Exploring Homeowners’ Attitudes and Climate-Smart Renovation Decisions: A Case Study in Kronoberg, Sweden

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study provides insights into the factors influencing climate-adaptive renovation behaviors among homeowners, leveraging an extended TPB. However, several areas require major revisions to strengthen the study’s clarity of interpretation. Detailed comments are listed below:

(1) The paper extends the TPB framework by incorporating additional factors, the rationale behind these extensions is not well-articulated. It is crucial to explain why these specific factors (e.g., inherent homeowner qualities and building attributes) were included and how they theoretically contribute to understanding climate-adaptive behavior. Additionally, a discussion on why other behavioral theories (such as Protection Motivation Theory or Norm Activation Theory) were not considered would provide stronger justification for the model choice.

(2) The methodology section needs greater transparency. The study mentions that the survey was designed based on the TPB and literature review, there is insufficient detail on how questions were structured, validated, and pre-tested. Providing sample survey items and reliability measures (e.g., Cronbach’s alpha) would improve credibility.

(3) The discussion notes a potential limitation due to sample size and skewness in age distribution. A power analysis should be conducted to justify whether the sample is statistically adequate for the regression models employed. Additionally, more details on how participants were recruited and whether they are representative of the broader population in Kronoberg are needed.

(4) The regression analyses need further justification and clearer presentation: The rationale behind choosing three specific models (OLS for different configurations and 2SLS for endogeneity control) should be explicitly stated.

(5) The results section should explain the implications of the differing R² values and potential omitted variable bias more explicitly.

(6) The significance of only one predictor variable (attitudes) calls into question whether the model adequately captures other influential factors. Could interaction effects or mediating variables be explored?

(7) The policy recommendations are valuable but need stronger empirical grounding. It is suggested that: The discussion should explicitly connect findings to policy interventions, emphasizing how attitude shifts can be effectively translated into behavioral change. The recommendation to use narratives and workshops is insightful, but citing empirical studies that demonstrate their effectiveness in climate behavior change would strengthen the argument.

(8) Some sections, particularly the discussion and conclusion, are repetitive. Consolidating overlapping points will enhance clarity. Table and figure references should be more seamlessly integrated into the narrative, with explicit discussions on what they reveal. Minor grammatical issues should be addressed for better readability.

Comments on the Quality of English Language

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

Author Response

Dear Reviewer,

Thank you for giving us the opportunity to submit a revised draft of the manuscript “Exploring Homeowners' Attitudes and Climate-Smart Renovation Decisions: A Case Study in Kronoberg, Sweden” for publication in the Sustainability journal. We appreciate the time and effort the reviewers dedicated to providing feedback on our manuscript and are grateful for the comments we received. We have incorporated the suggestions made by the reviewers. We appreciate your suggestion and believe this revision significantly strengthens the manuscript.

Below you can see a point-by-point response to the reviewers’ comments. Please refer to the revised manuscript file attached to this response for the changes made. 

Comment 1:
The paper extends the TPB framework by incorporating additional factors, but the rationale behind these extensions is not well-articulated. It is crucial to explain why these specific factors (e.g., inherent homeowner qualities and building attributes) were included and how they theoretically contribute to understanding climate-adaptive behavior. Additionally, a discussion on why other behavioral theories (such as Protection Motivation Theory or Norm Activation Theory) were not considered would provide stronger justification for the model choice.

Response 1:
Thank you for your valuable feedback. We have revised the manuscript to provide a clearer justification for extending the TPB framework with inherent homeowner qualities (IHQ) and building attributes (BA). Specifically, we now explain that these factors were included based on empirical evidence indicating their strong influence on home renovation decisions. IHQ captures key demographic and socioeconomic characteristics that shape decision-making, while BA accounts for the structural constraints and opportunities that directly impact climate-adaptive renovations. By incorporating these factors, the extended TPB model better reflects the real-world complexities of homeowner decision-making.

Additionally, we have included a discussion on why alternative behavioral theories, such as Protection Motivation Theory (PMT) and Norm Activation Theory (NAT), were not chosen. We clarify that while PMT is well-suited for contexts involving immediate risk perception and NAT focuses on moral obligations, TPB offers a more comprehensive framework that accounts for both social influences and perceived control—key elements in the decision-making process for climate-adaptive renovations. These additions ensure a stronger theoretical foundation for the study while directly addressing the concerns raised and can be found in the last paragraph of Section 2, page 6, starting at line 172.

Comment 2:
The methodology section needs greater transparency. The study mentions that the survey was designed based on the TPB and literature review, but there is insufficient detail on how questions were structured, validated, and pre-tested. Providing sample survey items and reliability measures (e.g., Cronbach’s alpha) would improve credibility.

Response 2:
We have revised the methodological section in the first paragraph of Section 3.2 (pre-testing), and the calculated value is presented in the first paragraph of Section 4, "Results & Discussion," page 11. The purpose and summary of feedback obtained from the pre-survey have been included on page 7, line 201. These respondents provided qualitative feedback that was used to prepare the final edition of the survey. The survey was then shared, and responses were collected.

The McDonald’s omega value was used as a reliability measure due to the type and range of questions asked in the survey, and the results and explanation for this are provided in the first paragraph of Section 4, page 11, starting at line 283. The formulation and structure of questions are discussed further in Section 3.2, page 10.

Comment 3:
The discussion notes a potential limitation due to sample size and skewness in age distribution. A power analysis should be conducted to justify whether the sample is statistically adequate for the regression models employed. Additionally, more details on how participants were recruited and whether they are representative of the broader population in Kronoberg are needed.

Response 3:
We acknowledge the reviewer's concern regarding the sample size and skewness in age distribution. To assess whether our sample is statistically adequate for the regression models used, we conducted a post hoc power analysis using a Monte Carlo simulation approach. This method allowed us to estimate the power of our Ordinary Least Squares (OLS) and Two-Stage Least Squares (2SLS) models given the observed effect sizes and sample size. The results indicate that our models achieve an acceptable level of power (e.g., >80%), suggesting that our findings are unlikely to be driven by Type II errors.

Furthermore, while the age distribution is skewed, the regression models control for relevant covariates, mitigating potential biases. These details have been incorporated into the revised manuscript in the last paragraph of Section 4.1, page 12, starting at line 301.

Comment 4:
The regression analyses need further justification and clearer presentation: The rationale behind choosing three specific models (OLS for different configurations and 2SLS for endogeneity control) should be explicitly stated.

Response 4:
Thank you for your insightful comment. We have revised the manuscript to provide a clearer justification for our choice of regression models. Specifically, we now explicitly state the rationale for using both ordinary least squares (OLS) regression and two-stage least squares (2SLS) regression. OLS was employed as a baseline method to examine the direct relationships between input variables and dependent variables (INT and BHV). However, given the possibility of endogeneity, where certain input variables might be correlated with the error term, we introduced 2SLS regression as a robustness check.

We now clarify that 2SLS helps address endogeneity concerns by using instrumental variables to isolate the causal effects of the independent variables. This approach follows established methodologies in previous studies applying the theory of planned behavior within similar empirical frameworks. Additionally, we explicitly outline how the regression models were implemented, including the criteria for selecting significant variables based on p-values. This is addressed on page 7, starting at line 240.

The revised section now includes these justifications and further details on the assumptions underlying each regression model. We believe these changes provide the necessary clarity and strengthen the analytical approach presented in the paper. These changes are in paragraph 1 of page 11.

Comment 5:
The results section should explain the implications of the differing R² values and potential omitted variable bias more explicitly.

Response 5:
Further discussion has been included in each subsection under Section 4.3, page 14, last paragraph.

Comment 6:
The significance of only one predictor variable (attitudes) calls into question whether the model adequately captures other influential factors. Could interaction effects or mediating variables be explored?

Response 6:
Thank you for your comment. We think that this is the main result as there are only 140 responses, meaning variables had to be limited significantly. This reduces the possibility of identifying less significant but still relevant interactions, for which we propose alternative methods of conducting the survey in Section 5, first paragraph, page 15.

Comment 7:
The policy recommendations are valuable but need stronger empirical grounding. It is suggested that: The discussion should explicitly connect findings to policy interventions, emphasizing how attitude shifts can be effectively translated into behavioral change. The recommendation to use narratives and workshops is insightful, but citing empirical studies that demonstrate their effectiveness in climate behavior change would strengthen the argument.

Response 7:
Policy suggestions are now explicitly discussed in the penultimate paragraph of Section 4.3, page 14.

Comment 8:
Some sections, particularly the discussion and conclusion, are repetitive. Consolidating overlapping points will enhance clarity. Table and figure references should be more seamlessly integrated into the narrative, with explicit discussions on what they reveal. Minor grammatical issues should be addressed for better readability.

Response 8:
Coherence and clarity have been improved, and repetitive sections have been consolidated. All authors have read through the manuscript to perform language and grammatical edits.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

1. The abstract mentions that the extended Theory of Planned Behavior is used in this paper. Here, a brief explanation of the optimization points of the Theory of Planned Behavior in this paper can be provided to help readers quickly grasp the key points during subsequent reading.
2. Line 161 mentions the main problem this paper aims to solve and then conducts specific data analysis in the following sections. However, in the conclusion part, there is no systematic summary of the solution to this problem. Appropriate supplementation can be made for this issue to ensure consistency and completeness of the article structure.
3. If possible, the content of Table 2 can be moved to the appendix at the end of the article to occupy less space in the main text. However, this is not mandatory and can be arranged according to the author's thinking.
4. The title of Table 7 is separated from the table content. The position of the table can be adjusted appropriately to ensure the complete display of the table content.
5. Table 6 appears again on Line 303. Is this redundant content? It should be checked and verified.
6. The last paragraph of the Conclusion section discusses the future application and shortcomings of this study. Therefore, this part can be classified as the sixth section "Limitation & Implication", or the title of the fifth section can be modified to "Conclusion & Limitation" to better reflect the content of the article.

Author Response

Dear Reviewer,

Thank you for giving us the opportunity to submit a revised draft of the manuscript “Exploring Homeowners' Attitudes and Climate-Smart Renovation Decisions: A Case Study in Kronoberg, Sweden” for publication in the Sustainability journal. We appreciate the time and effort the reviewers dedicated to providing feedback on our manuscript and are grateful for the comments we received. We have incorporated the suggestions made by the reviewers. We appreciate your suggestion and believe this revision significantly strengthens the manuscript.

Below you can see a point-by-point response to the reviewers’ comments. Please refer to the revised manuscript file attached to this response for the changes made. 

Comment 1:
The abstract mentions that the extended Theory of Planned Behavior is used in this paper. Here, a brief explanation of the optimization points of the Theory of Planned Behavior in this paper can be provided to help readers quickly grasp the key points during subsequent reading.

Response 1:
The abstract has been modified to more clearly delineate how TPB has been modified and tested in this paper.

Comment 2:
Line 161 mentions the main problem this paper aims to solve and then conducts specific data analysis in the following sections. However, in the conclusion part, there is no systematic summary of the solution to this problem. Appropriate supplementation can be made for this issue to ensure consistency and completeness of the article structure.

Response 2:
The conclusion has been restructured and altered to better answer the research question and is more in line with the explicitly stated aims of the paper. Line 444, page 15, addresses answers to the research question.

Comment 3:
If possible, the content of Table 2 can be moved to the appendix at the end of the article to occupy less space in the main text. However, this is not mandatory and can be arranged according to the author's thinking.

Response 3:
While carrying out this edit would certainly improve the organization of the paper, the way it is structured now provides more context for the succeeding sections of the paper, so the table has been left in its original position.

Comment 4:
The title of Table 7 is separated from the table content. The position of the table can be adjusted appropriately to ensure the complete display of the table content.

Response 4:
The positioning of the table has been fixed to have the caption appear above the table.

Comment 5:
Table 6 appears again on Line 303. Is this redundant content? It should be checked and verified.

Response 5:
The captions were numbered erroneously. This has been fixed now so the table figures are numbered appropriately.

Comment 6:
The last paragraph of the Conclusion section discusses the future application and shortcomings of this study. Therefore, this part can be classified as the sixth section "Limitation & Implication", or the title of the fifth section can be modified to "Conclusion & Limitation" to better reflect the content of the article.

Response 6:
A section has been added at the end (Section 5) titled "Limitations & Further Work." This section more clearly addresses the reviewers’ comments regarding limitations of the work and also addresses how further works can be enhanced to overcome these limitations.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

"Exploring Homeowners' Attitudes and Climate-Smart Renovation Decisions: A Case Study in Kronoberg, Sweden." The paper explores factors influencing homeowner behavior towards climate-adaptive renovations using an extended Theory of Planned Behavior (TPB). It employs a survey-based approach with multifactor regression analysis to assess the role of inherent homeowner qualities (IHQ) and building attributes (BA). The following sections provide an assessment of the research design, data collection, statistical analysis, and the reliability of conclusions.

The TPB framework, an established framework for behavior prediction based on attitudes, subjective norms, and perceived behavioral control, is suitably used in this study. The study deepens its analysis by expanding TPB to include inherent homeowner qualities (IHQ) and building attributes (BA). A structural equation modeling technique could have answered the extension's potential to complicate the interpretation of causal links.
The findings' generalizability is limited by the respondents' reported age distribution, which is biased towards older homeowners (overrepresentation of those aged 70–79 and underrepresentation of younger groups).
With its inclusion of thematic areas including attitudes, perceived behavioral control, subjective norms, and behavior, the survey structure conforms to TPB. Pre-testing with ten homeowners provides credibility to the validity of the questionnaire. However, the survey's reliance on self-reported measures introduces potential biases, including social desirability and recall bias.

The study utilizes multifactor analysis to identify significant variables before applying regression models (Ordinary Least Squares [OLS] and Two-Stage Least Squares [2SLS]). The methodology is well-documented, but some limitations exist:

• The R-squared values for different models (ranging from 0.47 to 0.82) indicate varying degrees of explanatory power. The higher R² in the 2SLS regression (0.82) suggests strong model fit, but the high standard errors signal possible overfitting or multicollinearity issues.
• The study finds attitudes (ATT) as the strongest predictor of climate-adaptive renovation behavior. However, subjective norms (SN) show a weaker correlation, which is attributed to Sweden’s individualistic culture. The study does not sufficiently explore alternative explanations, such as potential measurement limitations of SN.
• The study acknowledges that experience bias might influence results due to the Kronoberg region’s limited exposure to extreme climate events. A comparative analysis with more climate-affected regions would enhance validity.

These limitations affect the accuracy of outcomes, and should be indicated and highlighted, and here are recommendations to overcome them, that should be clearly reported

• Non-Response Bias: The study acknowledges non-response bias, particularly regarding age distribution. Future research should explore alternative recruitment methods (e.g., stratified sampling) to improve representativeness.
• Causal Inference Limitations: The study is cross-sectional, preventing causal claims. While regression analysis provides valuable insights, longitudinal studies would be necessary to confirm whether attitudes translate into actual renovation decisions over time.
• Policy Implications: The study suggests policy interventions should focus on shaping attitudes through public campaigns and workshops. While this recommendation is reasonable, the study does not test intervention effectiveness. Future research could incorporate experimental or quasi-experimental designs to assess policy impact.

• Overall, the study contributes valuable insights into the determinants of climate-adaptive renovations, with strong methodological foundations in TPB. However, limitations in sample representativeness, potential biases, and the absence of causal analysis reduce the generalizability of findings.

Author Response

Dear Reviewer,

 

Thank you for giving us the opportunity to submit a revised draft of the manuscript “Exploring Homeowners' Attitudes and Climate-Smart Renovation Decisions: A Case Study in Kronoberg, Sweden” for publication in the Sustainability journal. We appreciate the time and effort the reviewers dedicated to providing feedback on our manuscript and are grateful for the comments we received. We have incorporated the suggestions made by the reviewers. We appreciate your suggestion and believe this revision significantly strengthens the manuscript.

Below you can see a point-by-point response to the reviewers’ comments. Please refer to the revised manuscript file attached to this response for the changes made. 

Comment 1:
The TPB framework, an established framework for behavior prediction based on attitudes, subjective norms, and perceived behavioral control, is suitably used in this study. The study deepens its analysis by expanding TPB to include inherent homeowner qualities (IHQ) and building attributes (BA). A structural equation modeling technique could have answered the extension's potential to complicate the interpretation of causal links.

Response 1:
Thank you for your insightful comment. While structural equation modeling was evaluated as a potential method for analysis, due to the structure of the data, it was not possible to employ this method. Additionally, given that the survey was carried out at a point in time, it does not establish causality due to the cross-sectional nature of the study, so a regression-based method was used instead.

Comment 2:
The findings' generalizability is limited by the respondents' reported age distribution, which is biased towards older homeowners (overrepresentation of those aged 70–79 and underrepresentation of younger groups).

Response 2:
This issue is important for understanding the applicability of the results, so a power analysis is carried out to assess the generalizability of these results in the last paragraph of Section 4.1 and Table 5, line 313. It is acknowledged that with the skewness of the dataset, a significantly larger dataset is required to obtain generalizable results.

Comment 3:
With its inclusion of thematic areas including attitudes, perceived behavioral control, subjective norms, and behavior, the survey structure conforms to TPB. Pre-testing with ten homeowners provides credibility to the validity of the questionnaire. However, the survey's reliance on self-reported measures introduces potential biases, including social desirability and recall bias.

Response 3:
Thank you for this comment. While the questions within each section were designed to introduce redundancy and reduce the impact of biases while maintaining consistency in responses, the survey responses are still subject to recall bias. The limitations section has been modified to account for these biases.

Comment 4:
The study utilizes multifactor analysis to identify significant variables before applying regression models (Ordinary Least Squares [OLS] and Two-Stage Least Squares [2SLS]). The methodology is well-documented, but some limitations exist.

Response 4:
More explanation has been added to the methodology section to improve transparency.

Comment 5:
The R-squared values for different models (ranging from 0.47 to 0.82) indicate varying degrees of explanatory power. The higher R² in the 2SLS regression (0.82) suggests strong model fit, but the high standard errors signal possible overfitting or multicollinearity issues.

Response 5:
Thank you for this comment. The discussion regarding this has been added in Section 4.3 in the last paragraph, line 417.

Comment 6:
The study finds attitudes (ATT) as the strongest predictor of climate-adaptive renovation behavior. However, subjective norms (SN) show a weaker correlation, which is attributed to Sweden’s individualistic culture. The study does not sufficiently explore alternative explanations, such as potential measurement limitations of SN.

Response 6:
This has been added as another possible explanation for the results.

Comment 7:
The study acknowledges that experience bias might influence results due to the Kronoberg region’s limited exposure to extreme climate events. A comparative analysis with more climate-affected regions would enhance validity.

Response 7:
Thank you for your comment. This has been discussed in the second paragraph of Section 5, "Limitations & Further Work," starting at line 480, as a potential avenue for further research. This is also something I am currently working towards.

Comment 8:
These limitations affect the accuracy of outcomes and should be indicated and highlighted. Below are recommendations to overcome them that should be clearly reported:

• Non-Response Bias: The study acknowledges non-response bias, particularly regarding age distribution. Future research should explore alternative recruitment methods (e.g., stratified sampling) to improve representativeness.
• Causal Inference Limitations: The study is cross-sectional, preventing causal claims. While regression analysis provides valuable insights, longitudinal studies would be necessary to confirm whether attitudes translate into actual renovation decisions over time.
• Policy Implications: The study suggests policy interventions should focus on shaping attitudes through public campaigns and workshops. While this recommendation is reasonable, the study does not test intervention effectiveness. Future research could incorporate experimental or quasi-experimental designs to assess policy impact.

Response 8:
The "Limitations & Further Work" section has been modified to address all of these concerns. Policy implications have been discussed in the penultimate paragraph of Section 4.3, page 14, starting at line 397.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

Accept in present form.