Does Spatial Location Competition Shape Enterprises’ Green Technology Innovation? The Moderating Roles of Digital Transformation and Environmental Uncertainty

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

Based on the data of A-share listed companies in China, this paper discusses the influence of spatial location competition on green technology innovation of enterprises, and innovatively introduces the regulatory role of digital transformation and environmental uncertainty. The research problem has theoretical and practical value, the empirical design is generally standardized, and the conclusion is of reference significance to policy making. However, some theoretical logic, variable manipulation and result interpretation need to be further clarified to enhance the rigor of the paper.
1. The theoretical framework and hypothesis need to strengthen the logical chain. The theoretical mechanism of H1 hypothesis that "space competition negatively affects green innovation" is not well explained. Existing discourses (such as resource overlapping and competitive pressure restraining R&D investment) do not clearly link the resource-based theory. It is necessary to explain how space competition specifically affects the ability of enterprise resource acquisition and integration; H2 Hypothesis describes the mechanism of "digital transformation", such as reducing spatial dependence and transferring competitive factors, which is vague and needs to be combined with the characteristics of digital technology (such as virtual collaborative network and data element mobility).
2. Variable measure and model. The measurement of the dependent variable is to simply sum up the invention and utility model patents, which may confuse the quality of innovation. It is suggested that the supplement only uses the regression results of invention patents as a comparison; The measurement of the core explanatory variable is to measure the competition intensity by the reciprocal of "the average distance of the latest five listed companies in the same industry", which implicitly assumes that "the closer the distance, the fiercer the competition", but does not consider that industrial clusters may promote cooperation. It is suggested that alternative indicators should be supplemented in the robustness test; The measurement of digital transformation of regulatory variables is the proportion of word frequency in annual report, but the list of keywords is not specified, so it is necessary to quote or supplement the appendix.
3. In Table 4, the moderating effect direction of H2: the Distance5*Digital coefficient of the interaction term is significantly negative (-0.0203*), but the original text says that "digital transformation enhances the incentive effect of spatial competition on green innovation". If Distance5 itself is a negative predictive variable (Table 3), the negative interaction term means that digitalization strengthens the negative effect of spatial competition, which contradicts H2 expression. In addition, H3 (Adjustment of Environmental Uncertainty): The interaction term is significantly positive (0.0103*), indicating that high uncertainty weakens the negative effect of space competition, but the original text is attributed to "enterprises have no intention to innovate" and the logic is not direct.
4. Discussion and policy suggestions. Conclusion The key findings of heterogeneity test are not responded (for example, state-owned enterprises and manufacturing enterprises are more sensitive), and the policy recommendations are not targeted.
5. To supplement the empirical evidence of recent research on digital technology empowering green innovation; The grammatical expression of English needs further polishing; The format of references should be unified.

Author Response

Reviewer 1

Based on the data of A-share listed companies in China, this paper discusses the influence of spatial location competition on green technology innovation of enterprises, and innovatively introduces the regulatory role of digital transformation and environmental uncertainty. The research problem has theoretical and practical value, the empirical design is generally standardized, and the conclusion is of reference significance to policy making. However, some theoretical logic, variable manipulation and result interpretation need to be further clarified to enhance the rigor of the paper.
Q1. The theoretical framework and hypothesis need to strengthen the logical chain. The theoretical mechanism of H1 hypothesis that "space competition negatively affects green innovation" is not well explained. Existing discourses (such as resource overlapping and competitive pressure restraining R&D investment) do not clearly link the resource-based theory. It is necessary to explain how space competition specifically affects the ability of enterprise resource acquisition and integration; H2 Hypothesis describes the mechanism of "digital transformation", such as reducing spatial dependence and transferring competitive factors, which is vague and needs to be combined with the characteristics of digital technology (such as virtual collaborative network and data element mobility).

Reply: We rewrite the theoretical logic discussion in H1 hypothesis, H2 hypothesis and H3 hypothesis. In the part 2.3. Spatial location competition and enterprise green technology innovation, we add some more discussions from three aspects: firstly, regional market segmentation weakens the resource allocation efficiency and market foundation for green technological innovation (Richard and Toshihiro 2006; Verhoef et al 2021); secondly, excessive competition leads to "short-sighted" behavior, squeezing the space for green technological innovation investment; thirdly, negative spillover effects from neighboring regions disrupt the collaborative network for green technological innovation. In the part 2.4. The moderating effect of impact by digital transformation, we add some more discussions from two aspects: firstly, by reducing spatial dependence, it weakens the negative constraints of spatial location competition on green technological innovation; Secondly, by enhancing the transfer of competitive elements, it reshapes the connotation of spatial location competition and optimizes the environment for green technological innovation. In the part 2.5. The moderating effect of impact by environmental uncertainty, we add some more discussions from two aspects: Environmental uncertainty intensifies risk aversion tendencies, strengthening the inhibitory effect of spatial location competition on green innovation; Environmental uncertainty intensifies cost and cash flow pressure, further restricting the resource input for green innovation.

 

Q2. Variable measure and model. The measurement of the dependent variable is to simply sum up the invention and utility model patents, which may confuse the quality of innovation. It is suggested that the supplement only uses the regression results of invention patents as a comparison; The measurement of the core explanatory variable is to measure the competition intensity by the reciprocal of "the average distance of the latest five listed companies in the same industry", which implicitly assumes that "the closer the distance, the fiercer the competition", but does not consider that industrial clusters may promote cooperation. It is suggested that alternative indicators should be supplemented in the robustness test; The measurement of digital transformation of regulatory variables is the proportion of word frequency in annual report, but the list of keywords is not specified, so it is necessary to quote or supplement the appendix.

Reply: we add this as the limitations and in the part of future research.

 

Q3. In Table 4, the moderating effect direction of H2: the Distance5*Digital coefficient of the interaction term is significantly negative (-0.0203*), but the original text says that "digital transformation enhances the incentive effect of spatial competition on green innovation". If Distance5 itself is a negative predictive variable (Table 3), the negative interaction term means that digitalization strengthens the negative effect of spatial competition, which contradicts H2 expression. In addition, H3 (Adjustment of Environmental Uncertainty): The interaction term is significantly positive (0.0103*), indicating that high uncertainty weakens the negative effect of space competition, but the original text is attributed to "enterprises have no intention to innovate" and the logic is not direct.

Reply: In part 4.3. Moderating effects result, we add the description “As previously defined, spatial location competition serves as a negative predictive variable; therefore, a significant negative correlation suggests that a smaller average geographical distance between a company and the five nearest listed companies in the same industry intensifies the spatial location competition faced by the company, thereby strengthening the impetus for technological transformation and potentially accelerating the pace of green technology innovation” to describe that the result prove H2.

Additionally, we add “As previously defined, spatial location competition serves as a negative predictive variable; therefore, a significant negative correlation suggests that a smaller average geographical distance between a company and the five nearest listed companies in the same industry intensifies the spatial location competition faced by the company, thereby weakening the impetus for environmental uncertainty and potentially accelerating the pace of green technology innovation” to describe that the result prove H3.

 

 

 

 

Q4. Discussion and policy suggestions. Conclusion The key findings of heterogeneity test are not responded (for example, state-owned enterprises and manufacturing enterprises are more sensitive), and the policy recommendations are not targeted.

Reply: We have expanded the relevant content in part 5.1 “Conclusion” intro 5 aspects: (1) the first conclusion is about the basic relationship between spatial location competition and green technological innovation. (2) the second conclusion is about the moderating effect of digital transformation: (3) the third conclusion is about the moderating effect of environmental uncertainty. the fourth conclusion is about the heterogeneous impact of enterprise property nature (state-owned enterprises). (5) The fifth conclusion is about the incentive effect of government subsidies: (6) the last conclusion is about the industry heterogeneity characteristics of manufacturing listed companies.

 

Q5. To supplement the empirical evidence of recent research on digital technology empowering green innovation; The grammatical expression of English needs further polishing; The format of references should be unified.

Reply: We have expanded the relevant content in part 5.3 “Future research” intro 4 aspects: (1) Expansion of Research Perspectives; (2) Optimization of Research Methods; (3) Integration of Case Studies and Quantitative Analysis; (4) Deepening of Research Content.

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors!

I appreciate your efforts in addressing a timely and policy-relevant topic. Your empirical analysis is extensive, well-structured, and based on a large panel dataset of Chinese A-share listed companies. The inclusion of both digital transformation and environmental uncertainty as moderating variables is a strong contribution to the literature on green innovation under competitive pressure.

However, I would like to suggest the following revisions to improve the clarity, robustness, and theoretical depth of your manuscript:

1. Authors need to formulate the purpose of the study more precisely and reflect it in the abstract.
2.  While you reference foundational models of spatial competition (Cournot, Bertrand, Hotelling), the theoretical section would benefit from a broader engagement with contemporary innovation and sustainability frameworks, such as:

• the triple bottom line approach,

• institutional theory (especially in the Chinese context),

• or ESG-related theoretical perspectives.

This would enhance the conceptual contribution of your work.

2. Using MD&A keyword frequency is innovative, but its objectivity and replicability could be questioned without further validation. I recommend:

• discussing potential limitations of this approach, and

• if possible, triangulating with alternative digitalization indicators (e.g., IT investment data, adoption of ERP/AI systems, digital staff ratio, etc.).

3. Currently, the focus is limited to the number of green patents. However, these may not always reflect actual environmental impact. Future research (or at least a brief discussion in this paper) could include:

• CO₂ reduction metrics,

• green revenue share,

• environmental certifications (e.g., ISO 14001), or

• qualitative outcomes from green innovation.

4. The use of the Distance5 variable is interesting. Still, spatial clustering and autocorrelation effects might distort regression results. Have you considered employing:

• spatial econometric models (SAR, SEM), or

• Moran’s I test to detect spatial dependency?

Including such analysis would significantly strengthen your empirical claims.

5. In several instances (e.g., "hypotheses are supported"), the language is overly conclusive. Please consider softening these claims by using more cautious academic phrasing such as:

• “our findings suggest…”,

• “results indicate a possible effect…”.

This is particularly important given the complexity of causality in panel data settings.

Comments on the Quality of English Language The language of the article should be clear, precise, and free of grammatical errors. The author should thoroughly proofread the article to eliminate any language issues.

Author Response

Reviewer 2

Dear Authors!

I appreciate your efforts in addressing a timely and policy-relevant topic. Your empirical analysis is extensive, well-structured, and based on a large panel dataset of Chinese A-share listed companies. The inclusion of both digital transformation and environmental uncertainty as moderating variables is a strong contribution to the literature on green innovation under competitive pressure.

Q1. However, I would like to suggest the following revisions to improve the clarity, robustness, and theoretical depth of your manuscript:

1. Authors need to formulate the purpose of the study more precisely and reflect it in the abstract.
2.  While you reference foundational models of spatial competition (Cournot, Bertrand, Hotelling), the theoretical section would benefit from a broader engagement with contemporary innovation and sustainability frameworks, such as:

• the triple bottom line approach,
• institutional theory (especially in the Chinese context),
• or ESG-related theoretical perspectives.

This would enhance the conceptual contribution of your work.

Reply Q1-1: We take this suggestion, and we add this sentence to describe the purpose of the study in the abstract. We take this suggestion, and we add this paragraph to clarify the purpose of the study from four parts, respectively are research objective, the core goal (verifying the basic impact of spatial location competition on enterprise green technological innovation) and two the key goals (clarifying the moderating role of digital transformation and environmental uncertainty).

Reply Q1-2: We take this suggestion, and we add some more discussions from three different views, which respectively are triple bottom line theoretical perspective, institutional theoretical perspective and ESG (Environmental, Social, and Governance) theoretical perspective.

 

Q2. Using MD&A keyword frequency is innovative, but its objectivity and replicability could be questioned without further validation. I recommend:

• discussing potential limitations of this approach, and
• if possible, triangulating with alternative digitalization indicators (e.g., IT investment data, adoption of ERP/AI systems, digital staff ratio, etc.).

Reply: We take this good suggestion, and we add some contents that future research can do better, as “Future research can combine the MD&A keyword frequency method with objective digital indicators publicly available by enterprises, forming a dual measurement standard that combines qualitative expression and quantitative investment. Introduce IT investment data, such as the amount of intangible assets, software, and systems in the annual report of the enterprise; The proportion of R&D expenditure and digital technology R&D directly reflects the resource investment of enterprises in digital technology, which can effectively compensate for the deficiency of keyword frequency emphasizing expression and neglecting investment”.

 

Q3. Currently, the focus is limited to the number of green patents. However, these may not always reflect actual environmental impact. Future research (or at least a brief discussion in this paper) could include:

• CO₂ reduction metrics,
• green revenue share,
• environmental certifications (e.g., ISO 14001), or
• qualitative outcomes from green innovation.

Reply: We take this good suggestion, and we add some contents that future research can do better, as “As for the measurement of green technology innovation, currently, the focus is limited to the number of green patents. However, these may not always reflect actual environmental impact. In the future research, future research will measure green technology innovation from four aspects: CO ₂ reduction indicators, green revenue share, environmental certification (such as ISO 14001), and green innovation performance, and adjust indicator design based on specific industries (such as manufacturing and new energy)”.

 

Q4. The use of the Distance5 variable is interesting. Still, spatial clustering and autocorrelation effects might distort regression results. Have you considered employing:

• spatial econometric models (SAR, SEM), or
• Moran’s I test to detect spatial dependency?

Including such analysis would significantly strengthen your empirical claims.

Reply: We take this good suggestion, and we add some more description, as “As for the measurement of spatial location competition, The Distance5 variable used in this study can effectively capture the core characteristics of close-range spatial competition, but it does not fully consider the spatial clustering effect and spatial autocorrelation effect of green technology innovation of enterprises in the region, which may lead to the bias of traditional OLS regression results. In future research, a two-step analysis framework of "spatial dependency detection spatial econometric modeling" can be introduced. The existence of spatial effects can be verified through Moran I test, and regression bias can be corrected using SAR and SEM models. The specific implementation path is as follows: firstly, Moran I index is a core tool for measuring spatial autocorrelation, which can be used to verify whether there are spatial clustering characteristics in green technology innovation of enterprises, providing a basis for subsequent selection of spatial econometric models. Step two, based on the Moran I test results, select an appropriate spatial econometric model and incorporate spatial de-pendency into the regression framework of "spatial location competition green technology innovation". The above spatial econometric analysis has strong practicality. Regarding spatial data, the latitude and longitude coordinates of enterprises can be obtained through the registered address of the "National Enterprise Credit Information Publicity System" and the office address of listed companies' annual reports, combined with Baidu Maps API or Gaode Maps API conversion. The accuracy can reach street level and meet the calculation requirements of a 5-kilometer distance threshold.”

Q5. In several instances (e.g., "hypotheses are supported"), the language is overly conclusive. Please consider softening these claims by using more cautious academic phrasing such as:

• “our findings suggest…”,
• “results indicate a possible effect…”.

This is particularly important given the complexity of causality in panel data settings.

Reply: we agree with the suggestion, and we add the “our finding suggest that” before the “hypothesis 1 is supported”. And we also request for the language modification from the professional institution, and receive the certification of language.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

The review is attached

Comments for author File: Comments.pdf

Author Response

Reviewer 3

This study examines the impact mechanism of spatial location competition on green technology innovation among enterprises and investigates the moderating role of digital transformation. Utilizing panel data from Chinese A-share listed companies, the authors find a significant negative correlation between spatial location and green technology innovation. Furthermore, the degree of digital transformation within enterprises can effectively enhance the incentivizing effect on the relationship of the two. The paper is actual, and the authors made a big statistical job. However, there are some questions and remarks to be answered. Remarks.

Q1. The denotations in all formulas are very poor. Please correct them as it is accustomed with upper and/or lower subscripts, and so on. Sums are also not clear.

Reply: we agree with this good suggestion. We rewrite the part of “3. Methods” into 4 subtitles, which respectively are “3.1. Methodology and Data Collection Theoretical Model” “3.2. Variable Measures Spatial Location Competition (Distance5) Measurement” “3.3. Green Technology Innovation Measurement and Control Variables Measurement” “3.4. Data Collection”.

 

Q2. Trigonometric formulas (2) and (3) are absolutely not clear and quite strange. I doubt that they allow to calculate a distance.

Reply: Formula (2) and formula (3) are the formulas to calculate the spatial location competition, and we also replace the title of 3.2 Spatial Location Competition (Distance5) Measurement.

 

Q3. The formulas (1)-(3) are intended to explain a dependency between space location competition and green innovations. But how exactly is analyzed a regulating effect of the digital transformation and environmental uncertainty?

Reply: we agree with this good suggestion. In order to regulate effect of the digital transformation and environmental uncertainty, we add two formulas:

formula 2 “Gti_i,t/Tpg_i,t=β₀+β₁Distance5+β₂Digital+β₃Distance5×Digital +∑controls+∑year+∑Industry+ε_i,t”

and formula 3 “Gti_i,t/Tpg_i,t=γ₀+γ₁Distance5+γ₂EU+γ₃Distance5×EU+∑controls+∑year+∑Industry+ε_i,t”) ,

and add some descriptions to explain the two formulas.

 

 

 

 

Q4. In the Hypotheses 2 and 3 it is not clear what does mean "positive" or "negative" effect. Does it increase or decrease something? Please consider the remarks.

Reply: We agree with this good suggestion, and we add “When the level of digital transformation of enterprises is high, the driving effect of spatial location competition on green technology innovation is more significant; On the contrary, when the level of digital transformation is low, even facing high-intensity location competition, enterprises find it difficult to efficiently transform competitive pressure into green innovation drivers”to Hypothesis 2 (H2). We agree with this good suggestion, and we add “When the environmental uncertainty is high, enterprises face high-intensity spatial location competition and may reduce their investment in green innovation due to the instability of the external environment; On the contrary, when the environmental uncertainty is low, enterprises are more likely to clearly judge the direction of competition and control innovation risks, thereby efficiently transforming competitive pressure into green innovation momentum.” to Hypothesis 3 (H3).

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The author made a systematic response to the first round of peer review, but some key issues have not been fully solved, especially in the rationality of variable measurement, the consistency of interpretation of moderating effect results and the rigor of empirical design, which still need further revision and clarification. The specific views are as follows:
1. The theoretical framework and assumptions are in sections 2.3, 2.4 and 2.5. In this paper, the author supplements the theoretical discussions on regional market segmentation, excessive competition, negative spillover effect, weakening spatial dependence and factor transfer in digital transformation, and risk and cost mechanism of environmental uncertainty. However, although the article mentioned "resource allocation efficiency", it did not clearly connect with the core concept of resource theory (such as VRIN resources).
2. Variable measurement and model The author has not substantially improved this. There is no list of digital transformation keywords in the text or appendix, which affects the repeatability of the research; In the robustness test, the regression results of only using green invention patents are not supplemented, and the possible cooperative effect brought by industrial clusters is not considered; The construction of core variables is still based on the single assumption that the closer the distance is, the stronger the competition is, without considering the knowledge spillover and cooperative innovation that may be brought about by spatial agglomeration. Therefore, it is suggested that the author clearly list the key words of digital transformation in the text or appendix. In addition, regression using only green invention patents and industrial agglomeration index or Herfindal index as substitute variables is added to the robustness test.
3. Regulatory effect Interpretation of the results In Section 4.3, the author added a description of the adjustment effect, trying to explain the sign of the coefficient of the interaction term through the logic of "negative predictive variables". However, there are still logical contradictions in the explanation of this paper. H2 (digital transformation): the interaction coefficient term is negative (-0.0203*). If Distance5 is a negative predictive variable, then the negative interaction term means that digital transformation has strengthened the inhibition of space competition on green innovation, which is in direct contradiction with H2' s claim that "digital transformation enhances the incentive effect". H3 (environmental uncertainty): the interaction coefficient term is positive (0.0103*), and if the Distance5 is a negative predictive variable, the positive interaction term means that environmental uncertainty weakens the negative impact of space competition, which is inconsistent with the "enhanced inhibition effect of environmental uncertainty" advocated by H3. Therefore, it is suggested that the author re-examine the theoretical and empirical consistency of the moderating effect and revise the hypothesis or explanation of the result when necessary. If the result is inconsistent with the hypothesis, the possible reasons should be explained honestly in the discussion section.

Author Response

Reviewer 1 （Round 2）

The author made a systematic response to the first round of peer review, but some key issues have not been fully solved, especially in the rationality of variable measurement, the consistency of interpretation of moderating effect results and the rigor of empirical design, which still need further revision and clarification. The specific views are as follows:
Q1. The theoretical framework and assumptions are in sections 2.3, 2.4 and 2.5. In this paper, the author supplements the theoretical discussions on regional market segmentation, excessive competition, negative spillover effect, weakening spatial dependence and factor transfer in digital transformation, and risk and cost mechanism of environmental uncertainty. However, although the article mentioned "resource allocation efficiency", it did not clearly connect with the core concept of resource theory (such as VRIN resources).

Reply Q1: we adjust the contents of this paragraph by using the VRIN resources theory. The modified paragraph is as follows:

“Under the global wave of sustainable development, green technological in-novation has become the core path to resolving the contradiction between economic growth and environmental constraints. However, this innovation process is highly de-pendent on the efficient allocation of VRIN resources (resources that are valuable, scarce, difficult to imitate, and irreplaceable). Nevertheless, the widespread existence of regional market segmentation fundamentally undermines the mechanism for the optimal allocation of VRIN resources, setting multiple obstacles for green technological innovation and severely restricting the release of its development potential (Richard and Junhong 2006; Vihof et al. 2021). From the perspective of spatial competition logic, the spatial location competition among local governments based on performance evaluations easily gives rise to local protectionism, thereby solidifying the regional market segmentation pattern based on administrative boundaries. To safeguard the short-term interests of local industries, local governments often distort market mechanisms through three means: first, by setting differentiated technical standard barriers to artificially raise the threshold for external green technologies to enter the local market; second, by directly restricting the circulation of green products from other regions, fragmenting the unified market demand; third, by interfering with the cross-regional flow of talents, technologies, and funds, creating administrative barriers to the flow of VRIN resources. These actions not only undermine the fair competitive environment of the market but also directly prevent the VRIN resources necessary for green techno-logical innovation from being optimally allocated according to market rules, thereby weakening the resource foundation for innovation from the source.”

 

Q2. Variable measurement and model The author has not substantially improved this. There is no list of digital transformation keywords in the text or appendix, which affects the repeatability of the research; In the robustness test, the regression results of only using green invention patents are not supplemented, and the possible cooperative effect brought by industrial clusters is not considered; The construction of core variables is still based on the single assumption that the closer the distance is, the stronger the competition is, without considering the knowledge spillover and cooperative innovation that may be brought about by spatial agglomeration. Therefore, it is suggested that the author clearly list the key words of digital transformation in the text or appendix. In addition, regression using only green invention patents and industrial agglomeration index or Herfindal index as substitute variables is added to the robustness test.

Reply Q2-1: we add some clear key words of digital transformation. And the adding sentences describe the three steps for the measurement of the variable of “digital transformation.” The details are as follows:

“This paper builds a relatively complete digital dictionary by leveraging the semantic expressions of national policies related to the digital economy and uses a machine learning-based text analysis method to construct an index that comprehensively reflects the digitalization level of listed companies in China. The specific steps are as follows:

In first step, constructing a digitalization term dictionary for enterprises. Due to the lack of a specialized term dictionary in the field of the digital economy, this paper builds a digitalization term dictionary for enterprises based on the national policy se-mantic system. By drawing on the research of He and Liu (2019) and through manual screening of 30 important national-level digital economy policy documents released from 2012 to 2018, 197 keywords related to enterprise digitalization were extracted. After processing with Python word segmentation and manual identification, 197 terms with a frequency of 5 or more were finally selected, which constitute the digitalization term dictionary of this paper.

In second step, conducting text analysis on relevant sections of the annual report. This paper expands the 197 terms in the above digitalization term dictionary into the "jieba" Chinese word segmentation library of the Python software package, and then conducts text analysis on the "Management Discussion and Analysis" (MD&A) section of the annual reports of listed companies based on machine learning methods, to obtain the frequency of the 197 terms related to enterprise digitalization in the annual reports.

In third step, constructing the index of enterprise digitalization degree. Consider-ing the differences in the text length of the MD&A section of the annual report, after extracting the frequency of each keyword in the annual report of each listed company each year, this paper measures the micro-enterprise digitalization degree (DCG) by di-viding the total frequency of digitalization-related terms by the length of the MD&A section of the annual report. For convenience of expression, this paper multiplies this index by 100. The larger the Digital index value, the higher the degree of enterprise digital transformation.”

 

Reply Q2-2: we add Table 6 of “Robust Testing of Regional characteristics in this study.” The details are as follows:

“The explanatory variable Distance5 in this article may have regional char-act eristics, such as the geographical distance between listed companies in western regions and their competitors in the same industry may be remote, while listed companies in economic agglomeration areas such as Beijing, Shanghai, Guangzhou, and Shenzhen may be closer to their competitors in the same industry. The conclusion of this article may only indicate that the green technology innovation ability of enterprises in remote areas is insufficient, while listed companies in economic agglomeration areas may be forced to carry out green technology innovation due to institutional coercion in their location.

In order to further exclude alternative explanations that may arise from regional characteristics, this article conducted individual regional exclusion processing on the sample. First, remove the samples from relatively remote or sparsely populated areas (Xinjiang, Xizang, Inner Mongolia, Qinghai, Sichuan, Heilongjiang, Yunnan and Gansu, the top eight in terms of geographical area), and regress to get the results as shown in columns (1) and (2) of Table 6; Secondly, excluding samples from highly concentrated economic regions (Guangdong, Shanghai, Jiangsu, Zhejiang, and Beijing, which have the highest number of listed companies), the regression results are shown in columns (3) and (4) of Table 6; Thirdly, by simultaneously removing samples from the afore-mentioned two regions, the regression results are obtained as shown in columns (5) and (6) of Table 6. The results show that the coefficients of Distance5 in each column are significantly negative, and the robust test results can to some extent exclude the alternative explanations mentioned above.”

 

 

 

 

 

Q3. Regulatory effect Interpretation of the results In Section 4.3, the author added a description of the adjustment effect, trying to explain the sign of the coefficient of the interaction term through the logic of "negative predictive variables". However, there are still logical contradictions in the explanation of this paper. H2 (digital transformation): the interaction coefficient term is negative (-0.0203*). If Distance5 is a negative predictive variable, then the negative interaction term means that digital transformation has strengthened the inhibition of space competition on green innovation, which is in direct contradiction with H2' s claim that "digital transformation enhances the incentive effect". H3 (environmental uncertainty): the interaction coefficient term is positive (0.0103*), and if the Distance5 is a negative predictive variable, the positive interaction term means that environmental uncertainty weakens the negative impact of space competition, which is inconsistent with the "enhanced inhibition effect of environmental uncertainty" advocated by H3. Therefore, it is suggested that the author re-examine the theoretical and empirical consistency of the moderating effect and revise the hypothesis or explanation of the result when necessary. If the result is inconsistent with the hypothesis, the possible reasons should be explained honestly in the discussion section.

 

Reply Q3: It can be correctly understood as follows: In Section 4.3, the interaction coefficient term is negative (-0.0203*), and Distance5 is a negative predictor variable. A negative times a negative yield a positive, thus demonstrating that digital transformation has strengthened the relationship between the real variable (spatial location competition) and the green technological innovation of enterprises. This is a promoting effect, not an inhibitory one.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

No more comments.

Author Response

Thanks for the agreement.

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

According to the review comments, the author revised the articles one by one, and the quality of the articles was greatly improved. The only defect now is that I don't think the English expression is very good, and the quality of the article can be further improved through English polishing.

Author Response

Dear Professor Qian,

 

Thank you for providing the revised version of your manuscript, could you

please confirm the following issues:

 

1. Please revise the duplicated parts according to the attached iThenticate

report, especially the highlighted long sentences marked before Section 3.2.

To avoid any possible issues, please also confirm whether the sources need to

be properly cited in the paper.

Answer：we have corrected the duplicated parts in the article according the attached document. For instance, the highlighted long sentences marked before Section 3.2 was revised as “Model (3) introduces environmental uncertainty as a moderating variable. The measurement of this variable (EU) refers to the research method of Ghosh et al. (2009): First, calculate the unadjusted value of the industry, which is the ratio of the standard deviation of abnormal sales revenue in the past five years to the average sales revenue of the company in the same period [29]; then divide this value by the median of the unadjusted environmental uncertainty of all companies in the same industry in the same year to obtain the industry-adjusted environmental uncertainty index. The higher the ratio value, the higher the level of environmental uncertainty. ”

 

2. We found that Tao Wang's email <350234820002@ncu.edu.cn> you provided

previously is invalid. Please kindly provide a valid institutional email

address.

Answer：Tao Wang’s email can be the following one:

wangtao@stu.ncpu.edu.cn

 

3. Please add sections "Author Contributions:", "Funding:", "Institutional

Review Board Statement:", "Informed Consent Statement:", "Data Availability

Statement:" and "Conflicts of Interest:". The template is attached to this

email.

Answer：We add as follows in the article.

Author Contributions: Conceptualization, Y.H.; Software, T.W., J.Q.; Formal analysis, T.W.; Resources, C.C., T.W.; Data curation, J.Q.; Writing original draft, T.W., Y. H.; Writing review and editing, C.C.; J.Q. Supervision, Y.H.; Project administration, Y.H.; J.Q.; C.C.; Funding acquisition, Y.J.; C.C., J.Q. All authors have read and agreed to the published version of the manuscript.

Funding: This work was supported by funding projects of: China National natural science foundation project (No. 72562016); China Social science foundation project (No. 25CGL004); Jiangxi Social Science Foundation Project (No. 25GL30).

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Informed consent was obtained from all sub-jects involved in the study.

Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author   The data presented in this study are available on request from the corresponding author.

Conflicts of Interest: The authors declare no conflict of interest.

 

4. We found that the title of reference 33 is inconsistent with the linked

DOI page. Please check and revise.

Answer：we have correct it to be the right citation.

Drakeman, D.; Oraiopoulos, N. (2020). The Risk of De-Risking Innovation: Optimal R&D Strategies in Ambiguous Environments. California Management Review, 62(3), 42-63. DOI10.1177/0008125620915289.

 

5. We found the citation style in your paper is "(name, year)". Owing that we

are an ACS journal, please revise citations in the main text into "[number]".

References must be numbered in order of appearance in the text (including

citations in tables and legends) and listed individually at the end of the

manuscript. You can get more information in our template attached.

Answer：We have changed all the citation in the main text into "[number]".

 

6. Please check whether references 3&4, 5&9, 28&29 are duplicates. If they

are the same, kindly remove the redundant entry and rearrange the references.

Answer：References 3&4 are not duplicates; 5&9, 28&29 are not duplicates; 28&29 are duplicates, and we delete on of them. And we also check on other references.

 

7. Please let us know if *all the Tables* in your manuscript are originally

created by yourself with the result of your paper and can be used without any

copyright constraints. If this is not the case, please provide copyright

permissions where they are necessary. If you adapt or use only a part of a

table published in a non-open access resource, copyright permission is still

needed. Copyright permissions can usually be obtained via an online form or

by e-mailing the copyright holder, and it is the authors' responsibility to

obtain them. For more about copyright related issues, please visit

https://www.mdpi.com/authors/rights.

Answer：The data is available on the website of the following:

https://www.stats.gov.cn/sj/