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Peer-Review Record

Research on Autonomous Vehicle Technology Innovation Ecosystem in China Based on System Dynamics

Systems 2025, 13(4), 269; https://doi.org/10.3390/systems13040269
by Ruiyu Feng 1,*, Yingqi Liu 1, Mu Li 1 and Fei Zhou 2
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Reviewer 3: Anonymous
Reviewer 4: Anonymous
Systems 2025, 13(4), 269; https://doi.org/10.3390/systems13040269
Submission received: 13 February 2025 / Revised: 4 April 2025 / Accepted: 7 April 2025 / Published: 9 April 2025
(This article belongs to the Section Systems Practice in Social Science)

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

  1. A large part of the Introduction is a failed tentative to compare “two different technology routes […] derived in the field of autonomous driving: single-vehicle intelligent autonomous driving and vehicle-road-cloud integrated autonomous driving” (p.1, lines 39–40). Since there is no neat face-to-face comparison and no clearly stated purpose then: why to compare them? It is recommended to address this issue.
  2. The introductory part (p.3) contains formulations like “this article studies …” (lines 93–94) and “this article aims to improve …” (lines 105–108); both are long phrases which, unfortunately, failed to formulate the research objectives. It is strongly recommended to clearly and unambiguously state the study objective/s.
  3. The formulation “… continuous maturation of autonomous driving …” (p.1, line 35) is arguable, since “maturation” is just a stage of the life cycle. Revision is suggested.
  4. The literature review related to innovation ecosystems (2.1) looks outdated (with one exception, references are more than a decade old) and fails to identify the literature gap and set the background for the research in discussion. It is highly recommended to update the references; also, to extend and improve the above section of the paper as suggested.
  5. The methodology section: The author/s propose a “system dynamics model” (p.5, line 231–232) which assumes that “technological innovation ecosystem of autonomous vehicles is a continuous and progressive process” (p.6, lines 239– 240). This dynamic and progressive process looks in contradiction with other basic assumptions (p.6): “time delay is not considered in this article” (2); and “there is no change in the general direction of the national policy guidelines” (3). Therefore, coherent and documented discussion on this matter is highly recommended.
  6. It is recommended to highlight the original elements of the study – as compared to the state-of-the-art international literature.
  7. Limitations and further research mainly should be identified and reported.
  8. It is recommended that final part of the proposed paper to include (besides general statements) more concrete and specific recommendations addressed to the main stakeholders of the ecosystem described.
  9. It would be advisable to develop and insert a discussion about the social and managerial implications of the research presented.
  10. There are phrases that looks unfinished (e.g., “Domestic car companies represented by Ideal, Azure, Changan, Geely, and smart driving suppliers such as Huawei, Sensetime, and Haomo.” (p.2, lines 55–56). It is strongly recommended to revise the proposed paper entirely from this standpoint.
  11. The author/s mention “the research of Xu and Hu” (p.12, line 470) but neither citations nor references are provided. This issue should be addressed.
  12. The author/s claim “15 years of prediction” (p.13, line 515). Nevertheless, the formulation must be revised since the “prediction” is associated with 7 (seven) years (2023–2030) only. The 15 years might be total years of study.
  13. In addition: as it is rather risky to make predictions in that case when the prediction horizon (7 years: 2023–2030) is comparable to the period of available data (8 years: 2015–2023), a documented discussion on this topic is strongly recommended.
  14. The graphs (figures 1-to-5) are useful and nice. Nonetheless, it is suggested to turn writing more legible.
  15. The same applies to diagrams (figures 6-to-15).
  16. For the sake of ease of reading and communicating author/s’ ideas, it is suggested to split excessively long paragraphs (by respective ideas).
  17. It is suggested to avoid and reformulate tautologic definitions (e.g., “Autonomous vehicle technology innovation ecosystem is an innovation ecosystem … “ (p.5, line 220).
  18. For avoiding potential confusions, it is suggested to analyse the possibility of using a more suitable word than “route” in the context of this paper.
  19. Spacing, punctuation and orthography should be double-checked across the paper.

Comments on the Quality of English Language

(per comments addressed to author/s)

Author Response

Dear reviewer,please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The competition in the autonomous vehicle industry is provoked through key innovative technologies. Therefore, for the emerging autonomous vehicle industry, breakthroughs in technology and innovation are key elements of development. They depend on the most advanced technological achievements. Quite a few automobile companies are actively involved in the research and development of autonomous vehicle technologies, which promotes the development of the autonomous vehicle industry. Therefore, the article and the modeling and research findings presented in it are very relevant and significant in today's research.

Very interesting article. I have a few suggestions:
1. When describing Fig. 1, 2, 3  it is not clear how it relates to the presented model (Fig. 4), because it are named as subsystems, and in the model itself they are not visible, they merge. I would suggest highlighting or highlighting that place in the model with different colors (Fig. 4).
2. You claim that the members of the research group traveled to different cities - Beijing, Chongqing etc., conducting on-site research and interviewing numerous autonomous vehicle companies and consumers in the market. You just did not provide information about the specifics of those interviews? Where are their results? How did this affect your model?
3. Very interesting research, but I missed comparing the results of your research with the results of other researchers.

Author Response

Dear reviewer,please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This paper focuses on the research of China's autonomous driving technology innovation ecosystem, which has important practical significance and theoretical value. From the perspective of the innovation ecosystem, the paper uses the system dynamics method to build a model and conduct simulation analysis, exploring the impact of different elements on autonomous driving technology innovation. The research ideas are clear, the method selection is reasonable, and the conclusions are forward-looking and guiding to a certain extent.

However, there are still some relatively major flaws in the paper:

1. Although the paper conducts historical tests in model validation, the settings of some parameters still have certain ambiguity. Is it possible to add an appendix to explain the data sources of each parameter, whether it is enterprise-level micro-data, international comparison data, or personal hypothesized data, so as to enhance the accuracy and persuasiveness of the model.

2. In the first sentence of the methodology section, it is stated that "Autonomous vehicle technology innovation ecosystem is an innovation ecosystem composed of five technological innovation main bodies, including government, universities and research institutes, intermediary organizations, and market consumers" Here, enterprises are not mentioned. However, in the subsequent elaboration, enterprises are emphasized while market consumers are downplayed.

3. In the innovation resource supply subsystem, it seems that all the power sources are government support. Isn't market demand a driving force for increasing the supply of innovation resources? Even in Figure 4, I don't see that market demand can promote the supply of innovation resources. Does market demand only affect the diffusion rate? This is a relatively big problem because it seems to deny the economic theory that the market drives technological innovation.

4. How is the "Financial scale" in the model characterized? It seems to be a variable that is not affected by changes in other factors. Does government support have no impact on it? Generally speaking, the technology fields supported by the government are more likely to obtain financing and loans.

5. In the technology innovation diffusion subsystem, the author uses many variables that are not explained. I can understand that these variables may come from the technology innovation diffusion theory, but the specific connotations of these variables in this study need to be explained.

6. Figure 1 and Figure 3 seem to have a lot of repetitive content. Figure 3 only seems to have one more element of industry-university-research cooperation, but the text description of the paper includes various incentives and guarantees. Can these incentives and guarantees correspond to the relationships presented in Figure 3?

7. Some equations in the model are confusing. For example, where do the several weight factors in the calculation formula for autonomous driving R&D investment come from? Where does the initial value of 378 for the technological innovation level come from?

8. The paper conducts a sensitivity analysis for the research results, which is good. But can the authors draw some more specific policy recommendations through the sensitivity analysis? Currently, the policy recommendations in the paper seem to be relatively weak.

9. The paper indicates in both the abstract and the introduction that it aims to solve the problem of autonomous driving technology route decision-making. However, the research method does not distinguish between the two technology routes. The research results also cannot support the research conclusions. The key point mentioned in the paper is that the government's investment in infrastructure can improve the level of technological innovation. But is it possible that investing this part of the funds in other aspects can also improve the level of technological innovation, such as education, R&D, and corporate tax reduction? The inconsistency among the purpose, method, result, and conclusion may be the biggest flaw of the paper.

Author Response

Dear reviewer,please see the attachment.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

  1. In Section 3.1, the paper proposes to analyze the technological development of autonomous vehicles through a system dynamics model. However, there is limited explanation of how the two technology routes, single-vehicle intelligence and vehicle-road-cloud integration, are quantitatively compared within this model. The paper would benefit from a clearer description of the metrics and parameters used to evaluate and compare the impacts of these two approaches on technological innovation. Specifically, how are the advantages of vehicle-road-cloud integration, such as the role of 5G and infrastructure, being weighed against the more self-contained approach of single-vehicle intelligence?
  2. To enhance the depth and richness of the article, it is recommended to include references to the following papers that further elaborate on the advantages and characteristics of autonomous vehicles.  Qin, Y., Luo, Q., & Xiao, T. (2024). Capacity modeling for mixed traffic with connected automated vehicles on minor roads at priority intersections. Transportation Planning and Technology, 1-25.
  1. The model makes several assumptions about the stability of external factors, such as national policy guidelines and infrastructure developments. These assumptions may oversimplify the real-world dynamics of autonomous vehicle development.
  2. The paper focuses heavily on the two technological routes (single-vehicle intelligence vs. vehicle-road-cloud integration), but there is a lack of detailed discussion regarding the synergies between these technologies, especially in hybrid models.
  3. While the paper discusses the technological and economic elements of autonomous vehicle innovation, it offers little exploration of consumer behavior and societal impacts, which are crucial for the successful adoption of autonomous driving technology.
  4. The paper mentions that data for the system dynamics model is sourced from official publications, such as the China Statistical Yearbook and the China Automotive Industry Yearbook. However, the methodology for data collection and processing is not sufficiently detailed. For example, how were the variables and parameters derived from these sources? Were any assumptions made regarding data completeness or accuracy? More transparency regarding data selection, processing, and any potential limitations or biases would strengthen the paper's credibility and allow for better replication of the results.

Author Response

Dear reviewer,please see the attachment.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

  1. (formerly #2). The author/s’ long answer (instead to clearly state the research objective/s) reads that “It [the study] constructs an innovation ecosystem …” – which is confusing if not plainly wrong: “It” cannot construct an innovation ecosystem which is already in place (“It” may barely construct a model of …). Rephrasing (in shorter and clear statements) and reformulation are highly recommended.
  2. (formerly #4). The list of references has been improved; however the literature gap is not emphasized. It is suggested to be.
  3. (formerly #5). The contradiction between the core concept of the proposed model (“system dynamics” which implies continuous changes) and study hypotheses (not considering the significant time delay – hypothesis 1; and “no change in the general direction” – hypothesis 2) is still on. Complexity of the system (hypothesis 3) is not a reason to bypass this obvious contradiction (both in theory and practice). It is recommended to address this issue and solve the problem.
  4. (formerly #7). Some research limitations and further study are presented. However, one of the limitations (contradiction dynamic/change versus static/non-change) is not discussed. It should be.
  5. (formerly #8). The author/s have formulated recommendations addressed to policy makers in more details; however, recommendations to other main stakeholders (companies producing autonomous vehicles, R&D units, etc.) are not visible. It is suggested to be.
  6. (formerly #10 & #16). There still are long phrases, sometimes extended as a paragraph (e.g., p.9) as well as phrases that look unfinished (e.g., first paragraph under 3.2.4, lines 392–397, et al.). For the sake of clarity, it is recommended to carefully revise the paper from this standpoint.

Comments on the Quality of English Language

See suggestions to authors (comment vi).

Author Response

Response to Reviewer X Comments

 

1. Summary

 

 

Dear Editor and Reviewers,

We sincerely thank the review team for your time and effort in carefully reviewing our paper (Manuscript: systems-3501516. Title: Autonomous Driving in China: Collaborative Innovation Advantage Leads to New Choice of Technology Route), and for your many constructive comments, which help us improve our work.

Having carefully considered all the review team’s comments, we did our best to revise the paper to meet the reviewers’ requirements. Thanks to your suggestions, we believe that the revised paper is greatly improved and makes a valuable contribution to the literature and practice. To facilitate the review team’s checking, we mark the major changes in red in the revised paper. We provide point-by-point responses to the reviewers’ comments in the following.

Once again, we thank the editor and reviewers for your helpful comments and valuable suggestions. We hope that you find the revised paper acceptable for publication in Systems.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes

 

Are all the cited references relevant to the research?

Yes

 

Is the research design appropriate?

Must be improved

Based on your suggestion, we have provided additional clarification on the research hypothesis.

Are the methods adequately described?

Can be improved

We have revised the describe of methods.

Are the results clearly presented?

Can be improved

We have revised and improved the conclusion.

Are the conclusions supported by the results?

Yes

 

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: The author/s’ long answer (instead to clearly state the research objective/s) reads that “It [the study] constructs an innovation ecosystem …” – which is confusing if not plainly wrong: “It” cannot construct an innovation ecosystem which is already in place (“It” may barely construct a model of …). Rephrasing (in shorter and clear statements) and reformulation are highly recommended.

Response 1: Thank you for point out that we did not describe clearly. As suggested by the reviewer, we have rewritten it to provide a clearer presentation. The changes are as follows:

1. Introduction

This article takes China's autonomous vehicle as the research object, regards autonomous vehicle technology innovation as a dynamic system, and uses system dynamics method to simulate and examine the system from the perspective of innovation ecosystem. By exploring the collaborative innovation mode among the innovation subjects in the system, and the dynamic relationship among the innovation elements. The article aims to provide a new way for the research in the field of technological innovation of autonomous vehicle, and the research conclusions can be used for reference by autonomous vehicle related enterprises and institutions, so as to improve the ability of technological innovation and promote the sustainable development of autonomous vehicle industry.

(Please refer to page 2, paragraph 4 of the revised paper)

Comments 2: The list of references has been improved; however, the literature gap is not emphasized. It is suggested to be.

Response 2: Thank you for your constructive comments. We have supplemented the content of the literature gap section. The changes are as follows:

2.1. Research on Innovation Ecosystems

Through literature review, it can be found that the evolution of innovation eco-systems is the foundation of technological innovation in emerging industries, but the research on their mechanisms needs to be strengthened and expanded. At present, most research focuses on the evolution and upgrading of a single innovation ecosystem, the implementation path of technological innovation in emerging industries, and so on. There is few research on how the evolution process of the innovation ecosystem promotes the upgrading of the technological innovation path in emerging industries, and analyzes the mechanism from the perspective of the subsystems included in the innovation ecosystem.

(Please refer to page 3, paragraph 4 of the revised paper)

Comments 3: The contradiction between the core concept of the proposed model (“system dynamics” which implies continuous changes) and study hypotheses (not considering the significant time delay – hypothesis 1; and “no change in the general direction” – hypothesis 2) is still on. Complexity of the system (hypothesis 3) is not a reason to bypass this obvious contradiction (both in theory and practice). It is recommended to address this issue and solve the problem.

Response 3: We sincerely appreciate your careful review and the valuable comment you provided. Your concern regarding the potential contradictions between the core concept of the system dynamics model and our research assumptions is of great significance. Regarding the contradiction pointed out, we would like to provide the following explanations.

For hypothesis 1, the decision not to consider time delays initially was aimed at constructing a concise model to highlight the core mechanisms within the system. While time delays are indeed important in real-world scenarios, our preliminary analysis indicated that their impact on the overall system dynamics is relatively minor compared to the main factors considered. Therefore, in the initial stage of model building, simplifying this factor allowed us to better focus on the fundamental relationships among variables. In our future research, we plan to incorporate time - delay factors to further refine the model and make it more realistic.

For hypothesis 2, the setting of a stable national policy guideline direction was based on the relative coherence of policies within the research period. Based on recent policy analyses and industry reports, the general direction of policies supporting autonomous vehicle development has remained consistent over the past few years. This stability allows us to reasonably assume that significant policy shifts are unlikely in the short term. Thus, it does not mean that we ignore the potential changes in policies. Instead, we considered the overall policy stability during the specific time frame of our study. In subsequent research, we will explore the impact of dynamic policy adjustments on the system, such as changes in subsidy policies or regulatory requirements.

For hypothesis 3, it is not in conflict with the previous two assumptions. Although the autonomous vehicle industry has a complex industrial chain, by focusing on the five main stakeholders (enterprises, government, universities and research institutions, intermediary organizations, and market consumers), we can analyze the key relationships more clearly.

In summary, we have explained the reasons for the settings of these assumptions. We will improve the relevant content in the revised version of the paper. Specifically, in the section of model limitations, we will clearly elaborate on the limitations of the hypothesis and the future research directions. We have added a discussion on the limitations of this simplification and suggest that future research should gradually incorporate additional stakeholders to provide a more comprehensive view of the innovation ecosystem.

To address this issue, we have made the following modifications to the manuscript:

1. Added a detailed explanation of the rationale behind each hypothesis.

2. Included a discussion on the limitations of the hypothesis and suggested future research directions.

3. Provided additional literature and data to support the hypothesis, particularly regarding the stability of policy directions.

We believe that these modifications have addressed the contradictions highlighted by the reviewer while maintaining the focus and feasibility of our study. We appreciate your understanding.

3.1. Boundaries and Assumptions of the System Model

Determining the system boundary and assumptions is the basis for establishing the system dynamics model. The release of “Made in China 2025” in 2015 marked the starting point of the development of autonomous vehicle in China. Since then, the “Strategy for the Innovative Development of Intelligent Vehicles” and the “Technology Roadmap for Energy-saving and New Energy Vehicles” have specified 2025 and 2030 as the two key nodes of technological innovation. Therefore, this article takes autonomous vehicle in China as the main research object and selects 2015-2030 as the time boundary to explore the technological innovation of autonomous vehicle. During the past decade, the government has continuously issued relevant policies to support and encourage the development of autonomous vehicles, creating a favorable policy environment for them. In the selection of innovation subjects, this paper mainly considers subjects that have significant influence on the system. Referring to the studies of Yang et al. [23] and Xi [24], according to the research needs, the following basic assumptions are made for the model:

(1) In order to simplify the system dynamics model, the time delay problem is not considered in this article.

(2) The development situation of the autonomous vehicle industry is basically stable during the operation of the model, and there is no change in the general direction of the national policy guidelines.

(3) The autonomous vehicle industry chain is relatively complex, and this article only considers the roles of five parties: enterprises, government, universities and re-search institutes, intermediary organizations, and market consumers, while the roles of other secondary subjects are ignored.

Reference

[23]      Zhao, X. G; Wang, W. Wu, L. A dynamic analysis of research and development incentive on China's photovoltaic industry based on system dynamics model. Energy,2021,233(15):121141

[24]      Xi R. Research on the influencing factors and optimization path of technological innovation in China's intelligent connected vehicle industry. Beijing Jiaotong University, 2021.

(Please refer to page 5-6 of the revised paper)

5. Conclusion

Although this research has made some achievements in exploring the evolution of technological innovation of autonomous vehicle, it also has the following limitations. Current model simplifies reality by excluding time - delay factors, which are present in technology development, policy implementation, and market response. Incorporating time - delays in future work will enhance the realism of the model. The study assumes stable national policies during the research period, yet policies in this field change constantly due to technological progress, social needs, and international trends. Future research should explore how dynamic policy adjustments, like subsidy or regulation changes, affect the system. The model only accounts for five main stakeholders, while the industry's complex supply chain includes many secondary stakeholders. Future models should incorporate these to better represent industry relationships. Data col-lection is limited to the domestic market, overlooking international factors influencing autonomous vehicle technology innovation, like different regional tech requirements, consumer preferences, and regulations. Expanding data sources to include international data will lead to a more comprehensive analysis.

(Please refer to page 23 of the revised paper)

Comments 4: Some research limitations and further study are presented. However, one of the limitations (contradiction dynamic/change versus static/non-change) is not discussed. It should be.

Response 4: Thank you very much for your careful review in our manuscript. We have further explained the limitations of the article and future research directions. The changes are as follows:

5. Conclusion

Although this research has made some achievements in exploring the evolution of technological innovation of autonomous vehicle, it also has the following limitations. Current model simplifies reality by excluding time - delay factors, which are present in technology development, policy implementation, and market response. Incorporating time - delays in future work will enhance the realism of the model. The study assumes stable national policies during the research period, yet policies in this field change constantly due to technological progress, social needs, and international trends. Future research should explore how dynamic policy adjustments, like subsidy or regulation changes, affect the system. The model only accounts for five main stakeholders, while the industry's complex supply chain includes many secondary stakeholders. Future models should incorporate these to better represent industry relationships. Data col-lection is limited to the domestic market, overlooking international factors influencing autonomous vehicle technology innovation, like different regional tech requirements, consumer preferences, and regulations. Expanding data sources to include international data will lead to a more comprehensive analysis.

(Please refer to page 23 of the revised paper)

Comments 5: The author/s have formulated recommendations addressed to policy makers in more details; however, recommendations to other main stakeholders (companies producing autonomous vehicles, R&D units, etc.) are not visible. It is suggested to be.

Response 5: Thank you for your professional comments on our manuscript. Based on your suggestion, we have made modifications to the proposed section from the perspective of the main stakeholders of technological innovation. The changes are as follows:

5. Conclusion

Based on the research results, in order to better enhance the technological innovation capability of autonomous vehicle, this study puts forward suggestions from the perspectives of the main stakeholders of technological innovation. In the increasingly fierce competition of global autonomous driving technology, government should deeply grasp the cutting-edge trends and development patterns of autonomous driving technology at home and abroad, continuously refine support mechanisms, and provide precise policy support such as funding and talent for the innovation and development of autonomous driving technology.

Autonomous driving enterprises should actively participate in the construction of new infrastructure and promote the technology from the verification stage to large-scale marketization. Enterprises can cooperate with the government to accelerate the coverage of 5G network in the whole region to guarantee the real-time acquisition and transmission of data for autonomous vehicles. By integrating the IoT and big data resources to improve the intelligence level of transportation infrastructure, and build a city-level intelligent transportation perception platform.

Universities and research institutes should continue to expand the construction of technological innovation and exchange platforms, and deepen the cooperation be-tween industry, academia and research. In the process of cooperation, universities and research institutes can provide theoretical support for the R&D projects of enterprises, while enterprises can provide practical application scenarios for the research results of universities and research institutes to promote the transformation of scientific research results.

As the ultimate users of technology implementation, consumers' demand perception and feedback are crucial for technological iteration. Government and enterprises should establish a consumer demand feedback mechanism, and timely obtain consumers' needs and suggestions for autonomous driving technology through market research, user experience testing, etc. At the same time, consumers should actively participate in technology testing and demonstration applications, provide real scenario data for technology optimization, promote technology to be closer to market demand, and form a virtuous cycle of innovation-market-innovation.

Financial institutions should explore diversified financing models to support the R&D and commercialization of autonomous driving technologies. They can provide financial support to startups through equity financing and bond issuance. In addition, special venture capital funds can be set up to focus on supporting technology projects with innovative and market potential.

(Please refer to page 22 of the revised paper)

Comments 6: There still are long phrases, sometimes extended as a paragraph (e.g., p.9) as well as phrases that look unfinished (e.g., first paragraph under 3.2.4, lines 392–397, et al.). For the sake of clarity, it is recommended to carefully revise the paper from this standpoint.

Response 6: Thank you very much for your careful review of the sentences in our manuscript. As suggested by the reviewer, we have split excessively long paragraphs in the manuscript according to their content for easier reading.

3.2.3. Incentives and guarantees subsystem

As an emerging industry, autonomous vehicles in the early stages of technological research and development are characterized by high innovation complexity, significant technical challenges, and considerable uncertainty regarding future prospects. The process of technological innovation often necessitates access to knowledge and resources that exceed the enterprise's existing capabilities and conditions. This limitation makes it difficult for enterprises to possess all the necessary resources and knowledge required for exploratory and applied research. Consequently, the initial investment in technological development and the anticipated returns from innovation often fall short of expectations, thereby diminishing enterprises' confidence in pursuing technological innovation for autonomous vehicles.

Given these challenges, the technological innovation of autonomous vehicles requires comprehensive support from various sectors of society. Effective incentives and safeguards can significantly enhance the synergistic innovation capabilities within the technological innovation ecosystem of autonomous vehicles, thereby further advancing the overall level of technological innovation. As shown in Figure 3, the incentives and safeguards subsystem of the autonomous vehicle technology innovation ecosystem primarily encompasses four elements: policy incentives, enterprise incentives, resource safeguards, and environmental safeguards.

Policy incentives are primarily reflected in the government's support and guidance for autonomous vehicle technology as an emerging field in industrial development, encompassing financial subsidies, infrastructure construction, and other supportive policies. In the early stages of technological innovation, autonomous vehicles rely on the government to provide a clear development path and goals through a combination of top-level design and local implementation. Additionally, the government employs a series of measures to motivate the main actors involved in technological innovation, encouraging them to engage in innovative activities and stimulating their innovative vitality.

Enterprise incentives are mainly reflected in the need to strengthen the role of enterprises as the primary drivers of technological innovation. Within the autonomous vehicle technology innovation ecosystem, enterprises are the main organizers and participants of technological innovation activities. They are also the key decision-makers in technological innovation, responsible for R&D investment, scientific research organization, and the transformation of innovation outcomes. Promoting the aggregation of various innovation factors within enterprises and enhancing their role as the main body of technological innovation are crucial for driving progress.

Resource guarantees are primarily embodied in the provision of essential re-sources and elements, such as knowledge, talent, technology, capital, information, and infrastructure, to ensure the smooth execution and successful operation of technological innovation activities within the autonomous vehicle technology innovation eco-system. The aggregation, collaboration, and rational distribution of these innovation resources and elements will inevitably provide strong support and momentum for autonomous vehicle technological innovation.

Environmental guarantees are mainly reflected in the creation of a fair and competitive market environment within the autonomous vehicle technology innovation ecosystem. This environment serves as the foundation and key to technological innovation in autonomous vehicles. Strengthening the standardization and governance of market order, as well as protecting the outcomes of technological innovation, are essential for fostering a sustainable and innovative ecosystem.

3.2.4. Systematic causality diagram

Based on the comprehensive analysis of the three subsystems and the influencing factors within China's autonomous vehicle technology innovation ecosystem, the elements within the system boundary have been clearly defined. By integrating the interactions among these subsystems and utilizing Vensim PLE software to eliminate redundant causal relationship chains, the overall causal relationship diagram of China's autonomous vehicle technology innovation ecosystem has been constructed. This diagram provides a structured representation of the dynamic interactions and feed-back loops that drive the innovation process within the ecosystem.

(Please refer to page 8-10 of the revised paper)

4. Response to Comments on the Quality of English Language

Point 1: The English could be improved to more clearly express the research.

Response 1: Thank you very much for your insightful suggestions. As suggested by the reviewer, we have reprocessed the languages in the manuscript to express the research more clearly.

5. Additional clarifications

 

Reviewer 3 Report

Comments and Suggestions for Authors

I accept the vast majority of the authors' revisions. However, I still have some concerns regarding several equations in Appendix A:

  1. In Equation (8), the direct addition of three parameters seems a bit arbitrary. There should be differences in the influence weights of different parameters.
  2. How was Equation (12) derived? In fact, there are real data available for the number of R&D patents. Has the simulation result been compared with the real data?
  3. Why is market demand negatively correlated with the level of technological innovation in Equation (14)? This seems to contradict the causal diagram.
  4. Why is the adoption rate negatively correlated with market demand in Equation (19)? This relationship is not reflected in the causal diagram either.

Author Response

Response to Reviewer X Comments

 

1. Summary

 

 

Dear Editor and Reviewers,

We sincerely thank the review team for your time and effort in carefully reviewing our paper (Manuscript: systems-3501516. Title: Autonomous Driving in China: Collaborative Innovation Advantage Leads to New Choice of Technology Route), and for your many constructive comments, which help us improve our work.

Having carefully considered all the review team’s comments, we did our best to revise the paper to meet the reviewers’ requirements. Thanks to your suggestions, we believe that the revised paper is greatly improved and makes a valuable contribution to the literature and practice. To facilitate the review team’s checking, we mark the major changes in red in the revised paper. We provide point-by-point responses to the reviewers’ comments in the following.

Once again, we thank the editor and reviewers for your helpful comments and valuable suggestions. We hope that you find the revised paper acceptable for publication in Systems.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes

 

Are all the cited references relevant to the research?

Yes

 

Is the research design appropriate?

Can be improved

Based on your comments, we have revised the research design.

Are the methods adequately described?

Can be improved

We have revised the describe of methods.

Are the results clearly presented?

Yes

 

Are the conclusions supported by the results?

Yes

 

3. Point-by-point response to Comments and Suggestions for Authors

Comments 1: In Equation (8), the direct addition of three parameters seems a bit arbitrary. There should be differences in the influence weights of different parameters.

Response 1: Thank you for your careful review and insightful suggestion on our manuscript. In Equation (8) Industry-University-Research Cooperation = Innovation platform construction + Intensity of enterprise R&D inputs + R&D personnel inputs, due to the following reasons, we choose to add them directly.

Firstly, in the process of model construction, we standardized the variables of innovation platform construction, intensity of enterprise R&D inputs, and R&D personnel inputs to make them have the same dimension and value range. Through standardization, the parameters of each variable can be directly added together without calculation bias caused by dimensional differences.

Secondly, the three variables are calculated through multiple regression analysis and factor analysis on a large amount of data. These methods can determine the contribution of each variable to the strength of industry university research cooperation based on the inherent rules of the data, and thus obtain corresponding parameters.

Based on the above reasons, we choose to add innovation platform construction, intensity of enterprise R&D inputs and R&D personnel inputs directly. This treatment is more common in system dynamics models, which can effectively reduce the computational complexity of the model while maintaining the explanatory power and predictive ability of the model. In subsequent research, parameter weights can be adjusted based on empirical data to make the model more accurate.

Comments 2: How was Equation (12) derived? In fact, there are real data available for the number of R&D patents. Has the simulation result been compared with the real data?

Response 2: Thank you very much for your careful review and valuable feedback on our manuscript. Our response to the question is as follows:

Equation (12) R&D patents = Autonomous vehicle R&D inputs * Industry-University-Research Cooperation^2,is derived based on the following theoretical and empirical research.

Firstly, previous studies have shown a significant positive correlation between R&D inputs and R&D patents. The increase in R&D inputs will promote technological innovation and the growth of R&D patents numbers. The impact of Industry-University-Research cooperation on R&D patents is non-linear. According to the theory of innovation ecosystem, Industry-University-Research cooperation can significantly improve innovation efficiency through knowledge sharing, resource integration, and technological collaboration. We assume that the impact of Industry-University-Research cooperation on R&D patents follows a quadratic relationship to reflect the non-linear characteristics of its synergistic effect.

Secondly, regarding formula derivation. Our research team conducted simulation experiments in the system dynamics model using the collected data. In the simulation process, we input the actual R&D inputs of autonomous vehicle and Industry-University-Research cooperation data into the model, and calculate the number of simulated R&D patents through the formula.

Finally, in the article, the level of technological innovation is a state variable, and its value is obtained by inputting the value of the R&D patents. Therefore, in the section of 4.1 historical testing, we compare the simulated level of technological innovation with the actual level of technological innovation, which can also reflect the comparison between the simulated and actual values of the number of R&D patents. Table 1 shows the results of the historical testing. It can be seen that the absolute error between the simulated value and the actual value of the variable is within 10%, which indicates that the model has passed the historical testing, its operation status and actual status have a high degree of fit, and can more accurately simulate the actual situation of autonomous vehicle technology innovation.

(Please refer to page 12-13 of the revised paper)

Comments 3: Why is market demand negatively correlated with the level of technological innovation in Equation (14)? This seems to contradict the causal diagram.

Response 3: Thank you very much for your insightful suggestions, which has great guiding significance for us to improve the manuscript. There is a positive correlation between market demand and level of technological innovation. Technological innovation can improve product performance, reduce costs, or create new application scenarios, thereby attracting more consumers and driving market demand growth.

After re-simulating and simulating the data, our research team modified Equation (14) to: Market demand = 1.28 * Government support^1/2 * Level of technological innovation.

The parameters in the equation are derived by applying regression analysis to reflect the non-linear relationship between market demand, government support and level of technological innovation through the form of index. The parameter 1/2 represents the elasticity coefficient of the level of technological innovation to government support and measures the percentage change in market demand for each percentage point change in the level of government support.

Comments 4: Why is the adoption rate negatively correlated with market demand in Equation (19)? This relationship is not reflected in the causal diagram either.

Response 4: Thank you very much for your constructive suggestion. In the Equation (19), “Adoption rate = Willingness to adopt technology / Market demand", it shows that the adoption rate is negatively related to market demand, which is inconsistent with our original intention. Our original intention was to express that the adoption rate is positively correlated with market demand. We deeply apologize for this and make the following corrections and explanations.

In order to accurately reflect the positive correlation between adoption rate and market demand, we have revised the formula to:

Adoption rate = Willingness to adopt technology * Market demand.

We apologize for the errors in the equation design and have proposed a correction plan. This revision can more accurately express the logic that the adoption rate of technological innovation increases with market demand, which is in line with the intention of our research.

4. Response to Comments on the Quality of English Language

Point 1: The English is fine and does not require any improvement.

Response 1: Thank you very much for recognizing the quality of English language in this article!

5. Additional clarifications

 

Reviewer 4 Report

Comments and Suggestions for Authors

I have no further comments.

Author Response

Dear reviewer, thank you so much for your reviewing! We deeply appreciate your recognition of our research work.

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

I. It is recommended that revised Abstract to explicitly mention the period under scrutiny (2015–2030).

II. Relative to the research hypotheses (p.6):

  • It is suggested to declare that hypotheses (1) and (3) are simplifying hypotheses.
  • It is recommended to significantly reformulate the hypothesis (2) [“The development situation of the autonomous vehicle industry is basically stable during the operation of the model, and there is no change in the general direction of the national policy guidelines.” (lines 244–246)], making clear that “stability” refers strictly to the “national policy guidelines” only (and not to the vehicle industry itself).

III. The main “research object” (line 235, et al.) that refers to (2015-2030) period should explicitly be written as “research interval / time horizon", etc., and understood as such (in time terms).

IV. It was suggested (and still is) that research limitations (last part of Conclusions) to be highlighted as a distinct sub/section.

V. It was suggested (and still is) that paper Conclusion to be written in short, punchy statements (and not in general terms) declaring to what extent the research objective/s were reached.

VI. In the same line of thought, several concluding remarks (pp.21–22) are long phrases not documented, with uncertain time horizon or simply wrong. It is recommended to revise this part (reformulate, document, or give up / remove) in terms of:

  • time dimension / time horizon – e.g., “the revenue from technological innovation will show a better [?] growth trend in the future [what “future”?], and the level of industrial development and the competitiveness of enterprises will reach a higher level in the future [which future?]” (lines 851–853); “by then [when?], China's ability in artificial intelligence algorithms and decision-making chips in the field of autonomous driving will also be further [what “further” means?] improved” (lines 854–855);
  • certainty and precision (there is excess of “should”): e.g., “Autonomous driving enterprises should actively participate in the construction of new infrastructure” (line 877); “Universities and research institutes should continue to expand the construction of technological innovation and exchange platforms” (lines 883–884); “Government and enterprises should establish a consumer demand feedback mechanism” (line 891); “consumers should actively participate in technology testing” (line 894); “Financial institutions should explore diversified financing models to support the R&D” (line 898); of course they should but how (specifically)?

VII. It is recommended to redesign the figures (6–to–15) to make their texts legible.

Comments on the Quality of English Language

See comments to author/s.

Author Response

Comments 1: It is recommended that revised Abstract to explicitly mention the period under scrutiny (2015–2030).
Response 1: Thank you for your constructive comments. Based on your feedback, we have clearly added the period under scrutiny (2015–2030) in the abstract. The changes are as follows:


Abstract
Based on the perspective of innovation ecosystem, the system dynamics research method is used to construct a technological innovation ecosystem model of autonomous vehicle in China. Using Vensim PLE software for simulation to obtain the development trend of technological innovation from 2015 to 2030, and to explore the impact of various elements inside the system on the overall system. The research finds that the dynamic mechanism of China's autonomous vehicle technology innovation ecosystem mainly includes the innovation resource supply subsystem, the technology innovation diffusion subsystem, and the incentive and guarantee subsystem. Each subsystem interacts to jointly promote continuous innovation and iterative upgrading of technology. Education investment, infrastructure construction, innovation platform construction and other factors all have a positive impact on the technological innovation level of autonomous vehicle, and the effect of multiple parameter changes is far more significant than that of single factor changes. The number of research and development patents, level of technological innovation, actual adopters of technological innovation, and benefits of technological innovation are all showing a good growth trend in the future. Accordingly, it is concluded that there should be optimization of scientific research investment strategies, acceleration of infrastructure layout, and expansion of application scenarios. These insights provide theoretical basis and practical guidance for promoting the high-quality development of autonomous vehicle technology in China.
(Please refer to page 1 of the revised paper)


Comments 2: Relative to the research hypotheses (p.6):
It is suggested to declare that hypotheses (1) and (3) are simplifying hypotheses.
It is recommended to significantly reformulate the hypothesis (2) [“The development situation of the autonomous vehicle industry is basically stable during the operation of the model, and there is no change in the general direction of the national policy guidelines.” (lines 244–246)], making clear that “stability” refers strictly to the “national policy guidelines” only (and not to the vehicle industry itself).
Response 2: Thank you very much for your insightful suggestion on the hypotheses. As suggested, we have reformulated hypothesis (2) to clarify that "stability" strictly. The changes are as follows:


3.1. Boundaries and Assumptions of the System Model

Referring to the studies of Zhao et al. [23] and Xi [24], according to the research needs, the following basic assumptions are made for the model:
(1) In order to simplify the system dynamics model, the time delay problem is not considered in this article.
(2) During the operation of the model, the national policy guidelines remain stable with no significant changes in the general direction of these guidelines.
(3) The autonomous vehicle industry chain is relatively complex. In order to simplify the model, this article only considers the roles of five parties: enterprises, government, universities and research institutes, intermediary organizations, and market consumers, while the roles of other secondary subjects are ignored.
(Please refer to page 6 of the revised paper)


Comments 3: The main “research object” (line 235, et al.) that refers to (2015-2030) period should explicitly be written as “research interval / time horizon", etc., and understood as such (in time terms).
Response 3: We sincerely appreciate your careful review and the valuable comment. As suggested, we have revised the text to clarify that 2015–2030 denotes the time horizon of our study.


3.1. Boundaries and Assumptions of the System Model
Determining the system boundary and assumptions is the basis for establishing the system dynamics model. The release of “Made in China 2025” in 2015 marked the starting point of the development of autonomous vehicle in China. Since then, the “Strategy for the Innovative Development of Intelligent Vehicles” and the “Technology Roadmap for Energy-saving and New Energy Vehicles” have specified 2025 and 2030 as the two key nodes of technological innovation.
Accordingly, this article focuses on China’s autonomous vehicle industry, with the time horizon set from 2015 to 2030, to explore the dynamics of technological innovation within this period. During the past decade, the government has continuously is-sued relevant policies to support and encourage the development of autonomous vehicles, creating a favorable policy environment for them. In the selection of innovation subjects, this paper mainly considers subjects that have significant influence on the system. Referring to the studies of Zhao et al. [23] and Xi [24], according to the research needs, the following basic assumptions are made for the model.
(Please refer to page 5, line 237-239 of the revised paper)


Comments 4: It was suggested (and still is) that research limitations (last part of Conclusions) to be highlighted as a distinct sub/section.
Response 4: Thank you very much for your valuable comments in our manuscript. We fully agree with your suggestion of making the research limitations as a distinct section.


5.3 Limitations and Future Research
Although this research has made some achievements in exploring the evolution of technological innovation of autonomous vehicle, it also has the following limitations. Current model simplifies reality by excluding time - delay factors, which are present in technology development, policy implementation, and market response. Incorporating time - delays in future work will enhance the realism of the model. The study assumes stable national policies during the research period, yet policies in this field change constantly due to technological progress, social needs, and international trends. Future research should explore how dynamic policy adjustments, like subsidy or regulation changes, affect the system. The model only accounts for five main stakeholders, while the industry's complex supply chain includes many secondary stakeholders. Future models should incorporate these to better represent industry relationships. Data col-lection is limited to the domestic market, overlooking international factors influencing autonomous vehicle technology innovation, like different regional tech requirements, consumer preferences, and regulations. Expanding data sources to include international data will lead to a more comprehensive analysis.
(Please refer to page 23 of the revised paper)


Comments 5: It was suggested (and still is) that paper Conclusion to be written in short, punchy statements (and not in general terms) declaring to what extent the research objective/s were reached.
Response 5: Thank you for your professional comments on our manuscript. Based on your suggestion, we have revised the statement in the conclusion section to clearly demonstrate the research objectives. The changes are as follows:


5.1 Conclusions
Based on the system dynamics method, this article constructs a model of China's autonomous vehicle technology innovation ecosystem, obtains a development trend map for 2015-2030 through dynamic simulation of the model, and explores the effect of different elements in the autonomous technology innovation ecosystem on the industry's future technological innovation results output.
The main results of this study can be summarized as follows:
1. This article identified the components and interactions within the autonomous vehicle technology innovation ecosystem. The model confirmed that the synergistic effects of multiple innovation main bodies, environment and subsystems are crucial for driving the iterative innovation and upgrading of autonomous driving technology.
2. The simulation results of model clearly show the continuous growth trend of relevant innovation indicators during the period of 2024-2030. This is consistent with the 2030 milestones in the Intelligent Networked Vehicle Technology Roadmap 2.0, indicating that China's autonomous vehicle industry is on track for large-scale commercial operation and industry transformation.
3. It is demonstrated the significant positive impacts of infrastructure construction, innovation platform construction, educational investment and other elements on technological innovation levels. The analysis highlighted that considering multiple parameter changes provides a more accurate prediction of technological and market trends, offering a robust basis for policy formulation and enterprise decision-making.
(Please refer to page 21-22 of the revised paper)


Comments 6: In the same line of thought, several concluding remarks (pp.21–22) are long phrases not documented, with uncertain time horizon or simply wrong. It is recommended to revise this part (reformulate, document, or give up / remove) in terms of:
time dimension / time horizon – e.g., “the revenue from technological innovation will show a better [?] growth trend in the future [what “future”?], and the level of industrial development and the competitiveness of enterprises will reach a higher level in the future [which future?]” (lines 851–853); “by then [when?], China's ability in artificial intelligence algorithms and decision-making chips in the field of autonomous driving will also be further [what “further” means?] improved” (lines 854–855);
certainty and precision (there is excess of “should”): e.g., “Autonomous driving enterprises should actively participate in the construction of new infrastructure” (line 877); “Universities and research institutes should continue to expand the construction of technological innovation and exchange platforms” (lines 883–884); “Government and enterprises should establish a consumer demand feedback mechanism” (line 891); “consumers should actively participate in technology testing” (line 894); “Financial institutions should explore diversified financing models to support the R&D” (line 898); of course they should but how (specifically)?
Response 6: We sincerely appreciate your valuable feedback regarding the clarity of the time dimension / time horizon and the issue of certainty in our manuscript. As suggested by the reviewer, we have revised the relevant statements with more precise time references based on simulation results and specific implementation paths. The changes are as follows:


5.1 Conclusions
2. The simulation results of model clearly show the continuous growth trend of relevant innovation indicators during the period of 2024-2030. This is consistent with the 2030 milestones in the Intelligent Networked Vehicle Technology Roadmap 2.0, indicating that China's autonomous vehicle industry is on track for large-scale commercial operation and industry transformation.
5.2 Policy Suggestions
Based on the research results, in order to better enhance the technological innovation capability of autonomous vehicle, this study puts forward suggestions from the perspectives of the main stakeholders of technological innovation.
In the context of increasingly fierce competition in global autonomous driving technology, government can establish a research group on autonomous driving technology to closely monitor the policy trends of foreign governments in the field of autonomous driving, in order to grasp cutting-edge trends and development models. In terms of financial support, government can allocate special funds from the fiscal budget to provide direct funding support for innovative and promising autonomous driving technology research and development projects. At the same time, government can introduce preferential policies for talent introduction to attract outstanding talents from home and abroad to participate in the innovation and development of autonomous driving technology.
Autonomous driving companies actively participate in the construction of new infrastructure based on their technological advantages, promoting the technology from the validation stage to large-scale marketization. On the one hand, by cooperation with the government to accelerate the coverage of 5G networks across the region to ensure real-time data collection and transmission for autonomous vehicles. On the other hand, by integrating IoT and big data resources, it has improved the intelligence level of transportation infrastructure and constructed a municipal intelligent transportation sensing platform.
Universities and research institutes can attract domestic and foreign peers to participate and expand the influence of technology innovation exchange platforms by holding annual international forums on autonomous driving technology and regularly organizing online academic exchange seminars. In deepening industry university re-search cooperation, cooperation agreements are signed with universities and enterprises to establish demonstration bases for the application of research and development achievements within enterprises. The research achievements of universities and re-search institutes are tested and optimized in actual production and operation scenarios to promote the transformation of scientific research achievements. As the end-users of technology implementation, consumer demand perception and feedback are crucial for technological iteration. Government and enterprises can establish a consumer demand feedback mechanism by conducting regular market re-search, establishing user experience testing platforms, setting up consumer feedback hotlines and email addresses, etc., to timely obtain consumers' needs and suggestions for autonomous driving technology. At the same time, consumers also need to actively participate in technology testing and demonstration applications, provide real scenario data for technology optimization, promote technology to be closer to market demand, and form a virtuous cycle of innovative market innovation.
Financial institutions can explore diversified financing models by establishing special venture capital funds, conducting intellectual property pledge financing, issuing special bonds, and other means to support the research and commercialization of autonomous driving technology. They can provide financial support to startups through equity financing and bond issuance. In addition, a dedicated venture capital fund can be established to focus on supporting technology projects with innovation and market potential.
(Please refer to page 22-23 of the revised paper)


Comments 7: It is recommended to redesign the figures (6–to–15) to make their texts legible.
Response 7: Thank you very much for pointing out the issue. We have redesigned figures (6-15 to make them clearer. The specific modifications have been presented in the manuscript.


4. Response to Comments on the Quality of English Language
Point 1: The English could be improved to more clearly express the research.
Response 1: Thank you very much for your insightful suggestions. As suggested by the reviewer, we have reprocessed the languages in the manuscript to express the research more clearly.


5. Additional clarifications
We sincerely thank you for all the constructive and helpful comments on our paper.
Before closing, we thank you and the review team for helping us improve the quality of our paper. Thank you very much.

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