Overall, I found the study to be well-conducted and the manuscript to be well-written. The authors have made a valuable contribution by investigating the factors influencing contractors' Carbon Emission Reduction Index (CERI) in the construction industry. The findings highlight the significant impact of government regulations and support policies on CERI, with social norms and perceived behavioral control playing important mediating roles. The manuscript provides meaningful insights and practical implications for promoting carbon emission reduction in construction projects. However, the following comments should be addressed:

The abstract effectively presents the research topic, objectives, methodology, key findings, and practical implications. However, consider adding a sentence about the sample size to provide additional context.
The authors need to provide more specific information on China's emission reduction goals: Instead of just mentioning China's commitment to peak carbon emissions by 2030 and achieve carbon neutrality, consider including specific targets or percentages to provide a clearer understanding of the magnitude of the goals.
Clarify the significance of construction contractors: While the authors mention that the construction industry is vital for achieving national emission targets, they further emphasize the role of construction contractors specifically. Highlight their influence and decision-making power in the construction process and their potential impact on carbon emissions.
While the studies mentioned in the literature review are valuable, clearly articulate how they relate to the research. The authors need to explain how these previous studies inform the gaps or limitations their study aims to address.
The authors could consider grouping the studies based on themes or categories (e.g., carbon reduction strategies, factors influencing behavior, decision-making processes) to provide a more organized and coherent flow of information.
While the authors mention the three constructs of TPB (attitude toward the behavior, subjective norms, and perceived behavioral control), consider briefly explaining each construct to ensure clarity and understanding for readers who may need to become more familiar with TPB.
The authors need to clarify the relevance of TPB to thier study: Explicitly state how TPB aligns with the research objectives and why it is an appropriate framework for investigating contractors' carbon emission reduction intention (CERI). This will help readers understand the theoretical basis of their study and the rationale behind using TPB as the theoretical framework.
When mentioning the need for contextual adaptation of TPB, briefly explain what it entails and why it is important. Consider providing an example or referencing specific studies that have successfully adapted TPB to specific contexts.
Highlight Li et al.'s contribution: When referring to Li et al.'s research on waste reduction among construction contractors and their adaptation of the TPB framework, clearly state the specific contributions or insights from their study.
In this section (The Impact of Personal Norms (PN) ), the authors need to consider including specific examples or findings from the referenced studies to support the influence of personal norms on contractors' behavior.
The authors need to provide more details on the snowball sampling method and discuss steps taken to ensure data quality.
It would be beneficial to provide more specific implications and practical recommendations based on the study's findings to strengthen the conclusion section. This could include suggestions for government policymakers, construction industry stakeholders, and contractors on promoting and enhancing contractors' carbon emission reduction intentions.

Author Response

Response to Reviewer：Your recognition of our research is much appreciated. Authors of the study have carefully revised the paper after reading your comments and made responses accordingly. Thank you again for your sincere advice.

1.However, the following comments should be addressed:The abstract effectively presents the research topic, objectives, methodology, key findings, and practical implications. However, consider adding a sentence about the sample size to provide additional context.

Response to Reviewer：Thank you for the constructive suggestion, which we found very helpful.We carefully considered the issues in the abstract and added a sentence about the sample size to provide additional context. The revised part is as follows:

The study collected 311 valid questionnaires, which are suitable for SEM research, and the results indicate that: The results show that the model proposed in the study has an explanatory rate of 69% for developers' willingness to reduce carbon emissions.

The authors need to provide more specific information on China's emission reduction goals: Instead of just mentioning China's commitment to peak carbon emissions by 2030 and achieve carbon neutrality, consider including specific targets or percentages to provide a clearer understanding of the magnitude of the goals.

Response to Reviewer: Thank you for your sincere comment. We provided specific instructions on carbon peaking and carbon neutrality. The revised part is as follows:

As one of the world's largest carbon emitters, China has committed to achieving "carbon peak" by 2030 and "carbon neutrality" by 2060. Carbon peaking: By 2030, carbon dioxide emission will no longer increase to its peak and then gradually decrease. Carbon neutrality: Before 2060, through afforestation, energy conservation and emission reduction, offset the carbon dioxide emissions generated by oneself and achieve "zero emissions" of carbon dioxide [1].

Clarify the significance of construction contractors: While the authors mention that the construction industry is vital for achieving national emission targets, they further emphasize the role of construction contractors specifically. Highlight their influence and decision-making power in the construction process and their potential impact on carbon emissions.

Response to Reviewer: Your suggestion was beneficial, as clarifying the importance of building contractors further enhances the persuasiveness of the article. Therefore, we have added the contractor's role in the introduction section. The revised area is as follows:

While the studies mentioned in the literature review are valuable, clearly articulate how they relate to the research. The authors need to explain how these previous studies inform the gaps or limitations their study aims to address.
The authors could consider grouping the studies based on themes or categories (e.g., carbon reduction strategies, factors influencing behavior, decision-making processes) to provide a more organized and coherent flow of information.

Response to Reviewer:(4&5) We found your suggestion very helpful. We grouped the literature review and explained that this study proposes a new perspective based on previous research. We answered both questions together. The revised section is as follows:

2.1. Contractors' Carbon Emission Reduction Research

2.1.1. Carbon Emission Reduction strategies

Given the significant role of contractors' carbon emissions in the life cycle of carbon emissions, many scholars have conducted specific research on carbon emissions in construction. For instance, Tang et al. [10] proposed a simulation approach to evaluate the effectiveness of alternative management strategies in controlling greenhouse gas emissions. They demonstrated that an appropriate selection of management strategies could reduce greenhouse gas emissions without increasing contractor costs or delaying project schedules. In this context, enhancing contractors' CERI holds considerable value. Therefore, more scholars have been focusing on how to achieve carbon reduction in practical contexts. For instance, Chan et al. [12] argued that using low-carbon materials during the construction phase can effectively reduce the energy consumption of a building throughout its life cycle. They also highlighted that the main obstacle construction professionals face in using low-carbon building materials is the need for more information regarding material performance. Nässén et al. [14] also researched carbon dioxide emissions during construction, considering building materials, transportation, construction activities, and machine production. Yan et al. [15] calculated the four sources of greenhouse gas emissions in construction, which include the manufacturing and transportation of building materials, energy consumption of construction equipment, energy consumption for resource handling, and disposal of construction waste. To effectively assess contractors' carbon reduction behaviour in construction projects, Wong's [16] study adopted the European Construction Research and Development Organization's work to evaluate Australian contractors' carbon reduction strategies. Therefore, contractors can reduce their carbon emissions in construction projects by implementing various measures such as adopting more efficient building designs and construction methods, using energy-saving materials and equipment, and enhancing monitoring and management of building energy consumption, thereby contributing to global emission reduction targets.

2.1.2. Factors Affecting Carbon Emission Reduction

Studies on carbon reduction behaviour and developers' willingness at the management decision-making level have become relatively mature. For example, Mahmoud et al. [17] developed a multi-objective optimization model that identified 134 decision variables and provided a near-optimal Pareto frontier solution balancing low cost and sustainable development performance for building stakeholders. Lam et al. [18] classified and analyzed the factors influencing the implementation of green construction specifications, including the development of green technologies, the quality and reliability of specifications, leadership and responsibility allocation, stakeholder involvement, and the improvement of assessment benchmarks. Li et al. [19] focused on project environmental practices and defined project environmental practices from a lifecycle perspective. They studied the relationships between green design, green procurement, green construction, investment recovery, and their impacts on ecological and organizational performance.

However, these studies only investigated certain engineering content and factors affecting building carbon emissions reduction, and these influencing factors were not studied from the perspective of building implementers or contractors nor from the perspective of behavioural willingness. Therefore, this study comprehensively and systematically examines the influencing factors of contractor CERI from the contractors' perspective, aiming to fill this unexplored gap and provide new insights into the field of carbon reduction for construction contractors. Based on the TPB, this study identifies factors influencing contractors' CERI and analyzes key factors and mechanisms influencing contractors' CERI through establishing SEM. The study aims to reveal the underlying mechanisms of developers' carbon reduction decision-making in the Chinese context. Based on the TPB, this study identifies factors influencing contractors' CERI and analyzes key factors and mechanisms influencing contractors' CERI through establishing SEM. The study aims to reveal the underlying mechanisms of developers' carbon reduction decision-making in the Chinese context.

While the authors mention the three constructs of TPB (attitude toward the behavior, subjective norms, and perceived behavioral control), consider briefly explaining each construct to ensure clarity and understanding for readers who may need to become more familiar with TPB.
The authors need to clarify the relevance of TPB to thier study: Explicitly state how TPB aligns with the research objectives and why it is an appropriate framework for investigating contractors' carbon emission reduction intention (CERI). This will help readers understand the theoretical basis of their study and the rationale behind using TPB as the theoretical framework.
When mentioning the need for contextual adaptation of TPB, briefly explain what it entails and why it is important. Consider providing an example or referencing specific studies that have successfully adapted TPB to specific contexts.
Highlight Li et al.'s contribution: When referring to Li et al.'s research on waste reduction among construction contractors and their adaptation of the TPB framework, clearly state the specific contributions or insights from their study.
In this section (The Impact of Personal Norms (PN) ), the authors need to consider including specific examples or findings from the referenced studies to support the influence of personal norms on contractors' behavior.

Response to Reviewer: (6&7&8&9&10) We find your suggestions very helpful, and making these changes will enhance the rigour of our research and readability for readers. We answered both questions together. Due to the concentration of these issues, we have decided to reply together. The revised section is as follows:

TPB is widely applied. This theory posits that behavioural intentions(BI) and actual behaviour are influenced by three constructs: attitude toward the behaviour attitude (BA), subjective norms (SN), and perceived behavioural control (PBC) [20]. BA towards the behaviour refers to an individual's favourable or unfavourable evaluation or degree of assessing the behaviour. (Q6) If individuals believe their actions benefit the environment, they are more likely to be willing to take environmentally friendly actions. On the contrary, if individuals believe their behaviour harms the environment, they are less likely to act environmentally friendly. SN refers to the influence of external social factors on individual behaviour, such as social expectations, norms, and values. Individuals are more likely to adopt such behaviour if a behaviour aligns with social expectations and norms. PBC refers to individuals' perception of the ease or difficulty of performing the behaviour of interest. When individuals perceive that engaging in environmentally friendly actions is easy, they are likelier to take such measures.

In recent years, TPB has become one of the most influential theories for understanding, predicting, and changing various behaviours. It has gained increasing attention and application in research on the relationships between beliefs, BA, BI, and behaviours in multiple fields, such as tourism, advertising, environmental management, and project management [21-23]. Particularly in environmental psychology, it has been increasingly promoted as a critical theory for predicting and promoting various pro-environmental behaviours [24-26].(Q8) For instance, Yuan et al. [27] studied the predictive factors of project managers' intention to reduce waste based on TPB, and the results showed that BA was the strongest predictor of project managers' intention to reduce waste. Yang et al. [28] explored the key influencing factors of green procurement behaviour among developers. They pointed out that SN, PBC, and other factors can indirectly influence green procurement behaviour through BI. Therefore, this study chose TPB as the theoretical basis for the construction contractor CERI. Due to the difficulty in tracking actual behaviours [27,29], this study examines the factors influencing contractors' CERI. As an abstract macro framework, TPB often requires contextual adaptation (including cultural and social backgrounds) to enhance its explanatory power in specific situations [20].(Q9) For instance, Li et al. [30] incorporated GR, PN, and economic feasibility into the TPB model when studying the waste reduction behaviour of construction contractors, constructing a more practical framework for predicting waste reduction behaviour. The results showed that surface intention significantly impacted their behaviour, followed by GR and PBC. (Q7) This study also studies behaviour intention in environmental psychology, which is also part of TPB. This study now applies it to a new field to expand its specific application scope. Therefore, drawing on studies [30-32], this study also attempts an appropriate adaptation of the TPB framework to improve the understanding of the mechanisms and decision logic underlying contractors' CERI.

2.3. The Impact of Personal Norms (PN)

PN is often considered an essential factor in contractors' pro-environmental behaviour [30]. Especially in environmental behaviour, the influence of moral factors on BI should not be overlooked [33]. PN plays a significant role in determining environmental behaviour [34]. Effective carbon reduction behaviour can mitigate environmental damage and positively impact societal and environmental quality; thus, it can be seen as an environmental conservation behaviour. Therefore, it is reasonable to consider PN influencing CERI. In Kaiser's study [33], PN was identified as the solid antecedent of BA and BI.(Q10) Botetzagias et al. [35] and Wang et al. [36] found that PN significantly impacted BI and directly predicted BI and BA. Similarly, in the field of construction contractors, Li [30] indicated that personal norms have a particularly stimulating effect on contractors' waste reduction behaviour. When contractors have strong personal norms, they adopt waste-reduction behaviour to fulfil their moral obligations.

The authors need to provide more details on the snowball sampling method and discuss steps taken to ensure data quality.

Response to Reviewer: Your suggestion was constructive, explaining that snowball sampling would be more conducive to this research's rigour and readers' readability. The revised section is as follows:

This study conducted an online questionnaire survey from September to November 2022. All respondents to this study were willing to participate in this experiment. The questionnaire was communicated through WeChat to those who met the requirements of this study. All respondents answered anonymously; their personal information is confidential and does not involve sensitive issues. The questionnaire was distributed using snowball sampling. Snowball sampling is one of the most popular methods in qualitative research, with its core feature being networking and referral [83]. Fewer specific populations meet the research criteria, so snowball sampling suits this study. This study began with 40 initial contacts (seeds) who met the research criteria and were invited to participate. Then, with the consent of the participants, they were asked to recommend other contacts who met the research criteria, were also willing participants, and so on. A total of 400 questionnaires were distributed, and 364 were collected. This study found through the pre-experiment that the respondents completed their responses within at least 90 seconds. Therefore, the decision made in this study to complete the responses within 90 seconds is considered as not answering seriously and is considered an invalid questionnaire. After excluding 26 incomplete and 53 questionnaires with a response time of fewer than 90 seconds, the final valid sample size was 311, resulting in an effective response rate of 85.44%.

It would be beneficial to provide more specific implications and practical recommendations based on the study's findings to strengthen the conclusion section. This could include suggestions for government policymakers, construction industry stakeholders, and contractors on promoting and enhancing contractors' carbon emission reduction intentions.

Response to Reviewer:We found your suggestion very helpful, and we carefully modified and adjusted the conclusion section. The revised section is as follows:

This study synthesized the factors influencing contractors' CERI through a literature review and constructed a conceptual model and structural equation model for the factors based on existing research. Then, a questionnaire survey was conducted to collect data, which were processed using SPSS and AMOS software to verify the hypotheses in the model and analyze the mechanisms of contractors' CERI. Based on the results of the model analysis, this paper draws the following conclusions:

1.The results show that GR has the most significant impact on CERI. The relevant government departments not only explicitly require low-carbon requirements through policies, legal norms, standards and other documents, but more importantly, they carry out strict supervision, punish construction contractors who do not meet low-carbon standards, and suspend production or even suspend business licenses for enterprises that seriously pollute the environment. Only by strictly implementing policies can relevant government departments contribute to achieving the goals of "carbon peaking" and "carbon neutrality".

2.PS, SN, PBC have a significant impact on CERI. In addition to issuing strict regulations and supervision, economic measures can be taken to enhance contractors' awareness and autonomy in carbon reduction, such as tax incentives, financial subsidies and green construction certification. Contractors can learn advanced construction technologies from each other through organizing enterprise forums, international exchanges and cooperation, and improve their low-carbon awareness through communication. In addition to technical factors, contractors can also achieve innovation by reducing construction plans, construction concepts, management methods, and other aspects similar to recyclable waste.

3.The government indirectly affects CERI by influencing SN and PBC. The government can entrust third parties and stakeholders to supervise contractors. The government's incentive policies can also improve the ability and power of enterprises to innovate and reduce waste technology, increase the use of Renewable resources, and reduce environmental pollution.

With best regards

Thanks for your recognition and sincere comments. With your help, we have a more quality paper, and your comments inspired us greatly. Again, thanks for your kindness.

Author Response File: Author Response.pdf

Reviewer 2 Report

Based on the theory of planned behavior (TPB), this paper incorporates three potential influencing factors and constructs a structural equation model to predict the influencing factors of construction contractors' willingness to carbon emission reduction intentions (CERI). The key factors and mechanisms affecting the CERI of construction contractors are analyzed. The results show that the most significant factor affecting the CERI of construction contractors is GR, followed by GR, PS, subjective norm (SN) and perceptual behavior control (PBC).

The research results can provide theoretical and practical basis for the government to promote the contractor 's corporate responsibility investment. The structure of the article is generally complete, and the research method is reliable, but it still has some problems. The questions are as follows:

1、the conceptual framework, this study only considers GR,PS, and PN based on TPB, What are the reasons for choosing these factors?

2、This article excludes 53 questionnaires with an answer time of less than 90 seconds. Why 90 seconds? Please give the relevant basis or the corresponding specification.

3、Is “GS” in “From Table 6, it can be observed that GS has the greatest impact on CERI. ” is “GR”?

4、“The construction industry in China is more sensitive to government regulations than other industries, leading to a strong correlation between GR and contractors' CERI. ”，How does this conclusion come to ? The article does not give a corresponding explanation.

5、“Hypothesis 9 (H9): PBC mediates the relationship between PS and CERI. ” Should it be “Hypothesis 11 (H11)”?

6、The construction industry is a professional field. According to the questionnaire, the respondents in the article give their intentions for the construction contractor to reduce carbon emissions, but can this reflect the attitude of the construction contractor?

7、What is the meaning of the index in Table 5? Should be explained in detail.

8、This paper assumes the relationship of Behavioral Attitude (BA), Subjective Norms (SN), Perceived Behavioral Control (PBC), Personal Norms (PN), Government Regulation (GR) and Policy Support (PS) to CERI and their potential relationship. What are the reasons for choosing them?

9、The structural equation model is the focus of this paper, which should be described in more detail in Figure 2.

10、In the conclusion part, the author should focus more clearly on the most important findings, and ensure that no data analysis is carried out in the conclusion part, so as to maintain very descriptive and informative. All explanatory content should be moved to the subsequent discussion section.

11 The introduction does not specify which issues were studied in this study. Specific research questions need to be added at the end of the introduction. Enable readers to have a clearer understanding of the particular content of this study.

12 Section 4.2 is too brief. Do the respondents intend to participate in this study? How is the questionnaire published online? Are all respondents anonymous, is their personal information confidential, and does not involve sensitive issues? Completing this information can help readers understand the rigour of this research.

13 The complete spelling of C.R. in Table 3 and Table 6 is Composite Reliability, and the correct expression should be CR.

14 The author needs to provide a robustness test. There are significant differences in Demography or other personal characteristics information, so it is necessary to introduce the above control variables into the structural equation model to ensure the stability of the structural equation model analysis results.

15 The author needs to provide the Variance Inflation Factor (VIF). VIF refers to the ratio of variance when there is Multicollinearity between explanatory variables to conflict when there is no Multicollinearity. Providing VIF can further enhance the persuasiveness of the structural model, and it is recommended that the author provide VIF data.

16 The author needs to provide R² in Table 6. In regression analysis, R² is used to evaluate the explanatory power of the independent variable to the dependent variable. Provide R² to explain the interpretation rate of each Latent and observable variable to the willingness and how much variation of the dependent variable is defined by the independent variable of this study. Make research more scientific.

17 The corresponding variable abbreviation should be identified below Figure 2 so that readers can clearly distinguish the variables in the picture.

Author Response

The research method is reliable, but it still has some problems. The questions are as follows:

The conceptual framework, this study only considers GR,PS, and PN based on TPB, What are the reasons for choosing these factors?

Response to Reviewer：thanks for your comments. The following articles incorporate these factors into similar studies:

Li, J.; Wu, Q.; Wang, C.C.; Du, H.; Sun, J. Triggering Factors of Construction Waste Reduction Behavior: Evidence from Contractors in Wuhan, China. Journal of Cleaner Production 2022, 337, 130396, doi:https://doi.org/10.1016/j.jclepro.2022.130396.

Ebekozien, A.; Aigbavboa, C.; Aigbedion, M. Construction Industry Post-COVID-19 Recovery: Stakeholders Perspective on Achieving Sustainable Development Goals. International Journal of Construction Management 2021, 1–11, doi:https://doi.org/10.1080/15623599.2021.1973184.

Tang, Z.; Ng, S.T. Sustainable Building Development in China – a System Thinking Study. Procedia Engineering 2014, 85, 493–500, doi:https://doi.org/10.1016/j.proeng.2014.10.576.

We summarized and summarized these three factors based on TPB selection. And made the following modifications to the article. The revised part is as follows:

2.1.2. Factors Affecting Carbon Emission Reduction

2.2. The Theory of Planned Behavior (TPB)

TPB is widely applied. This theory posits that behavioural intentions(BI) and actual behaviour are influenced by three constructs: attitude toward the behaviour attitude (BA), subjective norms (SN), and perceived behavioural control (PBC) [20]. BA towards the behaviour refers to an individual's favourable or unfavourable evaluation or degree of assessing the behaviour. If individuals believe their actions benefit the environment, they are more likely to be willing to take environmentally friendly actions. On the contrary, if individuals believe their behaviour harms the environment, they are less likely to act environmentally friendly. SN refers to the influence of external social factors on individual behaviour, such as social expectations, norms, and values. Individuals are more likely to adopt such behaviour if a behaviour aligns with social expectations and norms. PBC refers to individuals' perception of the ease or difficulty of performing the behaviour of interest. When individuals perceive that engaging in environmentally friendly actions is easy, they are likelier to take such measures.

In recent years, TPB has become one of the most influential theories for understanding, predicting, and changing various behaviours. It has gained increasing attention and application in research on the relationships between beliefs, BA, BI, and behaviours in multiple fields, such as tourism, advertising, environmental management, and project management [21-23]. Particularly in environmental psychology, it has been increasingly promoted as a critical theory for predicting and promoting various pro-environmental behaviours [24-26].For instance, Yuan et al. [27] studied the predictive factors of project managers' intention to reduce waste based on TPB, and the results showed that BA was the strongest predictor of project managers' intention to reduce waste. Yang et al. [28] explored the key influencing factors of green procurement behaviour among developers. They pointed out that SN, PBC, and other factors can indirectly influence green procurement behaviour through BI. Therefore, this study chose TPB as the theoretical basis for the construction contractor CERI. Due to the difficulty in tracking actual behaviours [27,29], this study examines the factors influencing contractors' CERI. As an abstract macro framework, TPB often requires contextual adaptation (including cultural and social backgrounds) to enhance its explanatory power in specific situations [20].For instance, Li et al. [30] incorporated GR, PN, and economic feasibility into the TPB model when studying the waste reduction behaviour of construction contractors, constructing a more practical framework for predicting waste reduction behaviour. The results showed that surface intention significantly impacted their behaviour, followed by GR and PBC. This study also studies behaviour intention in environmental psychology, which is also part of TPB. This study now applies it to a new field to expand its specific application scope. Therefore, drawing on studies [30-32], this study also attempts an appropriate adaptation of the TPB framework to improve the understanding of the mechanisms and decision logic underlying contractors' CERI.

2.3. The Impact of Personal Norms (PN)

PN is often considered an essential factor in contractors' pro-environmental behaviour [30]. Especially in environmental behaviour, the influence of moral factors on BI should not be overlooked [33]. PN plays a significant role in determining environmental behaviour [34]. Effective carbon reduction behaviour can mitigate environmental damage and positively impact societal and environmental quality; thus, it can be seen as an environmental conservation behaviour. Therefore, it is reasonable to consider PN influencing CERI. In Kaiser's study [33], PN was identified as the solid antecedent of BA and BI. Botetzagias et al. [35] and Wang et al. [36] found that PN significantly impacted BI and directly predicted BI and BA. Similarly, in the field of construction contractors, Li [30] indicated that personal norms have a particularly stimulating effect on contractors' waste reduction behaviour. When contractors have strong personal norms, they adopt waste-reduction behaviour to fulfil their moral obligations.

2.4. The Impact of Government Regulation (GR)

GR is often considered a predictive factor for carbon reduction intention [37]. Numerous studies have shown that government supervision and corresponding laws and regulations significantly influence contractors' pro-environmental behaviour, with the role of government being critical in developing countries [16,38,39]. Awang [40] and Khan [41] argued that government regulations are the most critical driving force for companies to implement environmental measures. Regulatory pressure from the government will encourage stakeholders to execute environmentally friendly actions. In recent years, fines and penalties for violating regulations have led to increased consideration of ecologically friendly behaviour among construction companies [42]. Strict environmental regulations and high penalties for non-compliance may compel the construction industry to find new approaches that improve resource utilization [43]. Similarly, Ding [44] and Nejat [45] pointed out the vital role of government regulations and corresponding monitoring in promoting positive behaviour among contractors.

2.5. The Impact of Policy Support (PS)

However, Bigerna et al. [46] argued that positive incentives (subsidies, rewards, tax exemptions, and loans) are more effective than penalties in terms of environmental behaviour. Most countries have implemented various subsidies to promote green development, each with specific objectives. For instance, Australia provided production subsidies for variable renewable energy plants to achieve decarbonization in the electricity market [47], and the United States allocated significant capital grants and subsidy programs to wind energy and the grid during the financial crisis's green stimulus plan. Subsidies issued by different government departments have diverse requirements and focuses. Environmental-related subsidies may emphasize clean production and pollution reduction, while research and development-related subsidies may focus on new technologies, processes, equipment, and materials. In the context of the construction industry, Tang et al. [32] found that government incentive measures influence the vision of construction enterprises, thereby altering their sustainable development strategies. Similarly, Alwan et al. [48] highlighted that government subsidies are effective policy tools for addressing the environmental aspects of construction, and economic subsidies serve as means to provide positive incentives.

This article excludes 53 questionnaires with an answer time of less than 90 seconds. Why 90 seconds? Please give the relevant basis or the corresponding specification.

Response to Reviewer：Thank you for your sincere comment. We immediately supplemented the information on why the exclusion occurred. The revised part is as follows:

3&5&13

Response to Reviewer：Thank you very much for seeing the article's errors in 3, 5, and 13. We sincerely apologize for our negligence, which led to the appearance of the appeal error. We have corrected this and have read and checked the entire text to avoid the occurrence of the above errors.

“The construction industry in China is more sensitive to government regulations than other industries, leading to a strong correlation between GR and contractors' CERI. ”，How does this conclusion come to ? The article does not give a corresponding explanation.

Response to Reviewer：We found your suggestion very helpful, and the following literature explains why the construction industry is more sensitive to building regulations than other industries.

Liu, Z.; Deng, Z.; He, G.; Wang, H.; Zhang, X.; Lin, J.; Qi, Y.; Liang, X. Challenges and Opportunities for Carbon Neutrality in China. Nature Reviews Earth & Environment 2022, 3, 141–155, doi:https://doi.org/10.1038/s43017-021-00244-x.

The revised part is as follows:

From Table 6, it can be observed that GR has the greatest impact on CERI. It has a significant direct effect on CERI and an indirect effect through SN. Table 7 shows that the direct effect is 0.418, and the total effect is 0.504. The construction industry in China is more sensitive to government regulations than other industries [91], leading to a strong correlation between GR and contractors' CERI. Consistent with the findings of this study, many other studies have also indicated that government laws and regulations are key factors influencing contractors' green and environmentally friendly behaviours [30,38]. The study [92] results demonstrate that mandatory government institutional arrangements can stimulate companies' willingness to engage in low-carbon production.

The construction industry is a professional field. According to the questionnaire, the respondents in the article give their intentions for the construction contractor to reduce carbon emissions, but can this reflect the attitude of the construction contractor?

Response to Reviewer: Thank you for your sincere comment. Our study's research subjects were all construction contractors' employees, so their willingness to reduce carbon emissions can represent construction contractors' CERI. The revised part is as follows:

What is the meaning of the index in Table 5? Should be explained in detail.

Response to Reviewer: thanks for your comments. We added an explanation for index. The revised part is as follows:

Chi-square tests the discrepancy between the sample and the matrices of covariance fitted in the model. CMIN/df value of 3 or less is considered an excellent model fit measure [88]. Goodness-of-fit statistic (GFI) estimates the proportion of the variance provided by the projected covariance of the population. It ranges from 0 to 1. In general, the recommended threshold is 0.80 [89]. Adjusted Goodness-of-fit statistic (AGFI) tries to adjust the GFI with degrees of freedom. It also ranges from 0 to 1. Generally, the widely recommended threshold is 0.80 [88]. The Comparative Fit Index (CFI) compares the model fit with a null or independent model. The major difference is that it talks about latent factors rather than indicators. A threshold value of 0.90 and above suggests a good model fit [88]. Root Mean Square Error of Approximation (RMSEA) is considered the best informative fit index. It goes for an optimal number of parameters (lesser) to fit the final population covariance matrix. An excellent model fit should have an RMSEA value of 0.08 or less [90]. The Normed Fit Index (NFI) index evaluated the model by comparing the chi-square value of the model and the same null or independent model. A threshold value of 0.80 and above suggests a good model fit [90]. To determine the best-fitting structural equation model, this study primarily relied on the indicators and fit criteria in Table 5 to evaluate the model fit. Comparative analysis revealed that the goodness-of-fit indices met the reference standards, indicating a good model fit and sufficient adaptability to the collected data.

This paper assumes the relationship of Behavioral Attitude (BA), Subjective Norms (SN), Perceived Behavioral Control (PBC), Personal Norms (PN), Government Regulation (GR) and Policy Support (PS) to CERI and their potential relationship. What are the reasons for choosing them?

Response to Reviewer: We found your suggestion very helpful. SEM research constructs research models and hypotheses based on the theoretical framework and past practical support (i.e. conclusions from relevant empirical research in the past). This study extends the TPB model, including BA, PBC, and SN. In this study, we referred to the following literature to propose the above hypothesis.

Tang, Z.; Ng, S.T. Sustainable Building Development in China – a System Thinking Study. Procedia Engineering 2014, 85, 493–500, doi:https://doi.org/10.1016/j.proeng.2014.10.576.

The revised part is as follows:

2.1.2. Factors Affecting Carbon Emission Reduction

2.2. The Theory of Planned Behavior (TPB)

TPB is widely applied. This theory posits that behavioural intentions(BI) and actual behaviour are influenced by three constructs: attitude toward the behaviour attitude (BA), subjective norms (SN), and perceived behavioural control (PBC) [20]. BA towards the behaviour refers to an individual's favourable or unfavourable evaluation or degree of assessing the behaviour. If individuals believe their actions benefit the environment, they are more likely to be willing to take environmentally friendly actions. On the contrary, if individuals believe their behaviour harms the environment, they are less likely to act environmentally friendly. SN refers to the influence of external social factors on individual behaviour, such as social expectations, norms, and values. Individuals are more likely to adopt such behaviour if a behaviour aligns with social expectations and norms. PBC refers to individuals' perception of the ease or difficulty of performing the behaviour of interest. When individuals perceive that engaging in environmentally friendly actions is easy, they are likelier to take such measures.

In recent years, TPB has become one of the most influential theories for understanding, predicting, and changing various behaviours. It has gained increasing attention and application in research on the relationships between beliefs, BA, BI, and behaviours in multiple fields, such as tourism, advertising, environmental management, and project management [21-23]. Particularly in environmental psychology, it has been increasingly promoted as a critical theory for predicting and promoting various pro-environmental behaviours [24-26].For instance, Yuan et al. [27] studied the predictive factors of project managers' intention to reduce waste based on TPB, and the results showed that BA was the strongest predictor of project managers' intention to reduce waste. Yang et al. [28] explored the key influencing factors of green procurement behaviour among developers. They pointed out that SN, PBC, and other factors can indirectly influence green procurement behaviour through BI. Therefore, this study chose TPB as the theoretical basis for the construction contractor CERI. Due to the difficulty in tracking actual behaviours [27,29], this study examines the factors influencing contractors' CERI. As an abstract macro framework, TPB often requires contextual adaptation (including cultural and social backgrounds) to enhance its explanatory power in specific situations [20].For instance, Li et al. [30] incorporated GR, PN, and economic feasibility into the TPB model when studying the waste reduction behaviour of construction contractors, constructing a more practical framework for predicting waste reduction behaviour. The results showed that surface intention significantly impacted their behaviour, followed by GR and PBC. This study also studies behaviour intention in environmental psychology, which is also part of TPB. This study now applies it to a new field to expand its specific application scope. Therefore, drawing on studies [30-32], this study also attempts an appropriate adaptation of the TPB framework to improve the understanding of the mechanisms and decision logic underlying contractors' CERI.

2.3. The Impact of Personal Norms (PN)

PN is often considered an essential factor in contractors' pro-environmental behaviour [30]. Especially in environmental behaviour, the influence of moral factors on BI should not be overlooked [33]. PN plays a significant role in determining environmental behaviour [34]. Effective carbon reduction behaviour can mitigate environmental damage and positively impact societal and environmental quality; thus, it can be seen as an environmental conservation behaviour. Therefore, it is reasonable to consider PN influencing CERI. In Kaiser's study [33], PN was identified as the solid antecedent of BA and BI. Botetzagias et al. [35] and Wang et al. [36] found that PN significantly impacted BI and directly predicted BI and BA. Similarly, in the field of construction contractors, Li [30] indicated that personal norms have a particularly stimulating effect on contractors' waste reduction behaviour. When contractors have strong personal norms, they adopt waste-reduction behaviour to fulfil their moral obligations.

2.4. The Impact of Government Regulation (GR)

2.5. The Impact of Policy Support (PS)

The structural equation model is the focus of this paper, which should be described in more detail in Figure 2.

Response to Reviewer: Thank you for your sincere comment. We modified and added a path explanation for the resulting graph of the structural equation model. The revised part is as follows:

The results of the hypothesis testing are presented in Table 6, indicating that only H8 and H9 hypotheses were rejected, while the remaining hypotheses were supported. This study will discuss and analyze the significant paths. From the analysis of Table 6, it can be observed that out of the nine hypotheses investigated in this study, seven were supported. Specifically, GR (β=0.418, p<0.001), PS (β=0.231, p<0.001), and SN (β=0.171, p<0.001) significantly influenced CERI. GR (β=0.505, p<0.001) significantly influenced SN, PN (β=0.518, p<0.001) significantly influenced BA, PS (β=0.505, p<0.001) significantly influenced PBC, and PBC (β=0.505, p<0.01) influenced CERI. It is necessary to explain the meaning of path coefficients. Path coefficients (β) visually indicate the strength of the relationships between variables. Taking GR's influence on CERI as an example (β=0.418), for every one-unit increase in GR, CERI is expected to increase by 0.404 units. Therefore, the study supports the original hypotheses H1, H2, H3, H4, H5, H6, and H7, while H8 and H9 were not supported in this research.

10. In the conclusion part, the author should focus more clearly on the most important findings, and ensure that no data analysis is carried out in the conclusion part, so as to maintain very descriptive and informative. All explanatory content should be moved to the subsequent discussion section.

Response to Reviewer: We found your suggestion very helpful, and we carefully modified and adjusted the conclusion section. The revised section is as follows:

Response to Reviewer: Thank you for the constructive suggestion, which we found very helpful. We adjusted the introduction and added questions at the end to give readers a clearer understanding of the specific content of this study. The revised part is as follows:

Furthermore, by implementing appropriate construction management strategies, contractors can reduce greenhouse gas emissions without increasing financial burdens or causing project delays [10]. Malindu [11] proposed a Construction Emission Estimation Tool (CEET) through which management can obtain a comprehensive solution in the decision-making process to minimize emissions during the construction phase of the building. That is to say, the decisions of contractors have a significant impact on carbon emissions. Therefore, starting from the goals of energy conservation, emission reduction, and environmental protection, it is essential to study the factors influencing carbon emissions from construction contractors and scientifically propose energy-saving and emission reduction measures for the construction industry to achieve carbon peak and carbon neutrality in our country.

Previous studies have primarily focused on estimating carbon emissions from contractors [9,11] and exploring specific carbon reduction measures for contractors [12,13] without examining the influencing factors of carbon emission reduction intention from the perspective of behavioural willingness in construction contractors. Therefore, this study aims to comprehensively and systematically study the influencing factors of construction contractors' carbon emission reduction intention (CERI) and identify the key factors and mechanisms that affect construction contractors' CERI. This study is based on the Theory of Planned Behavior (TPB) to explore construction contractors' CERI, identify the factors influencing their CERI, establish a structural equation model (SEM), and analyze the key factors and mechanisms affecting their CERI. This study fills the gap in the factors influencing construction contractors' CERI and expands the application scope of TPB. Additionally, this study provides managerial insights to support the improvement of carbon emission reduction governance mechanisms for both the government and construction contractors, aiming to achieve carbon peak and carbon neutrality at an earlier date.

The remaining sections of this paper are organized as follows. Chapter 2 provides an overview of the research status on carbon emission reduction by contractors, as well as explanations of the Theory of Planned Behavior and additional factors. Chapter 3 outlines the basic framework and provides explanations for behavioural attitude (BA), subjective norm (SN), perceived behavioural control (PBC), personal norm (PN), government regulation (GR), and policy support (PS). The research design is presented in Chapter 4, followed by the analysis of research results in Chapter 5, and the discussion and managerial insights provided in Chapter 6. Finally, Chapter 7 summarizes the main conclusions, highlights the theoretical and practical implications, and identifies the limitations of the research. This study mainly focuses on the following issues: 1) Identify the critical factors of construction contractor CERI. 2) Identify the impact mechanism and action mechanism of the construction contractor CERI. 3) Identify key mediating roles to explore potential logical relationships between variables belong the model.

12.Section 4.2 is too brief. Do the respondents intend to participate in this study? How is the questionnaire published online? Are all respondents anonymous, is their personal information confidential, and does not involve sensitive issues? Completing this information can help readers understand the rigour of this research.

Response to Reviewer: Thank you for your sincere comment. Our careful completion of these modifications can help readers understand this study's rigour. The revised part is as follows:

Response to Reviewer: Thank you for your sincere comment.We added tests on robustness. Meanwhile, the test results of the impact of each control variable on the actual use of facial recognition were insignificant, indicating that the model passed the robustness test. The revised part is as follows:

5.5. Robustness T est of the Model

In this study, Years of work experience, corporate position and type of enterprise were introduced into the model as control variables to the test robustness of the hypothesis model. The test results areshown in figure 3.

Figure 3 shows that although control variables such as Years of work experience, corporate position and type of enterprise were introduced, the relationship and significance level of each factor in the model are consistent with the conclusion of the hypothesis test results mentioned above. Meanwhile, the test results of the impact of each control variable on the actual use of facial recognition were insignificant, indicating that the model passed the robustness test.

Figure 3. The structural equation model. (BA-Behavioral Attitude; SN-Subjective Norms; PBC-Perceived Behavioral Control; PN-Personal Norms; GR-Government Regulation; PS-Policy Support; CERI-Carbon Emissions Reduction Intention; YWE-Years of work experience; CP-corporate position; TE-type of enterprise)

15.The author needs to provide the Variance Inflation Factor (VIF). VIF refers to the ratio of variance when there is Multicollinearity between explanatory variables to conflict when there is no Multicollinearity. Providing VIF can further enhance the persuasiveness of the structural model, and it is recommended that the author provide VIF data.

Response to Reviewer: We found your suggestion very helpful. By calculating the VIF, we verified that our research does not have Multicollinearity, and explained it in the text.

16.The author needs to provide R2 in Table 6. In regression analysis, R2 is used to evaluate the explanatory power of the independent variable to the dependent variable. Provide R2 to explain the interpretation rate of each Latent and observable variable to the willingness and how much variation of the dependent variable is defined by the independent variable of this study. Make research more scientific.

Response to Reviewer: Thank you for your constructive suggestion. We added R² to Table 6.

17.The corresponding variable abbreviation should be identified below Figure 2 so that readers can clearly distinguish the variables in the picture.

Response to Reviewer: Thank you for your sincere comment.We added the full name of the abbreviation so that readers can clearly distinguish the variables in Figure 2. The revised part is as follows:

(BA-Behavioral Attitude; SN-Subjective Norms; PBC-Perceived Behavioral Control; PN-Personal Norms; GR-Government Regulation; PS-Policy Support; CERI-Carbon Emissions Reduction Intention)

With best regards

Thanks for your recognition and sincere comments. With your help, we have a more quality paper, and your comments inspired us greatly. Again, thanks for your kindness.

Author Response File: Author Response.pdf

Reviewer 3 Report

The authors had done a study which is in need for sustainable future. The following comments needs to be addressed to publish in this journal.

The authors have mentioned as various keywords it should be more precise.

The abstract should state briefly the purpose of the research, the principal results and major conclusions. An abstract is often presented separately from the article, so it must be able to stand alone.

What is the novelty of this research? The authors should present a clearer and concise presentation to highlight the novelty and significance of this paper.

The major defect of this study is the debate or Argument is not clear stated in the introduction session. Hence, the contribution is weak in this manuscript. I would suggest the author to enhance your theoretical discussion and arrives your debate or argument.

Since most of the hypothesis were made to relate with CERI, why hypothesis 4,6 and 10 were made?

Why the authors did not include reutilization of waste factor and other sustainable factors in the manuscript? Those factors plays vital role in carbon emissions reduction

Much more explanations and interpretations must be added for the Results and also compare with other similar researchers.

The conclusion should be improved.

Author Response

The authors had done a study which is in need for sustainable future. The following comments needs to be addressed to publish in this journal.

The authors have mentioned as various keywords it should be more precise.

Response to Reviewer：Thank you for the constructive suggestion, which we found very helpful. We carefully considered the choice of keywords, deleted two keywords, and then adjusted the position of the keywords.The revised part is as follows:

Keywords: carbon emission reduction intentions; construction contractors; sustainable development; Theory of Planned Behavior; structural equation model; dual carbon targets

The abstract should state briefly the purpose of the research, the principal results and major conclusions. An abstract is often presented separately from the article, so it must be able to stand alone.

Response to Reviewer: Thank you for your sincere comment. We carefully revised the abstract and added research objectives and conclusion data. The revised part is as follows:

The high carbon emissions of the construction industry affect China's sustainable development. Therefore, reducing the carbon emissions of the construction industry is crucial for China to achieve "carbon peak" by 2030 and "carbon neutrality" by 2060. To understand the factors that affect contractors' willingness to reduce carbon emissions. This study is based on the Theory of Planned Behavior (TPB) and incorporates three potential influencing factors: personal norms (PN), government regulation (GR), and policy support (PS). It constructs a structural equation model (SEM) to predict the influencing factors of carbon emission reduction intentions (CERI) among construction contractors. This study analyzes the key factors and mechanisms influencing construction contractors' CERI. The study collected 311 valid questionnaires, which are suitable for SEM research, and the results indicate that: The results show that the model proposed in the study has an explanatory rate of 69% for developers' willingness to reduce carbon emissions.

What is the novelty of this research? The authors should present a clearer and concise presentation to highlight the novelty and significance of this paper.
The major defect of this study is the debate or Argument is not clear stated in the introduction session. Hence, the contribution is weak in this manuscript. I would suggest the author to enhance your theoretical discussion and arrives your debate or argument.

Response to Reviewer: (3&4) We found your suggestions very helpful, and we included this study's innovative points and theoretical discussions in the introduction section and put the two suggestions together to answer. The revised area is as follows:

Malindu [11] proposed a Construction Emission Estimation Tool (CEET) through which management can obtain a comprehensive solution in the decision-making process to minimize emissions during the construction phase of the building. That is to say, the decisions of contractors have a significant impact on carbon emissions. Therefore, starting from the goals of energy conservation, emission reduction, and environmental protection, it is essential to study the factors influencing carbon emissions from construction contractors and scientifically propose energy-saving and emission reduction measures for the construction industry to achieve carbon peak and carbon neutrality in our country.

5，Since most of the hypothesis were made to relate with CERI, why hypothesis 4,6 and 10 were made?

Response to Reviewer: Thank you for the constructive suggestion. SEM often uses mediation analysis to understand the indirect impact mechanisms present in models, which is also the direct approach of SEM research. This study discusses three sets of mediators, so three hypotheses are needed. The following papers also have a similar Mesomeric effect to this paper.

Tang, Z.; Ng, S.T. Sustainable Building Development in China – a System Thinking Study. Procedia Engineering 2014, 85, 493–500, doi:https://doi.org/10.1016/j.proeng.2014.10.576.

Li, J.; Zuo, J.; Cai, H.; Zillante, G. Construction Waste Reduction Behavior of Contractor Employees: An Extended Theory of Planned Behavior Model Approach. Journal of Cleaner Production 2018, 172, 1399–1408, doi:https://doi.org/10.1016/j.jclepro.2017.10.138.

6，Why the authors did not include reutilization of waste factor and other sustainable factors in the manuscript? Those factors plays vital role in carbon emissions reduction

Response to Reviewer: Thank you for the constructive suggestion. We are very sorry, but our research may lack an analysis of the above essential factors. We have added an analysis of these critical factors in the conclusion section and the factors you mentioned in the short section. The revised area is as follows:

7.1. Conclusions

7.4. Limitations and Suggestions for Further Studies

This study has several limitations. Firstly, the empirical analysis is based on data from construction companies in Wuhan rather than including a range of other cities in China. While most cities in China have similar policies and operate under the central government's guidance, the social environment for carbon emissions reduction can vary across different regions and cities. Therefore, the research findings may need adjustments before being applied to other cities in China. Secondly, this study mainly focuses on capturing the construction' CERI based on TPB without directly examining their actual behaviour. Further research could explore the carbon reduction behaviours of contractors to gain a more comprehensive understanding of the factors influencing carbon reduction behaviour. Finally, regarding the conceptual framework, this study only considers GR, PS, and PN based on TPB, potentially overlooking the influence of other factors, such as economic benefits, organizational culture, waste reduction and recyclability factors. Future research should consider a broader range of potential influencing factors to enrich the study and broaden the research scope. Despite these limitations, this study provides a guideline for future research.

7，Much more explanations and interpretations must be added for the Results and also compare with other similar researchers.

Response to Reviewer: We found your suggestions very helpful. We carefully added comparisons with the researchers and conducted. The revised area is as follows:

Discussion and Implications

6.1. Factors Influencing CERI

6.1.1. The Impact of GR on CERI

The relevant government departments should gradually specify the low-carbon requirements contractors should meet during construction through policies, legal norms, standards, and other documents, based on assessing the average level of low-carbon construction among contractors in China. The government environmental regulatory agencies can supervise the low-carbon environmental behaviours of construction-related enterprises directly or by commissioning third parties, ensuring the effective implementation of low-carbon environmental design schemes during construction and the effective implementation of documents related to contractors and project green environmental aspects.

6.1.2. The Impact of PS on CERI

Moreover, PS has a significant impact on CERI as well. Table 7 reveals a direct effect of 0.231 and a total effect of 0.324. Consistent with the findings of Li et al. [93], government support policies for environmental behaviours not only contribute to enhancing the environmental governance capacity of enterprises but also guide investments and the adoption of innovative technologies. Implementing incentive policies can enhance the capacity and motivation of enterprises to innovate waste-reduction technologies, reduce the demand for raw materials, increase the utilization of recycled resources, and simultaneously decrease environmental pollution.

The incentive mechanisms established by the government through relevant policies are the driving force behind the development of low-carbon construction [71]. The government's favourable measures significantly promote the reform of relevant enterprises towards low-carbon construction. Policies and regulations introduced by government departments at all levels should focus on transforming construction entities and strongly support low-carbon construction enterprises. To promote the industrialization of low-carbon construction, it is crucial to provide police protection and urge relevant authorities to prioritize approving construction progress-restricted documents, dedicated funds, bank loans, and infrastructure. Additionally, establishing corresponding incentives for construction entities with advanced technologies is essential.

6.1.3. The Impact of SN on CERI

SN has an effect of 0.171 on CERI. Similar to the results in [29], he indicated that SN positively impacted the contractor's willingness to recycle Construction waste. This once again validates the TPB proposed by Ajzen [20]. The driving forces behind construction contractors' implementation of carbon emission reduction management in projects are the requirements of clients, competition among peers, and normative pressures from the public. As one of the primary external drivers for low-carbon management in construction projects, market competition should play a significant role. However, this external force's impact is weakening due to the need for standardized market competition in our country's construction industry. Therefore, to enhance the driving force behind carbon emission reduction management in construction projects, it is crucial to establish a fair market order and create an environment of survival of the fittest and free competition. Additionally, it is vital to strengthen the low-carbon awareness of clients and leverage their role in guiding and supervising. The intense market competition requires all types of construction companies to prioritize the needs of project clients, and the low-carbon demands of clients can drive construction companies to implement carbon emission reduction management. Therefore, strengthening the low-carbon awareness of project clients, making them aware of the benefits of implementing low-carbon management and construction, and leveraging their role in guiding and coordinating efforts, can promote implementing carbon emission reduction management in construction companies.

6.1.4. The Impact of PBC on CERI

Data analysis confirms that PBC is another positive factor influencing CERI. Li [68] also demonstrated that PBC is essential in reducing waste for construction contractor employees. What is different is that in this study, its positive effect was minimal (the effect size was only 0.169). It may be because the effects of GR and PS weaken the impact of PBC on de CERI. Construction contractors with sufficient technical expertise, management capabilities, and financial resources can only engage in carbon emission reduction activities. The flexible application of technology can influence contractors' willingness to adopt low-carbon practices and help mitigate construction difficulties. Therefore, the development and application of technology are crucial in promoting carbon emission reduction practices across all stages of construction. The advancement and implementation of low-carbon building practices depend on technological innovation and application, emphasizing the need to concentrate on technological innovation. With a focus on technology, it is essential to enhance the innovation awareness of all parties involved, recognize technological gaps, and develop technologies suitable for China's specific characteristics. In addition to technological factors, low-carbon construction can be achieved through innovations in construction plans, construction concepts, and management methods.

6.2. The Impact of Mediation

GR significantly influences CERI through its significant impact on SN. The mediation accounts for 83% of the total effect. The government is considered the primary source of pressure for businesses, with various manifestations. Companies must meet government expectations to reduce uncertainty and achieve their goals [94]. The healthy and orderly development of the market also relies on government regulations and guidance. In the market environment of construction, the government should guide the market to protect the environment and reduce carbon emissions based on fair competition under the socialist market economy system. The government can also use the relationships between stakeholders to form constraints among various units in the low-carbon aspect of the project, such as requiring the design unit to design low-carbon plans, the construction unit to execute according to the design plan, and the public to supervise the construction unit to execute.

PS significantly influences CERI through its significant impact on PBC, with a mediation effect of 72%. It indicates that the government supports construction contractors in implementing low-carbon project management through tax incentives, loan guarantees, and financial subsidies [32,48]. Implementing low-carbon management in construction projects requires exploring new management models and applying new management techniques, tools, and methods in the project management process. It represents a transformation and challenge to the traditional project management approach. However, this transformation may increase various costs and risks for enterprises in the initial stage. Therefore, for business managers, if the existing management and operational models can achieve the desired objectives, they may be unwilling to make any changes, even if such changes potentially improve productivity and management capabilities. During the early stages of implementing low-carbon management in projects, the government can employ restorative measures such as tax incentives, loan guarantees, and financial subsidies to minimize the additional costs and risks construction companies face due to implementing low-carbon management. Moreover, companies that achieve better results in implementing low-carbon management should receive more incredible policy support to encourage more construction companies to apply low-carbon thinking in their practical project management. Furthermore, providing substantial financial subsidies and tax incentives implies contractors can save project costs and minimize penalties. Therefore, PS is an essential environmental policy that promotes contractors' CERI and significantly strengthens their PBC capabilities, enabling them to engage in carbon reduction activities more effectively.

The conclusion should be improved.

Response to Reviewer: We found your suggestions very helpful. We have carefully revised the conclusion section. The revised areas are as follows:

With best regards

Thanks for your recognition and sincere comments. With your help, we have a more quality paper, and your comments inspired us greatly. Again, thanks for your kindness.

Author Response File: Author Response.pdf

Reviewer 4 Report

This study is based on the Theory of Planned Behavior (TPB) and incorporates three potential influencing factors: personal norms (PN), government regulation (GR), and policy support (PS). It constructs a structural equation model (SEM) to predict the influencing factors of carbon emission reduction intentions (CERI) among construction contractors. This study analyzes the key factors and mechanisms influencing construction contractors' CERI. The study collected 311 valid questionnaires, and the results indicate that: The most significant influencing factor on construction contractors' CERI is GR, followed by PS, subjective norms (SN), and perceived behavioural control (PBC). PN and behavioural attitude (BA) towards behaviour do not significantly impact CERI. SN mediate the relationship between GR and CERI, while PBC mediates the relationship between PS and CERI. The research findings can guide the government and construction contractors to improve carbon emission reduction governance mechanisms and achieve peak carbon emissions and carbon neutrality.

Manuscript is well written and the subject is of high interest

The methodology is correct and suitable for the proposed work

The work is original and no signs of copy-paste or self citation or salami work has been detected.

The conclusions are sound, and important in the field.

Redaction is ok and the use of english adequate.

Author Response

Thank you for your positive feedback on my paper

Dear Reviewer

I hope this email finds you well. I am writing to express my sincere gratitude for your thoughtful evaluation and positive feedback on my [Construction Contractors' Carbon Emissions Reduction Intention: A Study Based on Structural Equation Model] paper. Your recognition of my work based on the Theory of Planned Behavior (TPB) and its application to analyze the influencing factors of carbon emission reduction intentions (CERI) among construction contractors means a lot to me.

I am delighted that you found the methodology suitable and the manuscript well-written. Your comments regarding the study's originality and the importance of the conclusions in the field further motivate me to continue my research in this area. I assure you that I have diligently conducted the study to ensure the absence of any signs of plagiarism or salami work.

Your expertise and valuable suggestions have significantly contributed to improving my research. I appreciate your acknowledgement of the excellent use of English and the quality of redaction in my paper.

Once again, thank you for taking the time to review my manuscript and for your positive assessment. Your feedback has given me confidence in the value of my research. I remain committed to further refining my work and addressing any additional comments or suggestions you may have.

Thank you for your valuable contribution to the advancement of my research.

Sincerely,

Zhaoxin He

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Construction Contractors’ Carbon Emissions Reduction Intention: A Study Based on Structural Equation Model

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