# Synergy and Communication of IoT Industry Development Strategies in the Perspective of Low Carbon Economy

Authors: Shaobo Lu
Journal: Complexity
 (2021)

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## Abstract

In this paper, from the perspective of a low-carbon economy, the development mode and influencing factors of the IoT industry are studied, and development strategies are proposed in a targeted manner. Through the analysis of relevant literature, the connotation of the concept of cooperation and dissemination of IoT industry development strategy under low-carbon economy is enriched, emphasizing its systemic, multisubjectivity, and low-carbon effect, and its mechanistic effect is analyzed and different types of models are analyzed and established. The analysis of the model of IoT industry development strategy cooperation and dissemination in low-carbon economy finds that there is industrial variability in the overall trend of continuous increase of IoT industry development strategy cooperation and dissemination in the low-carbon economy, and the reasonable industrial categories and their characteristics are pointed out through cluster analysis. Based on the viewpoint of the IoT industry and regional differentiation, the analysis establishes the path analysis model of enterprise, industry, and region. From the perspective of enterprises using game analysis method research shows that the development process of IoT industry under low-carbon economy first adopts the important benchmarking significance of enterprise technology innovation behavior and its effect on potential enterprises and then pays attention to the government financial and tax policies and market cultivation on the coincidence between different types of IoT enterprises. The research shows that there are fault paths, distorted paths, and pathway types in the development of the IoT industry under a low-carbon economy. Finally, based on the policy analysis, we summarize our shortcomings and the above-mentioned development path views and make suggestions for better policy formulation of IoT industry development in a low-carbon economy from enterprise-level, industry level, and regional level. The above perspectives, conclusions, and policy recommendations on the synergy and dissemination of development strategies will have important positive theoretical and practical guidance for promoting the development of the low-carbon economy and IoT industry.


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## Body

## 1. Introduction

In the context of creating innovative countries, climate change issues and environmental issues are increasingly prominent. The low-carbon economy has become an important choice for the future economic development of countries. W indicates a type. Since entering the information age, with the rapid development of science and technology, the speed of socialization of science and social science has accelerated significantly, and the relationship between science and technology and society has become closer, while the Internet of Things (IoT) technology, as the frontier of information technology, has increasingly entered people’s vision and caused a far-reaching impact on the whole society, becoming a hot spot for research at present. From the perspective of science and technology and society, the study of IoT technology lies in why it can accelerate the change of society and whether it has been separated from the Internet and become a new discipline [1]. While under the premise of integrating various disciplines, the ethical, environmental, and ecological issues arising from the birth of IoT technology so far are criticized, and suggestions are given. At the same time, the sociology of science is based on the impact of IoT technology on society and society on IoT technology [2]. The sociology of science, on the other hand, analyzes the status and interactions between IoT technology and society, starting from both aspects. Many articles are discussing the relationship between IoT technology and industrial development, among which theoretical studies account for most of them, and their main ideas focus on [3] the analysis of the motivation of IoT industry construction; the methods and approaches of IoT industry construction; the analysis of government functions in the construction of IoT industry. These theories have matured more and have been more accepted by the policy-making departments and have played a role in promoting the development of IoT technology. At the same time, these theories are too general, and there are no concrete examples that can make society recognize the value of IoT technology.The research on low-carbon development of the IoT industry further enriches the theoretical knowledge of the IoT industry in low-carbon operation and green development and clarifies the way for the healthy and long-term development of enterprises. Currently, the country is making comprehensive adjustments in industrial structure, energy structure, and transportation structure. Because of the uneven regional, technological, innovation capability, and macro policies, there are still large misunderstandings in low-carbon operation in actual industrial enterprises. The research in this paper is beneficial to the development of the IoT industry in terms of industrial structure adjustment, optimization of energy structure, and improvement of enterprise environmental protection, further enriching the theoretical basis of low-carbon development of IoT enterprises [4]. The IoT industry has achieved milestones in the low-carbon economy by promoting the implementation of low-carbon development, green development, and circular development. This paper provides a reliable template and model for IoT enterprises in industrial structure, resource utilization, emission reduction, garden factory construction, etc. Through the development of the IoT industry’s development law measures or ways in the new normal of the low-carbon economy and promotes the healthy development of IoT enterprises through The formulation of low-carbon economy development strategy provides a reference for the stable and healthy development of IoT enterprises as well as their future transformation and positioning [5].This paper chooses to study the direction of IoT industry development from the perspective of a low-carbon economy, combined with the theory of synergistic development, drawing on the IoT industry development patterns of developed countries and regions at home and abroad. Based on the relevant theoretical foundation and literature research, this paper explores and refines the development strategy of IoT industry under a low-carbon economy and establishes a model of synergy and propagation mechanism effect of a development strategy based on the perspective of energy saving and emission reduction; the issues studied in this paper involve several topics, each of which has corresponding theoretical support, including the theory of low-carbon economy and the theory of synergy and propagation of development strategy. Since these theories are still in the process of continuous exploration and improvement and accumulation of experience, the issues studied in this paper will be exploratory and ground-breaking, providing new concepts and new ideas to improve the theoretical system and the practical process of IoT industry development under the low-carbon economy. Based on the mechanism of development strategy cooperation and propagation of manufacturing industry in the low-carbon economy and the difference of its activity in industry and region, the path model is innovatively established based on the W structural equation model around the systemic and complex phenomenon of the discussed problem, and the path model is tested and analyzed by WPLS method. The WPLS method is used to test and analyze the path model, which systematically reflects the interrelationships and paths of the main influencing factors and their effects and provides a comprehensive analysis of the essential phenomena and problems of synergy and dissemination of development strategies in the low-carbon economy.


## 2. Related Work

As people’s consciousness keeps changing and living standards improve, society keeps developing, the external environment of enterprises has changed greatly, and enterprises change from sloppy management and simple pursuit of profits to strengthening internal management, improving management level, and reducing resource costs. They refine management and improve the utilization of resources as an important means to improve the competitiveness of enterprises, to improve their internal and external competitiveness from the surface to the inside. The proposal and development of the low-carbon economy are inseparable from the national conditions of the country, including energy use, technology level, and people’s needs. Kitouni et al. proposed an agent-based approach to effectively integrate IoT technology in agribusiness systems [6]. Yang et al. studied the application of the Internet of Things in the field of intelligent manufacturing [7]. Biswas et al. described how to efficiently extract information, classify information, and mine hidden patterns in the data in the Internet of Things [8].The IoT industry is also a pioneer in the development of a low-carbon economy, pointing out that the development of a low-carbon economy and the promotion of CO2 gas reduction are the main ways to combat climate warming. From the macro level, he describes the key changes that the IoT industry will face in the low-carbon economy model and analyzes the risks and challenges that the IoT industry will face in the low-carbon economy. Keshavarzi et al. point out that the current environmental issues are getting more and more attention, the urgency and necessity of developing a low-carbon economy and pursuing green growth are also increasing, and the core of the low-carbon green strategy is the enterprise turnaround management in crisis, which discusses in detail the current situation of low-carbon operation by IoT enterprises and the existence of environmental resources, economic development, technical misunderstandings, and other problems [9]. Barata et al. pointed out the strategic requirements for the development of IoT enterprises based on the perspective of the low-carbon economy. They believe that solving the problem of high pollution, high consumption, and reliance on resources of IoT has a pivotal role in the development of low-carbon economy [10]. Looking at the whole economic development cycle, a low-carbon economy can be seen as another new worldwide economic model following the industrial revolution and information revolution. Under this economic model, the development of a circular economy and the creation of ecological parks become the organizational form of a low-carbon economy [11]. Kumar et al. introduces the low-carbon index layer of industrial international competitiveness around the definition of the connotation of a low-carbon economy and gives a quantitative determination of low-carbon economy for the first time by using the Delphi method and AHP method to five first-level indicators of industrial international competitiveness under low-carbon economy [12].Under the new economic normal, the construction of ecological civilization has been raised to the level of national strategy. The traditional manufacturing industry must abandon the original high pollution, high energy consumption, low efficiency of the crude mode of operation, the development of new low-carbon, green, circular new management, or business model. With limited environmental capacity and pressure to remove production capacity, the low-carbon operation must be implemented to achieve long-term good development [13]. The IoT economy used to mostly study the influence of economic and social factors on the whole industry, lacking the consideration of environmental factors [14]. This paper adds six environmental indicators to analyze the influence factors of the IoT economy from the comprehensive consideration of various factors and conducts empirical analysis, which is innovative in concept and application. By using the PEST analysis method to analyze the development conditions and development environment of the IoT economy and combining the research findings of development model and influencing factors, the paper explores the development strategy of IoT economy from multiple perspectives, which have important theoretical and practical significance.


### 2.1. Study on the Synergy and Dissemination Model of IoT Industry Development Strategy from the Perspective of the Low-Carbon Economy

#### 2.1.1. IoT Industry Development Factor Study

The IoT industry is guided by application innovation, its application model, and the standards of each link in the industry chain are different, and no unified model and common standards can be formed at present. Due to the rapid development of the Internet of Things (IoT) around the world, the basis of development, technical conditions, and national policies vary from country to country, resulting in different development priorities and development models in various areas of the region [15–17]. Even in the same field, the operation and management modes of each country and region are not the same. On the other hand, the standard of each link of the IoT industry chain cannot be unified, and a unified platform cannot be built to achieve interconnection. IoT is widely used internationally and has a unique operation scheme in each field. Although there are many standards organizations currently defining IoT standards, they still have not reached a consensus on a large scale. The sensor aspect of IoT and its different sensor signals are also different, but if its sensors are interconnected, there must be common signal conversion and data transmission standards; how to protect network security and its privacy rights in a global context is also a common pressing issue. The standard system of IoT includes multiple levels of content, system architecture standards, technical standards, security standards, and classification standards [18–21]. To unify and standardize all the standards of IoT, the efforts of all parts of the IoT industry are still needed.To develop a low-carbon economy, we should start from two aspects. One is to control the emission of carbon dioxide in the process of social reproduction such as production, consumption, exchange, and distribution through technological innovation, institutional innovation, industrial transformation, and new energy development so that the emission of carbon dioxide can be reduced to the lowest possible level or even zero. On the other hand, we should develop economic industries that absorb carbon dioxide to control the stock in the air. To promote the healthy and rapid development of the high-tech industry, a good legal system environment is indispensable [22, 23]. In the process of the development of the high-tech industry, it involves many innovative subjects, and the technical connection and economic interests are very complicated, which need a sound and perfect legal system to provide a guarantee for its development. By constructing a good legal environment, it can effectively coordinate the interesting relationship, regulate the economic behavior of the subjects, and give full play to the incentive and restraint effect of the law on the high-tech industry.Collaborative innovation in strategic emerging industries means that under a certain political, economic, cultural, and legal environment, collaborative innovation subjects in strategic emerging industries complete all-round innovation in management, products, knowledge, technology, finance, and services through sharing, integration, transformation, application, and opening, realizing collaborative innovation in industries withW users as the middle also, forming a collaborative innovation network in strategic emerging industries, as shown in Figure 1.Figure 1
Structure of industrial collaborative innovation network.


#### 2.1.2. IoT Industry Development Strategy Cooperation and Dissemination Model Construction

According to ecology, there are competitive, neutral, cooperative, symbiotic, and reciprocal interactions between any two populations in ecology, and for any one of these populations, there are only three outcomes: facilitation (1), constraint (0), and no effect (None), as shown in Table1. Coevolution is not a planned economy, and its purpose is not to eliminate competition but to cooperate with the outside world in areas where the competition is too fierce or where the strength is weak, to eliminate the shortcomings in the barrel theory. The purpose of coevolution is not to eliminate competition but to cooperate with the outside world in areas where competition is too strong or where we are weak and to eliminate the shortcomings of the barrel theory.Table 1
Table of biological population relationships.
Relationship typeBiological population 1Biological population 2Detailed descriptionCompetition001 and 2 restrict each otherReciprocal symbiosis111 and 2 are interdependentRush 1101 bashing 2Rush 2012 bashing 1NeutralNoneNone1 has nothing to do with 2The operational goals of each IoT enterprise in the IoT evolutionary community are all aimed at profit maximization, except for policy-oriented enterprises and enterprises with similar operational goals to promote technological progress in the IoT industry [24–26]. In this way, the following equation can be used to describe the population growth of the population in a limited environment influenced by the dependent variables such as environmental resources as shown in equation (1), where G represents the population size, ϖ represents the average growth rate of the population size G in that period, and Max represents the maximum value of the population that the environment in which the population is located can bear.(1)∫dGdt=ϖ∗G∗1−GMax,t⊂0,1.The scale of enterprises owned after the synergistic cooperation such as exchanging information, sharing resources, and joint development is larger than the original scale of enterprises, and then it means that the technology-based enterprises represented by IoT enterprises have been better developed in the synergistic evolutionary cluster [27]. It is feasible to use the theory of synergistic evolution to guide the development of the IoT industry. According to the growth rate of enterprise size of IoT derived above, we can first have two IoT enterprises that both develop in the system, and the enterprise size of each of the two IoT enterprises is(2)∫dG1dt=ϖ1∗G1∗1−G1Max1+Max2∗G2Max1,∫dG2dt=ϖ2∗G2∗1−G2Max2+Max1∗G1Max2.Since the existing resources of a single IoT enterprise are limited and small compared to the resources of the whole cluster, then the resources of the cluster have a facilitating effect on the growth of each IoT enterprise. Therefore, the equation can be interpreted by us as the growth of each IoT enterprise size is proportional to the resources that the enterprise has not yet occupied, then when two IoT enterprises are in a stable synergistic relationship, the enterprise size of both reaches the maximum value at this point.(3)∫dG1dt=∫dG2dt=0.Solving this equation yields four equilibrium points.(4)O10,0;O2Max1,0;O30,Max2;O4Max1,Max2.O1–O4 denote the positions of the four different centers, respectively. In the IoT coevolutionary community, the limit value of anyone IoT enterprise after the interaction is higher than the limit value when the enterprise operates independently, and a solution where everyone benefits between enterprises is achieved. The use of evolutionary theory to guide the development of knowledge-intensive industries, including the IoT industry, has been described in many pieces of literature, but it has not been demonstrated using mathematical models. In this paper, we investigate the interaction between knowledge-intensive enterprises including IoT enterprises and other related enterprises in an industrial cluster environment, and the increase or decrease of their competitiveness after the interaction, and finally conclude that coevolutionary theory can be used to guide the development of IoT industry.


#### 2.1.3. Research on the Evaluation of Cooperation and Dissemination Model of IoT Industry Development Strategy

Constructing the judgment matrix formula is a key step in the AHP method. Representing the judgment of the relative importance degree among the relevant elements in this level, the judgment matrix P form is shown in the following equation:(5)P=pmn,m,n⊆1,2…k.Hierarchical single ranking refers to the calculation of relative importance weights between elements in hierarchical analysis from a single judgment matrix. Equation (2) is obtained by calculating the eigenvalues where X is a vector and ß judgment lifts the characteristic root of P.(6)PX=βX+ϖ.The maximum characteristic root of the judgment matrix is calculated as in equation (6). PXi is the i element of the i-vector PX; Xi is the weight value of the corresponding element single sort.(7)βmax=∑i=0mPXi∑i=0mϖXi+∑i=0nPXi∑i=0nϖXi.To test the consistency of the judgment matrix, it is necessary to calculate its consistency index.(8)BX=βmax−mm−1+βmax−nn−1.When BX = 0, the judgment matrix has complete consistency, and vice versa. The larger the BX, the worse the consistency of the judgment matrix. To test whether the judgment citation has satisfactory consistency, the BX needs to be compared with the average random consistency index likeness, as shown in Table2.Table 2
Average random consistency indexX1 and X2.
Order12345678910X10.010.020.540.921.131.261.371.441.511.59X20.050.060.631.141.191.291.431.561.621.71Enterprises occupy the nuclear also position in the collaborative innovation network of strategic emerging industries: universities and institutes are the sources of knowledge and talents for collaborative innovation of strategic emerging industries; the government is the macro regulator and policymaker of collaborative innovation of strategic emerging industries, providing policy support and supervising and managing to ensure the efficient operation of the system; intermediary service institutions build bridges and links for collaborative innovation of strategic emerging industries, for example, Financial institutions provide credit support for collaborative innovation in strategic emerging industries, provide a financial guarantee for the transformation of scientific and technological achievements, and improve the efficiency of commercialization of scientific and technological achievements. Ultimately, through collaborative innovation, resources are shared, advantages are complementary, risks are shared and benefits are shared. The key nodes in the network, the chain of relationships, and various innovation resources form an open and shared organic ecosystem [28]. Our data were obtained through questionnaires, and the coverage of our survey is very extensive, so the data are indicative and generalizable.According to the principles and bases of the evaluation index system, the whole evaluation index system is divided into three levels: the comprehensive index layer illustrates the overall state or ability of the coordinated development of IoT industry ecology and IoT industry, which is used to evaluate the overall contribution and effect of IoT industry to the development of low-carbon economy in Inner Mongolia. The classification index layer is composed of various factors that affect the coordinated development of the ecology of the IoT industry and the IoT industry, including ecological and environmental protection indicators, economic and social progress indicators, and resource utilization indicators, which also reflect the perspective of the low-carbon economy and the multitarget development of ecological, economic, and social benefits of forest resources. The specific individual indicators are selected mainly for the sake of representativeness and accessibility of data collection, and indicators that can show the measurable and comparable indicators of each classification are used. Such a hierarchy of indicators can avoid crossover and duplication among indicators due to too many levels of indicators and avoid the problem of low accuracy due to too few levels.


## 2.1. Study on the Synergy and Dissemination Model of IoT Industry Development Strategy from the Perspective of the Low-Carbon Economy

### 2.1.1. IoT Industry Development Factor Study

The IoT industry is guided by application innovation, its application model, and the standards of each link in the industry chain are different, and no unified model and common standards can be formed at present. Due to the rapid development of the Internet of Things (IoT) around the world, the basis of development, technical conditions, and national policies vary from country to country, resulting in different development priorities and development models in various areas of the region [15–17]. Even in the same field, the operation and management modes of each country and region are not the same. On the other hand, the standard of each link of the IoT industry chain cannot be unified, and a unified platform cannot be built to achieve interconnection. IoT is widely used internationally and has a unique operation scheme in each field. Although there are many standards organizations currently defining IoT standards, they still have not reached a consensus on a large scale. The sensor aspect of IoT and its different sensor signals are also different, but if its sensors are interconnected, there must be common signal conversion and data transmission standards; how to protect network security and its privacy rights in a global context is also a common pressing issue. The standard system of IoT includes multiple levels of content, system architecture standards, technical standards, security standards, and classification standards [18–21]. To unify and standardize all the standards of IoT, the efforts of all parts of the IoT industry are still needed.To develop a low-carbon economy, we should start from two aspects. One is to control the emission of carbon dioxide in the process of social reproduction such as production, consumption, exchange, and distribution through technological innovation, institutional innovation, industrial transformation, and new energy development so that the emission of carbon dioxide can be reduced to the lowest possible level or even zero. On the other hand, we should develop economic industries that absorb carbon dioxide to control the stock in the air. To promote the healthy and rapid development of the high-tech industry, a good legal system environment is indispensable [22, 23]. In the process of the development of the high-tech industry, it involves many innovative subjects, and the technical connection and economic interests are very complicated, which need a sound and perfect legal system to provide a guarantee for its development. By constructing a good legal environment, it can effectively coordinate the interesting relationship, regulate the economic behavior of the subjects, and give full play to the incentive and restraint effect of the law on the high-tech industry.Collaborative innovation in strategic emerging industries means that under a certain political, economic, cultural, and legal environment, collaborative innovation subjects in strategic emerging industries complete all-round innovation in management, products, knowledge, technology, finance, and services through sharing, integration, transformation, application, and opening, realizing collaborative innovation in industries withW users as the middle also, forming a collaborative innovation network in strategic emerging industries, as shown in Figure 1.Figure 1
Structure of industrial collaborative innovation network.


### 2.1.2. IoT Industry Development Strategy Cooperation and Dissemination Model Construction

According to ecology, there are competitive, neutral, cooperative, symbiotic, and reciprocal interactions between any two populations in ecology, and for any one of these populations, there are only three outcomes: facilitation (1), constraint (0), and no effect (None), as shown in Table1. Coevolution is not a planned economy, and its purpose is not to eliminate competition but to cooperate with the outside world in areas where the competition is too fierce or where the strength is weak, to eliminate the shortcomings in the barrel theory. The purpose of coevolution is not to eliminate competition but to cooperate with the outside world in areas where competition is too strong or where we are weak and to eliminate the shortcomings of the barrel theory.Table 1
Table of biological population relationships.
Relationship typeBiological population 1Biological population 2Detailed descriptionCompetition001 and 2 restrict each otherReciprocal symbiosis111 and 2 are interdependentRush 1101 bashing 2Rush 2012 bashing 1NeutralNoneNone1 has nothing to do with 2The operational goals of each IoT enterprise in the IoT evolutionary community are all aimed at profit maximization, except for policy-oriented enterprises and enterprises with similar operational goals to promote technological progress in the IoT industry [24–26]. In this way, the following equation can be used to describe the population growth of the population in a limited environment influenced by the dependent variables such as environmental resources as shown in equation (1), where G represents the population size, ϖ represents the average growth rate of the population size G in that period, and Max represents the maximum value of the population that the environment in which the population is located can bear.(1)∫dGdt=ϖ∗G∗1−GMax,t⊂0,1.The scale of enterprises owned after the synergistic cooperation such as exchanging information, sharing resources, and joint development is larger than the original scale of enterprises, and then it means that the technology-based enterprises represented by IoT enterprises have been better developed in the synergistic evolutionary cluster [27]. It is feasible to use the theory of synergistic evolution to guide the development of the IoT industry. According to the growth rate of enterprise size of IoT derived above, we can first have two IoT enterprises that both develop in the system, and the enterprise size of each of the two IoT enterprises is(2)∫dG1dt=ϖ1∗G1∗1−G1Max1+Max2∗G2Max1,∫dG2dt=ϖ2∗G2∗1−G2Max2+Max1∗G1Max2.Since the existing resources of a single IoT enterprise are limited and small compared to the resources of the whole cluster, then the resources of the cluster have a facilitating effect on the growth of each IoT enterprise. Therefore, the equation can be interpreted by us as the growth of each IoT enterprise size is proportional to the resources that the enterprise has not yet occupied, then when two IoT enterprises are in a stable synergistic relationship, the enterprise size of both reaches the maximum value at this point.(3)∫dG1dt=∫dG2dt=0.Solving this equation yields four equilibrium points.(4)O10,0;O2Max1,0;O30,Max2;O4Max1,Max2.O1–O4 denote the positions of the four different centers, respectively. In the IoT coevolutionary community, the limit value of anyone IoT enterprise after the interaction is higher than the limit value when the enterprise operates independently, and a solution where everyone benefits between enterprises is achieved. The use of evolutionary theory to guide the development of knowledge-intensive industries, including the IoT industry, has been described in many pieces of literature, but it has not been demonstrated using mathematical models. In this paper, we investigate the interaction between knowledge-intensive enterprises including IoT enterprises and other related enterprises in an industrial cluster environment, and the increase or decrease of their competitiveness after the interaction, and finally conclude that coevolutionary theory can be used to guide the development of IoT industry.


### 2.1.3. Research on the Evaluation of Cooperation and Dissemination Model of IoT Industry Development Strategy

Constructing the judgment matrix formula is a key step in the AHP method. Representing the judgment of the relative importance degree among the relevant elements in this level, the judgment matrix P form is shown in the following equation:(5)P=pmn,m,n⊆1,2…k.Hierarchical single ranking refers to the calculation of relative importance weights between elements in hierarchical analysis from a single judgment matrix. Equation (2) is obtained by calculating the eigenvalues where X is a vector and ß judgment lifts the characteristic root of P.(6)PX=βX+ϖ.The maximum characteristic root of the judgment matrix is calculated as in equation (6). PXi is the i element of the i-vector PX; Xi is the weight value of the corresponding element single sort.(7)βmax=∑i=0mPXi∑i=0mϖXi+∑i=0nPXi∑i=0nϖXi.To test the consistency of the judgment matrix, it is necessary to calculate its consistency index.(8)BX=βmax−mm−1+βmax−nn−1.When BX = 0, the judgment matrix has complete consistency, and vice versa. The larger the BX, the worse the consistency of the judgment matrix. To test whether the judgment citation has satisfactory consistency, the BX needs to be compared with the average random consistency index likeness, as shown in Table2.Table 2
Average random consistency indexX1 and X2.
Order12345678910X10.010.020.540.921.131.261.371.441.511.59X20.050.060.631.141.191.291.431.561.621.71Enterprises occupy the nuclear also position in the collaborative innovation network of strategic emerging industries: universities and institutes are the sources of knowledge and talents for collaborative innovation of strategic emerging industries; the government is the macro regulator and policymaker of collaborative innovation of strategic emerging industries, providing policy support and supervising and managing to ensure the efficient operation of the system; intermediary service institutions build bridges and links for collaborative innovation of strategic emerging industries, for example, Financial institutions provide credit support for collaborative innovation in strategic emerging industries, provide a financial guarantee for the transformation of scientific and technological achievements, and improve the efficiency of commercialization of scientific and technological achievements. Ultimately, through collaborative innovation, resources are shared, advantages are complementary, risks are shared and benefits are shared. The key nodes in the network, the chain of relationships, and various innovation resources form an open and shared organic ecosystem [28]. Our data were obtained through questionnaires, and the coverage of our survey is very extensive, so the data are indicative and generalizable.According to the principles and bases of the evaluation index system, the whole evaluation index system is divided into three levels: the comprehensive index layer illustrates the overall state or ability of the coordinated development of IoT industry ecology and IoT industry, which is used to evaluate the overall contribution and effect of IoT industry to the development of low-carbon economy in Inner Mongolia. The classification index layer is composed of various factors that affect the coordinated development of the ecology of the IoT industry and the IoT industry, including ecological and environmental protection indicators, economic and social progress indicators, and resource utilization indicators, which also reflect the perspective of the low-carbon economy and the multitarget development of ecological, economic, and social benefits of forest resources. The specific individual indicators are selected mainly for the sake of representativeness and accessibility of data collection, and indicators that can show the measurable and comparable indicators of each classification are used. Such a hierarchy of indicators can avoid crossover and duplication among indicators due to too many levels of indicators and avoid the problem of low accuracy due to too few levels.


## 2.1.1. IoT Industry Development Factor Study

The IoT industry is guided by application innovation, its application model, and the standards of each link in the industry chain are different, and no unified model and common standards can be formed at present. Due to the rapid development of the Internet of Things (IoT) around the world, the basis of development, technical conditions, and national policies vary from country to country, resulting in different development priorities and development models in various areas of the region [15–17]. Even in the same field, the operation and management modes of each country and region are not the same. On the other hand, the standard of each link of the IoT industry chain cannot be unified, and a unified platform cannot be built to achieve interconnection. IoT is widely used internationally and has a unique operation scheme in each field. Although there are many standards organizations currently defining IoT standards, they still have not reached a consensus on a large scale. The sensor aspect of IoT and its different sensor signals are also different, but if its sensors are interconnected, there must be common signal conversion and data transmission standards; how to protect network security and its privacy rights in a global context is also a common pressing issue. The standard system of IoT includes multiple levels of content, system architecture standards, technical standards, security standards, and classification standards [18–21]. To unify and standardize all the standards of IoT, the efforts of all parts of the IoT industry are still needed.To develop a low-carbon economy, we should start from two aspects. One is to control the emission of carbon dioxide in the process of social reproduction such as production, consumption, exchange, and distribution through technological innovation, institutional innovation, industrial transformation, and new energy development so that the emission of carbon dioxide can be reduced to the lowest possible level or even zero. On the other hand, we should develop economic industries that absorb carbon dioxide to control the stock in the air. To promote the healthy and rapid development of the high-tech industry, a good legal system environment is indispensable [22, 23]. In the process of the development of the high-tech industry, it involves many innovative subjects, and the technical connection and economic interests are very complicated, which need a sound and perfect legal system to provide a guarantee for its development. By constructing a good legal environment, it can effectively coordinate the interesting relationship, regulate the economic behavior of the subjects, and give full play to the incentive and restraint effect of the law on the high-tech industry.Collaborative innovation in strategic emerging industries means that under a certain political, economic, cultural, and legal environment, collaborative innovation subjects in strategic emerging industries complete all-round innovation in management, products, knowledge, technology, finance, and services through sharing, integration, transformation, application, and opening, realizing collaborative innovation in industries withW users as the middle also, forming a collaborative innovation network in strategic emerging industries, as shown in Figure 1.Figure 1
Structure of industrial collaborative innovation network.


## 2.1.2. IoT Industry Development Strategy Cooperation and Dissemination Model Construction

According to ecology, there are competitive, neutral, cooperative, symbiotic, and reciprocal interactions between any two populations in ecology, and for any one of these populations, there are only three outcomes: facilitation (1), constraint (0), and no effect (None), as shown in Table1. Coevolution is not a planned economy, and its purpose is not to eliminate competition but to cooperate with the outside world in areas where the competition is too fierce or where the strength is weak, to eliminate the shortcomings in the barrel theory. The purpose of coevolution is not to eliminate competition but to cooperate with the outside world in areas where competition is too strong or where we are weak and to eliminate the shortcomings of the barrel theory.Table 1
Table of biological population relationships.
Relationship typeBiological population 1Biological population 2Detailed descriptionCompetition001 and 2 restrict each otherReciprocal symbiosis111 and 2 are interdependentRush 1101 bashing 2Rush 2012 bashing 1NeutralNoneNone1 has nothing to do with 2The operational goals of each IoT enterprise in the IoT evolutionary community are all aimed at profit maximization, except for policy-oriented enterprises and enterprises with similar operational goals to promote technological progress in the IoT industry [24–26]. In this way, the following equation can be used to describe the population growth of the population in a limited environment influenced by the dependent variables such as environmental resources as shown in equation (1), where G represents the population size, ϖ represents the average growth rate of the population size G in that period, and Max represents the maximum value of the population that the environment in which the population is located can bear.(1)∫dGdt=ϖ∗G∗1−GMax,t⊂0,1.The scale of enterprises owned after the synergistic cooperation such as exchanging information, sharing resources, and joint development is larger than the original scale of enterprises, and then it means that the technology-based enterprises represented by IoT enterprises have been better developed in the synergistic evolutionary cluster [27]. It is feasible to use the theory of synergistic evolution to guide the development of the IoT industry. According to the growth rate of enterprise size of IoT derived above, we can first have two IoT enterprises that both develop in the system, and the enterprise size of each of the two IoT enterprises is(2)∫dG1dt=ϖ1∗G1∗1−G1Max1+Max2∗G2Max1,∫dG2dt=ϖ2∗G2∗1−G2Max2+Max1∗G1Max2.Since the existing resources of a single IoT enterprise are limited and small compared to the resources of the whole cluster, then the resources of the cluster have a facilitating effect on the growth of each IoT enterprise. Therefore, the equation can be interpreted by us as the growth of each IoT enterprise size is proportional to the resources that the enterprise has not yet occupied, then when two IoT enterprises are in a stable synergistic relationship, the enterprise size of both reaches the maximum value at this point.(3)∫dG1dt=∫dG2dt=0.Solving this equation yields four equilibrium points.(4)O10,0;O2Max1,0;O30,Max2;O4Max1,Max2.O1–O4 denote the positions of the four different centers, respectively. In the IoT coevolutionary community, the limit value of anyone IoT enterprise after the interaction is higher than the limit value when the enterprise operates independently, and a solution where everyone benefits between enterprises is achieved. The use of evolutionary theory to guide the development of knowledge-intensive industries, including the IoT industry, has been described in many pieces of literature, but it has not been demonstrated using mathematical models. In this paper, we investigate the interaction between knowledge-intensive enterprises including IoT enterprises and other related enterprises in an industrial cluster environment, and the increase or decrease of their competitiveness after the interaction, and finally conclude that coevolutionary theory can be used to guide the development of IoT industry.


## 2.1.3. Research on the Evaluation of Cooperation and Dissemination Model of IoT Industry Development Strategy

Constructing the judgment matrix formula is a key step in the AHP method. Representing the judgment of the relative importance degree among the relevant elements in this level, the judgment matrix P form is shown in the following equation:(5)P=pmn,m,n⊆1,2…k.Hierarchical single ranking refers to the calculation of relative importance weights between elements in hierarchical analysis from a single judgment matrix. Equation (2) is obtained by calculating the eigenvalues where X is a vector and ß judgment lifts the characteristic root of P.(6)PX=βX+ϖ.The maximum characteristic root of the judgment matrix is calculated as in equation (6). PXi is the i element of the i-vector PX; Xi is the weight value of the corresponding element single sort.(7)βmax=∑i=0mPXi∑i=0mϖXi+∑i=0nPXi∑i=0nϖXi.To test the consistency of the judgment matrix, it is necessary to calculate its consistency index.(8)BX=βmax−mm−1+βmax−nn−1.When BX = 0, the judgment matrix has complete consistency, and vice versa. The larger the BX, the worse the consistency of the judgment matrix. To test whether the judgment citation has satisfactory consistency, the BX needs to be compared with the average random consistency index likeness, as shown in Table2.Table 2
Average random consistency indexX1 and X2.
Order12345678910X10.010.020.540.921.131.261.371.441.511.59X20.050.060.631.141.191.291.431.561.621.71Enterprises occupy the nuclear also position in the collaborative innovation network of strategic emerging industries: universities and institutes are the sources of knowledge and talents for collaborative innovation of strategic emerging industries; the government is the macro regulator and policymaker of collaborative innovation of strategic emerging industries, providing policy support and supervising and managing to ensure the efficient operation of the system; intermediary service institutions build bridges and links for collaborative innovation of strategic emerging industries, for example, Financial institutions provide credit support for collaborative innovation in strategic emerging industries, provide a financial guarantee for the transformation of scientific and technological achievements, and improve the efficiency of commercialization of scientific and technological achievements. Ultimately, through collaborative innovation, resources are shared, advantages are complementary, risks are shared and benefits are shared. The key nodes in the network, the chain of relationships, and various innovation resources form an open and shared organic ecosystem [28]. Our data were obtained through questionnaires, and the coverage of our survey is very extensive, so the data are indicative and generalizable.According to the principles and bases of the evaluation index system, the whole evaluation index system is divided into three levels: the comprehensive index layer illustrates the overall state or ability of the coordinated development of IoT industry ecology and IoT industry, which is used to evaluate the overall contribution and effect of IoT industry to the development of low-carbon economy in Inner Mongolia. The classification index layer is composed of various factors that affect the coordinated development of the ecology of the IoT industry and the IoT industry, including ecological and environmental protection indicators, economic and social progress indicators, and resource utilization indicators, which also reflect the perspective of the low-carbon economy and the multitarget development of ecological, economic, and social benefits of forest resources. The specific individual indicators are selected mainly for the sake of representativeness and accessibility of data collection, and indicators that can show the measurable and comparable indicators of each classification are used. Such a hierarchy of indicators can avoid crossover and duplication among indicators due to too many levels of indicators and avoid the problem of low accuracy due to too few levels.


## 3. Analysis of Results

### 3.1. IoT Industry Development Indicators Analysis

According to the PLS path model requirements, after the principal component analysis of the six-observation model indicator sets for the unique dimensionality test, this process was analyzed using Shen SS25 software, and the results of the first principal component and eigenvalue of the relevant reactivity indicator sets are shown in Figure2. Figure 2 reflects that the first principal component of each observation model observation indicator set expresses most of the content of the observation model, corresponding to the first principal component eigenvalue greater than 1.5, while the second principal component is less than 0.9, which passed the unique dimensionality test, indicating that these indicator sets are appropriately placed in the model.Figure 2
Unique dimensional test of the model’s inverse nudeness indicator.Each point of this scatter plot represents each original variable. Thex-axis value is the correlation coefficient of this variable with the first principal component, and the y-axis value is the correlation coefficient of this variable with the second principal component. The y-axis value is the correlation coefficient between this variable and the second principal component. Therefore, the closer the point is to the axis, the more correlated the variable is with the corresponding principal component. From Figure 3, we can see that the observed variables and the structural variables in the PLS model are greater than 0.012, which indicates that the PLS path model has good predictive validity and practicality for the observed variables, structural variables, and the full model. The above-mentioned model test analysis shows that the diffusion model of technological innovation in the IoT industry under the low-carbon economy established in this paper describes the basic reality and has good predictive power for future prediction. Through the model path analysis, the technology-driven technology, such as patents and research and development investment in the IoT industry, has a positive but insignificant effect on energy consumption and carbon emissions during the diffusion of technology innovation in the low-carbon economy. In particular, the direct path coefficient of the technology-driven effect on carbon emissions is 0.028, which reflects that the current technology-driven IoT industry does not contribute to the real reduction of energy consumption and carbon emission levels. Reducing the energy consumption level of the IoT industry contributes to a very significant reduction in the carbon emission level of the manufacturing industry.Figure 3
Results for model observed and structural variables.


### 3.2. Synergistic Analysis of IoT Industry Development

The rating of the IoT economic development model comes from 10 experts, according to the divided level, scored by experts, the specific results are shown in Figure4. From Figure 4, it can be seen that the four indicators of information development level, research and development level, environmental support level, development effect are good, the equipment level is general, the service level, sustainable development ability is general. Although there are individual details in the evaluation process that is not very town dense, the whole can intuitively reflect the development status of the Internet of Things economy at this stage.Figure 4
Evaluation of IoT economic development model.
(a)(b)(c)(d)From Figure5, we can see that the average value of the absolute degree in the collaborative innovation network is 15.32, the minimum value is 1.23, and the maximum value is 49.21; the average value of the relative degree in the collaborative innovation network is 9.89, the minimum value is 0.69, and the maximum value is 36.12. The cooperation among the collaborative innovation subjects in the Internet of Things industry is not much at present, and the average value is about 20 times. The average is about 20 times, and the difference between collaborative innovation subjects is still large.Figure 5
IoT collaborative innovation network degree center degree statistics.The power index value of each collaborative innovation subject is calculated by the software, as shown in Figure6. The power distribution of each innovation subject is not uniform. The relative power index of the innovation subject with the largest power is 35.63, while the relative power index of the smallest subject is only 0.58. It shows that the power in the collaborative innovation network of the IoT industry is mostly in the hands of a few innovation subjects, and most of the collaborative innovation subjects have smaller power in the network.Figure 6
Power index of collaborative innovation network of IoT industry.


### 3.3. Empirical Analysis of IoT Industry Development

The survey uses questionnaire method to obtain the contact information of enterprises engaged in IoT-related activities, research institutions, relevant government agencies and experts and scholars conducting IoT research through government agencies, industry associations, universities, and relevant websites and then uses direct telephone survey, on-site questionnaire survey and mail survey, respectively. Regarding the source distribution of the investigators, the details are shown in Figure7.Figure 7
Results of survey respondents.In the process of processing the data, a sending situation emerged: the process of determining the degree of influence of a factor on the development of the industry was highly biased. In response to the situation of sending seeds that appeared in the survey findings, the following processing was carried out: the obtained data were tabulated and summarized, and the extreme values were removed separately. And according to the questionnaire results for comprehensive processing, according to the number of valid questionnaires from different sources as the extreme weight, the data obtained by comparing the results of the same set of factors are weighted average. The matrix constructed in this paper is compared with the traditional method, and the result is shown in Figure8.Figure 8
Comparison result of a judgment matrix.The total hierarchical ranking and weights of the influencing factors of IoT industry development are shown in Figure9. The results of the hierarchical analysis show that among the influencing factors of IoT industry development, the innovation environment factor has the largest proportion, accounting for 35.70%, and is the factor with the largest degree of influence, considering the first-level indicators. Next is the policy environment factor, accounting for 22.53%, the degree of influence is closer to the innovation environment factor. The degree of influence of the market demand factor is after the policy environment, accounting for 18.96% of the weight: the related industry factor and the enterprise factor account for 9.36% and 6.35%, respectively.Figure 9
Extreme values of the impact of the IoT industry development.
(a)(b)Under the condition of reform and opening market economy, IOT industrial policy is an important part of national economic, industrial policy, which is the government’s regulation and control of IoT industry in general and intervenes through corresponding policy instruments to achieve the expected macro-objectives of IoT industry. The IoT industry has many complex features, such as industrial chain dragging involving a wide range, so it needs the government’s active promotion to balance the linkage between industries and break the barriers between industries and regions. The active promotion of the government is the key to the rapid development of the Internet of Things in China. The development of the Internet of Things and other emerging strategic industries must be from the national development planning, industrial development planning to industry development planning, and finally, to each city’s Internet of Things, development planning detailed overall layout. Under the total layout can mobilize the coordinated development of the Internet of Things industry from the central to local levels.In terms of IoT investment and financing policy, the government focuses on optimizing capital guidance, improving IoT financing policy, trying to launch several projects to support the development of the IoT manufacturing industry, and based on the actual needs of economic and social development around the world, carrying out pilot applications of IoT, accumulating experience in technology development, industrial applications, business management, policy implementation, etc. Investment opportunities are concentrated in the field of information acquisition and transmission as well as industrial applications. In its application areas, the industry is generally willing to refer to the various application directions of the Internet to determine the development direction of the Internet of Things. In the development of IoT, industrial applications such as mobile payment and coordination enterprises are the first to develop and become the first commercialized application of IoT. Especially, enterprises in the field of information transfer, such as telecom operators and coordination enterprises, which are directly involved in downstream applications, will have better development opportunities in the future development and will be directly benefited at the same time.


## 3.1. IoT Industry Development Indicators Analysis

According to the PLS path model requirements, after the principal component analysis of the six-observation model indicator sets for the unique dimensionality test, this process was analyzed using Shen SS25 software, and the results of the first principal component and eigenvalue of the relevant reactivity indicator sets are shown in Figure2. Figure 2 reflects that the first principal component of each observation model observation indicator set expresses most of the content of the observation model, corresponding to the first principal component eigenvalue greater than 1.5, while the second principal component is less than 0.9, which passed the unique dimensionality test, indicating that these indicator sets are appropriately placed in the model.Figure 2
Unique dimensional test of the model’s inverse nudeness indicator.Each point of this scatter plot represents each original variable. Thex-axis value is the correlation coefficient of this variable with the first principal component, and the y-axis value is the correlation coefficient of this variable with the second principal component. The y-axis value is the correlation coefficient between this variable and the second principal component. Therefore, the closer the point is to the axis, the more correlated the variable is with the corresponding principal component. From Figure 3, we can see that the observed variables and the structural variables in the PLS model are greater than 0.012, which indicates that the PLS path model has good predictive validity and practicality for the observed variables, structural variables, and the full model. The above-mentioned model test analysis shows that the diffusion model of technological innovation in the IoT industry under the low-carbon economy established in this paper describes the basic reality and has good predictive power for future prediction. Through the model path analysis, the technology-driven technology, such as patents and research and development investment in the IoT industry, has a positive but insignificant effect on energy consumption and carbon emissions during the diffusion of technology innovation in the low-carbon economy. In particular, the direct path coefficient of the technology-driven effect on carbon emissions is 0.028, which reflects that the current technology-driven IoT industry does not contribute to the real reduction of energy consumption and carbon emission levels. Reducing the energy consumption level of the IoT industry contributes to a very significant reduction in the carbon emission level of the manufacturing industry.Figure 3
Results for model observed and structural variables.


## 3.2. Synergistic Analysis of IoT Industry Development

The rating of the IoT economic development model comes from 10 experts, according to the divided level, scored by experts, the specific results are shown in Figure4. From Figure 4, it can be seen that the four indicators of information development level, research and development level, environmental support level, development effect are good, the equipment level is general, the service level, sustainable development ability is general. Although there are individual details in the evaluation process that is not very town dense, the whole can intuitively reflect the development status of the Internet of Things economy at this stage.Figure 4
Evaluation of IoT economic development model.
(a)(b)(c)(d)From Figure5, we can see that the average value of the absolute degree in the collaborative innovation network is 15.32, the minimum value is 1.23, and the maximum value is 49.21; the average value of the relative degree in the collaborative innovation network is 9.89, the minimum value is 0.69, and the maximum value is 36.12. The cooperation among the collaborative innovation subjects in the Internet of Things industry is not much at present, and the average value is about 20 times. The average is about 20 times, and the difference between collaborative innovation subjects is still large.Figure 5
IoT collaborative innovation network degree center degree statistics.The power index value of each collaborative innovation subject is calculated by the software, as shown in Figure6. The power distribution of each innovation subject is not uniform. The relative power index of the innovation subject with the largest power is 35.63, while the relative power index of the smallest subject is only 0.58. It shows that the power in the collaborative innovation network of the IoT industry is mostly in the hands of a few innovation subjects, and most of the collaborative innovation subjects have smaller power in the network.Figure 6
Power index of collaborative innovation network of IoT industry.


## 3.3. Empirical Analysis of IoT Industry Development

The survey uses questionnaire method to obtain the contact information of enterprises engaged in IoT-related activities, research institutions, relevant government agencies and experts and scholars conducting IoT research through government agencies, industry associations, universities, and relevant websites and then uses direct telephone survey, on-site questionnaire survey and mail survey, respectively. Regarding the source distribution of the investigators, the details are shown in Figure7.Figure 7
Results of survey respondents.In the process of processing the data, a sending situation emerged: the process of determining the degree of influence of a factor on the development of the industry was highly biased. In response to the situation of sending seeds that appeared in the survey findings, the following processing was carried out: the obtained data were tabulated and summarized, and the extreme values were removed separately. And according to the questionnaire results for comprehensive processing, according to the number of valid questionnaires from different sources as the extreme weight, the data obtained by comparing the results of the same set of factors are weighted average. The matrix constructed in this paper is compared with the traditional method, and the result is shown in Figure8.Figure 8
Comparison result of a judgment matrix.The total hierarchical ranking and weights of the influencing factors of IoT industry development are shown in Figure9. The results of the hierarchical analysis show that among the influencing factors of IoT industry development, the innovation environment factor has the largest proportion, accounting for 35.70%, and is the factor with the largest degree of influence, considering the first-level indicators. Next is the policy environment factor, accounting for 22.53%, the degree of influence is closer to the innovation environment factor. The degree of influence of the market demand factor is after the policy environment, accounting for 18.96% of the weight: the related industry factor and the enterprise factor account for 9.36% and 6.35%, respectively.Figure 9
Extreme values of the impact of the IoT industry development.
(a)(b)Under the condition of reform and opening market economy, IOT industrial policy is an important part of national economic, industrial policy, which is the government’s regulation and control of IoT industry in general and intervenes through corresponding policy instruments to achieve the expected macro-objectives of IoT industry. The IoT industry has many complex features, such as industrial chain dragging involving a wide range, so it needs the government’s active promotion to balance the linkage between industries and break the barriers between industries and regions. The active promotion of the government is the key to the rapid development of the Internet of Things in China. The development of the Internet of Things and other emerging strategic industries must be from the national development planning, industrial development planning to industry development planning, and finally, to each city’s Internet of Things, development planning detailed overall layout. Under the total layout can mobilize the coordinated development of the Internet of Things industry from the central to local levels.In terms of IoT investment and financing policy, the government focuses on optimizing capital guidance, improving IoT financing policy, trying to launch several projects to support the development of the IoT manufacturing industry, and based on the actual needs of economic and social development around the world, carrying out pilot applications of IoT, accumulating experience in technology development, industrial applications, business management, policy implementation, etc. Investment opportunities are concentrated in the field of information acquisition and transmission as well as industrial applications. In its application areas, the industry is generally willing to refer to the various application directions of the Internet to determine the development direction of the Internet of Things. In the development of IoT, industrial applications such as mobile payment and coordination enterprises are the first to develop and become the first commercialized application of IoT. Especially, enterprises in the field of information transfer, such as telecom operators and coordination enterprises, which are directly involved in downstream applications, will have better development opportunities in the future development and will be directly benefited at the same time.


## 4. Conclusion

To promote the synergistic development of the IoT industry from a low-carbon economy perspective, this paper proposes a basic development model that can promote synergistic development within the IoT industry because of the current development status of the IoT industry. Increasing the promotion of scientific and technological innovation and thus ensuring the synergistic technology policy is the top priority to improve the public policy of the IoT industry based on the theory of coevolution. Modern economic growth has evolved from the era of natural resources and capital to the era of a knowledge-based economy, while innovation has become the core driver of national economic growth. At the same time, with the complexity of IoT technology, the convergence and uncertainty of innovation technologies, the innovation power of individual enterprises, research institutes, or universities appears to be increasingly weak, so we should pay special attention to the advantages of coevolution to develop technology innovation policies that meet the requirements of the global IoT market. The IoT industrial policy based on coevolutionary theory is a comprehensive policy system with the functions of promoting the development of the IoT industry, facilitating the industrial restructuring of the national economy, and improving international competitiveness. The implementation process of its policy is not only a huge system project but also will be a multilink process. It requires not only the exploration and research of the academic community but also the practice and promotion of the business community; not only the improvement and implementation of policies and regulations but also the active cooperation of enterprises and citizens. In future research, we should increase the questionnaire survey of domestic IoT enterprises, and from the perspective of industrial economics, we should classify and study the policies that should be formulated and implemented by IoT enterprises in different stages of the industrial life cycle.



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*Source: 2584238-2021-07-07.xml*