How the Complexity of Knowledge Influences Carbon Lock-In

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study empirically examined the relationship between knowledge complexity and carbon lock-in, following the measurement of both concepts. However, some writing issues are left in the current manuscript.

Introduction part: please add the references for all data or results referred to.

The abbreviations should be carefully revised to avoid definition repeats, and wrong definitions (i.e., KCI in Line 99).

Please summarize the previous work in brief and avoid repeated text.

The contributions of this paper should be described briefly.

Section 2, please consider adding a table or figure to list the hypotheses.

Section 3, please consider adding a flowchart to clearly show the whole analysis process.

The reason why the authors excluded Tibet from the analysis should be given.

Online references for data sources are better to be added.

The column of Obs in Table 2 is better to be removed.

The differences in CLI (1-4) in Table 3 should be explained or marked.

Please adjust the font in Tables 5, 6, and 8. and the layout of Table 8 and the surrounding text.

Please consider adding a figure to show the correlation between the two targets mentioned.

Please add a paragraph to describe the limitations and future steps of this study.

Comments on the Quality of English Language

Please describe the content briefly. There are so many long sentences in the text, which makes it difficult for the readers to understand.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Among the disadvantages of the presented paper, I would like to note the following:

1. The study employs panel regressions and an instrumental variable approach (using lagged knowledge complexity as an instrument), but it does not convincingly address endogeneity concerns arising from omitted variables or reverse causality, for example may be jointly determined by unobserved regional policies or industrial structures, making causal inference weak.
2. The study calculates carbon lock-in using a weighted entropy method, which, while comprehensive, heavily depends on indicator selection and weighting choices, as well as the methodology does not account for dynamic changes in carbon lock-in due to policy interventions or technological disruptions.
3. The study focuses solely on knowledge complexity while neglecting other influential factors such as international trade, financial investments in renewable energy, and policy shocks (e.g., China's carbon pricing or emissions trading schemes).
4. The paper uses patent-based network measures to quantify knowledge complexity but patent data may not fully capture tacit knowledge, informal knowledge diffusion, or open-source innovation, which are important in carbon reduction efforts.
5. The study assumes that knowledge complexity alone can drive low-carbon transitions but does not consider resistance from entrenched interests, lobbying by fossil fuel industries, or political constraints on green technology diffusion.

Conclusion

• The study does not propose concrete policy interventions to leverage knowledge complexity for carbon lock-in mitigation. How should governments or firms act based on these findings?
• There is no discussion on the role of intellectual property policies, R&D subsidies, or technology transfer mechanisms in accelerating knowledge complexity for green development.
• The paper presents a macroeconomic statistical approach but lacks qualitative case studies of successful carbon lock-in reductions through knowledge complexity (e.g., specific provinces or industries that have successfully transitioned), as well as comparative analysis with other countries could strengthen the study’s claims.
• The study aggregates data at the provincial level, missing the opportunity to analyze how different industries (e.g., energy, transport, manufacturing) respond to knowledge complexity differently.
• A firm-level study could reveal whether multinational corporations or state-owned enterprises exhibit distinct patterns in using knowledge complexity to reduce carbon lock-in.
• While the study mentions spatial spillover effects, it does not analyze barriers to green technology adoption. What prevents complex knowledge from being effectively utilized?
• There is no discussion on infrastructure readiness, digital transformation, or workforce skills necessary for knowledge complexity to translate into carbon lock-in mitigation.

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors Based on the panel data of 30 provinces in China from 2000 to 2022, this paper investigates the impact of knowledge complexity on carbon lock-in. It is found that knowledge complexity significantly mitigates carbon lock-in through optimizing factor allocation, improving efficiency and industrial upgrading, and the effect is more obvious in regions with a high proportion of state-owned enterprises, stringent environmental regulations, and high investment in science and education. The study also reveals the spatial spillover effect and puts forward suggestions for improving knowledge complexity, enhancing regional synergy and differentiated policies.However, several issues should be addressed before accepting for publication: 1.The study data ends in 2022 and does not include the full cycle impact of policy implementation after China's “dual-carbon” target (proposed in 2020). It is recommended to supplement the data with 2023-2024 data to observe the long-term effect. 2.The Carbon Lock-In Index (CLI) does not include data on the carbon footprint of the consumer side of the population (e.g., household energy consumption), and it is recommended that indicators of carbon emissions from the residential sector be added to improve the measurement system. 3.Existing patent data focuses on technical knowledge and ignores non-technical knowledge such as management and institutional innovation. It is suggested to introduce multi-dimensional indicators, such as social science patents and industry standardization. 4.The study found that there is a positive spatial spillover of knowledge complexity but did not explain the specific path, and suggested analyzing the spatial transmission mechanism of knowledge spillover (e.g., talent flow, technology trade) in conjunction with regional innovation networks. 5.The policy recommendations are macro and lack concrete implementation paths, and it is recommended that specific operational programs be added. 6.Existing groupings do not take into account regional differences in development levels (e.g., East, Central, and West), and it is recommended that a geographic partition test be added to reveal the heterogeneous response mechanisms in regions at different stages of development.   Comments on the Quality of English Language

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

Author Response

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Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Dear Author,

The manuscript titled “How the complexity of knowledge influences carbon lock-in”contributes to understanding the issues of carbon dioxide emission that negatively influences on  ecosystem and human health at the same time. The authors have given a short overview China achievements in reducing CO₂ emissions. Although, the carbon emission reduction is still twice small accordance to EU, this Manuscript gives opportunities to develop the crucial strategic-pathways to achieving low-carbon enhancement. The authors utilized the relevant panel data from 30 China provinces, for 22 year in the past (2020-2022), moreover, they created the theoretical framework including five hypotheses which are methodologically assess by utilising the previous relevant studies. The authors precisely selected the key-variables for analysing the influenced factors on CO2 emissions reduction and measuring the utilised province-knowledge and practices about green technologies. The unique term “knowledge complexity (KCI)” has given comprehensive inputs on research development, from CO₂ emissions reducing (CLI) inside enterprise solutions (industry, institutions, technologies, behaviour) to presenting “green based knowledge and practices” of China`s provinces. The obtained results provide on variables give a comprehensive current picture to prioritizing green technologies in reducing CO2 emissions. As well as, the obtained result including mediating variables have presented pathway for efficient green strategies development and green-economy transformation as well. Although, the author give critical reflection of the researching background, with result interpretations and principle applications. I suggest the authors to reorganise following parts in Manuscript:

1. Starting with briefly introducing (Part 1), only a few sentences about the current green practice situation in 30 Chinese provinces. The authors has mentioned issues, statistic facts and  government  initiatives for sustainable development.
2. The research five hypotheses are well-set up, the reading empirical research results the authors emphasize only a few hypothesis`s confirmation or rejection.
3. The authors need to improve data presentation in Table 2, Table 3, Table 4, Table 5, Table 6, Table 7 and Table 8. Emphasize is on variables description. Although, the variable are noticed in the text part of research, I suggest to put the label legend at the end of each table, with variable abbreviation explanations.
4. I suggest to boost reference lists with the latest research about carbon-lock-ins and its global perspectives in developing green economic efficiency.

Suzana Pasanec Preprotić

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors have addressed all of my comments.

Reviewer 2 Report

Comments and Suggestions for Authors

I have no additional remarks

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have made revisions to the manuscript based on comments, and the manuscript can be accepted.