array}\right.$$

(5)

$$\:\omega\:\left({E}_{S-S}\right)\:=\:\omega\:\left({s}_{i},{s}_{j}\right)\:=\:\left\{\begin{array}{cc}{\beta\:}_{ij}&\:\left({s}_{i},{s}_{j}\right)\:=\:1\:\text{a}\text{n}\text{d}\:0<{\beta\:}_{ij}\le\:1\\\:0&\:\left({s}_{i},{s}_{j}\right)\:=\:0\end{array}\right.$$

(6)

where a larger $\:{\beta\:}_{ij}$ indicates a greater strength of interaction between member $\:{s}_{i}$ and $\:{s}_{j}$.

Renewable energy enterprise green technology innovation policy sub-network

The renewable energy enterprise green technology innovation policy network is denoted as $\:{G}_{L}\:=\:(P,{E}_{P-P},\omega\:({E}_{P-P}\left)\right)$,where $\:P\:=\:\left\{{p}_{1},{p}_{2}\cdots\:{p}_{t}\right\}$is the set of renewable energy enterprise green technology innovation policy nodes, $\:{E}_{P-P}$the edges in the renewable energy enterprise green technology innovation policy network,$\:\omega\:\left({E}_{P-P}\right)$ the weights of the edges in the renewable energy enterprise green technology innovation policy network, and see Eqs. (7)-(9):

$$\:{E}_{P-P}\:=\:\left\{\left({p}_{i},{p}_{j}\right)\right\}\:i,j\:=\:\text{1,2}\cdots\:t$$

(7)

$$\:\left({p}_{i},p\right)\:=\:\left\{\begin{array}{cc}0&\:\text{p}\text{o}\text{l}\text{i}\text{c}\text{y}\:{p}_{i\:}\text{d}\text{o}\text{e}\text{s}\:\text{n}\text{o}\text{t}\:\text{i}\text{n}\text{t}\text{e}\text{r}\text{a}\text{c}\text{t}\:\text{w}\text{i}\text{t}\text{h}\:{p}_{j}\\\:1&\:\text{p}\text{o}\text{l}\text{i}\text{c}\text{y}\:{p}_{i\:}\text{h}\text{a}\text{s}\:\text{i}\text{n}\text{t}\text{e}\text{r}\text{a}\text{c}\text{t}\:\text{w}\text{i}\text{t}\text{h}\:{p}_{j}\end{array}\right.$$

(8)

$$\:\omega\:\left({E}_{P-P}\right)\:=\:\omega\:\left({p}_{i},{p}_{j}\right)\:=\:\left\{\begin{array}{cc}{\gamma\:}_{ij}&\:\left({p}_{i},{p}_{j}\right)\:=\:1\text{?}0<{\gamma\:}_{ij}\le\:1\\\:0&\:\left({p}_{i},{p}_{j}\right)\:=\:0\end{array}\right.$$

(9)

where a larger $\:{\gamma\:}_{ij}$ indicates a greater strength of interaction between policy $\:{l}_{i}$ and $\:{l}_{j}$.

Relationships between networks

(1) Renewable energy enterprise green technology innovation member network and renewable energy enterprise green technology innovation knowledge network: the set of relationship between renewable energy enterprise green technology innovation member network and renewable energy enterprise green technology innovation knowledge network is $\:{E}_{s-K}$, the relationship weight is $\:\omega\:\left({E}_{s-K}\right)$, and see Eqs. (10)-(12):

$$\:{E}_{S-K}\left({s}_{i},{k}_{j}\right)\:i\:=\:\text{1,2}\cdots\:n,\:j\:=\:\text{1,2}\cdots\:m$$

(10)

$$\:\left({s}_{i},{k}_{j}\right)\:=\:\left\{\begin{array}{cc}0&\:\text{m}\text{e}\text{m}\text{b}\text{e}\text{r}\:{s}_{i}\:\text{d}\text{o}\text{e}\text{s}\:\text{n}\text{o}\text{t}\:\text{h}\text{a}\text{v}\text{e}\:\text{a}\:\text{m}\text{a}\text{p}\text{p}\text{i}\text{n}\text{g}\:\text{r}\text{e}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}\text{s}\text{h}\text{i}\text{p}\:\text{w}\text{i}\text{t}\text{h}\:\text{k}\text{n}\text{o}\text{w}\text{l}\text{e}\text{d}\text{g}\text{e}\:{k}_{j}\\\:1&\:\text{m}\text{e}\text{m}\text{b}\text{e}\text{r}\:{s}_{i}\:\text{h}\text{a}\text{s}\:\text{a}\:\text{m}\text{a}\text{p}\text{p}\text{i}\text{n}\text{g}\:\text{r}\text{e}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}\text{s}\text{h}\text{i}\text{p}\:\text{w}\text{i}\text{t}\text{h}\:\text{k}\text{n}\text{o}\text{w}\text{l}\text{e}\text{d}\text{g}\text{e}\:{k}_{j}\end{array}\right.$$

(11)

$$\:\omega\:\left({E}_{S-K}\right)\:=\:\omega\:\left({s}_{i},{k}_{j}\right)\:=\:\left\{\begin{array}{cc}{\mu\:}_{ij}&\:\left({s}_{i},{k}_{j}\right)\:=\:1\:\text{a}\text{n}\text{d}\:0<{\mu\:}_{ij}\le\:1\\\:0&\:\left({s}_{i},{k}_{j}\right)\:=\:0\end{array}\right.$$

(12)

There are two kinds of mapping relationships between renewable energy enterprise green technology innovation member network and renewable energy enterprise green technology innovation knowledge network: one is the mapping from renewable energy enterprise green technology innovation members to renewable energy enterprise green technology innovation knowledge, which reflects which innovation knowledge is mastered by innovation members, and the other is the mapping from innovation knowledge to innovation members, which reflects which innovation knowledge is mastered by innovation members. mastered. where a larger $\:{\mu\:}_{ij}$ indicates a stronger association between renewable energy enterprise green technology innovation members $\:{s}_{i}$ and renewable energy enterprise green technology innovation knowledge $\:{k}_{j}$.

(2) Green technology innovation policy network of renewable energy enterprises and green technology innovation knowledge network of renewable energy enterprises: the set of relations between green technology innovation policy network of renewable energy enterprises and green technology innovation knowledge network of renewable energy enterprises is $\:{E}_{P-K}$, the weight of the relation is $\:\omega\:\left({E}_{P-K}\right)$, and see Eqs. (13)-(15):

$$\:{E}_{P-K}\:=\:\left({p}_{i},{k}_{j}\right)\:i\:=\:\text{1,2}\cdots\:t,\:j\:=\:\text{1,2}\cdots\:m$$

(13)

$$\:\left({p}_{i},{k}_{j}\right)\:=\:\left\{\begin{array}{cc}0&\:\text{p}\text{o}\text{l}\text{i}\text{c}\text{y}\:{p}_{i}\:\text{d}\text{o}\text{e}\text{s}\:\text{n}\text{o}\text{t}\:\text{h}\text{a}\text{v}\text{e}\:\text{a}\:\text{m}\text{a}\text{p}\text{p}\text{i}\text{n}\text{g}\:\text{r}\text{e}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}\text{s}\text{h}\text{i}\text{p}\:\text{w}\text{i}\text{t}\text{h}\:\text{k}\text{n}\text{o}\text{w}\text{l}\text{e}\text{d}\text{g}\text{e}\:{k}_{j}\\\:1&\:\text{p}\text{o}\text{l}\text{i}\text{c}\text{y}\:{p}_{i}\:\text{h}\text{a}\text{s}\:\text{a}\:\text{m}\text{a}\text{p}\text{p}\text{i}\text{n}\text{g}\:\text{r}\text{e}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}\text{s}\text{h}\text{i}\text{p}\:\text{w}\text{i}\text{t}\text{h}\:\text{k}\text{n}\text{o}\text{w}\text{l}\text{e}\text{d}\text{g}\text{e}\:{k}_{j}\end{array}\right.$$

(14)

$$\:\omega\:\left({E}_{P-K}\right)\:=\:\omega\:\left({p}_{i},{k}_{j}\right)\:=\:\left\{\begin{array}{cc}{\theta\:}_{ij}&\:\left({p}_{i},{k}_{j}\right)\:=\:1\:\text{a}\text{n}\text{d}\:0<{\theta\:}_{ij}\le\:1\\\:0&\:\left({p}_{i},{k}_{j}\right)\:=\:0\end{array}\right.$$

(15)

There are two mapping relationships between the green technology innovation policy network of renewable energy enterprises and the green technology innovation knowledge network of renewable energy enterprises: one is the mapping from green technology innovation policy to green technology innovation knowledge, which responds to which green technology innovation knowledge is supported by green technology innovation policy, and the other is the mapping from green technology innovation knowledge to green technology innovation policy, which responds to which green technology innovation knowledge is supported by which green technology innovation policies support. Where the larger $\:{\theta\:}_{ij}$ indicates the stronger association between the green technology innovation policy $\:{p}_{i}$ of renewable energy enterprises and the green technology innovation knowledge $\:{k}_{j}$ of renewable energy enterprises.

(3) Green technology innovation policy network of renewable energy enterprises and green technology innovation member network of renewable energy enterprises: the set of relationship between green technology innovation policy network of renewable energy enterprises and green technology innovation member network of renewable energy enterprises is $\:{E}_{P-S}$, and the relationship weight is $\:\omega\:\left({E}_{P-S}\right)$, and see Eqs. (16)-(18):

$$\:{E}_{P-S}\:=\:\left({p}_{i},{s}_{j}\right)\:i\:=\:\text{1,2}\cdots\:t,\:j\:=\:\text{1,2}\cdots\:n$$

(16)

$$\:({p}_{i},{s}_{j})\:=\:\left\{\begin{array}{cc}0&\:\text{p}\text{o}\text{l}\text{i}\text{c}\text{y}\:{p}_{i}\:\text{d}\text{o}\text{e}\text{s}\:\text{n}\text{o}\text{t}\:\text{h}\text{a}\text{v}\text{e}\:\text{a}\:\text{m}\text{a}\text{p}\text{p}\text{i}\text{n}\text{g}\:\text{r}\text{e}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}\text{s}\text{h}\text{i}\text{p}\:\text{w}\text{i}\text{t}\text{h}\:\text{m}\text{e}\text{m}\text{b}\text{e}\text{r}\:{s}_{j}\\\:1&\:\text{p}\text{o}\text{l}\text{i}\text{c}\text{y}\:{p}_{i}\:\text{h}\text{a}\text{s}\:\text{a}\:\text{m}\text{a}\text{p}\text{p}\text{i}\text{n}\text{g}\:\text{r}\text{e}\text{l}\text{a}\text{t}\text{i}\text{o}\text{n}\text{s}\text{h}\text{i}\text{p}\:\text{w}\text{i}\text{t}\text{h}\:\text{m}\text{e}\text{m}\text{b}\text{e}\text{r}\:{s}_{j}\end{array}\right.$$

(17)

$$\:\omega\:\left({E}_{P-S}\right)\:=\:\omega\:({p}_{i},{s}_{j})\:=\:\left\{\begin{array}{cc}{\phi\:}_{ij}&\:\left({p}_{i},{s}_{j}\right)\:=\:1\:\text{a}\text{n}\text{d}\:0<{\phi\:}_{ij}\le\:1\\\:0&\:\left({p}_{i},{s}_{j}\right)\:=\:0\end{array}\right.$$

(18)

There are two kinds of mapping relationships between renewable energy enterprise green technology innovation policy network and renewable energy enterprise green technology innovation member network: one is the mapping from renewable energy enterprise green technology innovation member to renewable energy enterprise green technology innovation policy, which reflects which renewable energy enterprise green technology innovation member has interaction with which renewable energy enterprise green technology innovation policy and the other is the mapping from renewable energy enterprise green technology innovation policy to renewable energy enterprise green technology innovation member, which reflects which renewable energy enterprise green technology innovation policy has interaction with which renewable energy enterprise green technology innovation member. The other is the mapping from renewable energy enterprises’ green technology innovation policy to renewable energy enterprises’ green technology innovation members, which reflects which renewable energy enterprises’ green technology innovation policy interacts with which renewable energy enterprises’ green technology innovation members. The larger $\:{\phi\:}_{ij}$ indicates the stronger interaction between renewable energy enterprise green technology innovation members $\:{s}_{j}$ and renewable energy enterprise green technology innovation policy $\:{p}_{i}$.

Member participation in innovation knowledge sharing model construction

The three factors influencing innovation are studied in the preparation stage, implementation stage, operation stage, and feedback enhancement stage. As shown in Fig. 2.

Fig. 2

Member participation in the innovation knowledge-sharing process

Preparation stage

A series of preparatory work was done for the successful completion of knowledge sharing: (1) Network modeling, where members screen relevant knowledge in each innovation policy that is beneficial to the technological innovation of renewable energy enterprises. The cornerstone of the construction of the supernetwork model of knowledge sharing lies in the study of knowledge elements and knowledge subjects. (2) Send out knowledge-sharing requests, members will intentionally take the initiative to send out knowledge-sharing requests to the knowledge-sharing center. (3) Knowledge query: after receiving the request, the sharing center will search for knowledge nodes in the knowledge-sharing network and provide knowledge to the requesting object if the knowledge node is included in the sharing network or provide the knowledge node with high relevance to the requesting object if it is not included. (4) Screening knowledge providers, screening those that are conducive to innovation and development among many providers, and sending knowledge-sharing requests to them.

Implementation phase

The sign of the beginning of knowledge sharing is that the knowledge owner accepts the knowledge-sharing request from the knowledge receiver, and the methods of knowledge sharing are mainly two kinds: online primarily transmits knowledge through social software and emails; offline mainly through face-to-face communication between the two parties, paper documents, on-site guidance, etc. In the process of sharing knowledge, two types of knowledge can be found, namely, tacit knowledge and explicit knowledge (Hussain et al. 2020). Explicit knowledge and implicit tacit knowledge can be transformed into each other, and there is also a mutually transformable relationship between the same type of knowledge, implicit or explicit. The process of transformation of knowledge in another kind or itself completes the transfer and sharing of knowledge between both the owner and the receiver.

Operation phase

At the end of the sharing, the party receiving the knowledge adjusts the related technological innovation work according to the shared knowledge content. It applies the new knowledge to the original technological innovation link.

Feedback enhancement phase

Based on the operation, we provide feedback on the already existing knowledge-sharing situation, generate new knowledge in conjunction with the sharing status, implement dynamic guardianship to fix potential problems, and update the entire knowledge-sharing network in a timely manner.

Research methodology

Simulation using simulation experimental method to investigate the model of innovation knowledge, research members, and innovation policies affecting technological innovation, the constructed hyper-network model of innovation R&D members’ participation in innovation knowledge sharing was modeled and simulated by Netlogo software, focusing on innovation knowledge sharing; R&D members’ sharing type personality, communication between members, members’ trust in the team, members’ own knowledge, members’ innovation spirit and number of shareholdings; tax policies for technological innovation of renewable energy enterprises (Diercks et al. 2019; Hussain et al. 2020; Hille et al. 2020), encouraging economic policies (Obobisa et al. 2022; Behera and Sethi 2022; Cao et al. 2022), carbon peaking and carbon neutral policies, oil rising and emission reduction policies, floating wind power technical support policies, renewable energy subsidy policies, domestic environmental policies (Behera and Sethi 2022; Cao et al. 2022; Tasker and Scoones 2022; Cafarella et al. 2022), aspects of the impact on technological innovation of renewable energy companies, and other comparison research methods.

Simulation hypothesis

Hypothesis 1

Assume that there are three heterogeneous nodes of knowledge, members, and policies and six-element relationships in the system.

Hypothesis 2

Assume that member nodes possess one or more kinds of knowledge and the total amount of knowledge owned by member nodes is greater than the total amount of knowledge requested by policy nodes.

Hypothesis 3

Assume that members randomly select a policy solution from the list of knowledge providers to send knowledge sharing requests, and the policy must provide corresponding knowledge sharing.

Hypothesis 4

Assume that after the knowledge-sharing is completed, the storage of knowledge and the individual influencing factors of members will be elevated accordingly according to the sharing completion result.

Hypothesis 5

Assume that the condition for the emergence of new knowledge nodes is established on the basis that the storage of old knowledge nodes exceeds the threshold value and its its knowledge storage decreases, and the condition for the emergence of new member nodes is established on the basis that the value of each influence factor of old members exceeds the threshold value, and the value of each influence factor itself decreases accordingly.

Simulation steps

Step 1

Data collection: data related to knowledge, members and policies are collected and coded.

Step 2

Establish the knowledge sub-network, member sub-network, and policy sub-network based on the data and coding, and establish the mapping relationships among the networks.

Step 3

The member sends a knowledge-sharing request to the knowledge-sharing center, and the sharing center first searches for the knowledge in the knowledge sub-network, then searches for policies with mapping relationships with the knowledge in the policy sub-network and sends the list of policies with mapping relationships to the members.

Step 4

If there is no policy with mapping relationship with the knowledge, it searches for the knowledge with the highest degree of association in the knowledge subnet and repeats Step3.

Step 5

After members receive the list of policies with mapping relationships, they send knowledge-sharing requests to the policies and start knowledge-sharing.

Simulation data

The study was carried out by means of field research and questionnaire distribution. The survey respondents are middle and senior managers of renewable energy enterprises distributed in the eastern region of China, with rich experience in enterprise management and knowledge accumulation in renewable energy. A total of 600 questionnaires were distributed in this survey, and the questionnaires with blanks, incomplete answers, consecutively choosing the same answer, and answers with obvious patterns or errors were defined as invalid questionnaires. After eliminating 156 invalid questionnaires, a total of 444 valid questionnaires were obtained, and the effective recovery rate of the questionnaires was 74%. In actuality, the central surveyed enterprises are small and medium-sized, the establishment years are concentrated in 3–5 years, and the employees’ working years are mostly 3–5 years. The average survey data was selected for this study.

The data of listed companies of manufacturing companies are selected from annual reports of listed companies, a yearly library of data of listed companies, etc., and the results are obtained through simulation, which is consistent with the results obtained from the questionnaire.

Therefore, a hypernetwork model containing ten knowledge nodes, 20 member nodes, and 15 policy nodes is constructed. The amount of knowledge stored in each knowledge node and the trust level of each member node are shown in Tables 1 and 2.

Table 1 Knowledge storage of knowledge nodes

Table 2 Member node trust degree values

Simulation experiments

The Netlogo platform is used for simulation analysis. A knowledge-sharing hyper-network model containing 10 knowledge nodes, 20 member nodes, and 15 policy nodes is established, as shown in Fig. 3, in which the round nodes are knowledge nodes, the yellow “human-shaped” nodes are member nodes, and the red “human-shaped” nodes are policy nodes. The lines between the nodes represent the relationship between homogeneous nodes and the mapping relationship between heterogeneous nodes.

Fig. 3

Initial model diagram

The results of the model are shown in Fig. 4. Under the condition of the clock counter ticks = 460, the total storage of technological innovation knowledge of renewable energy enterprises changed from 18.9 to 64.1 after the sharing of knowledge by the innovation policy, and the number of new knowledge nodes and new member nodes was increased by 11 and 9, respectively. The first new renewable energy enterprise technology innovation knowledge node appeared at time = 20, and the first new renewable energy enterprise technology innovation membership node appeared at time = 13. This suggests that knowledge sharing enhances the total storage of innovation knowledge, increases the number of new knowledge nodes and new member nodes, and speeds up the generation of new knowledge nodes and new client nodes.

Fig. 4

Simulation diagram in the initial state

The impact of the state of knowledge sharing on technological innovation in renewable energy companies

Figure 5 shows the experimental results after the knowledge-sharing capacity is increased by 10% (the number of knowledge nodes and member nodes is increased by 20%), and Fig. 6 shows the comparison graph. In Fig. 6, the left figure shows the knowledge-sharing results in the initial state, and the correct figure shows the simulation results after a 10% increase in sharing capability.

The simulation result data is shown in Table 3. Compared with the initial state, the number of new knowledge nodes is 14 (initial state = 11), the number of new members is 15 (initial state = 9), the first new knowledge node is generated at time = 43 (initial state Time = 20), and the first new member node is generated at time = 37 (initial state Time = 13) after the knowledge sharing capacity is increased by 10%. The comparison shows that the improvement of sharing capacity can increase the number of new knowledge nodes, new member nodes, and the storage of system knowledge and speed up the generation of new knowledge nodes and new client nodes.

Fig. 5

Simulation results of 10% improvement in knowledge sharing capability

Fig. 6

Knowledge-sharing capacity increased by 10% vs. initial state

Table 3 10% improvement in knowledge sharing capacity

The impact of renewable energy firms’ technological innovation members on technological innovation

Impact of an increase in the number of members with sharing personalities on technological innovation

Figure 7 shows the simulation results after a 10% increase in the members of the shared personality, and Fig. 8 shows the comparison. In Fig. 8, the left side shows the initial state and the right side shows the simulation results after a 10% increase in shared personality members. The simulation data results are shown in Table 4, and the increment of new knowledge nodes and new member nodes are 15 (initial state = 11) and 17 (initial state = 9), respectively; the first new renewable energy enterprise technology innovation is generated at time = 23 (initial state Time = 20), the first new member node of renewable energy enterprise technological innovation is generated at time = 13 (initial state Time = 13). It is shown that the sharing desire of enterprise technology innovation R & D members is enhanced, and the knowledge mobility of the enterprise is improved, which leads to the enhancement of the enterprise’s knowledge-sharing ability and the improvement of the enterprise’s technological innovation ability.

Fig. 7

Member sharing type character increase 10% simulation effect

Fig. 8

Comparison of a 10% increase in the number of shared members to the initial state

Table 4 10% increase in the number of members with a sharing personality

Impact of increased frequency of communication between members on technological innovation

The simulation results when the communication frequency between members is increased by 10% (the number of knowledge nodes is increased by 20%) are shown in Fig. 9. Figure 10 shows the comparison graph with the results of renewable energy enterprise technology innovation knowledge sharing in the initial state on the left and the simulation effect when the communication frequency between members is increased by 10% on the right. The simulation data results are shown in Table 5. The increments of new knowledge nodes and new member nodes are 14 (initial state = 11) and 17 (initial state = 9), respectively; the first new renewable energy enterprise technology innovation knowledge node is generated at time = 30 (initial state Time = 20). The first new renewable energy enterprise technology innovation member node is created at time = 10 (initial state Time = 13). Thus, the communication frequency of the R & D members of the enterprise technology innovation increases, and the amount of knowledge held by the members in the enterprise increases, thus enhancing the enterprise technology innovation capability.

Fig. 9

Simulation results of a 10% increase in the frequency of communication between members

Fig. 10

Comparison of a 10% increase in member communication frequency

Table 5 10% increase in frequency of communication among members

The impact of members’ trust in the corporate team on technological innovation

The simulation results when members’ trust in the enterprise team is increased by 10% (the number of member nodes is increased by 30%) are shown in Fig. 11. Figure 12 shows the comparison graph with the results of renewable energy enterprise technology innovation knowledge sharing in the initial state on the left and the simulation effect when the frequency of communication between members is increased by 10% on the right. The simulation data results are shown in Table 6. The increments of new knowledge nodes and new member nodes are 13 (initial state = 11) and 21 (initial state = 9), respectively. The first new renewable energy enterprise technology innovation knowledge node is generated at time = 17 (initial state Time = 20). The first new renewable energy enterprise technology innovation member node is created at time = 20 (initial state Time = 13). It is shown that the trust of enterprise technology innovation R&D members in the enterprise team increases, and the knowledge nodes and membership nodes in the enterprise also increase on the original basis. Thus, the technological innovation capability of the enterprise increases with both.

Fig. 11

Simulation results of a 10% increase in member trust

Fig. 12

Comparison chart of a 10% increase in member trust

Table 6 10% increase in member trust

The impact of members’ own knowledge on technological innovation

The simulation results when the technology innovation members’ knowledge is increased by 10% (the number of knowledge points is increased by 30%) are shown in Fig. 13. Figure 14 shows the comparison graph with the results of technology innovation knowledge sharing of renewable energy enterprises in the initial state on the left and the simulation results when the members’ knowledge is increased by 10% on the right. Table 7 shows the simulation data results, and the increments of new knowledge nodes and new member nodes are 12 (initial state = 11) and 22 (initial state = 9) respectively. The first new renewable energy enterprise technology innovation knowledge node is generated at time = 43 (initial state Time = 20). The first new renewable energy enterprise technology innovation member node is created at time = 17 (initial state Time = 13). It can be seen that the amount of knowledge possessed by the enterprise technology innovation R&D members themselves increases, and the knowledge nodes and member nodes in the enterprise increase on the basis of the original one, and the green technology innovation capability increases.

Fig. 13

Simulation results of a 10% increase in members’ own knowledge

Fig. 14

Comparison chart of a 10% increase in members’ own knowledge

Table 7 10% increase in members’ own knowledge

The impact of members’ innovative spirit on technological innovation

The simulation results when the innovation spirit of technology innovation members is increased by 10% (the number of member nodes is increased by 10%) are shown in Fig. 15. Figure 16 shows the results of technology innovation knowledge sharing of renewable energy enterprises in the initial state of the comparison chart on the left and the simulation results when the innovation spirit of members is increased by 10% on the right. Table 8 shows the simulation results and the increments of new knowledge nodes and new member nodes are 14 (initial state = 11) and 13 (initial state = 9) respectively, and the first new renewable energy enterprise technology innovation knowledge node is generated at time = 10 (initial state Time = 20). The first new renewable energy enterprise technology innovation member node is created at time = 17 (initial state Time = 13). Thus, the innovation spirit of the enterprise technology innovation R & D members increases, and the knowledge nodes and member nodes in the enterprise also increase on the original basis, and the green technology innovation capability increases.

Fig. 15

Simulation results of a 10% increase in members’ innovation

Fig. 16

Comparison chart of a 10% increase in members’ innovative spirit

Table 8 10% increase in member innovation

The impact of the number of shares held by R&D personnel on technological innovation

The simulation results when the number of technology innovation members’ shareholding is increased by 10% (the number of knowledge nodes is increased by 10%, and the number of member nodes is increased by 10%) are shown in Fig. 17. Figure 18 shows the results of technology innovation knowledge sharing of renewable energy enterprises in the initial state on the left side of the comparison graph and the simulation results when the number of members’ shareholding is increased by 10% on the right side. Table 9 shows the simulation results, and the increments of new knowledge nodes and new member nodes are 15 (initial state = 11) and 24 (initial state = 9), respectively. The first new renewable energy enterprise technology innovation knowledge node is generated at time = 130 (initial state Time = 20). The first new renewable energy enterprise technology innovation member node is created at time = 13 (initial state Time = 13). It can be seen that the number of shares held by the R&D members of the enterprise technology innovation increases, the employees are more motivated to innovate, and the knowledge nodes and membership nodes in the enterprise are also increasing on the original basis. The green technology innovation capability is increased.

Fig. 17

Simulation results of a 10% increase in the number of shares held by R&D staff

Fig. 18

Comparison of a 10% increase in the number of shares held by R&D personnel

Table 9 10% increase in the number of shares held by R&D staff

The impact of technological innovation policies on technological innovation in renewable energy companies

The impact of reasonable reduction of income tax revenue from technological innovation on technological innovation

The simulation results of the corresponding increase in the tax policy regarding the reduction of renewable energy enterprises’ income in technological innovation, i.e., a 10% increase in the tax policy (10% increase in the number of knowledge nodes and 20% increase in the number of member nodes) are shown in Fig. 19. Figure 20 shows the simulation results comparing the initial state of renewable energy enterprises’ technological innovation knowledge sharing on the left side and the simulation effect when the tax policy is increased by 10% on the right side. Table 10 shows the simulation result data, and the increments of new knowledge nodes and new member nodes are 13 (initial state = 11) and 18 (initial state = 9) respectively. The first new renewable energy enterprise technology innovation knowledge node is generated at time = 27 (initial state Time = 20). The first new renewable energy enterprise technology innovation membership node is created at time = 11 (initial state Time = 13). Thus, it can be seen that the enterprise technology innovation R&D results bring income tax relief, the enterprise is stimulated to innovate, the knowledge nodes and membership nodes in the enterprise are increased on the basis of the original one, and the green technology innovation capacity is increased.

Fig. 19

Simulation results of a 10% increase in tax policy

Fig. 20

Reasonable tax relief for technological innovation income tax policy increased by 10%

Table 10 Reasonable relief from technical income tax policy increased by 10%

The impact of incentive economic policies on technological innovation

The simulation results for a 10% increase in the incentive economic policy (a 20% increase in the number of knowledge nodes and a 10% increase in the number of membership nodes) are shown in Fig. 21. Figure 22 shows the comparison graph with the results of renewable energy enterprise technology innovation knowledge sharing in the initial state on the left and the simulation effect when the incentive economic policy is increased by 10% on the right. Table 11 shows the simulation result data, and the increment of new knowledge node and new member node is 13 (initial state = 11; initial state = 9), and the first new renewable energy enterprise technology innovation knowledge node is generated at time = 37 (initial state Time = 20). The first new renewable energy enterprise technology innovation membership node was created at time = 17 (initial state Time = 13). Thus, encouraging economic policies promotes enterprise innovation. The knowledge nodes and membership nodes in the enterprise are increasing on the basis of the original one, and the green technology innovation capability is increased.

Fig. 21

Simulation results for a 10% increase in economic policy

Fig. 22

Incentive economic policy increase of 10% comparison chart

Table 11 Incentive economic policies increased by 10%

Impact of carbon peaking and carbon neutral policies on technological innovation

The simulation results for a 10% increase in carbon peaking and carbon neutral policy (10% increase in the number of policy nodes, 20% increase in the number of knowledge nodes, and 10% increase in the number of member nodes) are shown in Fig. 23. Figure 24 shows the comparison graph with the results of renewable energy enterprise technology innovation knowledge sharing in the initial state on the left and the simulation results for a 10% increase in carbon peaking and carbon neutral policy on the right. Table 12 shows the simulation result data, and the increment of the new knowledge node and new member node are 15 (initial state = 11) and 20 (initial state = 9), respectively. The first new renewable energy enterprise technology innovation knowledge node is generated at time = 19 (initial state Time = 20). The first new renewable energy enterprise technology innovation member node is created at time = 10 (initial state Time = 13). It is shown that the carbon peak and carbon neutral policies also promote enterprise innovation, and the knowledge nodes and membership nodes in the enterprise are increased from the original ones, which improves the green technology innovation capability of the enterprise.

Fig. 23

Simulation results of a 10% increase in carbon peaking and carbon neutral policies

Fig. 24

Comparison of carbon peaking and carbon neutral policy increase of a 10%

Table 12 Carbon peak, carbon neutral policy increase of 10%

The impact of rising oil and emission reduction policies on technological innovation

The simulation results for oil rise and a 10% increase in emission reduction policy (20% increase in the number of policy nodes, 20% increase in the number of knowledge nodes, and 10% increase in the number of member nodes) are shown in Fig. 25. Figure 26 shows the comparison graph with the results of renewable energy enterprise technology innovation knowledge sharing in the initial state on the left and the simulation effect when the oil rise and emission reduction policy is increased by 10% on the right. Table 13 shows the simulation results, and the increment of new knowledge nodes and new member nodes are 12 (initial state = 11) and 17 (initial state = 9) respectively. The first new renewable energy enterprise technology innovation knowledge node is generated at time = 37 (initial state Time = 20). The first new renewable energy enterprise technology innovation membership node is created at time = 33 (initial state Time = 13). Thus, the rising oil and emission reduction policies also promote enterprise innovation, and the knowledge nodes and membership nodes in the enterprises are increasing on the basis of the original ones, which enhances the green technology innovation capability of the enterprises.

Fig. 25

Simulation results of oil rise and 10% increase in abatement policy

Fig. 26

Comparison of oil rise and 10% increase in emission reduction policy

Table 13 Rising oil and abatement policy increase of 10%

The impact of technical support policies for floating wind power on technological innovation

The simulation results for a 10% increase in the technical support policy for floating wind power (20% increase in the number of policy nodes, 20% increase in the number of knowledge nodes, and 20% increase in the number of member nodes) are shown in Fig. 27. Figure 28 shows the comparison results on the left for the initial state of renewable energy enterprise technology innovation knowledge sharing and on the right for the simulation effect when the technical support policy for floating wind power is increased by 10%. Table 14 shows the simulation result data, and the increments of new knowledge nodes and new member nodes are 12 (initial state = 11) and 28 (initial state = 9) respectively. The first new renewable energy enterprise technology innovation knowledge node is generated at time = 63 (initial state Time = 20). The first new renewable energy enterprise technology innovation member node is created at time = 7 (initial state Time = 13). This shows that the floating wind power technology support also promotes enterprise innovation, and the knowledge nodes and membership nodes in the enterprise are increasing from the original one, which increases the green technology innovation capability.

Fig. 27

Simulation results of a 10% increase in technical support policy for floating wind power

Fig. 28

Comparison of 10% increase in floating wind power technology policy

Table 14 10% increase in floating wind power technology policy

The impact of renewable energy subsidies on technological innovation

The simulation results for a 10% increase in renewable energy subsidy policy (20% increase in the number of policy nodes, 10% increase in the number of knowledge nodes, and 20% increase in the number of member nodes) are shown in Fig. 29. Figure 30 shows the comparison graphs with the results of renewable energy enterprise technology innovation knowledge sharing in the initial state on the left and the simulation results when the renewable energy subsidy policy is increased by 10% on the right. Table 15 shows the simulation results, and the increments of new knowledge nodes and new member nodes are 13 (initial state = 11) and 20 (initial state = 9) respectively. The first new renewable energy enterprise technology innovation knowledge node is generated at time = 13 (initial state Time = 20). The first new renewable energy enterprise technology innovation membership node is created at time = 10 (initial state Time = 13). Thus, the renewable energy subsidy policy also promotes enterprise innovation. The knowledge nodes and membership nodes in the enterprise are increased from the original one, and the level of green technology innovation capability is increased.

Fig. 29

Simulation results of a 10% increase in renewable energy subsidy policy

Fig. 30

Comparison of renewable energy subsidy policy increase by 10%

Table 15 Renewable energy subsidy policy increased by 10%

The impact of domestic environmental policies on technological innovation

The simulation results for a 10% increase in domestic environmental policy (20% increase in the number of policy nodes, 30% increase in the number of knowledge nodes, and 30% increase in the number of member nodes) are shown in Fig. 31. Figure 32 shows the comparison graph with the results of renewable energy enterprise technology innovation knowledge sharing in the initial state on the left and the simulation results for a 10% increase in domestic environmental policy on the right. Table 16 shows the simulation results and the increments of new knowledge nodes and new member nodes are 15 (initial state = 11) and 24 (initial state = 9), respectively, and the first new renewable energy enterprise technology innovation knowledge node is generated at time = 17 (initial state Time = 20). The first new renewable energy firm technology innovation membership node was created at time = 9 (initial state Time = 13). It is shown that domestic environmental policies also contribute to the innovation of the enterprise, and the knowledge nodes and membership nodes in the enterprise increase from the original ones, and the level of green technology innovation capability increases.

Fig. 31

Simulation results for a 10% increase in domestic environmental policy

Fig. 32

Comparison of domestic environmental policy increase of a 10%

Table 16 10% increase in domestic environmental policies

Sensitivity analysis

Sensitivity analysis can help assess the stability and resilience of a simulation model in the face of uncertainty and volatility.

To ensure the robustness of the simulation results under different conditions, the study was re-simulated by varying different parameters (certain intervals). Observing the simulation results, it is found that the conclusions drawn remain unchanged and consistent with those before the parameters were increased. Therefore, the simulation results are robust.

Conclusion and recommendations

Conclusions and shortcomings

Figure 33 shows the relationship of innovation influencing factors derived from the simulation. The results show that:

Fig. 33

Relationship between factors influencing green technology innovation

• (1) In terms of innovation, the green technological innovation of enterprises is enhanced with the improvement of knowledge-sharing ability, indicating that innovation knowledge-sharing has a driving effect on green technological innovation.

• (2) In terms of innovative members, the green technology innovation ability of renewable energy enterprises improves with the increase in the proportion of six factors, such as the sharing personality of members, the frequency of communication between members, and the trust of members in the team, thus indicating that the six factors have a positive and significant impact on the green technology innovation of renewable energy enterprises.

• (3) In terms of innovation policies, seven innovation and economic policies about renewable energy, such as reasonable reduction of income tax policy on technological innovation, encouraging economic policy, carbon peak, carbon neutral policy, etc., were selected. The newborn knowledge nodes, member nodes, and total knowledge in the comparison between simulation results and original simulation results significantly improved compared to the original conditions. It was found that the increase of relevant policies would stimulate renewable energy enterprises’ innovation in green technology and strengthen their innovation ability.

This paper mainly investigates the factors that influence green technology innovation through innovation knowledge, innovation R&D personnel, and innovation policy. Other aspects should be covered. There are some shortcomings for the hypothetical conditions of the simulation: the policy node can also be increased by one unit when the knowledge node and membership node grow to a certain magnitude subsequently. These two aspects will continue to be improved in future studies. Meanwhile, this study focuses on the positive impacts of green technology innovation and ignores potential negative factors or barriers that may hinder innovation. Possible challenges, such as financial constraints, regulatory barriers or market competition, will be discussed in future studies with a view to providing a more balanced picture of the innovation landscape.

Countermeasures and recommendations

Based on the above simulation results, the following countermeasures are proposed:

• (1) Strengthening cooperation and exchanges: Encourage cooperation and exchanges among enterprises and promote the sharing and dissemination of knowledge on green technology innovation through the establishment of joint R&D projects and shared laboratories. Establishment of an open innovation platform: Enterprises can establish an open innovation platform, invite internal employees and external experts to participate, share innovation results and experiences, and promote the sharing and promotion of green technology innovation.

• (2) Establishing an open communication mechanism: Enterprises should encourage members to maintain a good communication frequency among themselves, promote information sharing and exchange of innovative ideas, improve communication efficiency, and stimulate innovative vitality. Build a culture of trust: Enterprises should focus on cultivating trust among team members and establishing a team atmosphere of mutual respect and honest cooperation. Enhance team members’ confidence in each other through reward systems, team building activities and leadership demonstrations; Enhance the sense of knowledge sharing: Encourage members to share their personalities and expertise, and promote knowledge sharing and learning among members through internal training, experience exchange and cross-departmental cooperation; Emphasize the culture of innovation: Companies should establish a culture that encourages innovation, advocate members to show their innovative spirit, encourage them to come up with new ideas and solutions, and at the same time provide appropriate incentives and support; Set up incentive mechanism: Set up a shareholding incentive program for R&D personnel, so that they can share the results of the enterprise’s growth, enhance their sense of belonging and responsibility to the enterprise, and thus stimulate their motivation to participate in green technological innovation; Focus on talent selection and cultivation: Enterprises should focus on recruiting talents with rich knowledge and innovative spirit, and at the same time continuously improve members’ professionalism and creative ability through training and development programs.

• (3) Formulate more incentive policies: The government and relevant departments can continue to formulate more incentive policies for the renewable energy sector, such as lowering the income tax on technological innovation, providing financial support for research and development, and launching an innovation incentive program, in order to further incentivize enterprises to increase their investment in green technological innovation; Provide exceptional funding support: The government can set up special funds to support green technology innovation projects, encourage enterprises to carry out renewable energy-related technology research and development and innovation, and promote the technological level of the industry; Strengthening policy publicity and guidance: The government can strengthen the publicity and interpretation of innovative economic policies, and guide enterprises to make full use of the support and preferential policies of relevant policies, so as to increase their motivation and investment in green technology innovation.

The hyper-network model can be considered for application in the field of transportation in the future for optimizing the logistics network, traffic flow management, and the design of intelligent transportation systems. By analyzing the relationship between different transportation nodes and paths, transportation efficiency and safety can be improved. In manufacturing, hypernetwork models can be used to optimize supply chain management, production processes, and resource allocation. By analyzing the interaction and knowledge sharing between different manufacturing units, production efficiency and innovation can be improved. In addition, the hypernetwork model can be applied to traditional energy industries, such as oil, gas, and nuclear energy, to increase the efficiency of energy production and distribution and to reduce environmental impacts.